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 : 942930 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 : ['4337278'] with mtr_portfolio_ids : ['30251199'] and first list_photo_ids : [] new path : /proc/942930/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 16 ; length of list_pids : 16 ; length of list_args : 16 time to download the photos : 2.6298062801361084 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 Thu Jan 29 14:10:33 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 : 2790 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-01-29 14:10:35.833054: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-29 14:10:35.862532: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-29 14:10:35.864488: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2ce0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-29 14:10:35.864531: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-29 14:10:35.867707: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-29 14:10:36.014790: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xb87c520 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-29 14:10:36.014835: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-29 14:10:36.015834: 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 2026-01-29 14:10:36.016215: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-29 14:10:36.019157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-29 14:10:36.021657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-29 14:10:36.022128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-29 14:10:36.024777: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-29 14:10:36.025983: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-29 14:10:36.030782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-29 14:10:36.031895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-29 14:10:36.031960: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-29 14:10:36.032584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-29 14:10:36.032602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-29 14:10:36.032613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-29 14:10:36.033668: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2338 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. 2026-01-29 14:10:36.288762: 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 2026-01-29 14:10:36.288871: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-29 14:10:36.288898: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-29 14:10:36.288922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-29 14:10:36.288945: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-29 14:10:36.288967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-29 14:10:36.288989: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-29 14:10:36.289012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-29 14:10:36.290269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-29 14:10:36.291422: 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 2026-01-29 14:10:36.291464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-29 14:10:36.291488: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-29 14:10:36.291511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-29 14:10:36.291531: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-29 14:10:36.291552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-29 14:10:36.291575: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-29 14:10:36.291596: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-29 14:10:36.292588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-29 14:10:36.292617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-29 14:10:36.292628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-29 14:10:36.292637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-29 14:10:36.293675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2338 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 : [] 2026-01-29 14:10:47.668911: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-29 14:10:47.835542: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-29 14:10:49.210666: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.210727: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.217102: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.217124: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.266139: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.266213: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.306895: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.306919: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.350950: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.350975: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-29 14:10:49.352986: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.28G (1379336192 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.353479: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.16G (1241402624 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.353492: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2026-01-29 14:10:49.367771: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.368719: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.395457: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.395960: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.397425: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.397921: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.403358: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.403860: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.405469: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.405968: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.411603: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.412103: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.413580: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.414080: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.440625: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.441132: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.441628: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.442123: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.445573: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.446084: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.461811: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.462329: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.462827: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.463320: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.476138: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.476645: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.477142: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.477638: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.481871: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.482403: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.486908: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.487409: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.499797: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.500299: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.504471: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.504972: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.505468: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.505964: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.506614: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.507119: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.518183: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.518697: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.519216: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.519710: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.520206: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.520702: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.535265: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.535766: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.593339: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.594098: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.602806: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.603405: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.619019: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.619566: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.620084: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.620591: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.624546: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.625108: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.625618: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.626122: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.627029: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.627046: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2026-01-29 14:10:49.637109: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.637618: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.646353: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.646864: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.647375: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.647877: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.648388: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-29 14:10:49.648891: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.78G (1916207104 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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 : 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 : 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 : 22 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 : 19 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 : 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 : 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 : 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 : 8 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 : 6 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 : 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 : 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 : 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 : 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 : 14 Detection mask done ! Trying to reset tf kernel 943277 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1597 tf kernel not reseted sub process len(results) : 16 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 16 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 : 2790 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 = [168, 1980, 984, 2766] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0062868595123291016 nb_pixel_total : 228534 time to create 1 rle with new method : 0.03316617012023926 length of segment : 681 DEBUG bbox = [1950, 1260, 2160, 1638] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007243156433105469 nb_pixel_total : 50832 time to create 1 rle with old method : 0.059557199478149414 length of segment : 193 DEBUG bbox = [432, 1344, 834, 1932] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015540122985839844 nb_pixel_total : 100565 time to create 1 rle with old method : 0.10987544059753418 length of segment : 315 DEBUG bbox = [594, 2622, 960, 3120] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0014121532440185547 nb_pixel_total : 92430 time to create 1 rle with old method : 0.10881638526916504 length of segment : 339 DEBUG bbox = [2004, 1740, 2100, 1878] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000194549560546875 nb_pixel_total : 7767 time to create 1 rle with old method : 0.009820222854614258 length of segment : 88 DEBUG bbox = [186, 1626, 546, 1878] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007908344268798828 nb_pixel_total : 51004 time to create 1 rle with old method : 0.058068037033081055 length of segment : 326 DEBUG bbox = [948, 1548, 1302, 1998] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010986328125 nb_pixel_total : 80202 time to create 1 rle with old method : 0.08943819999694824 length of segment : 325 DEBUG bbox = [60, 2526, 462, 2934] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0014200210571289062 nb_pixel_total : 83520 time to create 1 rle with old method : 0.09496259689331055 length of segment : 360 DEBUG bbox = [408, 1290, 816, 1518] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008094310760498047 nb_pixel_total : 56567 time to create 1 rle