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 : 1978711 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 : ['3735569'] with mtr_portfolio_ids : ['26946470'] and first list_photo_ids : [] new path : /proc/1978711/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 18 ; length of list_pids : 18 ; length of list_args : 18 time to download the photos : 2.500627279281616 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Wed Sep 17 14:00:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10365 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-17 14:00:35.288120: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-17 14:00:35.316580: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-17 14:00:35.318602: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f44e0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-17 14:00:35.318682: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-17 14:00:35.323334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-17 14:00:35.552056: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x139bc420 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-17 14:00:35.552108: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-17 14:00:35.554178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-17 14:00:35.554494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:00:35.556740: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:00:35.559172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:00:35.559542: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:00:35.562011: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:00:35.563202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:00:35.568040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:00:35.569692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:00:35.569799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:00:35.570625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-17 14:00:35.570643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-17 14:00:35.570653: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-17 14:00:35.572824: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9456 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-09-17 14:00:36.058643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-17 14:00:36.058736: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:00:36.058757: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:00:36.058775: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:00:36.058793: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:00:36.058810: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:00:36.058826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:00:36.058843: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:00:36.060209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:00:36.061547: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-17 14:00:36.061590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:00:36.061606: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:00:36.061621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:00:36.061635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:00:36.061649: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:00:36.061663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:00:36.061678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:00:36.062944: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:00:36.062981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-17 14:00:36.062990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-17 14:00:36.062998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-17 14:00:36.064318: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9456 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-09-17 14:00:47.094517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:00:47.469448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 18 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 25.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 19.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 19.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 16.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 23.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 23.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 19.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 12 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 23.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 12 Detection mask done ! Trying to reset tf kernel 1982022 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 106 tf kernel not reseted sub process len(results) : 18 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 18 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 : 5395 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.0007023811340332031 nb_pixel_total : 14931 time to create 1 rle with old method : 0.021166086196899414 length of segment : 187 time for calcul the mask position with numpy : 0.0002143383026123047 nb_pixel_total : 10549 time to create 1 rle with old method : 0.014791011810302734 length of segment : 89 time for calcul the mask position with numpy : 0.0006229877471923828 nb_pixel_total : 16928 time to create 1 rle with old method : 0.023869752883911133 length of segment : 435 time for calcul the mask position with numpy : 0.002692699432373047 nb_pixel_total : 102167 time to create 1 rle with old method : 0.11986398696899414 length of segment : 500 time for calcul the mask position with numpy : 0.0002205371856689453 nb_pixel_total : 5541 time to create 1 rle with old method : 0.0068035125732421875 length of segment : 73 time for calcul the mask position with numpy : 0.0004627704620361328 nb_pixel_total : 8485 time to create 1 rle with old method : 0.010528326034545898 length of segment : 180 time for calcul the mask position with numpy : 0.0019893646240234375 nb_pixel_total : 83880 time to create 1 rle with old method : 0.09696698188781738 length of segment : 487 time for calcul the mask position with numpy : 0.00027441978454589844 nb_pixel_total : 4336 time to create 1 rle with old method : 0.005066633224487305 length of segment : 136 time for calcul the mask position with numpy : 0.0002837181091308594 nb_pixel_total : 7512 time to create 1 rle with old method : 0.008928775787353516 length of segment : 88 time for calcul the mask position with numpy : 0.02860403060913086 nb_pixel_total : 1331766 time to create 1 rle with new method : 0.13989520072937012 length of segment : 1606 time for calcul the mask position with numpy : 0.0004143714904785156 nb_pixel_total : 8761 time to create 1 rle with old method : 0.013470649719238281 length of segment : 131 time for calcul the mask position with numpy : 0.0002772808074951172 nb_pixel_total : 7365 time to create 1 rle with old method : 0.009068965911865234 length of segment : 116 time for calcul the mask position with numpy : 0.0004723072052001953 nb_pixel_total : 8886 time to create 1 rle with old method : 0.011417865753173828 length of segment : 167 time for calcul the mask position with numpy : 0.00035452842712402344 nb_pixel_total : 8370 time to create 1 rle with old method : 0.009816646575927734 length of segment : 107 time for calcul the mask position with numpy : 0.00017070770263671875 nb_pixel_total : 6678 time to create 1 rle with old method : 0.011040449142456055 length of segment : 62 time for calcul the mask position with numpy : 0.00024056434631347656 nb_pixel_total : 5882 time to create 1 rle with old method : 0.007067680358886719 length of segment : 96 time for calcul the mask position with numpy : 0.00040149688720703125 nb_pixel_total : 11970 time to create 1 rle with old method : 0.013825178146362305 length of segment : 153 time for calcul the mask position with numpy : 0.0007545948028564453 nb_pixel_total : 29487 time to create 1 rle with old method : 0.0333402156829834 length of segment : 177 time for calcul the mask position with numpy : 0.0005536079406738281 nb_pixel_total : 8333 time to create 1 rle with old method : 0.00979304313659668 length of segment : 178 time for calcul the mask position with numpy : 0.00034236907958984375 nb_pixel_total : 14034 time to create 1 rle with old method : 0.01727771759033203 length of segment : 110 time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 23549 time to create 1 rle with old method : 0.02803778648376465 length of segment : 148 time for calcul the mask position with numpy : 0.0001323223114013672 nb_pixel_total : 5617 time to create 1 rle with old method : 0.007195472717285156 length of segment : 90 time for calcul the mask position with numpy : 0.00150299072265625 nb_pixel_total : 66167 time to create 1 rle with old method : 0.07679867744445801 length of segment : 694 time for calcul the mask position