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 : 2917203 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 : ['2539826'] with mtr_portfolio_ids : ['20176487'] and first list_photo_ids : [] new path : /proc/2917203/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 5 ; length of list_pids : 5 ; length of list_args : 5 time to download the photos : 1.091256856918335 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Mon Feb 3 14:00:29 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 : 10774 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-03 14:00:33.692483: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-03 14:00:33.727697: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493020000 Hz 2025-02-03 14:00:33.902728: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f18c0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-03 14:00:33.902769: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-03 14:00:33.906413: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-03 14:00:34.123248: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x851f930 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-03 14:00:34.123300: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-03 14:00:34.124560: 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 2025-02-03 14:00:34.124934: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-03 14:00:34.127294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-03 14:00:34.129727: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-03 14:00:34.130214: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-03 14:00:34.132792: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-03 14:00:34.134361: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-03 14:00:34.138685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-03 14:00:34.140222: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-03 14:00:34.140303: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-03 14:00:34.141096: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-03 14:00:34.141111: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-03 14:00:34.141119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-03 14:00:34.142908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9841 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. 2025-02-03 14:00:34.535125: 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 2025-02-03 14:00:34.535227: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-03 14:00:34.535254: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-03 14:00:34.535276: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-03 14:00:34.535299: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-03 14:00:34.535352: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-03 14:00:34.535377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-03 14:00:34.535396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-03 14:00:34.536903: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-03 14:00:34.538154: 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 2025-02-03 14:00:34.538202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-03 14:00:34.538221: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-03 14:00:34.538236: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-03 14:00:34.538252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-03 14:00:34.538267: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-03 14:00:34.538281: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-03 14:00:34.538295: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-03 14:00:34.539556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-03 14:00:34.539597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-03 14:00:34.539607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-03 14:00:34.539616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-03 14:00:34.540943: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9841 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 : [] 2025-02-03 14:00:48.556023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-03 14:00:48.777746: 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 : 5 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 60 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 51 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 86 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 55 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 60 Detection mask done ! Trying to reset tf kernel 2919423 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5485 tf kernel not reseted sub process len(results) : 5 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 5 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 : 10621 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 : 0.005192279815673828 nb_pixel_total : 115201 time to create 1 rle with old method : 0.17192316055297852 length of segment : 432 time for calcul the mask position with numpy : 0.0008637905120849609 nb_pixel_total : 28089 time to create 1 rle with old method : 0.03846001625061035 length of segment : 144 time for calcul the mask position with numpy : 0.001306295394897461 nb_pixel_total : 44150 time to create 1 rle with old method : 0.04837965965270996 length of segment : 303 time for calcul the mask position with numpy : 0.0003120899200439453 nb_pixel_total : 12035 time to create 1 rle with old method : 0.013606548309326172 length of segment : 106 time for calcul the mask position with numpy : 0.0006220340728759766 nb_pixel_total : 25906 