with old method : 0.06940960884094238 length of segment : 414 DEBUG bbox = [1494, 1728, 1710, 1866] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00033402442932128906 nb_pixel_total : 19761 time to create 1 rle with old method : 0.02271294593811035 length of segment : 187 DEBUG bbox = [1572, 1992, 1962, 2508] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0017061233520507812 nb_pixel_total : 93545 time to create 1 rle with old method : 0.10733175277709961 length of segment : 384 DEBUG bbox = [348, 2112, 594, 2538] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008571147918701172 nb_pixel_total : 51990 time to create 1 rle with old method : 0.061533451080322266 length of segment : 276 DEBUG bbox = [1122, 2466, 1326, 2724] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00045108795166015625 nb_pixel_total : 23921 time to create 1 rle with old method : 0.02808213233947754 length of segment : 185 DEBUG bbox = [264, 1500, 486, 1590] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0002446174621582031 nb_pixel_total : 14239 time to create 1 rle with old method : 0.016601085662841797 length of segment : 218 DEBUG bbox = [1290, 2514, 1602, 2598] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000278472900390625 nb_pixel_total : 17128 time to create 1 rle with old method : 0.01873946189880371 length of segment : 292 DEBUG bbox = [1674, 1644, 2040, 2040] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010023117065429688 nb_pixel_total : 65126 time to create 1 rle with old method : 0.07191157341003418 length of segment : 393 DEBUG bbox = [1722, 2484, 1998, 2814] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006508827209472656 nb_pixel_total : 32274 time to create 1 rle with old method : 0.03607821464538574 length of segment : 232 DEBUG bbox = [132, 3132, 426, 3312] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00046062469482421875 nb_pixel_total : 30208 time to create 1 rle with old method : 0.03651928901672363 length of segment : 316 DEBUG bbox = [348, 1698, 630, 1890] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00046253204345703125 nb_pixel_total : 22026 time to create 1 rle with old method : 0.025145530700683594 length of segment : 225 DEBUG bbox = [1440, 1374, 1806, 1614] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013804435729980469 nb_pixel_total : 47844 time to create 1 rle with old method : 0.05730628967285156 length of segment : 389 DEBUG bbox = [840, 2028, 1434, 2268] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0023651123046875 nb_pixel_total : 93377 time to create 1 rle with old method : 0.10521316528320312 length of segment : 607 DEBUG bbox = [270, 1068, 396, 1182] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003247261047363281 nb_pixel_total : 8628 time to create 1 rle with old method : 0.009535074234008789 length of segment : 109 DEBUG bbox = [756, 1746, 966, 2004] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008077621459960938 nb_pixel_total : 36478 time to create 1 rle with old method : 0.04018521308898926 length of segment : 205 DEBUG bbox = [1260, 2346, 1482, 2694] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009844303131103516 nb_pixel_total : 40730 time to create 1 rle with old method : 0.04463696479797363 length of segment : 211 DEBUG bbox = [606, 1908, 744, 2028] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00037360191345214844 nb_pixel_total : 7071 time to create 1 rle with old method : 0.008224010467529297 length of segment : 100 DEBUG bbox = [1116, 1710, 1290, 1950] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000621795654296875 nb_pixel_total : 26416 time to create 1 rle with old method : 0.032099008560180664 length of segment : 171 DEBUG bbox = [900, 1680, 1410, 2334] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.003873109817504883 nb_pixel_total : 166741 time to create 1 rle with new method : 0.008813858032226562 length of segment : 470 DEBUG bbox = [240, 2340, 444, 2562] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006902217864990234 nb_pixel_total : 22362 time to create 1 rle with old method : 0.026021242141723633 length of segment : 176 DEBUG bbox = [402, 2238, 672, 2460] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008106231689453125 nb_pixel_total : 24320 time to create 1 rle with old method : 0.027573585510253906 length of segment : 237 DEBUG bbox = [126, 930, 282, 1128] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005011558532714844 nb_pixel_total : 19387 time to create 1 rle with old method : 0.022806882858276367 length of segment : 149 DEBUG bbox = [1254, 1500, 1428, 1680] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00047135353088378906 nb_pixel_total : 18042 time to create 1 rle with old method : 0.020955324172973633 length of segment : 165 DEBUG bbox = [132, 1158, 378, 1362] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007793903350830078 nb_pixel_total : 26383 time to create 1 rle with old method : 0.029850244522094727 length of segment : 217 DEBUG bbox = [234, 912, 390, 1158] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00039267539978027344 nb_pixel_total : 21852 time to create 1 rle with old method : 0.024396657943725586 length of segment : 135 DEBUG bbox = [204, 2874, 486, 3018] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006880760192871094 nb_pixel_total : 24301 time to create 1 rle with old method : 0.02659320831298828 length of segment : 251 DEBUG bbox = [1548, 1896, 1764, 2094] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006463527679443359 nb_pixel_total : 23098 time to create 1 rle with old method : 0.025839567184448242 length of segment : 195 DEBUG bbox = [540, 3132, 978, 3480] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0018305778503417969 nb_pixel_total : 73048 time to create 1 rle with old method : 0.08113384246826172 length of segment : 363 DEBUG bbox = [174, 900, 624, 1404] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0028929710388183594 nb_pixel_total : 133563 time to create 1 rle with old method : 0.14900541305541992 length of segment : 489 DEBUG bbox = [0, 2916, 252, 3282] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0012516975402832031 nb_pixel_total : 52637 time to create 1 rle with old method : 0.06062483787536621 length of segment : 231 DEBUG bbox = [144, 2262, 756, 2778] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0037717819213867188 nb_pixel_total : 173214 time to create 1 rle with new method : 0.00915074348449707 length of segment : 504 DEBUG bbox = [0, 888, 198, 1212] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00092315673828125 nb_pixel_total : 42335 time to create 1 rle with old method : 0.05054736137390137 length of segment : 188 DEBUG bbox = [1038, 2022, 1224, 2184] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005068778991699219 nb_pixel_total : 15699 time to create 1 rle with old method : 0.017738819122314453 length of segment : 163 DEBUG bbox = [54, 690, 264, 894] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005326271057128906 nb_pixel_total : 28394 time to create 1 rle with old method : 0.031282901763916016 length of segment : 203 DEBUG bbox = [114, 1332, 354, 1674] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009441375732421875 nb_pixel_total : 35204 time to create 1 rle with old method : 0.03936362266540527 length of segment : 302 DEBUG bbox = [1644, 1656, 2022, 2034] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0019345283508300781 nb_pixel_total : 75987 time to create 1 rle with old method : 0.08319282531738281 length of segment : 304 DEBUG bbox = [426, 1782, 684, 2220] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013098716735839844 nb_pixel_total : 56634 time to create 1 rle with old method : 0.06227421760559082 length of segment : 216 DEBUG bbox = [504, 3396, 690, 3510] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004444122314453125 nb_pixel_total : 13709 time to create 1 rle with old method : 0.015116691589355469 length of segment : 158 DEBUG bbox = [948, 1680, 1236, 2100] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 71343 time to create 1 rle with old method : 0.10834097862243652 length of segment : 266 DEBUG bbox = [1326, 1596, 1500, 1758] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005154609680175781 nb_pixel_total : 16361 time to create 1 rle with old method : 0.01860642433166504 length of segment : 142 DEBUG bbox = [1098, 2262, 1428, 2484] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009889602661132812 nb_pixel_total : 46029 time to create 1 rle with old method : 0.05093574523925781 length of segment : 327 DEBUG bbox = [606, 1674, 972, 2226] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002458810806274414 nb_pixel_total : 104668 time to create 1 rle with old method : 0.1143045425415039 length of segment : 346 DEBUG bbox = [72, 3534, 276, 3756] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006585121154785156 nb_pixel_total : 26641 time to create 1 rle with old method : 0.0311276912689209 length of segment : 155 DEBUG bbox = [1026, 1122, 1266, 1350] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007550716400146484 nb_pixel_total : 27866 time to create 1 rle with old method : 0.03203439712524414 length of segment : 198 DEBUG bbox = [1284, 2196, 1452, 2430] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005140304565429688 nb_pixel_total : 16732 time to create 1 rle with old method : 0.019526243209838867 length of segment : 137 DEBUG bbox = [414, 1104, 570, 1254] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00036525726318359375 nb_pixel_total : 16886 time to create 1 rle with old method : 0.01928877830505371 length of segment : 142 DEBUG bbox = [1794, 1614, 2058, 1872] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008463859558105469 nb_pixel_total : 43331 time to create 1 rle with old method : 0.04820537567138672 length of segment : 256 DEBUG bbox = [786, 3006, 1008, 3150] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005581378936767578 nb_pixel_total : 19823 time to create 1 rle with old method : 0.02227616310119629 length of segment : 214 DEBUG bbox = [900, 2622, 1062, 2736] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003516674041748047 nb_pixel_total : 13951 time to create 1 rle with old method : 0.01570296287536621 length of segment : 153 DEBUG bbox = [1320, 2364, 1620, 2742] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001256704330444336 nb_pixel_total : 72564 time to create 1 rle with old method : 0.08049225807189941 length of segment : 286 DEBUG bbox = [612, 2724, 828, 2988] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006983280181884766 nb_pixel_total : 41579 time to create 1 rle with old method : 0.04505801200866699 length of segment : 208 DEBUG bbox = [804, 1866, 1014, 2010] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000576019287109375 nb_pixel_total : 19779 time to create 1 rle with old method : 0.02158355712890625 length of segment : 191 DEBUG bbox = [1968, 1452, 2130, 1752] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007131099700927734 nb_pixel_total : 28578 time to create 1 rle with old method : 0.03310751914978027 length of segment : 194 DEBUG bbox = [48, 1170, 204, 1332] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005381107330322266 nb_pixel_total : 14418 time to create 1 rle with old method : 0.017331838607788086 length of segment : 145 DEBUG bbox = [378, 1674, 630, 1962] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0011899471282958984 nb_pixel_total : 44280 time to create 1 rle with old method : 0.05023789405822754 length of segment : 209 DEBUG bbox = [1086, 1866, 1368, 2418] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001998424530029297 nb_pixel_total : 73936 time to create 1 rle with old method : 0.0835723876953125 length of segment : 323 DEBUG bbox = [0, 882, 360, 1164] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0012845993041992188 nb_pixel_total : 35912 time to create 1 rle with old method : 0.03923225402832031 length of segment : 379 DEBUG bbox = [1014, 2640, 1344, 2976] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015721321105957031 nb_pixel_total : 42978 time to create 1 rle with old method : 0.048187971115112305 length of segment : 339 DEBUG bbox = [1086, 1932, 1380, 2418] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010900497436523438 nb_pixel_total : 72805 time to create 1 rle with old method : 0.08300447463989258 length of segment : 326 DEBUG bbox = [792, 1014, 924, 1158] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004210472106933594 nb_pixel_total : 9353 time to create 1 rle with old method : 0.010879278182983398 length of segment : 102 DEBUG bbox = [1140, 1488, 1254, 1656] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003650188446044922 nb_pixel_total : 12393 time to create 1 rle with old method : 0.014128684997558594 length of segment : 110 DEBUG bbox = [1524, 1512, 1764, 1728] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008699893951416016 nb_pixel_total : 32135 time to create 1 rle with old method : 0.03679704666137695 length of segment : 226 DEBUG bbox = [1230, 1998, 1374, 2094] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003285408020019531 nb_pixel_total : 8350 time to create 1 rle with old method : 0.009392499923706055 length of segment : 121 DEBUG bbox = [168, 1926, 324, 2256] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000728607177734375 nb_pixel_total : 28496 time to create 1 rle with old method : 0.033197879791259766 length of segment : 145 DEBUG bbox = [204, 2694, 576, 3168] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0023958683013916016 nb_pixel_total : 103526 time to create 1 rle with old method : 0.11141490936279297 length of segment : 369 DEBUG bbox = [570, 1884, 1086, 2490] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0045282840728759766 nb_pixel_total : 247826 time to create 1 rle with new method : 0.009246826171875 length of segment : 601 DEBUG bbox = [1614, 1974, 1980, 2508] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0023152828216552734 nb_pixel_total : 131823 time to create 1 rle with old method : 0.1432664394378662 length of segment : 352 DEBUG bbox = [126, 1716, 426, 2340] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002016305923461914 nb_pixel_total : 102991 time to create 1 rle with old method : 0.11511945724487305 length of segment : 280 DEBUG bbox = [468, 3216, 774, 3300] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005142688751220703 nb_pixel_total : 19360 time to create 1 rle with old method : 0.021419525146484375 length of segment : 291 DEBUG bbox = [636, 2088, 966, 2334] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0011146068572998047 nb_pixel_total : 56112 time to create 1 rle with old method : 0.06168508529663086 length of segment : 295 DEBUG bbox = [342, 2724, 510, 2886] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004830360412597656 nb_pixel_total : 18756 time to create 1 rle with old method : 0.020527362823486328 length of segment : 158 DEBUG bbox = [522, 2046, 738, 2562] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013077259063720703 nb_pixel_total : 69845 time to create 1 rle with old method : 0.07674336433410645 length of segment : 224 DEBUG bbox = [1206, 1620, 1458, 1950] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0011572837829589844 nb_pixel_total : 51402 time to create 1 rle with old method : 0.05787301063537598 length of segment : 234 DEBUG bbox = [1314, 1542, 1476, 1698] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00026869773864746094 nb_pixel_total : 15568 time to create 1 rle with old method : 0.01750469207763672 length of segment : 161 DEBUG bbox = [1830, 2562, 1980, 2676] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 13665 time to create 1 rle with old method : 0.015671491622924805 length of segment : 146 DEBUG bbox = [1836, 1482, 2130, 1890] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013613700866699219 nb_pixel_total : 51584 time to create 1 rle with old method : 0.05869483947753906 length of segment : 264 DEBUG bbox = [1734, 2478, 2118, 2862] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0019524097442626953 nb_pixel_total : 94666 time to create 1 rle with old method : 0.10475516319274902 length of segment : 377 DEBUG bbox = [1692, 1824, 2112, 2280] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0019147396087646484 nb_pixel_total : 110694 time to create 1 rle with old method : 0.12389159202575684 length of segment : 373 DEBUG bbox = [1440, 1194, 1770, 1878] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0026848316192626953 nb_pixel_total : 135258 time to create 1 rle with old method : 0.14780068397521973 length of segment : 441 DEBUG bbox = [1458, 2406, 1614, 2544] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000400543212890625 nb_pixel_total : 11255 time to create 1 rle with old method : 0.012960195541381836 length of segment : 155 DEBUG bbox = [540, 1872, 630, 2094] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00030350685119628906 nb_pixel_total : 11212 time to create 1 rle with old method : 0.012938261032104492 length of segment : 104 time spent for convertir_results : 7.692325115203857 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 89 chid ids of type : 3594 Number RLEs to save : 23122 save missing photos in datou_result : time spend for datou_step_exec : 64.0497522354126 time spend to save output : 1.58786940574646 total time spend for step 1 : 65.63762164115906 step2:crop_condition Thu Jan 29 14:11:38 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 ! 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 : 16 ! batch 1 Loaded 89 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 47 About to insert : list_path_to_insert length 47 new photo from crops ! About to upload 47 photos upload in portfolio : 3736932 init cache_photo without model_param we have 47 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1769692311_942930 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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361572_0.png', 0, 693, 680, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361575_0.png', 0, 493, 317, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361577_0.png', 0, 234, 326, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361578_0.png', 0, 399, 306, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361579_0.png', 0, 392, 360, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361580_0.png', 0, 222, 378, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361581_0.png', 0, 138, 187, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361582_0.png', 0, 479, 375, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361583_0.png', 0, 419, 225, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361584_0.png', 0, 229, 170, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361585_0.png', 0, 85, 218, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361586_0.png', 0, 69, 292, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361588_0.png', 0, 317, 229, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361591_0.png', 0, 222, 324, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361597_0.png', 0, 238, 154, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361598_0.png', 0, 598, 470, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361599_0.png', 0, 205, 176, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361600_0.png', 0, 179, 234, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361601_0.png', 0, 198, 143, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361602_0.png', 0, 158, 165, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361605_0.png', 0, 141, 251, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361606_0.png', 0, 193, 195, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361607_0.png', 0, 333, 362, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361611_0.png', 0, 321, 177, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361616_0.png', 0, 408, 185, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361617_0.png', 0, 104, 158, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361618_0.png', 0, 398, 240, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361619_0.png', 0, 155, 140, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361621_0.png', 0, 538, 300, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361623_0.png', 0, 210, 196, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361624_0.png', 0, 216, 136, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361627_0.png', 0, 118, 214, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a_rle_crop_4115361628_0.png', 0, 112, 153, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a_rle_crop_4115361630_0.png', 0, 247, 208, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361631_0.png', 0, 142, 190, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361632_0.png', 0, 297, 155, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361636_0.png', 0, 242, 344, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361637_0.png', 0, 263, 312, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361638_0.png', 0, 448, 262, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361639_0.png', 0, 134, 101, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113_rle_crop_4115361642_0.png', 0, 86, 121, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2_rle_crop_4115361648_0.png', 0, 74, 290, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54_rle_crop_4115361652_0.png', 0, 287, 231, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54_rle_crop_4115361653_0.png', 0, 154, 160, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361655_0.png', 0, 387, 252, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361659_0.png', 0, 131, 155, 0, 1769692321,'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(1769692321), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361660_0.png', 0, 218, 87, 0, 1769692321,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 47 photos in the portfolio 3736932 time of upload the photos Elapsed time : 12.68889856338501 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 ! 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/1769692326_942930 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(1769692327), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361594_0.png', 0, 227, 191, 0, 1769692327,'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(1769692327), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361596_0.png', 0, 106, 99, 0, 1769692327,'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(1769692327), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361613_0.png', 0, 191, 203, 0, 1769692327,'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(1769692327), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361635_0.png', 0, 471, 254, 0, 1769692327,'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(1769692327), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113_rle_crop_4115361643_0.png', 0, 324, 145, 0, 1769692327,'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 : 1.5766329765319824 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/1769692330_942930 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(1769692330), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361576_0.png', 0, 121, 88, 0, 1769692330,'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(1769692330), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361593_0.png', 0, 104, 109, 0, 1769692330,'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(1769692330), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361603_0.png', 0, 144, 216, 0, 1769692330,'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(1769692330), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361640_0.png', 0, 139, 110, 0, 1769692330,'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 : 1.2014520168304443 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 ! map_result returned by crop_photo_return_map_crop : length : 25 About to insert : list_path_to_insert length 25 new photo from crops ! About to upload 25 photos upload in portfolio : 3736932 init cache_photo without model_param we have 25 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1769692340_942930 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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361573_0.png', 0, 364, 192, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361574_0.png', 0, 561, 305, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361587_0.png', 0, 340, 339, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361589_0.png', 0, 152, 268, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361590_0.png', 0, 152, 225, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361604_0.png', 0, 234, 135, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361608_0.png', 