with numpy : 0.0005843639373779297 nb_pixel_total : 16934 time to create 1 rle with old method : 0.019531965255737305 length of segment : 178 time for calcul the mask position with numpy : 0.00029659271240234375 nb_pixel_total : 4556 time to create 1 rle with old method : 0.006420135498046875 length of segment : 101 time for calcul the mask position with numpy : 0.00019884109497070312 nb_pixel_total : 5382 time to create 1 rle with old method : 0.006712913513183594 length of segment : 60 time for calcul the mask position with numpy : 0.0004665851593017578 nb_pixel_total : 10104 time to create 1 rle with old method : 0.011770248413085938 length of segment : 216 time for calcul the mask position with numpy : 0.00014472007751464844 nb_pixel_total : 2614 time to create 1 rle with old method : 0.0031881332397460938 length of segment : 56 time for calcul the mask position with numpy : 0.0009708404541015625 nb_pixel_total : 36966 time to create 1 rle with old method : 0.047924041748046875 length of segment : 272 time for calcul the mask position with numpy : 0.0006780624389648438 nb_pixel_total : 18891 time to create 1 rle with old method : 0.04595589637756348 length of segment : 197 time for calcul the mask position with numpy : 0.0004372596740722656 nb_pixel_total : 10606 time to create 1 rle with old method : 0.012490272521972656 length of segment : 109 time for calcul the mask position with numpy : 0.00044798851013183594 nb_pixel_total : 10930 time to create 1 rle with old method : 0.012775421142578125 length of segment : 136 time for calcul the mask position with numpy : 0.0003829002380371094 nb_pixel_total : 8108 time to create 1 rle with old method : 0.009652853012084961 length of segment : 176 time for calcul the mask position with numpy : 0.0003883838653564453 nb_pixel_total : 11373 time to create 1 rle with old method : 0.014353752136230469 length of segment : 99 time for calcul the mask position with numpy : 0.00037789344787597656 nb_pixel_total : 4002 time to create 1 rle with old method : 0.004760265350341797 length of segment : 119 time for calcul the mask position with numpy : 0.0006978511810302734 nb_pixel_total : 30384 time to create 1 rle with old method : 0.0346837043762207 length of segment : 179 time for calcul the mask position with numpy : 0.0017085075378417969 nb_pixel_total : 52927 time to create 1 rle with old method : 0.060294151306152344 length of segment : 320 time for calcul the mask position with numpy : 0.0009562969207763672 nb_pixel_total : 40878 time to create 1 rle with old method : 0.04755282402038574 length of segment : 186 time for calcul the mask position with numpy : 0.00034427642822265625 nb_pixel_total : 12718 time to create 1 rle with old method : 0.01490926742553711 length of segment : 159 time for calcul the mask position with numpy : 0.00018072128295898438 nb_pixel_total : 4297 time to create 1 rle with old method : 0.0051250457763671875 length of segment : 78 time for calcul the mask position with numpy : 0.0006566047668457031 nb_pixel_total : 18048 time to create 1 rle with old method : 0.10004901885986328 length of segment : 210 time for calcul the mask position with numpy : 0.0005359649658203125 nb_pixel_total : 11465 time to create 1 rle with old method : 0.013193607330322266 length of segment : 179 time for calcul the mask position with numpy : 0.0004949569702148438 nb_pixel_total : 10267 time to create 1 rle with old method : 0.01780390739440918 length of segment : 137 time for calcul the mask position with numpy : 0.0005764961242675781 nb_pixel_total : 8661 time to create 1 rle with old method : 0.009898900985717773 length of segment : 217 time for calcul the mask position with numpy : 0.0002579689025878906 nb_pixel_total : 7653 time to create 1 rle with old method : 0.009279251098632812 length of segment : 67 time for calcul the mask position with numpy : 0.0003249645233154297 nb_pixel_total : 8948 time to create 1 rle with old method : 0.010469436645507812 length of segment : 141 time for calcul the mask position with numpy : 0.0005230903625488281 nb_pixel_total : 9755 time to create 1 rle with old method : 0.011189699172973633 length of segment : 180 time for calcul the mask position with numpy : 0.02099299430847168 nb_pixel_total : 765780 time to create 1 rle with new method : 0.31743335723876953 length of segment : 986 time for calcul the mask position with numpy : 0.0002791881561279297 nb_pixel_total : 5924 time to create 1 rle with old method : 0.0069696903228759766 length of segment : 85 time for calcul the mask position with numpy : 0.0011858940124511719 nb_pixel_total : 6496 time to create 1 rle with old method : 0.007880926132202148 length of segment : 302 time for calcul the mask position with numpy : 0.00017881393432617188 nb_pixel_total : 4935 time to create 1 rle with old method : 0.0059206485748291016 length of segment : 66 time for calcul the mask position with numpy : 0.0013012886047363281 nb_pixel_total : 33867 time to create 1 rle with old method : 0.0414586067199707 length of segment : 263 time for calcul the mask position with numpy : 0.0005235671997070312 nb_pixel_total : 8422 time to create 1 rle with old method : 0.009823799133300781 length of segment : 180 time for calcul the mask position with numpy : 0.0002799034118652344 nb_pixel_total : 5957 time to create 1 rle with old method : 0.007134914398193359 length of segment : 120 time for calcul the mask position with numpy : 0.00015664100646972656 nb_pixel_total : 3721 time to create 1 rle with old method : 0.0045719146728515625 length of segment : 41 time spent for convertir_results : 8.029608249664307 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 55 chid ids of type : 3594 Number RLEs to save : 11825 save missing photos in datou_result : time spend for datou_step_exec : 41.15576434135437 time spend to save output : 0.7984344959259033 total time spend for step 1 : 41.95419883728027 step2:crop_condition Wed Sep 17 14:01:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 18 ! batch 1 Loaded 55 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 ! map_result returned by crop_photo_return_map_crop : length : 29 About to insert : list_path_to_insert length 29 new photo from crops ! About to upload 29 photos upload in portfolio : 3736932 init cache_photo without model_param we have 29 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758110477_1978711 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 29 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.490172863006592 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/1758110485_1978711 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! 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 : 1.3419513702392578 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 18 About to insert : list_path_to_insert length 18 new photo from crops ! About to upload 18 photos upload in portfolio : 3736932 init cache_photo without model_param we have 18 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758110497_1978711 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 18 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.380266189575195 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758110503_1978711 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.9606125354766846 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758110505_1978711 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.49664878845214844 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 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 [1384189139, 1384189106, 1384189065, 1384189036, 1384188734, 1384188708, 1384188683, 1384188656, 1384188631, 1384188613, 1384188607, 1384188604, 1384188601, 1384188598, 1384188595, 1384188593, 1384188256, 1384188222] Looping around the photos to save general results len do output : 55 /1384211340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384211499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384212164Didn'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, '3735569') ('3318', '26946470', '1384189139', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189106', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189065', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189036', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188734', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188708', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188683', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188656', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188631', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188613', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188607', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188604', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188601', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188598', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188595', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188593', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188256', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188222', None, None, None, None, None, '3735569') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 