time to create 1 rle with old method : 0.028982162475585938 length of segment : 223 time for calcul the mask position with numpy : 0.0008242130279541016 nb_pixel_total : 28599 time to create 1 rle with old method : 0.03182792663574219 length of segment : 228 time for calcul the mask position with numpy : 0.0011963844299316406 nb_pixel_total : 55349 time to create 1 rle with old method : 0.06183457374572754 length of segment : 180 time for calcul the mask position with numpy : 0.00022745132446289062 nb_pixel_total : 4209 time to create 1 rle with old method : 0.004938364028930664 length of segment : 80 time for calcul the mask position with numpy : 0.0016560554504394531 nb_pixel_total : 60009 time to create 1 rle with old method : 0.06624317169189453 length of segment : 309 time for calcul the mask position with numpy : 0.006745100021362305 nb_pixel_total : 212313 time to create 1 rle with new method : 0.01904773712158203 length of segment : 599 time for calcul the mask position with numpy : 0.004520416259765625 nb_pixel_total : 247146 time to create 1 rle with new method : 0.016450881958007812 length of segment : 518 time for calcul the mask position with numpy : 0.002591371536254883 nb_pixel_total : 74383 time to create 1 rle with old method : 0.0862894058227539 length of segment : 331 time for calcul the mask position with numpy : 0.0002548694610595703 nb_pixel_total : 8715 time to create 1 rle with old method : 0.009996175765991211 length of segment : 73 time for calcul the mask position with numpy : 0.00044989585876464844 nb_pixel_total : 13814 time to create 1 rle with old method : 0.015594482421875 length of segment : 176 time for calcul the mask position with numpy : 0.0014145374298095703 nb_pixel_total : 66221 time to create 1 rle with old method : 0.07384896278381348 length of segment : 238 time for calcul the mask position with numpy : 0.0006504058837890625 nb_pixel_total : 30919 time to create 1 rle with old method : 0.05077981948852539 length of segment : 272 time for calcul the mask position with numpy : 0.0034596920013427734 nb_pixel_total : 118718 time to create 1 rle with old method : 0.13338112831115723 length of segment : 665 time for calcul the mask position with numpy : 0.0009412765502929688 nb_pixel_total : 37556 time to create 1 rle with old method : 0.04177689552307129 length of segment : 218 time for calcul the mask position with numpy : 0.003030538558959961 nb_pixel_total : 153930 time to create 1 rle with new method : 0.010425090789794922 length of segment : 418 time for calcul the mask position with numpy : 0.0005958080291748047 nb_pixel_total : 7113 time to create 1 rle with old method : 0.008533716201782227 length of segment : 188 time for calcul the mask position with numpy : 0.0004291534423828125 nb_pixel_total : 15602 time to create 1 rle with old method : 0.01765894889831543 length of segment : 232 time for calcul the mask position with numpy : 0.00912618637084961 nb_pixel_total : 367818 time to create 1 rle with new method : 0.02999281883239746 length of segment : 716 time for calcul the mask position with numpy : 0.0010809898376464844 nb_pixel_total : 44653 time to create 1 rle with old method : 0.05269789695739746 length of segment : 364 time for calcul the mask position with numpy : 0.003534555435180664 nb_pixel_total : 177559 time to create 1 rle with new method : 0.01234126091003418 length of segment : 649 time for calcul the mask position with numpy : 0.00045299530029296875 nb_pixel_total : 19547 time to create 1 rle with old method : 0.022322893142700195 length of segment : 134 time for calcul the mask position with numpy : 0.0008227825164794922 nb_pixel_total : 32422 time to create 1 rle with old method : 0.036382198333740234 length of segment : 255 time for calcul the mask position with numpy : 0.0005211830139160156 nb_pixel_total : 23545 time to create 1 rle with old method : 0.030344724655151367 length of segment : 172 time for calcul the mask position with numpy : 0.0003666877746582031 nb_pixel_total : 15383 time to create 1 rle with old method : 0.019213199615478516 length of segment : 144 time for calcul the mask position with numpy : 0.0001678466796875 nb_pixel_total : 6904 time to create 1 rle with old method : 0.008046627044677734 length of segment : 75 time for calcul the mask position with numpy : 0.0003509521484375 nb_pixel_total : 13336 time to create 1 rle with old method : 0.015214920043945312 length of segment : 174 time for calcul the mask position with numpy : 0.0004932880401611328 nb_pixel_total : 17344 time to create 1 rle with old method : 0.01929330825805664 length of segment : 308 time for calcul the mask position with numpy : 0.0006883144378662109 nb_pixel_total : 27958 time to create 1 rle with old method : 0.03100419044494629 length of segment : 281 time for calcul the mask position with numpy : 0.0006155967712402344 nb_pixel_total : 20782 time to create 1 rle with old method : 0.02323007583618164 length of segment : 254 time for calcul the mask position with numpy : 0.0004668235778808594 nb_pixel_total : 18779 time to create 1 rle with old method : 0.021240711212158203 length of segment : 132 time for calcul the mask position with numpy : 0.00032258033752441406 nb_pixel_total : 13845 time to create 1 rle with old method : 0.015913963317871094 length of segment : 127 time for calcul the mask position with numpy : 0.0003437995910644531 nb_pixel_total : 12008 time to create 1 rle with old method : 0.018164634704589844 length of segment : 141 time for