0, 492, 396, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361609_0.png', 0, 350, 231, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361610_0.png', 0, 479, 498, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361614_0.png', 0, 326, 233, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361615_0.png', 0, 352, 304, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361622_0.png', 0, 205, 155, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a_rle_crop_4115361629_0.png', 0, 359, 286, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361633_0.png', 0, 141, 140, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361634_0.png', 0, 253, 207, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2_rle_crop_4115361644_0.png', 0, 449, 366, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2_rle_crop_4115361645_0.png', 0, 600, 491, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2_rle_crop_4115361646_0.png', 0, 498, 330, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2_rle_crop_4115361647_0.png', 0, 583, 250, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970799_5d872a44176de128b6ac9668338d4380_rle_crop_4115361649_0.png', 0, 243, 295, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970767_c90e03691be1f87f37862df075a669ec_rle_crop_4115361651_0.png', 0, 514, 174, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54_rle_crop_4115361654_0.png', 0, 112, 146, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361656_0.png', 0, 334, 376, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361657_0.png', 0, 404, 359, 0, 1769692346,'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(1769692346), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361658_0.png', 0, 636, 300, 0, 1769692346,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 25 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.318934440612793 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1769692351_942930 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(1769692352), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361592_0.png', 0, 228, 591, 0, 1769692352,'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(1769692352), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361612_0.png', 0, 124, 163, 0, 1769692352,'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(1769692352), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361625_0.png', 0, 135, 142, 0, 1769692352,'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(1769692352), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361626_0.png', 0, 223, 254, 0, 1769692352,'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(1769692352), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113_rle_crop_4115361641_0.png', 0, 200, 221, 0, 1769692352,'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(1769692352), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970767_c90e03691be1f87f37862df075a669ec_rle_crop_4115361650_0.png', 0, 153, 158, 0, 1769692352,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.735807180404663 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 ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1769692354_942930 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(1769692355), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361595_0.png', 0, 302, 211, 0, 1769692355,'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(1769692355), 0.0, 0.0, 14, '', 0, 0, '1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361620_0.png', 0, 194, 327, 0, 1769692355,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8843114376068115 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 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 [1405971326, 1405971292, 1405971255, 1405971220, 1405971040, 1405971003, 1405970958, 1405970925, 1405970895, 1405970892, 1405970809, 1405970807, 1405970799, 1405970767, 1405969713, 1405969692] Looping around the photos to save general results len do output : 89 /1405977659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977701Didn't retrieve data 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/1405977791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1405977896Didn'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, '4337278') ('3318', '30251199', '1405971326', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971292', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971255', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971220', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971040', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971003', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970958', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970925', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970895', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970892', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970809', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970807', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970799', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970767', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969713', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969692', None, None, None, None, None, '4337278') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 283 time used for this insertion : 0.056006431579589844 save_final save missing photos in datou_result : time spend for datou_step_exec : 56.649595737457275 time spend to save output : 0.05940866470336914 total time spend for step 2 : 56.709004402160645 step3:rle_unique_nms_with_priority Thu Jan 29 14:12:35 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 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 89 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 3.1937003135681152 time for calcul the mask position with numpy : 0.8288660049438477 nb_pixel_total : 7683066 time to create 1 rle with new method : 0.6697163581848145 time for calcul the mask position with numpy : 0.02688455581665039 nb_pixel_total : 80202 time to create 1 rle with old method : 0.08971929550170898 time for calcul the mask position with numpy : 0.025823593139648438 nb_pixel_total : 51004 time to create 1 rle with old method : 0.058450937271118164 time for calcul the mask position with numpy : 0.02434563636779785 nb_pixel_total : 7767 time to create 1 rle with old method : 0.008645057678222656 time for calcul the mask position with numpy : 0.025362491607666016 nb_pixel_total : 92430 time to create 1 rle with old method : 0.10416364669799805 time for calcul the mask position with numpy : 0.024783611297607422 nb_pixel_total : 100565 time to create 1 rle with old method : 0.10816526412963867 time for calcul the mask position with numpy : 0.023705244064331055 nb_pixel_total : 50832 time to create 1 rle with old method : 0.055231332778930664 time for calcul the mask position with numpy : 0.024800539016723633 nb_pixel_total : 228534 time to create 1 rle with new method : 0.7372841835021973 create new chi : 2.8961286544799805 time to delete rle : 0.02161717414855957 batch 1 Loaded 15 chid ids of type : 3594 +++++++Number RLEs to save : 6694 TO DO : save crop sub photo not yet done ! save time : 0.4773366451263428 nb_obj : 12 nb_hashtags : 2 time to prepare the origin masks : 5.326233863830566 time for calcul the mask position with numpy : 0.7987260818481445 nb_pixel_total : 7784095 time to create 1 rle with new method : 0.8284807205200195 time for calcul the mask position with numpy : 0.02390575408935547 nb_pixel_total : 22026 time to create 1 rle with old method : 0.024892568588256836 time for calcul the mask position with numpy : 0.02523493766784668 nb_pixel_total : 30208 time to create 1 rle with old method : 0.03421306610107422 time for calcul the mask position with numpy : 0.025252819061279297 nb_pixel_total : 32274 time to create 1 rle with old method : 0.03722882270812988 time for calcul the mask position with numpy : 0.0258023738861084 nb_pixel_total : 65126 time to create 1 rle with old method : 0.07479476928710938 time for calcul the mask position with numpy : 0.025571584701538086 nb_pixel_total : 17128 time to create 1 rle with old method : 0.019843578338623047 time for calcul the mask position with numpy : 0.02585911750793457 nb_pixel_total : 14239 time to create 1 rle with old method : 0.016081809997558594 time for calcul the mask position with numpy : 0.024779319763183594 nb_pixel_total : 23921 time to create 1 rle with old method : 0.02706122398376465 time for calcul the mask position with numpy : 0.0269467830657959 nb_pixel_total : 51990 time to create 1 rle with old method : 0.05904078483581543 time for calcul the mask position with numpy : 0.02787470817565918 nb_pixel_total : 93545 time to create 1 rle with old method : 0.10985898971557617 time for calcul the mask position with numpy : 0.025168418884277344 nb_pixel_total : 19761 time to create 1 rle with old method : 0.022282838821411133 time for calcul the mask position with numpy : 0.024024486541748047 nb_pixel_total : 56567 time to create 1 rle with old method : 0.06272649765014648 time for calcul the mask position with numpy : 0.024723529815673828 nb_pixel_total : 83520 time to create 1 rle with old method : 0.0916438102722168 create new chi : 2.556218385696411 time to delete rle : 0.0011622905731201172 batch 1 Loaded 25 chid ids of type : 3594 +++++++++++++++++Number RLEs to save : 9124 TO DO : save crop sub photo not yet done ! save time : 0.6284596920013428 nb_obj : 7 nb_hashtags : 5 time to prepare the origin masks : 3.88828182220459 time for calcul the mask position with numpy : 0.49761533737182617 nb_pixel_total : 8033856 time to create 1 rle with new method : 0.8397207260131836 time for calcul the mask position with numpy : 0.022960424423217773 nb_pixel_total : 26416 time to create 1 rle with old method : 0.028479814529418945 time for calcul the mask position with numpy : 0.022909879684448242 nb_pixel_total : 7071 time to create 1 rle with old method : 0.007578372955322266 time for calcul the mask position with numpy : 0.023364543914794922 nb_pixel_total : 40730 time to create 1 rle with old method : 0.04438948631286621 time for calcul the mask position with numpy : 0.023405075073242188 nb_pixel_total : 36478 time to create 1 rle with old method : 0.0397496223449707 time for calcul the mask position with numpy : 0.024527788162231445 nb_pixel_total : 8628 time to create 1 rle with old method : 0.009770870208740234 time for calcul the mask position with numpy : 0.024176359176635742 nb_pixel_total : 93377 time to create 1 rle with old method : 0.10064291954040527 time for calcul the mask position with numpy : 0.023389577865600586 nb_pixel_total : 47844 time to create 1 rle with old method : 0.05196022987365723 create new chi : 1.8251066207885742 time to delete rle : 0.0007753372192382812 batch 1 Loaded 15 chid ids of type : 3594 +++++++Number RLEs to save : 5744 TO DO : save crop sub photo not yet done ! save time : 0.4225883483886719 nb_obj : 4 nb_hashtags : 1 time to prepare the origin masks : 1.6325311660766602 time for calcul the mask position with numpy : 0.6156215667724609 nb_pixel_total : 8061590 time to create 1 rle with new method : 0.7530274391174316 time for calcul the mask position with numpy : 0.0229952335357666 nb_pixel_total : 19387 time to create 1 rle with old method : 0.020872116088867188 time for calcul the mask position with numpy : 0.022853374481201172 nb_pixel_total : 24320 time to create 1 rle with old method : 0.02718663215637207 time for calcul the mask position with numpy : 0.024201631546020508 nb_pixel_total : 22362 time to create 1 rle with old method : 0.02469921112060547 time for calcul the mask position with numpy : 0.02448248863220215 nb_pixel_total : 166741 time to create 1 rle with new method : 0.786872386932373 create new chi : 2.3867299556732178 time to delete rle : 0.0006303787231445312 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 4224 TO DO : save crop sub photo not yet done ! save time : 0.3424689769744873 nb_obj : 4 nb_hashtags : 3 time to prepare the origin masks : 2.04323148727417 time for calcul the mask position with numpy : 0.641613245010376 nb_pixel_total : 8203822 time to create 1 rle with new method : 0.806342601776123 time for calcul the mask position with numpy : 0.023942947387695312 nb_pixel_total : 24301 time to create 1 rle with old method : 0.026606082916259766 time for calcul the mask