183 time used for this insertion : 0.029909133911132812 save_final save missing photos in datou_result : time spend for datou_step_exec : 31.848687648773193 time spend to save output : 0.032294273376464844 total time spend for step 2 : 31.880981922149658 step3:rle_unique_nms_with_priority Wed Sep 17 14:01:46 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.2217099666595459 time for calcul the mask position with numpy : 0.07618260383605957 nb_pixel_total : 2058669 time to create 1 rle with new method : 0.21623897552490234 time for calcul the mask position with numpy : 0.006433725357055664 nb_pixel_total : 14931 time to create 1 rle with old method : 0.016617298126220703 create new chi : 0.32558298110961914 time to delete rle : 0.02103447914123535 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1454 TO DO : save crop sub photo not yet done ! save time : 0.11683893203735352 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.08024835586547852 time for calcul the mask position with numpy : 0.18005704879760742 nb_pixel_total : 2050848 time to create 1 rle with new method : 0.279465913772583 time for calcul the mask position with numpy : 0.006703615188598633 nb_pixel_total : 12203 time to create 1 rle with old method : 0.013968706130981445 time for calcul the mask position with numpy : 0.007005214691162109 nb_pixel_total : 10549 time to create 1 rle with old method : 0.01172184944152832 create new chi : 0.5084168910980225 time to delete rle : 0.0003070831298828125 batch 1 Loaded 5 chid ids of type : 3594 +++++++++Number RLEs to save : 1877 TO DO : save crop sub photo not yet done ! save time : 0.1929621696472168 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.041905879974365234 time for calcul the mask position with numpy : 0.07549643516540527 nb_pixel_total : 1971433 time to create 1 rle with new method : 0.22333693504333496 time for calcul the mask position with numpy : 0.007356166839599609 nb_pixel_total : 102167 time to create 1 rle with old method : 0.11814165115356445 create new chi : 0.43355560302734375 time to delete rle : 0.0003342628479003906 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2080 TO DO : save crop sub photo not yet done ! save time : 0.1642627716064453 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 0.9745948314666748 time for calcul the mask position with numpy : 0.012268304824829102 nb_pixel_total : 632705 time to create 1 rle with new method : 0.09317159652709961 time for calcul the mask position with numpy : 0.0063822269439697266 nb_pixel_total : 8761 time to create 1 rle with old method : 0.009969949722290039 time for calcul the mask position with numpy : 0.21073246002197266 nb_pixel_total : 1322380 time to create 1 rle with new method : 0.09222126007080078 time for calcul the mask position with numpy : 0.008837223052978516 nb_pixel_total : 7512 time to create 1 rle with old method : 0.00877833366394043 time for calcul the mask position with numpy : 0.0077250003814697266 nb_pixel_total : 4336 time to create 1 rle with old method : 0.004897117614746094 time for calcul the mask position with numpy : 0.009100914001464844 nb_pixel_total : 83880 time to create 1 rle with old method : 0.11805963516235352 time for calcul the mask position with numpy : 0.006815671920776367 nb_pixel_total : 8485 time to create 1 rle with old method : 0.009685754776000977 time for calcul the mask position with numpy : 0.0066471099853515625 nb_pixel_total : 5541 time to create 1 rle with old method : 0.006291866302490234 create new chi : 0.6275560855865479 time to delete rle : 0.0008008480072021484 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 6406 TO DO : save crop sub photo not yet done ! save time : 0.3912842273712158 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03884387016296387 time for calcul the mask position with numpy : 0.0211637020111084 nb_pixel_total : 2066235 time to create 1 rle with new method : 0.032296180725097656 time for calcul the mask position with numpy : 0.007029056549072266 nb_pixel_total : 7365 time to create 1 rle with old method : 0.008483171463012695 create new chi : 0.06922149658203125 time to delete rle : 0.0002288818359375 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1312 TO DO : save crop sub photo not yet done ! save time : 0.11246633529663086 nb_obj : 4 nb_hashtags : 3 time to prepare the origin masks : 0.29267311096191406 time for calcul the mask position with numpy : 0.2431650161743164 nb_pixel_total : 2043784 time to create 1 rle with new method : 0.21999812126159668 time for calcul the mask position with numpy : 0.006863117218017578 nb_pixel_total : 5882 time to create 1 rle with old method : 0.006632328033447266 time for calcul the mask position with numpy : 0.00641942024230957 nb_pixel_total : 6678 time to create 1 rle with old method : 0.007528543472290039 time for calcul the mask position with numpy : 0.00652003288269043 nb_pixel_total : 8370 time to create 1 rle with old method : 0.009376764297485352 time for calcul the mask position with numpy : 0.0068471431732177734 nb_pixel_total : 8886 time to create 1 rle with old method : 0.009918451309204102 create new chi : 0.5341243743896484 time to delete rle : 0.00035452842712402344 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 1944 TO DO : save crop sub photo not yet done ! save time : 0.13327312469482422 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.042911529541015625 time for calcul the mask position with numpy : 0.020721912384033203 nb_pixel_total : 2032143 time to create 1 rle with new method : 0.09333038330078125 time for calcul the mask position with numpy : 0.006783246994018555 nb_pixel_total : 29487 time to create 1 rle with old method : 0.03310346603393555 time for calcul the mask position with numpy : 0.0064733028411865234 nb_pixel_total : 11970 time to create 1 rle with old method : 0.013447046279907227 create new chi : 0.18110966682434082 time to delete rle : 0.00031304359436035156 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1740 TO DO : save crop sub photo not yet done ! save time : 0.13825178146362305 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.2434077262878418 time for calcul the mask position with numpy : 0.16972970962524414 nb_pixel_total : 2051233 time to create 1 rle with new method : 0.08961606025695801 time for calcul the mask position with numpy : 0.006241559982299805 nb_pixel_total : 14034 time to create 1 rle with old method : 0.01567554473876953 time for calcul the mask position with numpy : 0.0066225528717041016 nb_pixel_total : 8333 time to create 1 rle with old method : 0.009427070617675781 create new chi : 0.30806517601013184 time to delete rle : 0.00030803680419921875 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1656 TO DO : save crop sub photo not yet done ! save time : 0.14942336082458496 nb_obj : 4 nb_hashtags : 4 time to prepare the origin masks : 0.25781702995300293 time for calcul the mask position with numpy : 0.5683856010437012 nb_pixel_total : 1961333 time to create 1 rle with new method : 0.0926201343536377 time for calcul the mask position with numpy : 0.006433248519897461 nb_pixel_total : 16934 time to create 1 rle with old method : 0.01895737648010254 time for calcul the mask position with numpy : 0.007049083709716797 nb_pixel_total : 66167 time to create 1 rle with old method : 0.10669612884521484 time for calcul the mask position with numpy : 0.006611347198486328 nb_pixel_total : 5617 time to create 1 rle with old method : 0.0064449310302734375 time for calcul the mask position with numpy : 0.007040500640869141 nb_pixel_total : 23549 time to create 1 rle with old method : 0.029535770416259766 create new chi : 0.8609488010406494 time to delete rle : 0.0006985664367675781 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 3300 TO DO : save crop sub photo not yet done ! save time : 0.2516024112701416 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.08054852485656738 time for calcul the mask position with numpy : 0.027376890182495117 nb_pixel_total : 2069044 time to create 1 rle with new method : 0.03570389747619629 time for calcul the mask position with numpy : 0.006341695785522461 nb_pixel_total : 4556 time to create 1 rle with old method : 0.00586247444152832 create new chi : 0.07553458213806152 time to delete rle : 0.00030994415283203125 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1282 TO DO : save crop