calcul the mask position with numpy : 0.0011761188507080078 nb_pixel_total : 19644 time to create 1 rle with old method : 0.02227783203125 length of segment : 200 time for calcul the mask position with numpy : 0.0004601478576660156 nb_pixel_total : 13893 time to create 1 rle with old method : 0.016984224319458008 length of segment : 154 time for calcul the mask position with numpy : 0.0011336803436279297 nb_pixel_total : 20574 time to create 1 rle with old method : 0.023125171661376953 length of segment : 169 time for calcul the mask position with numpy : 0.0015804767608642578 nb_pixel_total : 23781 time to create 1 rle with old method : 0.02682328224182129 length of segment : 334 time for calcul the mask position with numpy : 0.006665945053100586 nb_pixel_total : 187517 time to create 1 rle with new method : 0.40149927139282227 length of segment : 531 time for calcul the mask position with numpy : 0.0004630088806152344 nb_pixel_total : 4846 time to create 1 rle with old method : 0.008307695388793945 length of segment : 96 time for calcul the mask position with numpy : 0.0031294822692871094 nb_pixel_total : 74275 time to create 1 rle with old method : 0.09389472007751465 length of segment : 505 time for calcul the mask position with numpy : 0.0009272098541259766 nb_pixel_total : 19847 time to create 1 rle with old method : 0.02402973175048828 length of segment : 142 time for calcul the mask position with numpy : 0.00040602684020996094 nb_pixel_total : 6298 time to create 1 rle with old method : 0.007357120513916016 length of segment : 89 time for calcul the mask position with numpy : 0.005538463592529297 nb_pixel_total : 125883 time to create 1 rle with old method : 0.146026611328125 length of segment : 621 time for calcul the mask position with numpy : 0.002631664276123047 nb_pixel_total : 36771 time to create 1 rle with old method : 0.04462575912475586 length of segment : 314 time for calcul the mask position with numpy : 0.0013425350189208984 nb_pixel_total : 22045 time to create 1 rle with old method : 0.024598360061645508 length of segment : 313 time for calcul the mask position with numpy : 0.0005230903625488281 nb_pixel_total : 9703 time to create 1 rle with old method : 0.012187480926513672 length of segment : 101 time for calcul the mask position with numpy : 0.0009877681732177734 nb_pixel_total : 17315 time to create 1 rle with old method : 0.024158477783203125 length of segment : 121 time for calcul the mask position with numpy : 0.0019273757934570312 nb_pixel_total : 22758 time to create 1 rle with old method : 0.025542497634887695 length of segment : 244 time for calcul the mask position with numpy : 0.007966756820678711 nb_pixel_total : 132901 time to create 1 rle with old method : 0.15992259979248047 length of segment : 423 time for calcul the mask position with numpy : 0.002763986587524414 nb_pixel_total : 34593 time to create 1 rle with old method : 0.04041862487792969 length of segment : 275 time for calcul the mask position with numpy : 0.0009989738464355469 nb_pixel_total : 8512 time to create 1 rle with old method : 0.013987064361572266 length of segment : 135 time for calcul the mask position with numpy : 0.0030698776245117188 nb_pixel_total : 30995 time to create 1 rle with old method : 0.04899287223815918 length of segment : 323 time for calcul the mask position with numpy : 0.0011398792266845703 nb_pixel_total : 11778 time to create 1 rle with old method : 0.013664722442626953 length of segment : 135 time for calcul the mask position with numpy : 0.0008513927459716797 nb_pixel_total : 12893 time to create 1 rle with old method : 0.014757871627807617 length of segment : 111 time for calcul the mask position with numpy : 0.004571676254272461 nb_pixel_total : 71638 time to create 1 rle with old method : 0.08223438262939453 length of segment : 310 time for calcul the mask position with numpy : 0.005137443542480469 nb_pixel_total : 101304 time to create 1 rle with old method : 0.1163949966430664 length of segment : 576 time for calcul the mask position with numpy : 0.0049321651458740234 nb_pixel_total : 90751 time to create 1 rle with old method : 0.1056509017944336 length of segment : 370 time for calcul the mask position with numpy : 0.00403141975402832 nb_pixel_total : 61447 time to create 1 rle with old method : 0.07175111770629883 length of segment : 226 time for calcul the mask position with numpy : 0.00033593177795410156 nb_pixel_total : 6212 time to create 1 rle with old method : 0.007119178771972656 length of segment : 86 time for calcul the mask position with numpy : 0.0006737709045410156 nb_pixel_total : 8202 time to create 1 rle with old method : 0.009576797485351562 length of segment : 83 time for calcul the mask position with numpy : 0.0013773441314697266 nb_pixel_total : 12076 time to create 1 rle with old method : 0.020581483840942383 length of segment : 136 time for calcul the mask position with numpy : 0.0008497238159179688 nb_pixel_total : 12875 time to create 1 rle with old method : 0.02114725112915039 length of segment : 133 time for calcul the mask position with numpy : 0.0005245208740234375 nb_pixel_total : 11691 time to create 1 rle with old method : 0.013629436492919922 length of segment : 140 time for calcul the mask position with numpy : 0.0011830329895019531 nb_pixel_total : 15187 time to create 1 rle with old method : 0.017514467239379883 length of segment : 111 time for calcul the mask position with numpy : 0.0015180110931396484 nb_pixel_total : 19089 time to create 1 rle