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 : 2804915 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 : ['3711405'] with mtr_portfolio_ids : ['26787600'] and first list_photo_ids : [] new path : /proc/2804915/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 25 ; length of list_pids : 25 ; length of list_args : 25 time to download the photos : 3.6134841442108154 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Fri Sep 12 14:20:32 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 : 6812 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-12 14:20:35.033393: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-12 14:20:35.060863: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-12 14:20:35.063276: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb77c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-12 14:20:35.063325: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-12 14:20:35.067388: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-12 14:20:35.207727: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x36f49070 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-12 14:20:35.207803: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-12 14:20:35.209251: 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 2025-09-12 14:20:35.209751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-12 14:20:35.212986: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-12 14:20:35.215734: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-12 14:20:35.216187: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-12 14:20:35.219000: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-12 14:20:35.220430: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-12 14:20:35.226838: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-12 14:20:35.228284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-12 14:20:35.228478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-12 14:20:35.229256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-12 14:20:35.229276: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-12 14:20:35.229287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-12 14:20:35.230542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6260 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. 2025-09-12 14:20:35.558899: 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 2025-09-12 14:20:35.558996: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-12 14:20:35.559017: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-12 14:20:35.559035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-12 14:20:35.559053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-12 14:20:35.559071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-12 14:20:35.559088: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-12 14:20:35.559107: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-12 14:20:35.560499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-12 14:20:35.561873: 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 2025-09-12 14:20:35.561930: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-12 14:20:35.561946: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-12 14:20:35.561960: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-12 14:20:35.561973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-12 14:20:35.561987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-12 14:20:35.562001: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-12 14:20:35.562015: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-12 14:20:35.563115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-12 14:20:35.563161: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-12 14:20:35.563172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-12 14:20:35.563182: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-12 14:20:35.564447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6260 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 : [] 2025-09-12 14:20:45.781506: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-12 14:20:45.974101: 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 : 25 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 21.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 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: 21.00000 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: 27.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 21.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 2 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 : 6 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 : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 36.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 8 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 : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 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 : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 Detection mask done ! Trying to reset tf kernel 2805588 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 330 tf kernel not reseted sub process len(results) : 25 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 25 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 : 5619 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 : 0.0003223419189453125 nb_pixel_total : 10419 time to create 1 rle with old method : 0.013090848922729492 length of segment : 133 time for calcul the mask position with numpy : 0.00274658203125 nb_pixel_total : 116602 time to create 1 rle with old method : 0.16099214553833008 length of segment : 561 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 30835 time to create 1 rle with old method : 0.03686690330505371 length of segment : 176 time for calcul the mask position with numpy : 0.00030875205993652344 nb_pixel_total : 16564 time to create 1 rle with old method : 0.019458532333374023 length of segment : 154 time for calcul the mask position with numpy : 0.0003497600555419922 nb_pixel_total : 8683 time to create 1 rle with old method : 0.010631799697875977 length of segment : 117 time for calcul the mask position with numpy : 0.0004398822784423828 nb_pixel_total : 8951 time to create 1 rle with old method : 0.010758638381958008 length of segment : 174 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 2442 time to create 1 rle with old method : 0.003063201904296875 length of segment : 64 time for calcul the mask position with numpy : 0.0001323223114013672 nb_pixel_total : 5292 time to create 1 rle with old method : 0.0067136287689208984 length of segment : 117 time for calcul the mask position with numpy : 0.00031065940856933594 nb_pixel_total : 10881 time to create 1 rle with old method : 0.017908334732055664 length of segment : 184 time for calcul the mask position with numpy : 0.0003230571746826172 nb_pixel_total : 10017 time to create 1 rle with old method : 0.0166776180267334 length of segment : 187 time for calcul the mask position with numpy : 0.00038361549377441406 nb_pixel_total : 20387 time to create 1 rle with old method : 0.024863243103027344 length of segment : 235 time for calcul the mask position with numpy : 0.0015609264373779297 nb_pixel_total : 105326 time to create 1 rle with old method : 0.12350153923034668 length of segment : 558 time for calcul the mask position with numpy : 0.0020956993103027344 nb_pixel_total : 113699 time to create 1 rle with old method : 0.1375901699066162 length of segment : 560 time for calcul the mask position with numpy : 0.0002605915069580078 nb_pixel_total : 9243 time to create 1 rle with old method : 0.01462411880493164 length of segment : 173 time for calcul the mask position with numpy : 0.021076202392578125 nb_pixel_total : 968819 time to create 1 rle with new method : 0.24936914443969727 length of segment : 1856 time for calcul the mask position with numpy : 0.0007970333099365234 nb_pixel_total : 24258 time to create 1 rle with old method : 0.030208587646484375 length of segment : 223 time for calcul the mask position with numpy : 0.00028443336486816406 nb_pixel_total : 9813 time to create 1 rle with old method : 0.012076377868652344 length of segment : 100 time for calcul the mask position with numpy : 0.0025043487548828125 nb_pixel_total : 112677 time to create 1 rle with old method : 0.13921475410461426 length of segment : 537 time for calcul the mask position with numpy : 0.0003192424774169922 nb_pixel_total : 8272 time to create 1 rle with old method : 0.010075092315673828 length of segment : 172 time for calcul the mask position with numpy : 0.0001838207244873047 nb_pixel_total : 3227 time to create 1 rle with old method : 0.004143714904785156 length of segment : 56 time for calcul the mask position with numpy : 0.0002257823944091797 nb_pixel_total : 4206 time to create 1 rle with old method : 0.005242824554443359 length of segment : 102 time for calcul the mask position with numpy : 0.0001914501190185547 nb_pixel_total : 3802 time to create 1 rle with old method : 0.004848480224609375 length of segment : 71 time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 6533 time to create 1 rle with old method : 0.008046150207519531 length of segment : 126 time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 7921 time to create 1 rle with old method : 0.009752988815307617 length of segment : 106 time for calcul the mask position with numpy : 0.0010066032409667969 nb_pixel_total : 35438 time to create 1 rle with old method : 0.04503226280212402 length of segment : 315 time for calcul the mask position with numpy : 0.002207517623901367 nb_pixel_total : 108840 time to create 1 rle with old method : 0.13138723373413086 length of segment : 626 time for calcul the mask position with numpy : 0.0006468296051025391 nb_pixel_total : 14895 time to create 1 rle with old method : 0.01827263832092285 length of segment : 226 time for calcul the mask position with numpy : 0.003390073776245117 nb_pixel_total : 114584 time to create 1 rle with old method : 0.15715622901916504 length of segment : 538 time for calcul the mask position with numpy : 0.00031113624572753906 nb_pixel_total : 9817 time to create 1 rle with old method : 0.011664152145385742 length of segment : 181 time for calcul the mask position with numpy : 0.0012121200561523438 nb_pixel_total : 62541 time to create 1 rle with old method : 0.07788658142089844 length of segment : 301 time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 15425 time to create 1 rle with old method : 0.019100189208984375 length of segment : 204 time for calcul the mask position with numpy : 0.0004611015319824219 nb_pixel_total : 12950 time to create 1 rle with old method : 0.016085147857666016 length of segment : 117 time for calcul the mask position with numpy : 0.0028259754180908203 nb_pixel_total : 124241 time to create 1 rle with old method : 0.15482401847839355 length of segment : 563 time for calcul the mask position with numpy : 0.0005967617034912109 nb_pixel_total : 19785 time to create 1 rle with old method : 0.03348731994628906 length of segment : 210 time for calcul the mask position with numpy : 0.00046753883361816406 nb_pixel_total : 11098 time to create 1 rle with old method : 0.018819808959960938 length of segment : 144 time for calcul the mask position with numpy : 0.00015854835510253906 nb_pixel_total : 2079 time to create 1 rle with old method : 0.003649473190307617 length of segment : 56 time for calcul the mask position with numpy : 0.002834320068359375 nb_pixel_total : 102633 time to create 1 rle with old method : 0.14388179779052734 length of segment : 554 time for calcul the mask position with numpy : 0.0004444122314453125 nb_pixel_total : 11204 time to create 1 rle with old method : 0.013666152954101562 length of segment : 94 time for calcul the mask position with numpy : 0.000110626220703125 nb_pixel_total : 1585 time to create 1 rle with old method : 0.002094745635986328 length of segment : 32 time for calcul the mask position with numpy : 0.0027091503143310547 nb_pixel_total : 111572 time to create 1 rle with old method : 0.13003277778625488 length of segment : 508 time for calcul the mask position with numpy : 0.0005116462707519531 nb_pixel_total : 17585 time to create 1 rle with old method : 0.02086019515991211 length of segment : 178 time for calcul the mask position with numpy : 0.02375483512878418 nb_pixel_total : 1179444 time to create 1 rle with new method : 0.48096132278442383 length of segment : 1621 time for calcul the mask position with numpy : 0.0033669471740722656 nb_pixel_total : 118359 time to create 1 rle with old method : 0.14510703086853027 length of segment : 565 time for calcul the mask position with numpy : 0.0003902912139892578 nb_pixel_total : 7855 time to create 1 rle with old method : 0.01325082778930664 length of segment : 137 time for calcul the mask position with numpy : 0.0004677772521972656 nb_pixel_total : 14622 time to create 1 rle with old method : 0.01798415184020996 length of segment : 132 time spent for convertir_results : 5.6535608768463135 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 45 chid ids of type : 3594 Number RLEs to save : 14044 save missing photos in datou_result : time spend for datou_step_exec : 37.03647184371948 time spend to save output : 0.8266832828521729 total time spend for step 1 : 37.863155126571655 step2:crop_condition Fri Sep 12 14:21:10 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 : 25 ! batch 1 Loaded 45 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 ! map_result returned by crop_photo_return_map_crop : length : 10 About to insert : list_path_to_insert length 10 new photo from crops ! About to upload 10 photos upload in portfolio : 3736932 init cache_photo without model_param we have 10 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1757679671_2804915 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 10 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.384141445159912 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 ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1757679676_2804915 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 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2486307621002197 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! 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/1757679677_2804915 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 : 1.2973155975341797 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 31 About to insert : list_path_to_insert length 31 new photo from crops ! About to upload 31 photos upload in portfolio : 3736932 init cache_photo without model_param we have 31 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1757679694_2804915 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 ! 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 31 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.685035943984985 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 begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1383364875, 1383364830, 1383363867, 1383363796, 1383363731, 1383363666, 1383363596, 1383363563, 1383363541, 1383363538, 1383363531, 1383363526, 1383363523, 1383363497, 1383363495, 1383363492, 1383363488, 1383363483, 1383363482, 1383363465, 1383363463, 1383363460, 1383363455, 1383363450, 1383363446] Looping around the photos to save general results len do output : 45 /1383461521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1383461591Didn'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, '3711405') ('3318', '26787600', '1383364875', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383364830', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363867', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363796', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363731', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363666', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363596', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363563', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363541', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363538', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363531', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363526', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363523', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363497', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363495', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363492', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363488', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363483', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363482', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363465', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363463', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363460', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363455', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363450', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363446', None, None, None, None, None, '3711405') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 160 time used for this insertion : 0.028695344924926758 save_final save missing photos in datou_result : time spend for datou_step_exec : 33.11992383003235 time spend to save output : 0.030617713928222656 total time spend for step 2 : 33.15054154396057 step3:rle_unique_nms_with_priority Fri Sep 12 14:21:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 45 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 4 nb_hashtags : 1 time to prepare the origin masks : 0.4514188766479492 time for calcul the mask position with numpy : 0.05724143981933594 nb_pixel_total : 1899180 time to create 1 rle with new method : 0.11706113815307617 time for calcul the mask position with numpy : 0.006139516830444336 nb_pixel_total : 16564 time to create 1 rle with old method : 0.018987178802490234 time for calcul the mask position with numpy : 0.006174325942993164 nb_pixel_total : 30835 time to create 1 rle with old method : 0.03590250015258789 time for calcul the mask position with numpy : 0.006806373596191406 nb_pixel_total : 116602 time to create 1 rle with old method : 0.13244986534118652 time for calcul the mask position with numpy : 0.0060198307037353516 nb_pixel_total : 10419 time to create 1 rle with old method : 0.011916875839233398 create new chi : 0.4102210998535156 time to delete rle : 0.026041030883789062 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 3128 TO DO : save crop sub photo not yet done ! save