sub photo not yet done ! save time : 0.11517167091369629 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04453086853027344 time for calcul the mask position with numpy : 0.025794506072998047 nb_pixel_total : 2058114 time to create 1 rle with new method : 0.05016183853149414 time for calcul the mask position with numpy : 0.0067882537841796875 nb_pixel_total : 10104 time to create 1 rle with old method : 0.011094093322753906 time for calcul the mask position with numpy : 0.006547451019287109 nb_pixel_total : 5382 time to create 1 rle with old method : 0.005835771560668945 create new chi : 0.11305952072143555 time to delete rle : 0.00028252601623535156 batch 1 Loaded 5 chid ids of type : 3594 +++Number RLEs to save : 1632 TO DO : save crop sub photo not yet done ! save time : 0.1380608081817627 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.05761122703552246 time for calcul the mask position with numpy : 0.03240060806274414 nb_pixel_total : 2070986 time to create 1 rle with new method : 0.12760519981384277 time for calcul the mask position with numpy : 0.008048772811889648 nb_pixel_total : 2614 time to create 1 rle with old method : 0.00302886962890625 create new chi : 0.1806471347808838 time to delete rle : 0.00032591819763183594 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1192 TO DO : save crop sub photo not yet done ! save time : 0.11446547508239746 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.056242942810058594 time for calcul the mask position with numpy : 0.0762174129486084 nb_pixel_total : 2017743 time to create 1 rle with new method : 0.33399343490600586 time for calcul the mask position with numpy : 0.006561279296875 nb_pixel_total : 18891 time to create 1 rle with old method : 0.021165847778320312 time for calcul the mask position with numpy : 0.006582498550415039 nb_pixel_total : 36966 time to create 1 rle with old method : 0.06471371650695801 create new chi : 0.5205650329589844 time to delete rle : 0.0004031658172607422 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2018 TO DO : save crop sub photo not yet done ! save time : 0.16059088706970215 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 2.213881015777588 time for calcul the mask position with numpy : 0.0674290657043457 nb_pixel_total : 1998197 time to create 1 rle with new method : 0.9199388027191162 time for calcul the mask position with numpy : 0.011394739151000977 nb_pixel_total : 30384 time to create 1 rle with old method : 0.03576374053955078 time for calcul the mask position with numpy : 0.011455297470092773 nb_pixel_total : 4002 time to create 1 rle with old method : 0.004575014114379883 time for calcul the mask position with numpy : 0.007114410400390625 nb_pixel_total : 11373 time to create 1 rle with old method : 0.01278233528137207 time for calcul the mask position with numpy : 0.00754237174987793 nb_pixel_total : 8108 time to create 1 rle with old method : 0.009112834930419922 time for calcul the mask position with numpy : 0.007397174835205078 nb_pixel_total : 10930 time to create 1 rle with old method : 0.012391090393066406 time for calcul the mask position with numpy : 0.006924867630004883 nb_pixel_total : 10606 time to create 1 rle with old method : 0.012353181838989258 create new chi : 1.13665771484375 time to delete rle : 0.0005130767822265625 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 2716 TO DO : save crop sub photo not yet done ! save time : 0.19674468040466309 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.09290504455566406 time for calcul the mask position with numpy : 0.03638911247253418 nb_pixel_total : 1967077 time to create 1 rle with new method : 0.08559131622314453 time for calcul the mask position with numpy : 0.006891489028930664 nb_pixel_total : 12718 time to create 1 rle with old method : 0.014542579650878906 time for calcul the mask position with numpy : 0.011256933212280273 nb_pixel_total : 40878 time to create 1 rle with old method : 0.06624889373779297 time for calcul the mask position with numpy : 0.007441282272338867 nb_pixel_total : 52927 time to create 1 rle with old method : 0.06402969360351562 create new chi : 0.2929348945617676 time to delete rle : 0.000537872314453125 batch 1 Loaded 7 chid ids of type : 3594 ++++++++Number RLEs to save : 2410 TO DO : save crop sub photo not yet done ! save time : 0.1817948818206787 nb_obj : 4 nb_hashtags : 1 time to prepare the origin masks : 0.8492302894592285 time for calcul the mask position with numpy : 0.5388059616088867 nb_pixel_total : 2029523 time to create 1 rle with new method : 0.2386341094970703 time for calcul the mask position with numpy : 0.010946273803710938 nb_pixel_total : 10267 time to create 1 rle with old method : 0.011536121368408203 time for calcul the mask position with numpy : 0.006263256072998047 nb_pixel_total : 11465 time to create 1 rle with old method : 0.012903213500976562 time for calcul the mask position with numpy : 0.006465435028076172 nb_pixel_total : 18048 time to create 1 rle with old method : 0.06889533996582031 time for calcul the mask position with numpy : 0.0064198970794677734 nb_pixel_total : 4297 time to create 1 rle with old method : 0.004811763763427734 create new chi : 0.917020320892334 time to delete rle : 0.0005021095275878906 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2288 TO DO : save crop sub photo not yet done ! save time : 0.17484474182128906 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.7947943210601807 time for calcul the mask position with numpy : 0.045159101486206055 nb_pixel_total : 1274384 time to create 1 rle with new method : 4.552923917770386 time for calcul the mask position with numpy : 0.22452569007873535 nb_pixel_total : 764199 time to create 1 rle with new method : 1.1140577793121338 time for calcul the mask position with numpy : 0.017216920852661133 nb_pixel_total : 9755 time to create 1 rle with old method : 0.016839027404785156 time for calcul the mask position with numpy : 0.010373115539550781 nb_pixel_total : 8948 time to create 1 rle with old method : 0.011561870574951172 time for calcul the mask position with numpy : 0.010266304016113281 nb_pixel_total : 7653 time to create 1 rle with old method : 0.008571147918701172 time for calcul the mask position with numpy : 0.010008811950683594 nb_pixel_total : 8661 time to create 1 rle with old method : 0.01157522201538086 create new chi : 6.043267726898193 time to delete rle : 0.001291513442993164 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 4182 TO DO : save crop sub photo not yet done ! save time : 0.3555617332458496 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 0.20081758499145508 time for calcul the mask position with numpy : 0.5529232025146484 nb_pixel_total : 2004278 time to create 1 rle with new method : 0.36769890785217285 time for calcul the mask position with numpy : 0.011793136596679688 nb_pixel_total : 3721 time to create 1 rle with old method : 0.0042877197265625 time for calcul the mask position with numpy : 0.012640714645385742 nb_pixel_total : 5957 time to create 1 rle with old method : 0.006726264953613281 time for calcul the mask position with numpy : 0.009376764297485352 nb_pixel_total : 8422 time to create 1 rle with old method : 0.009572505950927734 time for calcul the mask position with numpy : 0.01163792610168457 nb_pixel_total : 33867 time to create 1 rle with old method : 0.040082454681396484 time for calcul the mask position with numpy : 0.008779048919677734 nb_pixel_total : 4935 time to create 1 rle with old method : 0.006974458694458008 time for calcul the mask position with numpy : 0.006906270980834961 nb_pixel_total : 6496 time to create 1 rle with old method : 0.0073277950286865234 time for calcul the mask position with numpy : 0.006561994552612305 nb_pixel_total : 5924 time to create 1 rle with old method : 0.006784915924072266 create new chi : 1.0812029838562012 time to delete rle : 0.0007345676422119141 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 3194 TO DO : save crop sub photo not yet done ! save time : 0.22497940063476562 map_output_result : {1384189139: (0.0, 'Should be the crop_list due to order', 0), 1384189106: (0.0, 'Should be the crop_list due to order', 0), 1384189065: (0.0, 'Should be the crop_list due to order', 0), 1384189036: (0.0, 'Should be the crop_list due to order', 0), 1384188734: (0.0, 'Should be the crop_list due to order', 0), 1384188708: (0.0, 'Should be the crop_list due to order', 0), 1384188683: (0.0, 'Should be the crop_list due to order', 0), 1384188656: (0.0, 'Should be the crop_list due to order', 0), 1384188631: (0.0, 'Should be the crop_list due to order', 0), 1384188613: (0.0, 'Should be the crop_list due to order', 0), 1384188607: (0.0, 'Should be the crop_list due to order', 0), 1384188604: (0.0, 'Should be the crop_list due to order', 0), 1384188601: (0.0, 'Should be the crop_list due to order', 0), 1384188598: (0.0, 'Should be the crop_list due to order', 0), 1384188595: (0.0, 'Should be the crop_list due to order', 0), 1384188593: (0.0, 'Should be the crop_list due to order', 0), 1384188256: (0.0, 'Should be the crop_list due to order', 0), 1384188222: (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 [1384189139, 1384189106, 1384189065, 1384189036, 1384188734, 1384188708, 1384188683, 1384188656, 1384188631, 1384188613, 1384188607, 1384188604, 1384188601, 1384188598, 1384188595, 1384188593, 1384188256, 1384188222] Looping around the photos to save general results len do output : 18 /1384189139.Didn't retrieve data . /1384189106.Didn't retrieve data . /1384189065.Didn't retrieve data . /1384189036.Didn't retrieve data . /1384188734.Didn't retrieve data . /1384188708.Didn't retrieve data . /1384188683.Didn't retrieve data . /1384188656.Didn't retrieve data . /1384188631.Didn't retrieve data . /1384188613.Didn't retrieve data . /1384188607.Didn't retrieve data . /1384188604.Didn't retrieve data . /1384188601.Didn't retrieve data . /1384188598.Didn't retrieve data . /1384188595.Didn't retrieve data . /1384188593.Didn't retrieve data . /1384188256.Didn't retrieve data . /1384188222.