position with numpy : 0.02303290367126465 nb_pixel_total : 21852 time to create 1 rle with old method : 0.023764610290527344 time for calcul the mask position with numpy : 0.023578166961669922 nb_pixel_total : 26383 time to create 1 rle with old method : 0.028289318084716797 time for calcul the mask position with numpy : 0.02367377281188965 nb_pixel_total : 18042 time to create 1 rle with old method : 0.019632577896118164 create new chi : 1.679919719696045 time to delete rle : 0.0005426406860351562 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 3696 TO DO : save crop sub photo not yet done ! save time : 0.2792816162109375 nb_obj : 9 nb_hashtags : 4 time to prepare the origin masks : 4.130194664001465 time for calcul the mask position with numpy : 0.6990010738372803 nb_pixel_total : 7717208 time to create 1 rle with new method : 0.8349630832672119 time for calcul the mask position with numpy : 0.02315688133239746 nb_pixel_total : 35204 time to create 1 rle with old method : 0.03801465034484863 time for calcul the mask position with numpy : 0.023729562759399414 nb_pixel_total : 28394 time to create 1 rle with old method : 0.03106689453125 time for calcul the mask position with numpy : 0.024550914764404297 nb_pixel_total : 15699 time to create 1 rle with old method : 0.017099380493164062 time for calcul the mask position with numpy : 0.0344998836517334 nb_pixel_total : 42335 time to create 1 rle with old method : 0.04581499099731445 time for calcul the mask position with numpy : 0.03006744384765625 nb_pixel_total : 173214 time to create 1 rle with new method : 0.8238420486450195 time for calcul the mask position with numpy : 0.024281740188598633 nb_pixel_total : 52637 time to create 1 rle with old method : 0.05742835998535156 time for calcul the mask position with numpy : 0.023871183395385742 nb_pixel_total : 133563 time to create 1 rle with old method : 0.14379429817199707 time for calcul the mask position with numpy : 0.02508258819580078 nb_pixel_total : 73048 time to create 1 rle with old method : 0.0813760757446289 time for calcul the mask position with numpy : 0.024138927459716797 nb_pixel_total : 23098 time to create 1 rle with old method : 0.02513909339904785 create new chi : 3.094754695892334 time to delete rle : 0.0010671615600585938 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++Number RLEs to save : 7436 TO DO : save crop sub photo not yet done ! save time : 0.5106058120727539 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 3.2237865924835205 time for calcul the mask position with numpy : 0.5398197174072266 nb_pixel_total : 7909669 time to create 1 rle with new method : 0.861325740814209 time for calcul the mask position with numpy : 0.022951364517211914 nb_pixel_total : 104668 time to create 1 rle with old method : 0.11057639122009277 time for calcul the mask position with numpy : 0.023728370666503906 nb_pixel_total : 46029 time to create 1 rle with old method : 0.049762725830078125 time for calcul the mask position with numpy : 0.023674488067626953 nb_pixel_total : 16361 time to create 1 rle with old method : 0.017260313034057617 time for calcul the mask position with numpy : 0.023861169815063477 nb_pixel_total : 71343 time to create 1 rle with old method : 0.07541394233703613 time for calcul the mask position with numpy : 0.024173498153686523 nb_pixel_total : 13709 time to create 1 rle with old method : 0.014593839645385742 time for calcul the mask position with numpy : 0.02373957633972168 nb_pixel_total : 56634 time to create 1 rle with old method : 0.06108736991882324 time for calcul the mask position with numpy : 0.023378849029541016 nb_pixel_total : 75987 time to create 1 rle with old method : 0.08185839653015137 create new chi : 2.0156288146972656 time to delete rle : 0.0007431507110595703 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 5678 TO DO : save crop sub photo not yet done ! save time : 0.42064714431762695 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 2.7911455631256104 time for calcul the mask position with numpy : 0.6414518356323242 nb_pixel_total : 8143121 time to create 1 rle with new method : 0.9791617393493652 time for calcul the mask position with numpy : 0.023295164108276367 nb_pixel_total : 19823 time to create 1 rle with old method : 0.02168416976928711 time for calcul the mask position with numpy : 0.023509979248046875 nb_pixel_total : 43331 time to create 1 rle with old method : 0.046982526779174805 time for calcul the mask position with numpy : 0.02448582649230957 nb_pixel_total : 16886 time to create 1 rle with old method : 0.01930403709411621 time for calcul the mask position with numpy : 0.024911880493164062 nb_pixel_total : 16732 time to create 1 rle with old method : 0.018990278244018555 time for calcul the mask position with numpy : 0.024252653121948242 nb_pixel_total : 27866 time to create 1 rle with old method : 0.030331850051879883 time for calcul the mask position with numpy : 0.023573637008666992 nb_pixel_total : 26641 time to create 1 rle with old method : 0.029022216796875 create new chi : 1.9709551334381104 time to delete rle : 0.0006344318389892578 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 4364 TO DO : save crop sub photo not yet done ! save time : 0.3229553699493408 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 1.6908867359161377 time for calcul the mask position with numpy : 0.6662507057189941 nb_pixel_total : 8166306 time to create 1 rle with new method : 0.7790563106536865 time for calcul the mask position with numpy : 0.022979021072387695 nb_pixel_total : 41579 time to create 1 rle with old method : 0.0443873405456543 time for calcul the mask position with numpy : 0.02616739273071289 nb_pixel_total : 72564 time to create 1 rle with old method : 0.07774496078491211 time for calcul the mask position with numpy : 0.034520864486694336 nb_pixel_total : 13951 time to create 1 rle with old method : 0.015350341796875 create new chi : 1.7063713073730469 time to delete rle : 0.00048351287841796875 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 3454 TO DO : save crop sub photo not yet done ! save time : 0.2754819393157959 nb_obj : 10 nb_hashtags : 4 time to prepare the origin masks : 4.480273723602295 time for calcul the mask position with numpy : 0.6010453701019287 nb_pixel_total : 8010818 time to create 1 rle with new method : 1.0210824012756348 time for calcul the mask position with numpy : 0.023369789123535156 nb_pixel_total : 12393 time to create 1 rle with old method : 0.013978719711303711 time for calcul the mask position with numpy : 0.023932695388793945 nb_pixel_total : 9353 time to create 1 rle with old method : 0.01079249382019043 time for calcul the mask position with numpy : 0.024908065795898438 nb_pixel_total : 1955 time to create 1 rle with old method : 0.002619504928588867 time for calcul the mask position with numpy : 0.02404499053955078 nb_pixel_total : 42978 time to create 1 rle with old method : 0.04782366752624512 time for calcul the mask position with numpy : 0.024037837982177734 nb_pixel_total : 35912 time to create 1 rle with old method : 0.04037141799926758 time for calcul the mask position with numpy : 0.024825572967529297 nb_pixel_total : 73936 time to create 1 rle with old method : 0.0805206298828125 time for calcul the mask position with numpy : 0.024292945861816406 nb_pixel_total : 44280 time to create 1 rle with old method : 0.048928022384643555 time for calcul the mask position with numpy : 0.023420333862304688 nb_pixel_total : 14418 time to create 1 rle with old method : 0.01553034782409668 time for calcul the mask position with numpy : 0.023610353469848633 nb_pixel_total : 28578 time to create 1 rle with old method : 0.030816316604614258 time for calcul the mask position with numpy : 0.023537635803222656 nb_pixel_total : 19779 time to create 1 rle with old method : 0.021538496017456055 create new chi : 2.218743324279785 time to delete rle : 0.0009362697601318359 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++Number RLEs to save : 6378 TO DO : save crop sub photo not yet done ! save time : 0.4483299255371094 nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 1.982980728149414 time for calcul the mask position with numpy : 0.7161855697631836 nb_pixel_total : 8225419 time to create 1 rle with new method : 0.6687545776367188 time for calcul the mask position with numpy : 0.02335500717163086 nb_pixel_total : 28496 time to create 1 rle with old method : 0.030948400497436523 time for calcul the mask position with numpy : 0.023562908172607422 nb_pixel_total : 8350 time to create 1 rle with old method : 0.008905887603759766 time for calcul the mask position with numpy : 0.023151874542236328 nb_pixel_total : 32135 time to create 1 rle with old method : 0.034673452377319336 create new chi : 1.5693655014038086 time to delete rle : 0.0004477500915527344 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 3144 TO DO : save crop sub photo not yet done ! save time : 0.25148773193359375 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 2.3127472400665283 time for calcul the mask position with numpy : 0.6338009834289551 nb_pixel_total : 7688874 time to create 1 rle with new method : 0.8238534927368164 time for calcul the mask position with numpy : 0.023280858993530273 nb_pixel_total : 19360 time to create 1 rle with old method : 0.021684646606445312 time for calcul the mask position with numpy : 0.024670839309692383 nb_pixel_total : 102991 time to create 1 rle with old method : 0.1139223575592041 time for calcul the mask position with numpy : 0.024055957794189453 nb_pixel_total : 131823 time to create 1 rle with old method : 0.1460728645324707 time for calcul the mask position with numpy : 0.024628877639770508 nb_pixel_total : 247826 time to create 1 rle with new method : 0.7111446857452393 time for calcul the mask position with numpy : 0.025203943252563477 nb_pixel_total : 103526 time to create 1 rle with old method : 0.11052989959716797 create new chi : 2.7474746704101562 time to delete rle : 0.000823974609375 batch 1 Loaded 11 chid ids of type : 3594 ++++++++Number RLEs to save : 5946 TO DO : save crop sub photo not yet done ! save time : 0.4215097427368164 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.7190091609954834 time for calcul the mask position with numpy : 0.5044305324554443 nb_pixel_total : 8238288 time to create 1 rle with new method : 0.6322050094604492 time for calcul the mask position with numpy : 0.022879362106323242 nb_pixel_total : 56112 time to create 1 rle with old method : 0.06060194969177246 create new chi : 1.261202335357666 time to delete rle : 0.0002796649932861328 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2750 TO DO : save crop sub photo not yet done ! save time : 0.23223280906677246 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 1.2311301231384277 time for calcul the mask position with numpy : 0.7571053504943848 nb_pixel_total : 8205799 time to create 1 rle with new method : 0.8689532279968262 time for calcul the mask position with numpy : 0.02330803871154785 nb_pixel_total : 69845 time to create 1 rle with old method : 0.07877659797668457 time for calcul the mask position with numpy : 0.024001359939575195 nb_pixel_total : 18756 time to create 1 rle with old method : 0.02081465721130371 create new chi : 1.8148620128631592 time to delete rle : 0.0004715919494628906 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2924 TO DO : save crop sub photo not yet done ! save time : 0.21573758125305176 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 1.5579209327697754 time for calcul the mask position with numpy : 0.6803684234619141 nb_pixel_total : 8213765 time to create 1 rle with new method : 0.6623036861419678 time for calcul the mask position with numpy : 0.023085594177246094 nb_pixel_total : 13665 time to create 1 rle with old method : 0.015115737915039062 time for calcul the mask position with numpy : 0.02365398406982422 nb_pixel_total : 15568 time to create 1 rle with old method : 0.017124176025390625 time for calcul the mask position with numpy : 0.024161100387573242 nb_pixel_total : 51402 time to create 1 rle with old method : 0.05623459815979004 create new chi : 1.5434467792510986 time to delete rle : 0.0004918575286865234 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 3242 TO DO : save crop sub photo not yet done ! save time : 0.25675344467163086 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 