time : 0.23965978622436523 No data in photo_id : 1383364830 No data in photo_id : 1383363867 No data in photo_id : 1383363796 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.1801753044128418 time for calcul the mask position with numpy : 0.1425623893737793 nb_pixel_total : 2055966 time to create 1 rle with new method : 0.1487562656402588 time for calcul the mask position with numpy : 0.006281614303588867 nb_pixel_total : 8951 time to create 1 rle with old method : 0.010257959365844727 time for calcul the mask position with numpy : 0.00640869140625 nb_pixel_total : 8683 time to create 1 rle with old method : 0.010472536087036133 create new chi : 0.32866644859313965 time to delete rle : 0.00028586387634277344 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1662 TO DO : save crop sub photo not yet done ! save time : 0.14053750038146973 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.0620121955871582 time for calcul the mask position with numpy : 0.2854764461517334 nb_pixel_total : 2054985 time to create 1 rle with new method : 0.08578276634216309 time for calcul the mask position with numpy : 0.006299734115600586 nb_pixel_total : 10881 time to create 1 rle with old method : 0.0123748779296875 time for calcul the mask position with numpy : 0.006216526031494141 nb_pixel_total : 5292 time to create 1 rle with old method : 0.006405830383300781 time for calcul the mask position with numpy : 0.006310462951660156 nb_pixel_total : 2442 time to create 1 rle with old method : 0.0029184818267822266 create new chi : 0.42263031005859375 time to delete rle : 0.0003170967102050781 batch 1 Loaded 7 chid ids of type : 3594 +++++Number RLEs to save : 1810 TO DO : save crop sub photo not yet done ! save time : 0.1490771770477295 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.051709890365600586 time for calcul the mask position with numpy : 0.18245792388916016 nb_pixel_total : 1937870 time to create 1 rle with new method : 0.23719453811645508 time for calcul the mask position with numpy : 0.007093667984008789 nb_pixel_total : 105326 time to create 1 rle with old method : 0.12304544448852539 time for calcul the mask position with numpy : 0.006402730941772461 nb_pixel_total : 20387 time to create 1 rle with old method : 0.024637699127197266 time for calcul the mask position with numpy : 0.006350994110107422 nb_pixel_total : 10017 time to create 1 rle with old method : 0.01191854476928711 create new chi : 0.6089169979095459 time to delete rle : 0.0005083084106445312 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 3040 TO DO : save crop sub photo not yet done ! save time : 0.2153642177581787 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.2459719181060791 time for calcul the mask position with numpy : 0.03382444381713867 nb_pixel_total : 984260 time to create 1 rle with new method : 0.27606701850891113 time for calcul the mask position with numpy : 0.013527154922485352 nb_pixel_total : 966398 time to create 1 rle with new method : 0.0881202220916748 time for calcul the mask position with numpy : 0.006278276443481445 nb_pixel_total : 9243 time to create 1 rle with old method : 0.010941028594970703 time for calcul the mask position with numpy : 0.0068891048431396484 nb_pixel_total : 113699 time to create 1 rle with old method : 0.13170814514160156 create new chi : 0.5838570594787598 time to delete rle : 0.0008363723754882812 batch 1 Loaded 7 chid ids of type : 3594 +++++++Number RLEs to save : 6139 TO DO : save crop sub photo not yet done ! save time : 0.3919639587402344 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.038184165954589844 time for calcul the mask position with numpy : 0.023609638214111328 nb_pixel_total : 2049342 time to create 1 rle with new method : 0.25994086265563965 time for calcul the mask position with numpy : 0.0077974796295166016 nb_pixel_total : 24258 time to create 1 rle with old method : 0.028443574905395508 create new chi : 0.32695746421813965 time to delete rle : 0.0002484321594238281 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1526 TO DO : save crop sub photo not yet done ! save time : 0.137467622756958 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.0790555477142334 time for calcul the mask position with numpy : 0.19545626640319824 nb_pixel_total : 1942838 time to create 1 rle with new method : 0.08237600326538086 time for calcul the mask position with numpy : 0.006270170211791992 nb_pixel_total : 8272 time to create 1 rle with old method : 0.011321783065795898 time for calcul the mask position with numpy : 0.006790876388549805 nb_pixel_total : 112677 time to create 1 rle with old method : 0.13161659240722656 time for calcul the mask position with numpy : 0.0067327022552490234 nb_pixel_total : 9813 time to create 1 rle with old method : 0.011755228042602539 create new chi : 0.4631614685058594 time to delete rle : 0.00031375885009765625 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2698 TO DO : save crop sub photo not yet done ! save time : 0.21063923835754395 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.051077842712402344 time for calcul the mask position with numpy : 0.09977006912231445 nb_pixel_total : 2062365 time to create 1 rle with new method : 0.279681921005249 time for calcul the mask position with numpy : 0.006243467330932617 nb_pixel_total : 3802 time to create 1 rle with old method : 0.00453639030456543 time for calcul the mask position with numpy : 0.006078243255615234 nb_pixel_total : 4206 time to create 1 rle with old method : 0.0049932003021240234 time for calcul the mask position with numpy : 0.0060613155364990234 nb_pixel_total : 3227 time to create 1 rle with old method : 0.003919124603271484 create new chi : 0.4213712215423584 time to delete rle : 0.0002739429473876953 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1538 TO DO : save crop sub photo not yet done ! save time : 0.13549065589904785 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03482246398925781 time for calcul the mask position with numpy : 0.019685745239257812 nb_pixel_total : 2067067 time to create 1 rle with new method : 0.25336599349975586 time for calcul the mask position with numpy : 0.006134748458862305 nb_pixel_total : 6533 time to create 1 rle with old method : 0.007649421691894531 create new chi : 0.2870628833770752 time to delete rle : 0.00022220611572265625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1332 TO DO : save crop sub photo not yet done ! save time : 0.12734127044677734 No data in photo_id : 1383363523 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.07402348518371582 time for calcul the mask position with numpy : 0.20657825469970703 nb_pixel_total : 2065679 time to create 1 rle with new method : 0.08022546768188477 time for calcul the mask position with numpy : 0.006086111068725586 nb_pixel_total : 7921 time to create 1 rle with old method : 0.009541988372802734 create new chi : 0.31447935104370117 time to delete rle : 0.00023293495178222656 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1292 TO DO : save crop sub photo not yet done ! save time : 0.11978840827941895 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03302884101867676 time for calcul the mask position with numpy : 0.12175154685974121 nb_pixel_total : 2038162 time to create 1 rle with new method : 0.07935500144958496 time for calcul the mask position with numpy : 0.00610804557800293 nb_pixel_total : 35438 time to create 1 rle with old method : 0.042845726013183594 create new chi : 0.26041245460510254 time to delete rle : 0.00027942657470703125 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1710 TO DO : save crop sub photo not yet done ! save time : 0.15441584587097168 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.033828020095825195 time for calcul the mask position with numpy : 0.15653276443481445 nb_pixel_total : 1964760 time to create 1 rle with new method : 0.2077350616455078 time for calcul the mask position with numpy : 0.0070226192474365234 nb_pixel_total : 108840 time to create 1 rle with old method : 0.125274658203125 create new chi : 0.5066993236541748 time to delete rle : 0.0003898143768310547 batch 1 Loaded 3 chid ids of type : 3594 ++Number RLEs to save : 2332 TO DO : save crop sub photo not yet done ! save time : 0.19296646118164062 No data in photo_id : 1383363488 No data in photo_id : 1383363483 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.33269357681274414 time for calcul the mask position with numpy : 0.16245651245117188 nb_pixel_total : 1856338 time to create 1 rle with new method : 0.2660808563232422 time for calcul the mask position with numpy : 0.0063936710357666016 nb_pixel_total : 15425 time to create 1 rle with old method : 0.01763463020324707 time for calcul the mask position with numpy : 0.006312847137451172 nb_pixel_total : 62541 time to create 1 rle with old method : 0.07045865058898926 time for calcul the mask position with numpy : 0.006201982498168945 nb_pixel_total : 9817 time to create 1 rle with old method : 0.011218786239624023 time for calcul the mask position with numpy : 0.006751060485839844 nb_pixel_total : 114584 time to create 1 rle with old method : 0.12935376167297363 time for calcul the mask position with numpy : 0.006766080856323242 