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, '3735569') ('3318', '26946470', '1384189139', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189106', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189065', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189036', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188734', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188708', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188683', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188656', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188631', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188613', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188607', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188604', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188601', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188598', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188595', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188593', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188256', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188222', None, None, None, None, None, '3735569') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 54 time used for this insertion : 0.014559030532836914 save_final save missing photos in datou_result : time spend for datou_step_exec : 24.923311948776245 time spend to save output : 0.020090818405151367 total time spend for step 3 : 24.943402767181396 step4:ventilate_hashtags_in_portfolio Wed Sep 17 14:02:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 26946470 get user id for portfolio 26946470 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`=26946470 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','environnement','flou','mal_croppe','metal','carton','papier','background','autre','pet_fonce','pehd')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=26946470 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','environnement','flou','mal_croppe','metal','carton','papier','background','autre','pet_fonce','pehd')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=26946470 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','environnement','flou','mal_croppe','metal','carton','papier','background','autre','pet_fonce','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/26947578,26947579,26947580,26947581,26947582,26947583,26947584,26947585,26947586,26947587,26947588?tags=pet_clair,environnement,flou,mal_croppe,metal,carton,papier,background,autre,pet_fonce,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1384189139, 1384189106, 1384189065, 1384189036, 1384188734, 1384188708, 1384188683, 1384188656, 1384188631, 1384188613, 1384188607, 1384188604, 1384188601, 1384188598, 1384188595, 1384188593, 1384188256, 1384188222] Looping around the photos to save general results len do output : 1 /26946470. 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, '3735569') ('3318', '26946470', '1384189139', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189106', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189065', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189036', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188734', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188708', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188683', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188656', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188631', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188613', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188607', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188604', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188601', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188598', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188595', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188593', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188256', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188222', None, None, None, None, None, '3735569') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.014229297637939453 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.865656852722168 time spend to save output : 0.015045166015625 total time spend for step 4 : 1.880702018737793 step5:final Wed Sep 17 14:02:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1384189139: ('0.07949328596536352',), 1384189106: ('0.07949328596536352',), 1384189065: ('0.07949328596536352',), 1384189036: ('0.07949328596536352',), 1384188734: ('0.07949328596536352',), 1384188708: ('0.07949328596536352',), 1384188683: ('0.07949328596536352',), 1384188656: ('0.07949328596536352',), 1384188631: ('0.07949328596536352',), 1384188613: ('0.07949328596536352',), 1384188607: ('0.07949328596536352',), 1384188604: ('0.07949328596536352',), 1384188601: ('0.07949328596536352',), 1384188598: ('0.07949328596536352',), 1384188595: ('0.07949328596536352',), 1384188593: ('0.07949328596536352',), 1384188256: ('0.07949328596536352',), 1384188222: ('0.07949328596536352',)} new output for save of step final : {1384189139: ('0.07949328596536352',), 1384189106: ('0.07949328596536352',), 1384189065: ('0.07949328596536352',), 1384189036: ('0.07949328596536352',), 1384188734: ('0.07949328596536352',), 1384188708: ('0.07949328596536352',), 1384188683: ('0.07949328596536352',), 1384188656: ('0.07949328596536352',), 1384188631: ('0.07949328596536352',), 1384188613: ('0.07949328596536352',), 1384188607: ('0.07949328596536352',), 1384188604: ('0.07949328596536352',), 1384188601: ('0.07949328596536352',), 1384188598: ('0.07949328596536352',), 1384188595: ('0.07949328596536352',), 1384188593: ('0.07949328596536352',), 1384188256: ('0.07949328596536352',), 1384188222: ('0.07949328596536352',)} [1384189139, 1384189106, 1384189065, 1384189036, 1384188734, 1384188708, 1384188683, 1384188656, 1384188631, 1384188613, 1384188607, 1384188604, 1384188601, 1384188598, 1384188595, 1384188593, 1384188256, 1384188222] Looping around the photos to save general results len do output : 18 /1384189139.Didn't retrieve data . /1384189106.Didn't retrieve data . /1384189065.Didn't retrieve data . /1384189036.Didn't retrieve data . /1384188734.Didn't retrieve data . /1384188708.Didn't retrieve data . /1384188683.Didn't retrieve data . /1384188656.Didn't retrieve data . /1384188631.Didn't retrieve data . /1384188613.Didn't retrieve data . /1384188607.Didn't retrieve data . /1384188604.Didn't retrieve data . /1384188601.Didn't retrieve data . /1384188598.Didn't retrieve data . /1384188595.Didn't retrieve data . /1384188593.Didn't retrieve data . /1384188256.Didn't retrieve data . /1384188222.