with old method : 0.02138233184814453 length of segment : 179 time for calcul the mask position with numpy : 0.0008521080017089844 nb_pixel_total : 10786 time to create 1 rle with old method : 0.012258052825927734 length of segment : 96 time for calcul the mask position with numpy : 0.0009236335754394531 nb_pixel_total : 9918 time to create 1 rle with old method : 0.011255502700805664 length of segment : 139 time for calcul the mask position with numpy : 0.0005130767822265625 nb_pixel_total : 5383 time to create 1 rle with old method : 0.006276369094848633 length of segment : 96 time for calcul the mask position with numpy : 0.0006644725799560547 nb_pixel_total : 13473 time to create 1 rle with old method : 0.015039205551147461 length of segment : 152 time for calcul the mask position with numpy : 0.0007638931274414062 nb_pixel_total : 9248 time to create 1 rle with old method : 0.01059865951538086 length of segment : 142 time for calcul the mask position with numpy : 0.0032885074615478516 nb_pixel_total : 46071 time to create 1 rle with old method : 0.05277276039123535 length of segment : 246 time for calcul the mask position with numpy : 0.0019345283508300781 nb_pixel_total : 24494 time to create 1 rle with old method : 0.030764341354370117 length of segment : 305 time for calcul the mask position with numpy : 0.0016970634460449219 nb_pixel_total : 15734 time to create 1 rle with old method : 0.017754077911376953 length of segment : 190 time for calcul the mask position with numpy : 0.0018622875213623047 nb_pixel_total : 25699 time to create 1 rle with old method : 0.028935670852661133 length of segment : 182 time for calcul the mask position with numpy : 0.022411108016967773 nb_pixel_total : 266739 time to create 1 rle with new method : 0.757002592086792 length of segment : 437 time for calcul the mask position with numpy : 0.004225730895996094 nb_pixel_total : 6952 time to create 1 rle with old method : 0.009116411209106445 length of segment : 77 time for calcul the mask position with numpy : 0.0017290115356445312 nb_pixel_total : 9133 time to create 1 rle with old method : 0.010387897491455078 length of segment : 107 time for calcul the mask position with numpy : 0.004600048065185547 nb_pixel_total : 20666 time to create 1 rle with old method : 0.03851604461669922 length of segment : 227 time for calcul the mask position with numpy : 0.0013816356658935547 nb_pixel_total : 5072 time to create 1 rle with old method : 0.005995273590087891 length of segment : 91 time for calcul the mask position with numpy : 0.020394086837768555 nb_pixel_total : 80329 time to create 1 rle with old method : 0.10670089721679688 length of segment : 377 time for calcul the mask position with numpy : 0.01799154281616211 nb_pixel_total : 15983 time to create 1 rle with old method : 0.02090311050415039 length of segment : 184 time for calcul the mask position with numpy : 0.027692317962646484 nb_pixel_total : 89504 time to create 1 rle with old method : 0.14934301376342773 length of segment : 340 time for calcul the mask position with numpy : 0.11904692649841309 nb_pixel_total : 94744 time to create 1 rle with old method : 0.165757417678833 length of segment : 330 time for calcul the mask position with numpy : 0.0963449478149414 nb_pixel_total : 62640 time to create 1 rle with old method : 0.08128714561462402 length of segment : 227 time for calcul the mask position with numpy : 0.16300392150878906 nb_pixel_total : 86924 time to create 1 rle with old method : 0.16051864624023438 length of segment : 410 time for calcul the mask position with numpy : 0.16809511184692383 nb_pixel_total : 58570 time to create 1 rle with old method : 0.07290077209472656 length of segment : 335 time for calcul the mask position with numpy : 0.04453468322753906 nb_pixel_total : 25433 time to create 1 rle with old method : 0.0343170166015625 length of segment : 234 time for calcul the mask position with numpy : 0.009028196334838867 nb_pixel_total : 6540 time to create 1 rle with old method : 0.012804031372070312 length of segment : 96 time for calcul the mask position with numpy : 1.0340917110443115 nb_pixel_total : 240070 time to create 1 rle with new method : 0.31995320320129395 length of segment : 1039 time for calcul the mask position with numpy : 0.22561264038085938 nb_pixel_total : 38385 time to create 1 rle with old method : 0.0604248046875 length of segment : 192 time for calcul the mask position with numpy : 0.01929616928100586 nb_pixel_total : 8423 time to create 1 rle with old method : 0.012148618698120117 length of segment : 94 time for calcul the mask position with numpy : 0.2657313346862793 nb_pixel_total : 31323 time to create 1 rle with old method : 0.04001879692077637 length of segment : 188 time for calcul the mask position with numpy : 0.012339591979980469 nb_pixel_total : 16522 time to create 1 rle with old method : 0.019825458526611328 length of segment : 95 time for calcul the mask position with numpy : 0.02625131607055664 nb_pixel_total : 30410 time to create 1 rle with old method : 0.03838229179382324 length of segment : 280 time for calcul the mask position with numpy : 0.11798286437988281 nb_pixel_total : 53495 time to create 1 rle with old method : 0.070831298828125 length of segment : 266 time for calcul the mask position with numpy : 0.10209536552429199 nb_pixel_total : 19825 time to create 1 rle with old method : 0.0389399528503418 length of segment : 208 time for calcul the mask position with numpy : 0.1271052360534668 nb_pixel_total : 27139 time to create 1 rle with old method : 0.05014777183532715 length of segment : 198 time for calcul the mask position with numpy : 0.19792938232421875 nb_pixel_total : 47747 time to create 1 rle with old method : 0.08066034317016602 length of segment : 292 time for calcul the mask position with numpy : 0.19121813774108887 nb_pixel_total : 42239 time to create 1 rle with old method : 0.06473922729492188 length of segment : 242 time for calcul the mask position with numpy : 0.3747711181640625 nb_pixel_total : 48284 time to create 1 rle with old method : 0.08092379570007324 length of segment : 180 time for calcul the mask position with numpy : 0.1582653522491455 nb_pixel_total : 6558 time to create 1 rle with old method : 0.010438919067382812 length of segment : 126 time for calcul the mask position with numpy : 0.04527640342712402 nb_pixel_total : 3280 time to create 1 rle with old method : 0.0052797794342041016 length of segment : 60 time for calcul the mask position with numpy : 1.2360293865203857 nb_pixel_total : 190966 time to create 1 rle with new method : 0.024364948272705078 length of segment : 587 time for calcul the mask position with numpy : 0.6837003231048584 nb_pixel_total : 115203 time to create 1 rle with old method : 0.14049768447875977 length of segment : 432 time for calcul the mask position with numpy : 0.10200953483581543 nb_pixel_total : 28097 time to create 1 rle with old method : 0.035265445709228516 length of segment : 144 time for calcul the mask position with numpy : 0.32993459701538086 nb_pixel_total : 44151 time to create 1 rle with old method : 0.05409646034240723 length of segment : 303 time for calcul the mask position with numpy : 0.05898690223693848 nb_pixel_total : 12040 time to create 1 rle with old method : 0.017146587371826172 length of segment : 106 time for calcul the mask position with numpy : 0.12552118301391602 nb_pixel_total : 25913 time to create 1 rle with old method : 0.03255128860473633 length of segment : 224 time for calcul the mask position with numpy : 0.15438413619995117 nb_pixel_total : 28598 time to create 1 rle with old method : 0.03482532501220703 length of segment : 228 time for calcul the mask position with numpy : 0.15256953239440918 nb_pixel_total : 55356 time to create 1 rle with old method : 0.08575654029846191 length of segment : 180 time for calcul the mask position with numpy : 0.026563644409179688 nb_pixel_total : 4213 time to create 1 rle with old method : 0.008282899856567383 length of segment : 80 time for calcul the mask position with numpy : 0.16526293754577637 nb_pixel_total : 60007 time to create 1 rle with old method : 0.08890676498413086 length of segment : 309 time for calcul the mask position with numpy : 0.40284228324890137 nb_pixel_total : 212615 time to create 1 rle with new method : 0.015002250671386719 length of segment : 599 time for calcul the mask position with numpy : 0.3090841770172119 nb_pixel_total : 74401 time to create 1 rle with old method : 0.09100532531738281 length of segment : 330 time for calcul the mask position with numpy : 0.586418867111206 nb_pixel_total : 247137 time to create 1 rle with new method : 0.016556501388549805 length of segment : 518 time for calcul the mask position with numpy : 0.03795886039733887 nb_pixel_total : 8719 time to create 1 rle with old method : 0.015352010726928711 length of segment : 73 time for calcul the mask position with numpy : 0.0743570327758789 nb_pixel_total : 13814 time to create 1 rle with old method : 0.02011871337890625 length of segment : 176 time for calcul the mask position with numpy : 0.16851139068603516 nb_pixel_total : 66229 time to create 1 rle with old method : 0.08043336868286133 length of segment : 238 time for calcul the mask position with numpy : 0.054923295974731445 nb_pixel_total : 30919 time to create 1 rle with old method : 0.04365825653076172 length of segment : 272 time for calcul the mask position with numpy : 0.4521522521972656 nb_pixel_total : 118713 time to create 1 rle with old method : 0.13156604766845703 length of segment : 665 time for calcul the mask position with numpy : 0.10098552703857422 nb_pixel_total : 37567 time to create 1 rle with old method : 0.04641556739807129 length of segment : 218 time for calcul the mask position with numpy : 0.22859764099121094 nb_pixel_total : 154805 time to create 1 rle with new method : 0.01049494743347168 length of segment : 423 time for calcul the mask position with numpy : 0.06278324127197266 nb_pixel_total : 7110 time to create 1 rle with old method : 0.012637138366699219 length of segment : 188 time for calcul the mask position with numpy : 0.021231889724731445 nb_pixel_total : 15598 time to create 1 rle with old method : 0.023714780807495117 length of segment : 232 time for calcul the mask position with numpy : 0.6772449016571045 nb_pixel_total : 367836 time to create 1 rle with new method : 0.030062437057495117 length of segment : 716 time spent for convertir_results : 22.524744510650635 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 273 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 33266 save missing photos in datou_result : time spend for datou_step_exec : 88.929438829422 time spend to save output : 4.647243022918701 total time spend for step 1 : 93.5766818523407 step2:crop_condition Mon Feb 3 14:02:03 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 : 5 ! batch 1 Loaded 273 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 139 About to insert : list_path_to_insert length 139 