nb_pixel_total : 14895 time to create 1 rle with old method : 0.017119646072387695 create new chi : 0.7162415981292725 time to delete rle : 0.0007784366607666016 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 3980 TO DO : save crop sub photo not yet done ! save time : 0.33077502250671387 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.08599162101745605 time for calcul the mask position with numpy : 0.08438229560852051 nb_pixel_total : 1903586 time to create 1 rle with new method : 0.08075952529907227 time for calcul the mask position with numpy : 0.006211519241333008 nb_pixel_total : 2079 time to create 1 rle with old method : 0.0024542808532714844 time for calcul the mask position with numpy : 0.00606846809387207 nb_pixel_total : 11098 time to create 1 rle with old method : 0.012775659561157227 time for calcul the mask position with numpy : 0.006007671356201172 nb_pixel_total : 19785 time to create 1 rle with old method : 0.023056745529174805 time for calcul the mask position with numpy : 0.00674128532409668 nb_pixel_total : 124102 time to create 1 rle with old method : 0.14360547065734863 time for calcul the mask position with numpy : 0.0065038204193115234 nb_pixel_total : 12950 time to create 1 rle with old method : 0.015384435653686523 create new chi : 0.4050583839416504 time to delete rle : 0.0005726814270019531 batch 1 Loaded 11 chid ids of type : 3594 +++++++Number RLEs to save : 3236 TO DO : save crop sub photo not yet done ! save time : 0.24306559562683105 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.062458038330078125 time for calcul the mask position with numpy : 0.03419661521911621 nb_pixel_total : 1959763 time to create 1 rle with new method : 0.09387063980102539 time for calcul the mask position with numpy : 0.006266593933105469 nb_pixel_total : 11204 time to create 1 rle with old method : 0.013094186782836914 time for calcul the mask position with numpy : 0.0073850154876708984 nb_pixel_total : 102633 time to create 1 rle with old method : 0.1262507438659668 create new chi : 0.28150224685668945 time to delete rle : 0.0005290508270263672 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2376 TO DO : save crop sub photo not yet done ! save time : 0.18753409385681152 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.5418672561645508 time for calcul the mask position with numpy : 0.03662681579589844 nb_pixel_total : 777193 time to create 1 rle with new method : 0.2531697750091553 time for calcul the mask position with numpy : 0.04366183280944824 nb_pixel_total : 1165665 time to create 1 rle with new method : 0.12302803993225098 time for calcul the mask position with numpy : 0.010460853576660156 nb_pixel_total : 17585 time to create 1 rle with old method : 0.020749807357788086 time for calcul the mask position with numpy : 0.010559320449829102 nb_pixel_total : 111572 time to create 1 rle with old method : 0.13049960136413574 time for calcul the mask position with numpy : 0.010325431823730469 nb_pixel_total : 1585 time to create 1 rle with old method : 0.001916646957397461 create new chi : 0.657721757888794 time to delete rle : 0.0008585453033447266 batch 1 Loaded 9 chid ids of type : 3594 ++++++++Number RLEs to save : 5743 TO DO : save crop sub photo not yet done ! save time : 0.37491297721862793 No data in photo_id : 1383363455 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.0494379997253418 time for calcul the mask position with numpy : 0.09454226493835449 nb_pixel_total : 1947386 time to create 1 rle with new method : 0.1678168773651123 time for calcul the mask position with numpy : 0.006589651107788086 nb_pixel_total : 7855 time to create 1 rle with old method : 0.00906229019165039 time for calcul the mask position with numpy : 0.006867885589599609 nb_pixel_total : 118359 time to create 1 rle with old method : 0.1331653594970703 create new chi : 0.42708492279052734 time to delete rle : 0.00036787986755371094 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2484 TO DO : save crop sub photo not yet done ! save time : 0.18409514427185059 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.032564640045166016 time for calcul the mask position with numpy : 0.019932985305786133 nb_pixel_total : 2058978 time to create 1 rle with new method : 0.2299356460571289 time for calcul the mask position with numpy : 0.006806850433349609 nb_pixel_total : 14622 time to create 1 rle with old method : 0.017186403274536133 create new chi : 0.28611183166503906 time to delete rle : 0.0003955364227294922 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1344 TO DO : save crop sub photo not yet done ! save time : 0.128143310546875 map_output_result : {1383364875: (0.0, 'Should be the crop_list due to order', 0), 1383364830: (0.0, 'Should be the crop_list due to order', 0.0), 1383363867: (0.0, 'Should be the crop_list due to order', 0.0), 1383363796: (0.0, 'Should be the crop_list due to order', 0.0), 1383363731: (0.0, 'Should be the crop_list due to order', 0), 1383363666: (0.0, 'Should be the crop_list due to order', 0), 1383363596: (0.0, 'Should be the crop_list due to order', 0), 1383363563: (0.0, 'Should be the crop_list due to order', 0), 1383363541: (0.0, 'Should be the crop_list due to order', 0), 1383363538: (0.0, 'Should be the crop_list due to order', 0), 1383363531: (0.0, 'Should be the crop_list due to order', 0), 1383363526: (0.0, 'Should be the crop_list due to order', 0), 1383363523: (0.0, 'Should be the crop_list due to order', 0.0), 1383363497: (0.0, 'Should be the crop_list due to order', 0), 1383363495: (0.0, 'Should be the crop_list due to order', 0), 1383363492: (0.0, 'Should be the crop_list due to order', 0), 1383363488: (0.0, 'Should be the crop_list due to order', 0.0), 1383363483: (0.0, 'Should be the crop_list due to order', 0.0), 1383363482: (0.0, 'Should be the crop_list due to order', 0), 1383363465: (0.0, 'Should be the crop_list due to order', 0), 1383363463: (0.0, 'Should be the crop_list due to order', 0), 1383363460: (0.0, 'Should be the crop_list due to order', 0), 1383363455: (0.0, 'Should be the crop_list due to order', 0.0), 1383363450: (0.0, 'Should be the crop_list due to order', 0), 1383363446: (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 [1383364875, 1383364830, 1383363867, 1383363796, 1383363731, 1383363666, 1383363596, 1383363563, 1383363541, 1383363538, 1383363531, 1383363526, 1383363523, 1383363497, 1383363495, 1383363492, 1383363488, 1383363483, 1383363482, 1383363465, 1383363463, 1383363460, 1383363455, 1383363450, 1383363446] Looping around the photos to save general results len do output : 25 /1383364875.Didn't retrieve data . /1383364830.Didn't retrieve data . /1383363867.Didn't retrieve data . /1383363796.Didn't retrieve data . /1383363731.Didn't retrieve data . /1383363666.Didn't retrieve data . /1383363596.Didn't retrieve data . /1383363563.Didn't retrieve data . /1383363541.Didn't retrieve data . /1383363538.Didn't retrieve data . /1383363531.Didn't retrieve data . /1383363526.Didn't retrieve data . /1383363523.Didn't retrieve data . /1383363497.Didn't retrieve data . /1383363495.Didn't retrieve data . /1383363492.Didn't retrieve data . /1383363488.Didn't retrieve data . /1383363483.Didn't retrieve data . /1383363482.Didn't retrieve data . /1383363465.Didn't retrieve data . /1383363463.Didn't retrieve data . /1383363460.Didn't retrieve data . /1383363455.Didn't retrieve data . /1383363450.Didn't retrieve data . /1383363446.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, '3711405') ('3318', '26787600', '1383364875', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383364830', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363867', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363796', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363731', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363666', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363596', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363563', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363541', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363538', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363531', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363526', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363523', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363497', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363495', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363492', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363488', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363483', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363482', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363465', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363463', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363460', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363455', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363450', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363446', None, None, None, None, None, '3711405') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 time used for this insertion : 0.015400886535644531 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.453841924667358 time spend