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, '3735569') ('3318', '26946470', '1384189139', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189106', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189065', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189036', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188734', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188708', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188683', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188656', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188631', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188613', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188607', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188604', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188601', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188598', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188595', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188593', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188256', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188222', None, None, None, None, None, '3735569') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 54 time used for this insertion : 0.014123201370239258 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.14104032516479492 time spend to save output : 0.015058517456054688 total time spend for step 5 : 0.1560988426208496 step6:blur_detection Wed Sep 17 14:02:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1758110429_1978711_1384189139_5589b31494a2f1e5fd3530b357b55812.jpg resize: (1080, 1920) 1384189139 1.2447378783392997 treat image : temp/1758110429_1978711_1384189106_e695468c803321e3a0f3385dc0403340.jpg resize: (1080, 1920) 1384189106 0.47132705572688466 treat image : temp/1758110429_1978711_1384189065_349608ad94b9120f735a7eace2df23d0.jpg resize: (1080, 1920) 1384189065 1.0337142700765325 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93.jpg resize: (1080, 1920) 1384189036 0.1790081933979683 treat image : temp/1758110429_1978711_1384188734_dc2ce85b0b27b328fc4c1e6bb4ca2faa.jpg resize: (1080, 1920) 1384188734 0.18435790146355802 treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7.jpg resize: (1080, 1920) 1384188708 -0.21567871951837192 treat image : temp/1758110429_1978711_1384188683_e199ca411f3f5e2db3bfd806bb19c8d4.jpg resize: (1080, 1920) 1384188683 0.5304822061293851 treat image : temp/1758110429_1978711_1384188656_a7743390e45fa0c3878eb91040d59748.jpg resize: (1080, 1920) 1384188656 0.36412161703600254 treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8.jpg resize: (1080, 1920) 1384188631 0.5519408802791437 treat image : temp/1758110429_1978711_1384188613_c3cf431b27345a1cbe6c17ad0fdb7c92.jpg resize: (1080, 1920) 1384188613 -0.08339734638213113 treat image : temp/1758110429_1978711_1384188607_9dcfb1cec85bce3af8c193d665512c7e.jpg resize: (1080, 1920) 1384188607 0.1873616420138775 treat image : temp/1758110429_1978711_1384188604_13ddd471c7891c8c3a35456616906feb.jpg resize: (1080, 1920) 1384188604 -0.9137470993408534 treat image : temp/1758110429_1978711_1384188601_88ea25798f5181e8d21fc1b3a47922ae.jpg resize: (1080, 1920) 1384188601 -0.49444906313002307 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846.jpg resize: (1080, 1920) 1384188598 0.11348596716810491 treat image : temp/1758110429_1978711_1384188595_2a0922ed162640a83915a88426f9bd42.jpg resize: (1080, 1920) 1384188595 0.22029293009803175 treat image : temp/1758110429_1978711_1384188593_45853675b5fb5a87b5bf511773c452cd.jpg resize: (1080, 1920) 1384188593 -0.11539038774935184 treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9.jpg resize: (1080, 1920) 1384188256 1.5326685273493903 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa.jpg resize: (1080, 1920) 1384188222 -0.495395177395106 treat image : temp/1758110429_1978711_1384189106_e695468c803321e3a0f3385dc0403340_rle_crop_3961532214_0.png resize: (89, 145) 1384211340 -1.2311768256179094 treat image : temp/1758110429_1978711_1384189106_e695468c803321e3a0f3385dc0403340_rle_crop_3961532215_0.png resize: (200, 282) 1384211342 -0.5654587084394891 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532217_0.png resize: (72, 101) 1384211345 -0.5789608150550674 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532218_0.png resize: (180, 100) 1384211348 -1.6158560089592393 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532220_0.png resize: (136, 42) 1384211350 -0.04221409688137033 treat image : temp/1758110429_1978711_1384188734_dc2ce85b0b27b328fc4c1e6bb4ca2faa_rle_crop_3961532224_0.png resize: (116, 83) 1384211352 0.27155619729188696 treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7_rle_crop_3961532225_0.png resize: (167, 88) 1384211355 -1.1773004504459232 treat image : temp/1758110429_1978711_1384188656_a7743390e45fa0c3878eb91040d59748_rle_crop_3961532231_0.png resize: (178, 100) 1384211358 -1.9331672763866337 treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8_rle_crop_3961532233_0.png resize: (141, 230) 1384211360 -1.2627245576776598 treat image : temp/1758110429_1978711_1384188613_c3cf431b27345a1cbe6c17ad0fdb7c92_rle_crop_3961532237_0.png resize: (101, 123) 1384211363 -1.0144729202278961 treat image : temp/1758110429_1978711_1384188607_9dcfb1cec85bce3af8c193d665512c7e_rle_crop_3961532238_0.png resize: (57, 121) 1384211365 0.4180919420790206 treat image : temp/1758110429_1978711_1384188607_9dcfb1cec85bce3af8c193d665512c7e_rle_crop_3961532239_0.png resize: (207, 116) 1384211368 -1.5010788350721243 treat image : temp/1758110429_1978711_1384188604_13ddd471c7891c8c3a35456616906feb_rle_crop_3961532240_0.png resize: (56, 65) 1384211371 -0.5144627948227959 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532245_0.png resize: (176, 99) 1384211374 -1.9543309137426208 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532247_0.png resize: (119, 71) 1384211377 -2.562934286005362 treat image : temp/1758110429_1978711_1384188595_2a0922ed162640a83915a88426f9bd42_rle_crop_3961532249_0.png resize: (413, 337) 1384211379 -0.8816203164933925 treat image : temp/1758110429_1978711_1384188595_2a0922ed162640a83915a88426f9bd42_rle_crop_3961532250_0.png resize: (175, 441) 1384211382 -0.7317162841060204 treat image : temp/1758110429_1978711_1384188593_45853675b5fb5a87b5bf511773c452cd_rle_crop_3961532252_0.png resize: (78, 66) 1384211385 1.436918929102913 treat image : temp/1758110429_1978711_1384188593_45853675b5fb5a87b5bf511773c452cd_rle_crop_3961532253_0.png resize: (204, 125) 1384211387 -1.5777833998369135 treat image : temp/1758110429_1978711_1384188593_45853675b5fb5a87b5bf511773c452cd_rle_crop_3961532254_0.png resize: (179, 157) 1384211391 -2.520688149171907 treat image : temp/1758110429_1978711_1384188593_45853675b5fb5a87b5bf511773c452cd_rle_crop_3961532255_0.png resize: (127, 128) 1384211394 -1.1429677503642433 treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9_rle_crop_3961532256_0.png resize: (217, 92) 1384211396 -2.0759136964384517 treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9_rle_crop_3961532257_0.png resize: (66, 155) 1384211399 2.207979034672767 treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9_rle_crop_3961532258_0.png resize: (137, 115) 1384211402 -1.1906059359741594 treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9_rle_crop_3961532259_0.png resize: (180, 106) 1384211404 -1.075318275797795 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532262_0.png resize: (309, 207) 1384211407 -2.1207493422493817 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532265_0.png resize: (180, 104) 1384211410 -1.9955301428675083 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532266_0.png resize: (94, 114) 1384211412 -1.937027881517634 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532267_0.png resize: (41, 125) 1384211415 0.24950287038482366 treat image : temp/1758110429_1978711_1384188683_e199ca411f3f5e2db3bfd806bb19c8d4_rle_crop_3961532229_0.png resize: (153, 107) 1384211487 -0.8668222188473467 treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8_rle_crop_3961532235_0.png resize: (483, 324) 1384211490 -1.5448623331814841 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532244_0.png resize: (136, 125) 1384211493 -1.7751155487144596 treat image : temp/1758110429_1978711_1384188595_2a0922ed162640a83915a88426f9bd42_rle_crop_3961532251_0.png resize: (159, 118) 1384211495 -1.1829936186074823 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532264_0.png resize: (262, 228) 1384211499 -1.732155681302336 treat image : temp/1758110429_1978711_1384189139_5589b31494a2f1e5fd3530b357b55812_rle_crop_3961532213_0.png resize: (187, 98) 1384212018 -1.1090258684634187 treat image : temp/1758110429_1978711_1384189065_349608ad94b9120f735a7eace2df23d0_rle_crop_3961532216_0.png resize: (499, 334) 1384212020 0.29692126235083316 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532219_0.png resize: (454, 340) 1384212022 -0.5889592539150904 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532221_0.png resize: (88, 116) 1384212025 -1.7816465523480296 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532222_0.png resize: (995, 1791) 1384212027 -1.0575202984521015 treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7_rle_crop_3961532226_0.png resize: (106, 93) 1384212029 -0.7487484877939283 treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7_rle_crop_3961532227_0.png resize: (61, 128) 1384212032 1.4934496399178083 treat image : temp/1758110429_1978711_1384188683_e199ca411f3f5e2db3bfd806bb19c8d4_rle_crop_3961532230_0.png resize: (174, 231) 1384212034 -1.0860605805506547 treat image : temp/1758110429_1978711_1384188656_a7743390e45fa0c3878eb91040d59748_rle_crop_3961532232_0.png resize: (108, 152) 1384212036 0.740506130736694 treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8_rle_crop_3961532234_0.png resize: (90, 84) 1384212038 -2.801786449902907 treat image : temp/1758110429_1978711_1384188601_88ea25798f5181e8d21fc1b3a47922ae_rle_crop_3961532241_0.png resize: (272, 220) 1384212041 -0.3091506751096476 treat image : temp/1758110429_1978711_1384188601_88ea25798f5181e8d21fc1b3a47922ae_rle_crop_3961532242_0.png resize: (152, 272) 1384212043 -2.108609927432597 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532243_0.png resize: (107, 129) 1384212045 -0.7000569133427055 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532246_0.png resize: (90, 189) 1384212049 -1.247983559804603 treat image : temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532248_0.png resize: (175, 222) 1384212051 -1.4147895154741734 treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9_rle_crop_3961532260_0.png resize: (976, 1097) 1384212053 0.22626209921904908 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532261_0.png resize: (84, 88) 1384212055 -1.058447324897715 treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532263_0.png resize: (65, 107) 1384212058 -3.3771247378043694 treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532223_0.png resize: (131, 105) 1384212130 -5.673595498021148 treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7_rle_crop_3961532228_0.png resize: (96, 81) 1384212133 0.9819448047984665 treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8_rle_crop_3961532236_0.png resize: (177, 131) 1384212164 -0.67568652586297 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 : 73 time used for this insertion : 0.0153656005859375 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 73 time used for this insertion : 0.015965700149536133 save missing photos in datou_result : time spend for datou_step_exec : 17.509757041931152 time spend to save output : 0.03673911094665527 total time spend for step 6 : 17.546496152877808 step7:brightness Wed Sep 17 14:02:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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/1758110429_1978711_1384189139_5589b31494a2f1e5fd3530b357b55812.jpg treat image : temp/1758110429_1978711_1384189106_e695468c803321e3a0f3385dc0403340.jpg treat image : temp/1758110429_1978711_1384189065_349608ad94b9120f735a7eace2df23d0.jpg treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93.jpg treat image : temp/1758110429_1978711_1384188734_dc2ce85b0b27b328fc4c1e6bb4ca2faa.jpg treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7.jpg treat image : temp/1758110429_1978711_1384188683_e199ca411f3f5e2db3bfd806bb19c8d4.jpg treat image : temp/1758110429_1978711_1384188656_a7743390e45fa0c3878eb91040d59748.jpg treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8.jpg treat image : temp/1758110429_1978711_1384188613_c3cf431b27345a1cbe6c17ad0fdb7c92.jpg treat image : 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temp/1758110429_1978711_1384188598_5a5c7c9331e67d6584198a1bda955846_rle_crop_3961532248_0.png treat image : temp/1758110429_1978711_1384188256_9423c454a19007fe2a1f9cad9dcdddc9_rle_crop_3961532260_0.png treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532261_0.png treat image : temp/1758110429_1978711_1384188222_0da658fd05008ad6bfdf18a0925bf4aa_rle_crop_3961532263_0.png treat image : temp/1758110429_1978711_1384189036_c677d1f2c87dc94e6a10f5e662cb6e93_rle_crop_3961532223_0.png treat image : temp/1758110429_1978711_1384188708_6586ac564e3849e92b606766ade92ca7_rle_crop_3961532228_0.png treat image : temp/1758110429_1978711_1384188631_f9c6857e905114ddb1f3b762c6e5e7b8_rle_crop_3961532236_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 : 73 time used for this insertion : 0.013283014297485352 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 73 time used for this insertion : 0.01595139503479004 save missing photos in datou_result : time spend for datou_step_exec : 4.9923789501190186 time spend to save output : 0.0339357852935791 total time spend for step 7 : 5.026314735412598 step8:velours_tree Wed Sep 17 14:02:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.6705739498138428 time spend to save output : 6.461143493652344e-05 total time spend for step 8 : 0.6706385612487793 step9:send_mail_cod Wed Sep 17 14:02:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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_P26946470_17-09-2025_14_02_36.pdf 26947578 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette269475781758110556 26947580 imagette269475801758110557 26947581 imagette269475811758110557 26947582 imagette269475821758110557 26947583 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette269475831758110557 26947584 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette269475841758110558 26947585 imagette269475851758110559 26947586 change filename to text .change filename to text .imagette269475861758110559 26947587 change filename to text .imagette269475871758110559 26947588 imagette269475881758110559 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=26946470 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/26947578,26947579,26947580,26947581,26947582,26947583,26947584,26947585,26947586,26947587,26947588?tags=pet_clair,environnement,flou,mal_croppe,metal,carton,papier,background,autre,pet_fonce,pehd args[1384189139] : ((1384189139, 1.2447378783392997, 492688767), (1384189139, 0.5037480787596591, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384189106] : ((1384189106, 0.47132705572688466, 492688767), (1384189106, 0.6671736257673958, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384189065] : ((1384189065, 1.0337142700765325, 492688767), (1384189065, 0.2665927480082211, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384189036] : ((1384189036, 0.1790081933979683, 492688767), (1384189036, 0.29855680247497207, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188734] : ((1384188734, 0.18435790146355802, 492688767), (1384188734, 0.8082927707674514, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188708] : ((1384188708, -0.21567871951837192, 492688767), (1384188708, 0.38084376473350984, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188683] : ((1384188683, 0.5304822061293851, 492688767), (1384188683, 0.6897775521845022, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188656] : ((1384188656, 0.36412161703600254, 492688767), (1384188656, 0.5744180952622058, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188631] : ((1384188631, 0.5519408802791437, 492688767), (1384188631, 0.576742536037629, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188613] : ((1384188613, -0.08339734638213113, 492688767), (1384188613, 0.5612958011507772, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188607] : ((1384188607, 0.1873616420138775, 492688767), (1384188607, 0.4114954552182934, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188604] : ((1384188604, -0.9137470993408534, 492688767), (1384188604, 0.51339423484756, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188601] : ((1384188601, -0.49444906313002307, 492688767), (1384188601, 0.7817862913666225, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188598] : ((1384188598, 0.11348596716810491, 492688767), (1384188598, 0.5293485025720248, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188595] : ((1384188595, 0.22029293009803175, 492688767), (1384188595, 0.89990968689949, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188593] : ((1384188593, -0.11539038774935184, 492688767), (1384188593, 0.5790086679929938, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188256] : ((1384188256, 1.5326685273493903, 492688767), (1384188256, 0.33164920099682765, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com args[1384188222] : ((1384188222, -0.495395177395106, 492688767), (1384188222, 0.4190327119856946, 2107752395), '0.07949328596536352') We are sending mail with results at report@fotonower.com refus_total : 0.07949328596536352 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=26946470 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_P26946470_17-09-2025_14_02_36.pdf results_Auto_P26946470_17-09-2025_14_02_36.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26946470_17-09-2025_14_02_36.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','26946470','results_Auto_P26946470_17-09-2025_14_02_36.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26946470_17-09-2025_14_02_36.pdf','pdf','','0.44','0.07949328596536352') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/26946470

https://www.fotonower.com/image?json=false&list_photos_id=1384189139