2.5071182250976562 time for calcul the mask position with numpy : 0.5874674320220947 nb_pixel_total : 7879731 time to create 1 rle with new method : 0.6154258251190186 time for calcul the mask position with numpy : 0.0227353572845459 nb_pixel_total : 11212 time to create 1 rle with old method : 0.01203775405883789 time for calcul the mask position with numpy : 0.022820472717285156 nb_pixel_total : 11255 time to create 1 rle with old method : 0.012262821197509766 time for calcul the mask position with numpy : 0.023656845092773438 nb_pixel_total : 135258 time to create 1 rle with old method : 0.14480280876159668 time for calcul the mask position with numpy : 0.027916431427001953 nb_pixel_total : 110694 time to create 1 rle with old method : 0.13878130912780762 time for calcul the mask position with numpy : 0.02413773536682129 nb_pixel_total : 94666 time to create 1 rle with old method : 0.10196638107299805 time for calcul the mask position with numpy : 0.02411031723022461 nb_pixel_total : 51584 time to create 1 rle with old method : 0.055419921875 create new chi : 1.8547685146331787 time to delete rle : 0.0007922649383544922 batch 1 Loaded 13 chid ids of type : 3594 ++++++++Number RLEs to save : 5588 TO DO : save crop sub photo not yet done ! save time : 0.4371306896209717 map_output_result : {1405971326: (0.0, 'Should be the crop_list due to order', 0), 1405971292: (0.0, 'Should be the crop_list due to order', 0), 1405971255: (0.0, 'Should be the crop_list due to order', 0), 1405971220: (0.0, 'Should be the crop_list due to order', 0), 1405971040: (0.0, 'Should be the crop_list due to order', 0), 1405971003: (0.0, 'Should be the crop_list due to order', 0), 1405970958: (0.0, 'Should be the crop_list due to order', 0), 1405970925: (0.0, 'Should be the crop_list due to order', 0), 1405970895: (0.0, 'Should be the crop_list due to order', 0), 1405970892: (0.0, 'Should be the crop_list due to order', 0), 1405970809: (0.0, 'Should be the crop_list due to order', 0), 1405970807: (0.0, 'Should be the crop_list due to order', 0), 1405970799: (0.0, 'Should be the crop_list due to order', 0), 1405970767: (0.0, 'Should be the crop_list due to order', 0), 1405969713: (0.0, 'Should be the crop_list due to order', 0), 1405969692: (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 [1405971326, 1405971292, 1405971255, 1405971220, 1405971040, 1405971003, 1405970958, 1405970925, 1405970895, 1405970892, 1405970809, 1405970807, 1405970799, 1405970767, 1405969713, 1405969692] Looping around the photos to save general results len do output : 16 /1405971326.Didn't retrieve data . /1405971292.Didn't retrieve data . /1405971255.Didn't retrieve data . /1405971220.Didn't retrieve data . /1405971040.Didn't retrieve data . /1405971003.Didn't retrieve data . /1405970958.Didn't retrieve data . /1405970925.Didn't retrieve data . /1405970895.Didn't retrieve data . /1405970892.Didn't retrieve data . /1405970809.Didn't retrieve data . /1405970807.Didn't retrieve data . /1405970799.Didn't retrieve data . /1405970767.Didn't retrieve data . /1405969713.Didn't retrieve data . /1405969692.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, '4337278') ('3318', '30251199', '1405971326', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971292', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971255', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971220', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971040', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971003', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970958', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970925', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970895', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970892', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970809', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970807', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970799', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970767', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969713', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969692', None, None, None, None, None, '4337278') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 48 time used for this insertion : 0.018903493881225586 save_final save missing photos in datou_result : time spend for datou_step_exec : 83.43512845039368 time spend to save output : 0.019349336624145508 total time spend for step 3 : 83.45447778701782 step4:ventilate_hashtags_in_portfolio Thu Jan 29 14:13:58 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 : 30251199 get user id for portfolio 30251199 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`=30251199 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','flou','pehd','autre','carton','background','pet_clair','pet_fonce','environnement','papier','mal_croppe')) 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`=30251199 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','flou','pehd','autre','carton','background','pet_clair','pet_fonce','environnement','papier','mal_croppe')) 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`=30251199 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','flou','pehd','autre','carton','background','pet_clair','pet_fonce','environnement','papier','mal_croppe')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/30251661,30251662,30251663,30251664,30251665,30251666,30251667,30251668,30251669,30251670,30251671?tags=metal,flou,pehd,autre,carton,background,pet_clair,pet_fonce,environnement,papier,mal_croppe Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1405971326, 1405971292, 1405971255, 1405971220, 1405971040, 1405971003, 1405970958, 1405970925, 1405970895, 1405970892, 1405970809, 1405970807, 1405970799, 1405970767, 1405969713, 1405969692] Looping around the photos to save general results len do output : 1 /30251199. 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, '4337278') ('3318', '30251199', '1405971326', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971292', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971255', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971220', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971040', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971003', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970958', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970925', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970895', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970892', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970809', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970807', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970799', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970767', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969713', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969692', None, None, None, None, None, '4337278') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 17 time used for this insertion : 0.017742395401000977 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.0984628200531006 time spend to save output : 0.018224000930786133 total time spend for step 4 : 2.1166868209838867 step5:final Thu Jan 29 14:14:01 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 ! 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 : {1405971326: ('0.03424730088975695',), 1405971292: ('0.03424730088975695',), 1405971255: ('0.03424730088975695',), 1405971220: ('0.03424730088975695',), 1405971040: ('0.03424730088975695',), 1405971003: ('0.03424730088975695',), 1405970958: ('0.03424730088975695',), 1405970925: ('0.03424730088975695',), 1405970895: ('0.03424730088975695',), 1405970892: ('0.03424730088975695',), 1405970809: ('0.03424730088975695',), 1405970807: ('0.03424730088975695',), 1405970799: ('0.03424730088975695',), 1405970767: ('0.03424730088975695',), 1405969713: ('0.03424730088975695',), 1405969692: ('0.03424730088975695',)} new output for save of step final : {1405971326: ('0.03424730088975695',), 1405971292: ('0.03424730088975695',), 1405971255: ('0.03424730088975695',), 1405971220: ('0.03424730088975695',), 1405971040: ('0.03424730088975695',), 1405971003: ('0.03424730088975695',), 1405970958: ('0.03424730088975695',), 1405970925: ('0.03424730088975695',), 1405970895: ('0.03424730088975695',), 1405970892: ('0.03424730088975695',), 1405970809: ('0.03424730088975695',), 1405970807: ('0.03424730088975695',), 1405970799: ('0.03424730088975695',), 1405970767: ('0.03424730088975695',), 1405969713: ('0.03424730088975695',), 1405969692: ('0.03424730088975695',)} [1405971326, 1405971292, 1405971255, 1405971220, 1405971040, 1405971003, 1405970958, 1405970925, 1405970895, 1405970892, 1405970809, 1405970807, 1405970799, 1405970767, 1405969713, 1405969692] Looping around the photos to save general results len do output : 16 /1405971326.Didn't retrieve data . /1405971292.Didn't retrieve data . /1405971255.Didn't retrieve data . /1405971220.Didn't retrieve data . /1405971040.Didn't retrieve data . /1405971003.Didn't retrieve data . /1405970958.Didn't retrieve data . /1405970925.Didn't retrieve data . /1405970895.Didn't retrieve data . /1405970892.Didn't retrieve data . /1405970809.Didn't retrieve data . /1405970807.Didn't retrieve data . /1405970799.Didn't retrieve data . /1405970767.Didn't retrieve data . /1405969713.Didn't retrieve data . /1405969692.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, '4337278') ('3318', '30251199', '1405971326', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971292', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971255', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971220', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971040', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971003', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970958', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970925', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970895', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970892', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970809', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970807', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970799', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970767', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969713', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969692', None, None, None, None, None, '4337278') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 48 time used for this insertion : 0.016620159149169922 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.22500896453857422 time spend to save output : 0.01733684539794922 total time spend for step 5 : 0.24234580993652344 step6:blur_detection Thu Jan 29 14:14:01 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 ! 