to save output : 0.01629042625427246 total time spend for step 3 : 14.47013235092163 step4:ventilate_hashtags_in_portfolio Fri Sep 12 14:21:57 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 : 26787600 get user id for portfolio 26787600 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`=26787600 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pet_fonce','pehd','background','carton','environnement','papier','flou','autre','metal','mal_croppe')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=26787600 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pet_fonce','pehd','background','carton','environnement','papier','flou','autre','metal','mal_croppe')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=26787600 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pet_fonce','pehd','background','carton','environnement','papier','flou','autre','metal','mal_croppe')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/26792635,26792636,26792637,26792638,26792639,26792640,26792641,26792642,26792643,26792644,26792645?tags=pet_clair,pet_fonce,pehd,background,carton,environnement,papier,flou,autre,metal,mal_croppe Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1383364875, 1383364830, 1383363867, 1383363796, 1383363731, 1383363666, 1383363596, 1383363563, 1383363541, 1383363538, 1383363531, 1383363526, 1383363523, 1383363497, 1383363495, 1383363492, 1383363488, 1383363483, 1383363482, 1383363465, 1383363463, 1383363460, 1383363455, 1383363450, 1383363446] Looping around the photos to save general results len do output : 1 /26787600. 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, '3711405') ('3318', '26787600', '1383364875', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383364830', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363867', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363796', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363731', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363666', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363596', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363563', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363541', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363538', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363531', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363526', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363523', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363497', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363495', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363492', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363488', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363483', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363482', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363465', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363463', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363460', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363455', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363450', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363446', None, None, None, None, None, '3711405') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 26 time used for this insertion : 0.016737699508666992 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.65443754196167 time spend to save output : 0.01708984375 total time spend for step 4 : 1.67152738571167 step5:final Fri Sep 12 14:21:59 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 : {1383364875: ('0.07135574845679012',), 1383364830: ('0.07135574845679012',), 1383363867: ('0.07135574845679012',), 1383363796: ('0.07135574845679012',), 1383363731: ('0.07135574845679012',), 1383363666: ('0.07135574845679012',), 1383363596: ('0.07135574845679012',), 1383363563: ('0.07135574845679012',), 1383363541: ('0.07135574845679012',), 1383363538: ('0.07135574845679012',), 1383363531: ('0.07135574845679012',), 1383363526: ('0.07135574845679012',), 1383363523: ('0.07135574845679012',), 1383363497: ('0.07135574845679012',), 1383363495: ('0.07135574845679012',), 1383363492: ('0.07135574845679012',), 1383363488: ('0.07135574845679012',), 1383363483: ('0.07135574845679012',), 1383363482: ('0.07135574845679012',), 1383363465: ('0.07135574845679012',), 1383363463: ('0.07135574845679012',), 1383363460: ('0.07135574845679012',), 1383363455: ('0.07135574845679012',), 1383363450: ('0.07135574845679012',), 1383363446: ('0.07135574845679012',)} new output for save of step final : {1383364875: ('0.07135574845679012',), 1383364830: ('0.07135574845679012',), 1383363867: ('0.07135574845679012',), 1383363796: ('0.07135574845679012',), 1383363731: ('0.07135574845679012',), 1383363666: ('0.07135574845679012',), 1383363596: ('0.07135574845679012',), 1383363563: ('0.07135574845679012',), 1383363541: ('0.07135574845679012',), 1383363538: ('0.07135574845679012',), 1383363531: ('0.07135574845679012',), 1383363526: ('0.07135574845679012',), 1383363523: ('0.07135574845679012',), 1383363497: ('0.07135574845679012',), 1383363495: ('0.07135574845679012',), 1383363492: ('0.07135574845679012',), 1383363488: ('0.07135574845679012',), 1383363483: ('0.07135574845679012',), 1383363482: ('0.07135574845679012',), 1383363465: ('0.07135574845679012',), 1383363463: ('0.07135574845679012',), 1383363460: ('0.07135574845679012',), 1383363455: ('0.07135574845679012',), 1383363450: ('0.07135574845679012',), 1383363446: ('0.07135574845679012',)} [1383364875, 1383364830, 1383363867, 1383363796, 1383363731, 1383363666, 1383363596, 1383363563, 1383363541, 1383363538, 1383363531, 1383363526, 1383363523, 1383363497, 1383363495, 1383363492, 1383363488, 1383363483, 1383363482, 1383363465, 1383363463, 1383363460, 1383363455, 1383363450, 1383363446] Looping around the photos to save general results len do output : 25 /1383364875.Didn't retrieve data . /1383364830.Didn't retrieve data . /1383363867.Didn't retrieve data . /1383363796.Didn't retrieve data . /1383363731.Didn't retrieve data . /1383363666.Didn't retrieve data . /1383363596.Didn't retrieve data . /1383363563.Didn't retrieve data . /1383363541.Didn't retrieve data . /1383363538.Didn't retrieve data . /1383363531.Didn't retrieve data . /1383363526.Didn't retrieve data . /1383363523.Didn't retrieve data . /1383363497.Didn't retrieve data . /1383363495.Didn't retrieve data . /1383363492.Didn't retrieve data . /1383363488.Didn't retrieve data . /1383363483.Didn't retrieve data . /1383363482.Didn't retrieve data . /1383363465.Didn't retrieve data . /1383363463.Didn't retrieve data . /1383363460.Didn't retrieve data . /1383363455.Didn't retrieve data . /1383363450.Didn't retrieve data . /1383363446.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, '3711405') ('3318', '26787600', '1383364875', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383364830', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363867', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363796', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363731', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363666', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363596', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363563', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363541', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363538', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363531', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363526', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363523', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363497', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363495', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363492', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363488', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363483', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363482', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363465', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363463', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363460', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363455', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363450', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363446', None, None, None, None, None, '3711405') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 time used for this insertion : 0.015888214111328125 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.11535239219665527 time spend to save output : 0.016945362091064453 total time spend for step 5 : 0.13229775428771973 step6:blur_detection Fri Sep 12 14:21:59 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/1757679628_2804915_1383364875_49e59219a37aeecf7f37fd49cfb2cc37.jpg resize: (1080, 1920) 1383364875 -2.7937223298238805 treat image : temp/1757679628_2804915_1383364830_5af572a2f20813ce5e3883dbbde8d26a.jpg resize: (1080, 1920) 1383364830 -2.148541142643297 treat image : temp/1757679628_2804915_1383363867_03ecefb5f3ecfadc2dc28ef557fbd58e.jpg resize: (1080, 1920) 1383363867 -2.6249938770210943 treat image : temp/1757679628_2804915_1383363796_5168dea19cb6b47d31199a2c68833a40.jpg resize: (1080, 1920) 1383363796 -1.3068528813711329 treat image : temp/1757679628_2804915_1383363731_b570e3017c38ee7ff3be2f312d65caa1.jpg resize: (1080, 1920) 1383363731 -2.6308719157893776 treat image : temp/1757679628_2804915_1383363666_312cd11c3017162cf2b0cc7c8fac76a1.jpg resize: (1080, 1920) 1383363666 -2.764514757734927 treat image : temp/1757679628_2804915_1383363596_d13fdddeb99eabaea50ec2309f86a289.jpg resize: (1080, 1920) 1383363596 -1.8436586629079956 treat image : temp/1757679628_2804915_1383363563_bdc705a4dfcf464060bea771471ce9df.jpg resize: (1080, 1920) 1383363563 -2.6525575675321913 treat image : temp/1757679628_2804915_1383363541_b962d8e9002b14cb6b94434da7ef333a.jpg resize: (1080, 1920) 1383363541 -2.419968421089536 treat image : temp/1757679628_2804915_1383363538_75db18820d4e8937c3d35dd864b84d75.jpg resize: (1080, 1920) 1383363538 -3.538908011100792 treat image : temp/1757679628_2804915_1383363531_da7bb16c5122fbd3f841a471aaa956ff.jpg resize: (1080, 1920) 1383363531 -1.0178801802137678 treat image : temp/1757679628_2804915_1383363526_fbebe41d465a6a031beec4c481d8a0b4.jpg resize: (1080, 1920) 1383363526 -2.518797292475122 treat image : temp/1757679628_2804915_1383363523_f2e270087f92fbe6fd2b62c0d3f70d66.jpg resize: (1080, 1920) 1383363523 -2.4465661424866236 treat image : temp/1757679628_2804915_1383363497_91ffa2b8a75b309280c857e745c3aca5.jpg resize: (1080, 1920) 1383363497 -2.657199687843288 treat image : temp/1757679628_2804915_1383363495_7700751566d12c70798468e9992873be.jpg resize: (1080, 1920) 1383363495 -1.3040536428699456 treat image : temp/1757679628_2804915_1383363492_3ef1bb4ca5b0372d642230248f0b71c7.jpg resize: (1080, 1920) 1383363492 -2.7603685887805556 treat image : temp/1757679628_2804915_1383363488_deba435fde774cec1ecbf0cb24adfdcb.jpg resize: (1080, 1920) 1383363488 -1.0417054679842321 treat image : temp/1757679628_2804915_1383363483_617d2508c716de2d1eea67ce2a7698ea.jpg resize: (1080, 1920) 1383363483 -1.504978962265165 treat image : temp/1757679628_2804915_1383363482_a6c778a6114281e58dd5c3408ecbe6bf.jpg resize: (1080, 1920) 1383363482 -2.753687281137536 treat image : temp/1757679628_2804915_1383363465_8fd8940c6ae03c9d6598416c43d1031e.jpg resize: (1080, 1920) 1383363465 -3.687374731138488 treat image : temp/1757679628_2804915_1383363463_bb95fc0db8f98ec436989ab2c614187f.jpg resize: (1080, 1920) 1383363463 -1.8567389684758406 treat image : temp/1757679628_2804915_1383363460_42b0453914b59d7cd5316bfb951a2ec4.jpg resize: (1080, 1920) 1383363460 -3.1750097973835207 treat image : temp/1757679628_2804915_1383363455_45a181a49805c6f575856ac5a54e9682.jpg resize: (1080, 1920) 1383363455 -2.900894024746839 treat image : temp/1757679628_2804915_1383363450_86c3c464950021b382c8e1aec40597ad.jpg resize: (1080, 1920) 1383363450 -2.5807059574796805 treat image : temp/1757679628_2804915_1383363446_79f7bb74694fe6369545778a7801c2d2.jpg resize: (1080, 1920) 1383363446 -1.9871979185925617 treat image : temp/1757679628_2804915_1383363666_312cd11c3017162cf2b0cc7c8fac76a1_rle_crop_3956849322_0.png resize: (107, 100) 1383461521 -2.4050455749883417 treat image : temp/1757679628_2804915_1383363666_312cd11c3017162cf2b0cc7c8fac76a1_rle_crop_3956849323_0.png resize: (184, 105) 1383461522 -0.49900253376765064 treat image : temp/1757679628_2804915_1383363596_d13fdddeb99eabaea50ec2309f86a289_rle_crop_3956849324_0.png resize: (187, 110) 1383461523 -1.9041836738266409 treat image : temp/1757679628_2804915_1383363563_bdc705a4dfcf464060bea771471ce9df_rle_crop_3956849328_0.png resize: (173, 99) 1383461524 -1.2942130415442172 treat image : temp/1757679628_2804915_1383363538_75db18820d4e8937c3d35dd864b84d75_rle_crop_3956849333_0.png resize: (172, 102) 1383461525 -2.079627122774167 treat image : temp/1757679628_2804915_1383363482_a6c778a6114281e58dd5c3408ecbe6bf_rle_crop_3956849343_0.png resize: (181, 109) 1383461526 -1.6906462321539124 treat image : temp/1757679628_2804915_1383363482_a6c778a6114281e58dd5c3408ecbe6bf_rle_crop_3956849345_0.png resize: (204, 122) 1383461527 -1.411269874351771 treat image : temp/1757679628_2804915_1383363465_8fd8940c6ae03c9d6598416c43d1031e_rle_crop_3956849350_0.png resize: (56, 49) 1383461528 1.8707744424343136 treat image : temp/1757679628_2804915_1383363460_42b0453914b59d7cd5316bfb951a2ec4_rle_crop_3956849353_0.png resize: (30, 63) 1383461529 -2.9716119542786017 treat image : temp/1757679628_2804915_1383363450_86c3c464950021b382c8e1aec40597ad_rle_crop_3956849358_0.png resize: (136, 97) 1383461530 -4.308578355140735 treat image : temp/1757679628_2804915_1383363731_b570e3017c38ee7ff3be2f312d65caa1_rle_crop_3956849319_0.png resize: (117, 116) 1383461534 -1.971813936383221 treat image : temp/1757679628_2804915_1383363596_d13fdddeb99eabaea50ec2309f86a289_rle_crop_3956849325_0.png resize: (233, 120) 1383461535 -0.7603008457198703 treat image : temp/1757679628_2804915_1383363596_d13fdddeb99eabaea50ec2309f86a289_rle_crop_3956849326_0.png resize: (524, 358) 1383461536 -0.399377243123437 treat image : temp/1757679628_2804915_1383363531_da7bb16c5122fbd3f841a471aaa956ff_rle_crop_3956849336_0.png resize: (71, 69) 1383461539 -0.46784160902609606 treat image : temp/1757679628_2804915_1383364875_49e59219a37aeecf7f37fd49cfb2cc37_rle_crop_3956849315_0.png resize: (132, 107) 1383461558 -3.392763343506966 treat image : temp/1757679628_2804915_1383364875_49e59219a37aeecf7f37fd49cfb2cc37_rle_crop_3956849316_0.png resize: (559, 339) 1383461559 0.09051419733961884 treat image : temp/1757679628_2804915_1383364875_49e59219a37aeecf7f37fd49cfb2cc37_rle_crop_3956849317_0.png resize: (171, 244) 1383461560 -3.2358602382484483 treat image : temp/1757679628_2804915_1383364875_49e59219a37aeecf7f37fd49cfb2cc37_rle_crop_3956849318_0.png resize: (152, 142) 1383461561 -3.4811745148424063 treat image : temp/1757679628_2804915_1383363731_b570e3017c38ee7ff3be2f312d65caa1_rle_crop_3956849320_0.png resize: (174, 95) 1383461562 -1.954010869406742 treat image : temp/1757679628_2804915_1383363666_312cd11c3017162cf2b0cc7c8fac76a1_rle_crop_3956849321_0.png resize: (63, 54) 1383461563 -1.5498185328242955 treat image : temp/1757679628_2804915_1383363563_bdc705a4dfcf464060bea771471ce9df_rle_crop_3956849327_0.png resize: (559, 328) 1383461564 0.21206252989084873 treat image : temp/1757679628_2804915_1383363563_bdc705a4dfcf464060bea771471ce9df_rle_crop_3956849329_0.png resize: (1006, 1353) 1383461565 -3.32021868032252 treat image : temp/1757679628_2804915_1383363541_b962d8e9002b14cb6b94434da7ef333a_rle_crop_3956849330_0.png resize: (220, 161) 1383461566 -4.409285376260185 treat image : temp/1757679628_2804915_1383363538_75db18820d4e8937c3d35dd864b84d75_rle_crop_3956849331_0.png resize: (99, 117) 1383461567 -2.9556292141335034 treat image : temp/1757679628_2804915_1383363538_75db18820d4e8937c3d35dd864b84d75_rle_crop_3956849332_0.png resize: (535, 350) 1383461568 -0.09405581387518784 treat image : temp/1757679628_2804915_1383363531_da7bb16c5122fbd3f841a471aaa956ff_rle_crop_3956849334_0.png resize: (56, 78) 1383461569 -3.982799863979494 treat image : temp/1757679628_2804915_1383363531_da7bb16c5122fbd3f841a471aaa956ff_rle_crop_3956849335_0.png resize: (102, 54) 1383461570 -0.8669255050514714 treat image : temp/1757679628_2804915_1383363526_fbebe41d465a6a031beec4c481d8a0b4_rle_crop_3956849337_0.png resize: (126, 87) 1383461571 -3.5918394934213245 treat image : temp/1757679628_2804915_1383363497_91ffa2b8a75b309280c857e745c3aca5_rle_crop_3956849338_0.png resize: (104, 112) 1383461572 -2.2362074349191294 treat image : temp/1757679628_2804915_1383363495_7700751566d12c70798468e9992873be_rle_crop_3956849339_0.png resize: (285, 167) 1383461573 -1.2670104580608208 treat image : temp/1757679628_2804915_1383363492_3ef1bb4ca5b0372d642230248f0b71c7_rle_crop_3956849340_0.png resize: (550, 357) 1383461574 -0.2623024214688649 treat image : temp/1757679628_2804915_1383363482_a6c778a6114281e58dd5c3408ecbe6bf_rle_crop_3956849341_0.png resize: (225, 135) 1383461575 -4.212095758929115 treat image : temp/1757679628_2804915_1383363482_a6c778a6114281e58dd5c3408ecbe6bf_rle_crop_3956849342_0.png resize: (531, 374) 1383461576 0.224090653561938 treat image : temp/1757679628_2804915_1383363482_a6c778a6114281e58dd5c3408ecbe6bf_rle_crop_3956849344_0.png resize: (298, 292) 1383461577 -0.6854201589896532 treat image : temp/1757679628_2804915_1383363465_8fd8940c6ae03c9d6598416c43d1031e_rle_crop_3956849346_0.png resize: (109, 188) 1383461578 -3.106608310953337 treat image : temp/1757679628_2804915_1383363465_8fd8940c6ae03c9d6598416c43d1031e_rle_crop_3956849347_0.png resize: (562, 364) 1383461580 0.2600323712327189 treat image : temp/1757679628_2804915_1383363465_8fd8940c6ae03c9d6598416c43d1031e_rle_crop_3956849348_0.png resize: (165, 178) 1383461581 -4.575852974024187 treat image : temp/1757679628_2804915_1383363465_8fd8940c6ae03c9d6598416c43d1031e_rle_crop_3956849349_0.png resize: (144, 98) 1383461582 -3.7101668226390214 treat image : temp/1757679628_2804915_1383363463_bb95fc0db8f98ec436989ab2c614187f_rle_crop_3956849351_0.png resize: (548, 336) 1383461584 -0.4477332062279295 treat image : temp/1757679628_2804915_1383363463_bb95fc0db8f98ec436989ab2c614187f_rle_crop_3956849352_0.png resize: (85, 173) 1383461585 -3.7735669301726693 treat image : temp/1757679628_2804915_1383363460_42b0453914b59d7cd5316bfb951a2ec4_rle_crop_3956849354_0.png resize: (506, 351) 1383461586 0.18060990939942603 treat image : temp/1757679628_2804915_1383363460_42b0453914b59d7cd5316bfb951a2ec4_rle_crop_3956849355_0.png resize: (176, 121) 1383461587 -1.9480715814639114 treat image : temp/1757679628_2804915_1383363460_42b0453914b59d7cd5316bfb951a2ec4_rle_crop_3956849356_0.png resize: (968, 1684) 1383461589 -1.7381134654028962 treat image : temp/1757679628_2804915_1383363450_86c3c464950021b382c8e1aec40597ad_rle_crop_3956849357_0.png resize: (565, 369) 1383461590 0.29891578415082615 treat image : temp/1757679628_2804915_1383363446_79f7bb74694fe6369545778a7801c2d2_rle_crop_3956849359_0.png resize: (132, 162) 1383461591 -2.254111162472727 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 : 70 time used for this insertion : 0.02016305923461914 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 70 time used for this insertion : 0.016903162002563477 save missing photos in datou_result : time spend for datou_step_exec : 21.859667539596558 time spend to save output : 0.04194927215576172 total time spend for step 6 : 21.90161681175232 step7:brightness Fri Sep 12 14:22:21 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/1757679628_2804915_1383364875_49e59219a37aeecf7f37fd49cfb2cc37.jpg treat image : temp/1757679628_2804915_1383364830_5af572a2f20813ce5e3883dbbde8d26a.jpg treat image : temp/1757679628_2804915_1383363867_03ecefb5f3ecfadc2dc28ef557fbd58e.jpg treat image : temp/1757679628_2804915_1383363796_5168dea19cb6b47d31199a2c68833a40.jpg treat image : temp/1757679628_2804915_1383363731_b570e3017c38ee7ff3be2f312d65caa1.jpg treat image : temp/1757679628_2804915_1383363666_312cd11c3017162cf2b0cc7c8fac76a1.jpg treat image : temp/1757679628_2804915_1383363596_d13fdddeb99eabaea50ec2309f86a289.jpg treat image : temp/1757679628_2804915_1383363563_bdc705a4dfcf464060bea771471ce9df.jpg treat image : temp/1757679628_2804915_1383363541_b962d8e9002b14cb6b94434da7ef333a.jpg treat image : temp/1757679628_2804915_1383363538_75db18820d4e8937c3d35dd864b84d75.jpg treat image : 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temp/1757679628_2804915_1383363460_42b0453914b59d7cd5316bfb951a2ec4_rle_crop_3956849356_0.png treat image : temp/1757679628_2804915_1383363450_86c3c464950021b382c8e1aec40597ad_rle_crop_3956849357_0.png treat image : temp/1757679628_2804915_1383363446_79f7bb74694fe6369545778a7801c2d2_rle_crop_3956849359_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 : 70 time used for this insertion : 0.01451253890991211 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 70 time used for this insertion : 0.016945362091064453 save missing photos in datou_result : time spend for datou_step_exec : 6.772326469421387 time spend to save output : 0.03663134574890137 total time spend for step 7 : 6.808957815170288 step8:velours_tree Fri Sep 12 14:22:28 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 : 0.17554140090942383 time spend to save output : 5.841255187988281e-05 total time spend for step 8 : 0.1755998134613037 step9:send_mail_cod Fri Sep 12 14:22:28 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_P26787600_12-09-2025_14_22_28.pdf 26792635 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 .imagette267926351757679748 26792636 imagette267926361757679749 26792637 imagette267926371757679749 26792638 imagette267926381757679749 26792639 change filename to text .change filename to text .change filename to text .imagette267926391757679749 26792641 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 .imagette267926411757679750 26792642 imagette267926421757679750 26792643 imagette267926431757679750 26792644 change filename to text .imagette267926441757679750 26792645 imagette267926451757679750 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=26787600 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/26792635,26792636,26792637,26792638,26792639,26792640,26792641,26792642,26792643,26792644,26792645?tags=pet_clair,pet_fonce,pehd,background,carton,environnement,papier,flou,autre,metal,mal_croppe args[1383364875] : ((1383364875, -2.7937223298238805, 492609224), (1383364875, 0.31894066918797875, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383364830] : ((1383364830, -2.148541142643297, 492609224), (1383364830, 0.5438810601726689, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363867] : ((1383363867, -2.6249938770210943, 492609224), (1383363867, 0.8018175089872366, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363796] : ((1383363796, -1.3068528813711329, 492688767), (1383363796, 0.705746480617872, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363731] : ((1383363731, -2.6308719157893776, 492609224), (1383363731, 0.5592009000170318, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363666] : ((1383363666, -2.764514757734927, 492609224), (1383363666, 0.48662168109111476, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363596] : ((1383363596, -1.8436586629079956, 492688767), (1383363596, 0.4194396095323345, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363563] : ((1383363563, -2.6525575675321913, 492609224), (1383363563, 0.3773950908718105, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363541] : ((1383363541, -2.419968421089536, 492609224), (1383363541, 0.5346227734765152, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363538] : ((1383363538, -3.538908011100792, 492609224), (1383363538, 0.3833521686556763, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363531] : ((1383363531, -1.0178801802137678, 492688767), (1383363531, 0.27735939791769015, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363526] : ((1383363526, -2.518797292475122, 492609224), (1383363526, 0.5429614420812493, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363523] : ((1383363523, -2.4465661424866236, 492609224), (1383363523, 0.700159162794107, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363497] : ((1383363497, -2.657199687843288, 492609224), (1383363497, 0.4485862181536387, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363495] : ((1383363495, -1.3040536428699456, 492688767), (1383363495, 0.47559716804355817, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363492] : ((1383363492, -2.7603685887805556, 492609224), (1383363492, 0.42484596264237245, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363488] : ((1383363488, -1.0417054679842321, 492688767), (1383363488, 0.6001700860748749, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363483] : ((1383363483, -1.504978962265165, 492688767), (1383363483, 0.6757643576117252, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363482] : ((1383363482, -2.753687281137536, 492609224), (1383363482, 0.714492056743772, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363465] : ((1383363465, -3.687374731138488, 492609224), (1383363465, 0.4326635103513079, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363463] : ((1383363463, -1.8567389684758406, 492688767), (1383363463, 0.6469500947214596, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363460] : ((1383363460, -3.1750097973835207, 492609224), (1383363460, 0.5753867222772953, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363455] : ((1383363455, -2.900894024746839, 492609224), (1383363455, 0.6237347881073138, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363450] : ((1383363450, -2.5807059574796805, 492609224), (1383363450, 0.44619163644846216, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com args[1383363446] : ((1383363446, -1.9871979185925617, 492688767), (1383363446, 0.4508182470316083, 2107752395), '0.07135574845679012') We are sending mail with results at report@fotonower.com refus_total : 0.07135574845679012 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=26787600 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_P26787600_12-09-2025_14_22_28.pdf results_Auto_P26787600_12-09-2025_14_22_28.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787600_12-09-2025_14_22_28.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','26787600','results_Auto_P26787600_12-09-2025_14_22_28.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787600_12-09-2025_14_22_28.pdf','pdf','','0.42','0.07135574845679012') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/26787600

https://www.fotonower.com/image?json=false&list_photos_id=1383364875
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383364830
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363867
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363796
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363731
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363666
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363596
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363563
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363541
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363538
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363531
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363526
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363523
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363497
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363495
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363492
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363488