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.2447378783392997)
https://www.fotonower.com/image?json=false&list_photos_id=1384189106
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384189065
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.0337142700765325)
https://www.fotonower.com/image?json=false&list_photos_id=1384189036
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188734
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188708
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188683
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188656
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188631
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188613
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188607
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188604
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188601
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188598
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188595
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188593
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384188256
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.5326685273493903)
https://www.fotonower.com/image?json=false&list_photos_id=1384188222
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 7.95%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/26947578?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/26947583?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/26947584?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/26947586?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/26947587?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26946470_17-09-2025_14_02_36.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/26947578,26947579,26947580,26947581,26947582,26947583,26947584,26947585,26947586,26947587,26947588?tags=pet_clair,environnement,flou,mal_croppe,metal,carton,papier,background,autre,pet_fonce,pehd.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 17 Sep 2025 12:02:41 GMT Content-Length: 0 Connection: close X-Message-Id: bowI7P9MQ1CeszXpw3DXsQ Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1384189139, 1384189106, 1384189065, 1384189036, 1384188734, 1384188708, 1384188683, 1384188656, 1384188631, 1384188613, 1384188607, 1384188604, 1384188601, 1384188598, 1384188595, 1384188593, 1384188256, 1384188222] 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, '3735569') ('3318', '26946470', '1384189139', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189106', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189065', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189036', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188734', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188708', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188683', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188656', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188631', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188613', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188607', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188604', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188601', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188598', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188595', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188593', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188256', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188222', None, None, None, None, None, '3735569') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.01385354995727539 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.373504161834717 time spend to save output : 0.014112710952758789 total time spend for step 9 : 5.387616872787476 step10:split_time_score Wed Sep 17 14:02:41 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('13', 18),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 17092025 26946470 Nombre de photos uploadées : 18 / 23040 (0%) 17092025 26946470 Nombre de photos taguées (types de déchets): 0 / 18 (0%) 17092025 26946470 Nombre de photos taguées (volume) : 0 / 18 (0%) elapsed_time : load_data_split_time_score 3.5762786865234375e-06 elapsed_time : order_list_meta_photo_and_scores 9.298324584960938e-06 ?????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0009114742279052734 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.21586251258850098 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 : 0.10858108874133637 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26931981_17-09-2025_09_30_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26931981 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`=26931981 AND mptpi.`type`=3594 To do Qualite : 0.07002329282407407 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26931985_17-09-2025_08_51_23.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26931985 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`=26931985 AND mptpi.`type`=3594 To do Qualite : 0.060776120580808085 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26931986_17-09-2025_08_41_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26931986 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`=26931986 AND mptpi.`type`=3594 To do Qualite : 0.0977959718714927 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26945362_17-09-2025_13_11_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26945362 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`=26945362 AND mptpi.`type`=3594 To do Qualite : 0.06575307264109347 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26941005_17-09-2025_13_02_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26941005 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`=26941005 AND mptpi.`type`=3594 To do Qualite : 0.12312721032664604 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26941009_17-09-2025_11_41_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26941009 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`=26941009 AND mptpi.`type`=3594 To do Qualite : 0.030984171382030176 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26944431_17-09-2025_12_41_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26944431 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`=26944431 AND mptpi.`type`=3594 To do Qualite : 0.07949328596536352 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26946470_17-09-2025_14_02_36.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26946470 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`=26946470 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26946471 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'17092025': {'nb_upload': 18, '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 [1384189139, 1384189106, 1384189065, 1384189036, 1384188734, 1384188708, 1384188683, 1384188656, 1384188631, 1384188613, 1384188607, 1384188604, 1384188601, 1384188598, 1384188595, 1384188593, 1384188256, 1384188222] Looping around the photos to save general results len do output : 1 /26946470Didn'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, '3735569') ('3318', '26946470', '1384189139', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189106', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189065', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384189036', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188734', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188708', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188683', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188656', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188631', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188613', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188607', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188604', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188601', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188598', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188595', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188593', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188256', None, None, None, None, None, '3735569') ('3318', None, None, None, None, None, None, None, '3735569') ('3318', '26946470', '1384188222', None, None, None, None, None, '3735569') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.01560354232788086 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.8347437381744385 time spend to save output : 0.015886545181274414 total time spend for step 10 : 1.850630283355713 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 18 set_done_treatment 65.45user 41.73system 2:19.64elapsed 76%CPU (0avgtext+0avgdata 3127416maxresident)k 2003408inputs+29896outputs (20166major+2814782minor)pagefaults 0swaps