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/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e.jpg resize: (2160, 3840) 1405971326 -6.5205331661365795 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450.jpg resize: (2160, 3840) 1405971292 -7.016858403181206 treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5.jpg resize: (2160, 3840) 1405971255 -6.692438477575007 treat image : temp/1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da.jpg resize: (2160, 3840) 1405971220 -6.6057982262968675 treat image : temp/1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2.jpg resize: (2160, 3840) 1405971040 -6.654191257451978 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3.jpg resize: (2160, 3840) 1405971003 -6.662386840771976 treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310.jpg resize: (2160, 3840) 1405970958 -6.772459015317848 treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38.jpg resize: (2160, 3840) 1405970925 -6.546076652423207 treat image : temp/1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a.jpg resize: (2160, 3840) 1405970895 -6.70301421052555 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683.jpg resize: (2160, 3840) 1405970892 -6.75232184750319 treat image : temp/1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113.jpg resize: (2160, 3840) 1405970809 -6.616224961019805 treat image : temp/1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2.jpg resize: (2160, 3840) 1405970807 -6.49996522513464 treat image : temp/1769692230_942930_1405970799_5d872a44176de128b6ac9668338d4380.jpg resize: (2160, 3840) 1405970799 -6.566706562367266 treat image : temp/1769692230_942930_1405970767_c90e03691be1f87f37862df075a669ec.jpg resize: (2160, 3840) 1405970767 -6.633276815443812 treat image : temp/1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54.jpg resize: (2160, 3840) 1405969713 -6.484713048498567 treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418.jpg resize: (2160, 3840) 1405969692 -5.019594625973719 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361572_0.png resize: (680, 693) 1405977659 -3.4711126514136845 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361575_0.png resize: (317, 493) 1405977660 -4.193697022357998 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361577_0.png resize: (326, 234) 1405977661 -4.2846124805822825 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361578_0.png resize: (306, 399) 1405977662 -6.0707290139044074 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361579_0.png resize: (360, 392) 1405977663 -5.474842720987553 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361580_0.png resize: (378, 222) 1405977664 -4.629999441125635 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361581_0.png resize: (187, 138) 1405977665 -4.615664188856652 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361582_0.png resize: (375, 479) 1405977666 -3.378909269055072 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361583_0.png resize: (225, 419) 1405977667 -5.556201331107051 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361584_0.png resize: (170, 229) 1405977668 -5.199687623793881 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361585_0.png resize: (218, 85) 1405977669 -4.06060434701838 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361586_0.png resize: (292, 69) 1405977670 -4.533238135417994 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361588_0.png resize: (229, 317) 1405977671 -3.2134114299510297 treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361591_0.png resize: (324, 222) 1405977672 -5.07159126953705 treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361597_0.png resize: (154, 238) 1405977673 -4.307255198307858 treat image : temp/1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361598_0.png resize: (470, 598) 1405977674 -4.634640578620681 treat image : temp/1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361599_0.png resize: (176, 205) 1405977675 -4.353008256136662 treat image : temp/1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361600_0.png resize: (234, 179) 1405977676 -5.0638374895569465 treat image : temp/1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da_rle_crop_4115361601_0.png resize: (143, 198) 1405977677 -4.229713742551463 treat image : temp/1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361602_0.png resize: (165, 158) 1405977678 -4.737029029309395 treat image : temp/1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361605_0.png resize: (251, 141) 1405977679 -4.098149543701822 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361606_0.png resize: (195, 193) 1405977680 -5.114811963873207 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361607_0.png resize: (362, 333) 1405977681 -4.951033499517993 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361611_0.png resize: (177, 321) 1405977682 -3.296901018303604 treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361616_0.png resize: (185, 408) 1405977683 -4.834671281772423 treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361617_0.png resize: (158, 104) 1405977684 -3.0270258078098604 treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361618_0.png resize: (240, 398) 1405977685 -5.220383677837352 treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361619_0.png resize: (140, 155) 1405977686 -4.740149646452466 treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361621_0.png resize: (300, 538) 1405977687 -4.3012114880659125 treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361623_0.png resize: (196, 210) 1405977688 -4.474409500529484 treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361624_0.png resize: (136, 216) 1405977689 -4.177015902332215 treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361627_0.png resize: (214, 118) 1405977690 -4.430556079518854 treat image : temp/1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a_rle_crop_4115361628_0.png resize: (153, 112) 1405977691 -4.314830474913404 treat image : temp/1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a_rle_crop_4115361630_0.png resize: (208, 247) 1405977692 -4.6845738332343805 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361631_0.png resize: (190, 142) 1405977693 -4.254971157700592 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361632_0.png resize: (155, 297) 1405977694 -4.727098048722183 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361636_0.png resize: (344, 242) 1405977695 -3.8785937795236607 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361637_0.png resize: (312, 263) 1405977696 -3.9821838500126012 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361638_0.png resize: (262, 448) 1405977697 -5.913021351863591 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361639_0.png resize: (101, 134) 1405977698 -3.4979962629156165 treat image : temp/1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113_rle_crop_4115361642_0.png resize: (121, 86) 1405977699 -2.6784490230999745 treat image : temp/1769692230_942930_1405970807_353c8ce8003b325afacda79572c620b2_rle_crop_4115361648_0.png resize: (290, 74) 1405977700 -3.8755283989256233 treat image : temp/1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54_rle_crop_4115361652_0.png resize: (231, 287) 1405977701 -4.930641892851018 treat image : temp/1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54_rle_crop_4115361653_0.png resize: (160, 154) 1405977702 -5.019147918101939 treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361655_0.png resize: (252, 387) 1405977703 -3.5909405304537123 treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361659_0.png resize: (155, 131) 1405977704 -3.9033170586527413 treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361660_0.png resize: (87, 218) 1405977705 -3.7116822961120417 treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361594_0.png resize: (191, 227) 1405977706 -4.25397354101478 treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361596_0.png resize: (99, 106) 1405977707 -3.1484263400290935 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361613_0.png resize: (203, 191) 1405977708 -4.991919935899157 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361635_0.png resize: (254, 471) 1405977709 -5.910227539844298 treat image : temp/1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113_rle_crop_4115361643_0.png resize: (145, 324) 1405977710 -2.869006760601203 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361576_0.png resize: (88, 121) 1405977711 -5.6874871852849305 treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361593_0.png resize: (109, 104) 1405977712 -4.810783094107702 treat image : temp/1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361603_0.png resize: (216, 144) 1405977713 -4.893841079559651 treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683_rle_crop_4115361640_0.png resize: (110, 139) 1405977714 -5.403624391880005 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361573_0.png resize: (192, 364) 1405977774 -4.834582436168318 treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e_rle_crop_4115361574_0.png resize: (305, 561) 1405977776 -4.621498108127093 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361587_0.png resize: (339, 340) 1405977777 -5.301052708976328 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361589_0.png resize: (268, 152) 1405977778 -5.285851591583024 treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450_rle_crop_4115361590_0.png resize: (225, 152) 1405977780 -4.736833671715143 treat image : temp/1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2_rle_crop_4115361604_0.png resize: (135, 234) 1405977781 -5.130068347677321 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361608_0.png resize: (396, 492) 1405977783 -4.880378988311202 treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361609_0.png resize: (231, 350) 1405977784 -4.428643768479387 treat image : 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temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361620_0.png resize: (327, 194) 1405977896 -6.860281577054932 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 : 105 time used for this insertion : 0.021738290786743164 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 105 time used for this insertion : 0.03713345527648926 save missing photos in datou_result : time spend for datou_step_exec : 60.366119146347046 time spend to save output : 0.06474637985229492 total time spend for step 6 : 60.43086552619934 step7:brightness Thu Jan 29 14:15:01 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 ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1769692230_942930_1405971326_b3583a0998efd71218005ae77f6f4f2e.jpg treat image : temp/1769692230_942930_1405971292_1de4b2859858f47c2776c1582c6ff450.jpg treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5.jpg treat image : temp/1769692230_942930_1405971220_5ec3069937e7ed76a00c6f0a4740b0da.jpg treat image : temp/1769692230_942930_1405971040_4a675b2860a29a36c62a194d347232d2.jpg treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3.jpg treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310.jpg treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38.jpg treat image : temp/1769692230_942930_1405970895_28d17e8c46809c9adbdc1fbbf037e50a.jpg treat image : temp/1769692230_942930_1405970892_ac46e361fba04b3c728beb38dad14683.jpg treat image : 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temp/1769692230_942930_1405970767_c90e03691be1f87f37862df075a669ec_rle_crop_4115361651_0.png treat image : temp/1769692230_942930_1405969713_7d63750c7f21cb10807d5397e323ff54_rle_crop_4115361654_0.png treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361656_0.png treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361657_0.png treat image : temp/1769692230_942930_1405969692_396ab74d839cbf668fce5aef25a71418_rle_crop_4115361658_0.png treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361592_0.png treat image : temp/1769692230_942930_1405971003_2725b86c8db997fdaddae8d2bd688bf3_rle_crop_4115361612_0.png treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361625_0.png treat image : temp/1769692230_942930_1405970925_0fefe3d05c1c62d130546b2a34702b38_rle_crop_4115361626_0.png treat image : temp/1769692230_942930_1405970809_41a7fab630177e1e0c792b2de0047113_rle_crop_4115361641_0.png treat image : temp/1769692230_942930_1405970767_c90e03691be1f87f37862df075a669ec_rle_crop_4115361650_0.png treat image : temp/1769692230_942930_1405971255_88fcbe9f019157f8718a5f494fac21c5_rle_crop_4115361595_0.png treat image : temp/1769692230_942930_1405970958_3dc29d1008fa1885f03c7952b6e74310_rle_crop_4115361620_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 : 105 time used for this insertion : 0.017522335052490234 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 105 time used for this insertion : 0.03968214988708496 save missing photos in datou_result : time spend for datou_step_exec : 13.775126934051514 time spend to save output : 0.0631873607635498 total time spend for step 7 : 13.838314294815063 step8:velours_tree Thu Jan 29 14:15:15 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 : 0.10883164405822754 time spend to save output : 4.410743713378906e-05 total time spend for step 8 : 0.10887575149536133 step9:send_mail_cod Thu Jan 29 14:15:15 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_P30251199_29-01-2026_14_15_15.pdf 30251661 change filename to text .change filename to text .change filename to text .change filename to text .imagette302516611769692515 30251662 imagette302516621769692516 30251663 imagette302516631769692516 30251664 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette302516641769692516 30251665 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette302516651769692516 30251666 imagette302516661769692517 30251667 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change 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.imagette302516701769692519 30251671 imagette302516711769692521 