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363483
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363482
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363465
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363463
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363460
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363455
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363450
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1383363446
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 7.14%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/26792635?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/26792639?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/26792641?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/26792644?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787600_12-09-2025_14_22_28.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/26792635,26792636,26792637,26792638,26792639,26792640,26792641,26792642,26792643,26792644,26792645?tags=pet_clair,pet_fonce,pehd,background,carton,environnement,papier,flou,autre,metal,mal_croppe.


L'équipe Fotonower 202 b'' Server: nginx Date: Fri, 12 Sep 2025 12:22:33 GMT Content-Length: 0 Connection: close X-Message-Id: TpjNvypvRSOkyBHxfbLt0Q 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 [1383364875, 1383364830, 1383363867, 1383363796, 1383363731, 1383363666, 1383363596, 1383363563, 1383363541, 1383363538, 1383363531, 1383363526, 1383363523, 1383363497, 1383363495, 1383363492, 1383363488, 1383363483, 1383363482, 1383363465, 1383363463, 1383363460, 1383363455, 1383363450, 1383363446] 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, '3711405') ('3318', '26787600', '1383364875', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383364830', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363867', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363796', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363731', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363666', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363596', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363563', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363541', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363538', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363531', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363526', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363523', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363497', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363495', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363492', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363488', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363483', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363482', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363465', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363463', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363460', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363455', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363450', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363446', None, None, None, None, None, '3711405') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 time used for this insertion : 0.016192913055419922 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.673438310623169 time spend to save output : 0.016542434692382812 total time spend for step 9 : 4.689980745315552 step10:split_time_score Fri Sep 12 14:22:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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'}] (('08', 25),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 12092025 26787600 Nombre de photos uploadées : 25 / 23040 (0%) 12092025 26787600 Nombre de photos taguées (types de déchets): 0 / 25 (0%) 12092025 26787600 Nombre de photos taguées (volume) : 0 / 25 (0%) elapsed_time : load_data_split_time_score 2.1457672119140625e-06 elapsed_time : order_list_meta_photo_and_scores 6.198883056640625e-06 ????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0011353492736816406 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.23237824440002441 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787584 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787592 order by id desc limit 1 Qualite : 0.07135574845679012 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787600_12-09-2025_14_22_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787600 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`=26787600 AND mptpi.`type`=3594 To do Qualite : 0.02230613425925926 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787601_12-09-2025_14_11_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787601 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`=26787601 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787615 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787618 order by id desc limit 1 Qualite : 0.04421078504579849 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787619_12-09-2025_13_13_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787619 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`=26787619 AND mptpi.`type`=3594 To do Qualite : 0.08489351851851851 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787621_12-09-2025_12_41_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787621 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`=26787621 AND mptpi.`type`=3594 To do Qualite : 0.09501412312610229 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787622_12-09-2025_12_32_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787622 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`=26787622 AND mptpi.`type`=3594 To do Qualite : 0.02157503858024691 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787651_12-09-2025_12_21_19.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787651 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`=26787651 AND mptpi.`type`=3594 To do Qualite : 0.045483592678326475 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26787652_12-09-2025_12_11_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26787652 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`=26787652 AND mptpi.`type`=3594 To do Qualite : 0.09784127443415638 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26791118_12-09-2025_13_41_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26791118 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`=26791118 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'12092025': {'nb_upload': 25, '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 [1383364875, 1383364830, 1383363867, 1383363796, 1383363731, 1383363666, 1383363596, 1383363563, 1383363541, 1383363538, 1383363531, 1383363526, 1383363523, 1383363497, 1383363495, 1383363492, 1383363488, 1383363483, 1383363482, 1383363465, 1383363463, 1383363460, 1383363455, 1383363450, 1383363446] Looping around the photos to save general results len do output : 1 /26787600Didn'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, '3711405') ('3318', '26787600', '1383364875', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383364830', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363867', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363796', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363731', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363666', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363596', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363563', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363541', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363538', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363531', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363526', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363523', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363497', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363495', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363492', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363488', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363483', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363482', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363465', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363463', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363460', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363455', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363450', None, None, None, None, None, '3711405') ('3318', None, None, None, None, None, None, None, '3711405') ('3318', '26787600', '1383363446', None, None, None, None, None, '3711405') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 26 time used for this insertion : 0.01880168914794922 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.849312782287598 time spend to save output : 0.019118785858154297 total time spend for step 10 : 4.868431568145752 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 25 set_done_treatment 75.79user 32.18system 2:13.58elapsed 80%CPU (0avgtext+0avgdata 3028792maxresident)k 592704inputs+37256outputs (8541major+2231265minor)pagefaults 0swaps