new photo from crops ! About to upload 139 photos upload in portfolio : 3736932 init cache_photo without model_param we have 139 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587736_2917203 we have uploaded 139 photos in the portfolio 3736932 time of upload the photos Elapsed time : 32.899677753448486 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 72 About to insert : list_path_to_insert length 72 new photo from crops ! About to upload 72 photos upload in portfolio : 3736932 init cache_photo without model_param we have 72 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587781_2917203 we have uploaded 72 photos in the portfolio 3736932 time of upload the photos Elapsed time : 17.512054443359375 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 : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587800_2917203 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8234245777130127 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 ! map_result returned by crop_photo_return_map_crop : length : 30 About to insert : list_path_to_insert length 30 new photo from crops ! About to upload 30 photos upload in portfolio : 3736932 init cache_photo without model_param we have 30 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587809_2917203 we have uploaded 30 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.711967945098877 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587819_2917203 we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2337069511413574 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587821_2917203 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7725129127502441 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 3736932 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738587825_2917203 we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.4159152507781982 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 258 /1334201470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334201999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334202022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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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, '2539826') ('3318', None, '1333266340', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266339', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266334', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266329', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266314', None, None, None, None, None, '2539826') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 779 time used for this insertion : 0.08477067947387695 save_final save missing photos in datou_result : time spend for datou_step_exec : 104.07460284233093 time spend to save output : 0.09201407432556152 total time spend for step 2 : 104.1666169166565 step3:rle_unique_nms_with_priority Mon Feb 3 14:03:47 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 273 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 4.36523175239563 time for calcul the mask position with numpy : 0.4802823066711426 nb_pixel_total : 5347867 time to create 1 rle with new method : 0.7845029830932617 time for calcul the mask position with numpy : 0.029105663299560547 nb_pixel_total : 28089 time to create 1 rle with old method : 0.0314943790435791 time for calcul the mask position with numpy : 0.02947688102722168 nb_pixel_total : 118718 time to create 1 rle with old method : 0.13357758522033691 time for calcul the mask position with numpy : 0.02910447120666504 nb_pixel_total : 55349 time to create 1 rle with old method : 0.05973196029663086 time for calcul the mask position with numpy : 0.02872633934020996 nb_pixel_total : 4209 time to create 1 rle with old method : 0.004853248596191406 time for calcul the mask position with numpy : 0.028602123260498047 nb_pixel_total : 28599 time to create 1 rle with old method : 0.0317385196685791 time for calcul the mask position with numpy : 0.028554201126098633 nb_pixel_total : 66221 time to create 1 rle with old method : 0.07010173797607422 time for calcul the mask position with numpy : 0.031850576400756836 nb_pixel_total : 367818 time to create 1 rle with new method : 0.7100086212158203 time for calcul the mask position with numpy : 0.029337644577026367 nb_pixel_total : 25906 time to create 1 rle with old method : 0.0304107666015625 time for calcul the mask position with numpy : 0.028582096099853516 nb_pixel_total : 7113 time to create 1 rle with old method : 0.008461475372314453 time for calcul the mask position with numpy : 0.028392553329467773 nb_pixel_total : 14372 time to create 1 rle with old method : 0.017210721969604492 time for calcul the mask position with numpy : 0.028444766998291016 nb_pixel_total : 115201 time to create 1 rle with old method : 0.1308298110961914 time for calcul the mask position with numpy : 0.028733253479003906 nb_pixel_total : 30919 time to create 1 rle with old method : 0.03782343864440918 time for calcul the mask position with numpy : 0.02863478660583496 nb_pixel_total : 153930 time to create 1 rle with new method : 0.5647478103637695 time for calcul the mask position with numpy : 0.031440019607543945 nb_pixel_total : 1014 time to create 1 rle with old method : 0.0015370845794677734 time for calcul the mask position with numpy : 0.028749942779541016 nb_pixel_total : 37556 time to create 1 rle with old method : 0.04135465621948242 time for calcul the mask position with numpy : 0.028429269790649414 nb_pixel_total : 74383 time to create 1 