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=30251199 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/30251661,30251662,30251663,30251664,30251665,30251666,30251667,30251668,30251669,30251670,30251671?tags=metal,flou,pehd,autre,carton,background,pet_clair,pet_fonce,environnement,papier,mal_croppe your option no_mail is active, we will not send the real mail to your client args[1405971326] : ((1405971326, -6.5205331661365795, 492609224), (1405971326, -0.8853621358237663, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405971292] : ((1405971292, -7.016858403181206, 492609224), (1405971292, -0.9326527472234665, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405971255] : ((1405971255, -6.692438477575007, 492609224), (1405971255, -1.0867438874446405, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405971220] : ((1405971220, -6.6057982262968675, 492609224), (1405971220, -1.033459291612723, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405971040] : ((1405971040, -6.654191257451978, 492609224), (1405971040, -1.0710133354491056, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405971003] : ((1405971003, -6.662386840771976, 492609224), (1405971003, -0.9954328044503937, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970958] : ((1405970958, -6.772459015317848, 492609224), (1405970958, -0.9222344755487991, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970925] : ((1405970925, -6.546076652423207, 492609224), (1405970925, -1.1517922591741798, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970895] : ((1405970895, -6.70301421052555, 492609224), (1405970895, -1.1696968634941092, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970892] : ((1405970892, -6.75232184750319, 492609224), (1405970892, -1.0236315888532013, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970809] : ((1405970809, -6.616224961019805, 492609224), (1405970809, -1.128718182493385, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970807] : ((1405970807, -6.49996522513464, 492609224), (1405970807, -0.9128667377776273, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970799] : ((1405970799, -6.566706562367266, 492609224), (1405970799, -1.1044842814981384, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405970767] : ((1405970767, -6.633276815443812, 492609224), (1405970767, -1.2960434547568973, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405969713] : ((1405969713, -6.484713048498567, 492609224), (1405969713, -1.3834943039905228, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com args[1405969692] : ((1405969692, -5.019594625973719, 492609224), (1405969692, -3.029738548532632, 501862349), '0.03424730088975695') We are sending mail with results at report@fotonower.com refus_total : 0.03424730088975695 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=30251199 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_P30251199_29-01-2026_14_15_15.pdf results_Auto_P30251199_29-01-2026_14_15_15.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30251199_29-01-2026_14_15_15.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','30251199','results_Auto_P30251199_29-01-2026_14_15_15.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30251199_29-01-2026_14_15_15.pdf','pdf','','0.79','0.03424730088975695') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1405971326, 1405971292, 1405971255, 1405971220, 1405971040, 1405971003, 1405970958, 1405970925, 1405970895, 1405970892, 1405970809, 1405970807, 1405970799, 1405970767, 1405969713, 1405969692] 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, '4337278') ('3318', '30251199', '1405971326', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971292', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971255', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971220', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971040', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971003', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970958', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970925', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970895', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970892', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970809', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970807', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970799', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970767', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969713', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969692', None, None, None, None, None, '4337278') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.01835179328918457 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.223578453063965 time spend to save output : 0.018661975860595703 total time spend for step 9 : 8.24224042892456 step10:split_time_score Thu Jan 29 14:15:23 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'}] (('13', 16),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 29012026 30251199 Nombre de photos uploadées : 16 / 23040 (0%) 29012026 30251199 Nombre de photos taguées (types de déchets): 0 / 16 (0%) 29012026 30251199 Nombre de photos taguées (volume) : 0 / 16 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 4.76837158203125e-06 ???????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0007867813110351562 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.26474857330322266 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 : 0.015514593724557506 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30245047_29-01-2026_07_14_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30245047 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`=30245047 AND mptpi.`type`=3726 To do Qualite : 0.021765745563271606 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30245096_29-01-2026_07_14_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30245096 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`=30245096 AND mptpi.`type`=3594 To do Qualite : 0.04542275936407159 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30245114_29-01-2026_07_10_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30245114 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`=30245114 AND mptpi.`type`=3726 To do Qualite : 0.06897667950480435 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30247852_29-01-2026_08_09_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30247852 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`=30247852 AND mptpi.`type`=3726 To do Qualite : 0.1448523365530624 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30248355_29-01-2026_08_36_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30248355 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`=30248355 AND mptpi.`type`=3726 To do Qualite : 0.09042217802607218 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30250069_29-01-2026_12_09_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30250069 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`=30250069 AND mptpi.`type`=3726 To do Qualite : 0.05113713977877101 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249124_29-01-2026_10_08_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249124 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`=30249124 AND mptpi.`type`=3726 To do Qualite : 0.18472005543821687 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249125_29-01-2026_10_06_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249125 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`=30249125 AND mptpi.`type`=3726 To do Qualite : 0.10591639205818319 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249127_29-01-2026_10_10_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249127 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`=30249127 AND mptpi.`type`=3726 To do Qualite : 0.06122370266378762 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249419_29-01-2026_12_10_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249419 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`=30249419 AND mptpi.`type`=3726 To do Qualite : 0.12639447852826655 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249422_29-01-2026_10_40_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249422 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`=30249422 AND mptpi.`type`=3726 To do Qualite : 0.06729275539816018 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249423_29-01-2026_10_35_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249423 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`=30249423 AND mptpi.`type`=3726 To do Qualite : 0.097633299877073 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30249764_29-01-2026_11_40_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30249764 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`=30249764 AND mptpi.`type`=3726 To do Qualite : 0.03074547954750841 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30250073_29-01-2026_12_08_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30250073 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`=30250073 AND mptpi.`type`=3726 To do Qualite : 0.05027438574383199 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30250848_29-01-2026_13_46_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30250848 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`=30250848 AND mptpi.`type`=3726 To do Qualite : 0.03159246568265744 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30250849_29-01-2026_13_38_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30250849 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`=30250849 AND mptpi.`type`=3726 To do Qualite : 0.14011750525855746 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30250850_29-01-2026_13_39_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30250850 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`=30250850 AND mptpi.`type`=3726 To do Qualite : 0.0691956093067128 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30250852_29-01-2026_13_35_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30250852 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`=30250852 AND mptpi.`type`=3726 To do Qualite : 0.03424730088975695 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30251199_29-01-2026_14_15_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30251199 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`=30251199 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'29012026': {'nb_upload': 16, '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 [1405971326, 1405971292, 1405971255, 1405971220, 1405971040, 1405971003, 1405970958, 1405970925, 1405970895, 1405970892, 1405970809, 1405970807, 1405970799, 1405970767, 1405969713, 1405969692] Looping around the photos to save general results len do output : 1 /30251199Didn'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, '4337278') ('3318', '30251199', '1405971326', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971292', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971255', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971220', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971040', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405971003', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970958', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970925', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970895', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970892', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970809', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970807', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970799', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405970767', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969713', None, None, None, None, None, '4337278') ('3318', None, None, None, None, None, None, None, '4337278') ('3318', '30251199', '1405969692', None, None, None, None, None, '4337278') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 17 time used for this insertion : 0.019940853118896484 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.8313007354736328 time spend to save output : 0.020209312438964844 total time spend for step 10 : 1.8515100479125977 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 16 set_done_treatment 139.26user 123.52system 4:59.78elapsed 87%CPU (0avgtext+0avgdata 5074116maxresident)k 22744inputs+126640outputs (136major+13042914minor)pagefaults 0swaps