rle with old method : 0.0875551700592041 time for calcul the mask position with numpy : 0.030260324478149414 nb_pixel_total : 12035 time to create 1 rle with old method : 0.012850522994995117 time for calcul the mask position with numpy : 0.0291135311126709 nb_pixel_total : 60009 time to create 1 rle with old method : 0.0637826919555664 time for calcul the mask position with numpy : 0.027857065200805664 nb_pixel_total : 8715 time to create 1 rle with old method : 0.009119749069213867 time for calcul the mask position with numpy : 0.028890609741210938 nb_pixel_total : 246367 time to create 1 rle with new method : 0.5538947582244873 time for calcul the mask position with numpy : 0.029435157775878906 nb_pixel_total : 187886 time to create 1 rle with new method : 0.4494638442993164 time for calcul the mask position with numpy : 0.0276639461517334 nb_pixel_total : 13814 time to create 1 rle with old method : 0.014618873596191406 time for calcul the mask position with numpy : 0.027216434478759766 nb_pixel_total : 44150 time to create 1 rle with old method : 0.04537701606750488 create new chi : 5.166932106018066 time to delete rle : 0.01606154441833496 batch 1 Loaded 49 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 15106 TO DO : save crop sub photo not yet done ! save time : 2.118457317352295 nb_obj : 27 nb_hashtags : 3 time to prepare the origin masks : 3.629704713821411 time for calcul the mask position with numpy : 0.8569068908691406 nb_pixel_total : 6051401 time to create 1 rle with new method : 0.7666637897491455 time for calcul the mask position with numpy : 0.02881026268005371 nb_pixel_total : 18779 time to create 1 rle with old method : 0.02090620994567871 time for calcul the mask position with numpy : 0.028484821319580078 nb_pixel_total : 13845 time to create 1 rle with old method : 0.014804363250732422 time for calcul the mask position with numpy : 0.02792215347290039 nb_pixel_total : 9703 time to create 1 rle with old method : 0.00988149642944336 time for calcul the mask position with numpy : 0.029072999954223633 nb_pixel_total : 177559 time to create 1 rle with new method : 0.479663610458374 time for calcul the mask position with numpy : 0.02927088737487793 nb_pixel_total : 20007 time to create 1 rle with old method : 0.023298978805541992 time for calcul the mask position with numpy : 0.029298067092895508 nb_pixel_total : 16918 time to create 1 rle with old method : 0.019098520278930664 time for calcul the mask position with numpy : 0.02864980697631836 nb_pixel_total : 20574 time to create 1 rle with old method : 0.021912574768066406 time for calcul the mask position with numpy : 0.028910398483276367 nb_pixel_total : 44653 time to create 1 rle with old method : 0.04784202575683594 time for calcul the mask position with numpy : 0.02867269515991211 nb_pixel_total : 32422 time to create 1 rle with old method : 0.034705162048339844 time for calcul the mask position with numpy : 0.02837228775024414 nb_pixel_total : 23708 time to create 1 rle with old method : 0.02544879913330078 time for calcul the mask position with numpy : 0.028074979782104492 nb_pixel_total : 13336 time to create 1 rle with old method : 0.014084339141845703 time for calcul the mask position with numpy : 0.028273344039916992 nb_pixel_total : 19847 time to create 1 rle with old method : 0.020777463912963867 time for calcul the mask position with numpy : 0.03003239631652832 nb_pixel_total : 36771 time to create 1 rle with old method : 0.039853811264038086 time for calcul the mask position with numpy : 0.029996395111083984 nb_pixel_total : 22045 time to create 1 rle with old method : 0.03481745719909668 time for calcul the mask position with numpy : 0.03279900550842285 nb_pixel_total : 6298 time to create 1 rle with old method : 0.007081747055053711 time for calcul the mask position with numpy : 0.027971506118774414 nb_pixel_total : 12008 time to create 1 rle with old method : 0.012800931930541992 time for calcul the mask position with numpy : 0.028910398483276367 nb_pixel_total : 125883 time to create 1 rle with old method : 0.13551068305969238 time for calcul the mask position with numpy : 0.0273587703704834 nb_pixel_total : 6904 time to create 1 rle with old method : 0.007141828536987305 time for calcul the mask position with numpy : 0.028502464294433594 nb_pixel_total : 19547 time to create 1 rle with old method : 0.020375728607177734 time for calcul the mask position with numpy : 0.02777719497680664 nb_pixel_total : 12696 time to create 1 rle with old method : 0.01347804069519043 time for calcul the mask position with numpy : 0.027888774871826172 nb_pixel_total : 17344 time to create 1 rle with old method : 0.018527507781982422 time for calcul the mask position with numpy : 0.02859783172607422 nb_pixel_total : 27958 time to create 1 rle with old method : 0.029303550720214844 time for calcul the mask position with numpy : 0.028161048889160156 nb_pixel_total : 20782 time to create 1 rle with old method : 0.021959304809570312 time for calcul the mask position with numpy : 0.028561830520629883 nb_pixel_total : 71506 time to create 1 rle with old method : 0.07862114906311035 time for calcul the mask position with numpy : 0.02865147590637207 nb_pixel_total : 187517 time to create 1 rle with new method : 0.6159117221832275 time for calcul the mask position with numpy : 0.030124187469482422 nb_pixel_total : 4846 time to create 1 rle with old method : 0.0052890777587890625 time for calcul the mask position with numpy : 0.028789043426513672 nb_pixel_total : 15383 time to create 1 rle with old method : 0.016456127166748047 create new chi : 4.268641710281372 time to delete rle : 0.001954317092895508 batch 1 Loaded 56 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 15373 TO DO : save crop sub photo not yet done ! save time : 0.8829250335693359 nb_obj : 41 nb_hashtags : 4 time to prepare the origin masks : 4.655468225479126 time for calcul the mask position with numpy : 0.42476916313171387 nb_pixel_total : 5721747 time to create 1 rle with new method : 0.7513346672058105 time for calcul the mask position with numpy : 0.028077363967895508 nb_pixel_total : 9133 time to create 1 rle with old method : 0.009900808334350586 time for calcul the mask position with numpy : 0.02710103988647461 nb_pixel_total : 8202 time to create 1 rle with old method : 0.008604288101196289 time for calcul the mask position with numpy : 0.0271604061126709 nb_pixel_total : 71638 time to create 1 rle with old method : 0.07396340370178223 time for calcul the mask position with numpy : 0.028109312057495117 nb_pixel_total : 19089 time to create 1 rle with old method : 0.01975250244140625 time for calcul the mask position with numpy : 0.02672600746154785 nb_pixel_total : 12076 time to create 1 rle with old method : 0.012764930725097656 time for calcul the mask position with numpy : 0.026521682739257812 nb_pixel_total : 15187 time to create 1 rle with old method : 0.015770673751831055 time for calcul the mask position with numpy : 0.027928829193115234 nb_pixel_total : 9918 time to create 1 rle with old method : 0.010600090026855469 time for calcul the mask position with numpy : 0.02772688865661621 nb_pixel_total : 15734 time to create 1 rle with old method : 0.017162084579467773 time for calcul the mask position with numpy : 0.026227235794067383 nb_pixel_total : 22758 time to create 1 rle with old method : 0.02332472801208496 time for calcul the mask position with numpy : 0.027083635330200195 nb_pixel_total : 10786 time to create 1 rle with old method : 0.011318206787109375 time for calcul the mask position with numpy : 0.028510332107543945 nb_pixel_total : 72707 time to create 1 rle with old method : 0.07558155059814453 time for calcul the mask position with numpy : 0.028283119201660156 nb_pixel_total : 6212 time to create 1 rle with old method : 0.006739616394042969 time for calcul the mask position with numpy : 0.028473854064941406 nb_pixel_total : 13712 time to create 1 rle with old method : 0.015054941177368164 time for calcul the mask position with numpy : 0.03073287010192871 nb_pixel_total : 34593 time to create 1 rle with old method : 0.03671002388000488 time for calcul the mask position with numpy : 0.0294497013092041 nb_pixel_total : 6952 time to create 1 rle with old method : 0.008062124252319336 time for calcul the mask position with numpy : 0.028726577758789062 nb_pixel_total : 5383 time to create 1 rle with old method : 0.005877971649169922 time for calcul the mask position with numpy : 0.027979135513305664 nb_pixel_total : 11778 time to create 1 rle with old method : 0.012488842010498047 time for calcul the mask position with numpy : 0.02845287322998047 nb_pixel_total : 132901 time to create 1 rle with old method : 0.13788461685180664 time for calcul the mask position with numpy : 0.027827024459838867 nb_pixel_total : 7707 time to create 1 rle with old method : 0.00812387466430664 time for calcul the mask position with numpy : 0.02729654312133789 nb_pixel_total : 61447 time to create 1 rle with old method : 0.06370019912719727 time for calcul the mask position with numpy : 0.028345346450805664 nb_pixel_total : 8228 time to create 1 rle with old method : 0.009444952011108398 time for calcul the mask position with numpy : 0.028547048568725586 nb_pixel_total : 8512 time to create 1 rle with old method : 0.008687019348144531 time for calcul the mask position with numpy : 0.028188705444335938 nb_pixel_total : 5361 time to create 1 rle with old method : 0.005918264389038086 time for calcul the mask position with numpy : 0.028675317764282227 nb_pixel_total : 30974 time to create 1 rle with old method : 0.032195329666137695 time for calcul the mask position with numpy : 0.02786850929260254 nb_pixel_total : 101304 time to create 1 rle with old method : 0.10698986053466797 time for calcul the mask position with numpy : 0.028841733932495117 nb_pixel_total : 80329 time to create 1 rle with old method : 0.08250570297241211 time for calcul the mask position with numpy : 0.027987003326416016 nb_pixel_total : 25699 time to create 1 rle with old method : 0.028180599212646484 time for calcul the mask position with numpy : 0.032862186431884766 nb_pixel_total : 24494 time to create 1 rle with old method : 0.039913177490234375 time for calcul the mask position with numpy : 0.02910327911376953 nb_pixel_total : 5072 time to create 1 rle with old method : 0.0055773258209228516 time for calcul the mask position with numpy : 0.02753305435180664 nb_pixel_total : 12893 time to create 1 rle with old method : 0.013799190521240234 time for calcul the mask position with numpy : 0.02812051773071289 nb_pixel_total : 145 time to create 1 rle with old method : 0.00030112266540527344 time for calcul the mask position with numpy : 0.027701139450073242 nb_pixel_total : 227 time to create 1 rle with old method : 0.0003440380096435547 time for calcul the mask position with numpy : 0.028424978256225586 nb_pixel_total : 9097 time to create 1 rle with old method : 0.009964704513549805 time for calcul the mask position with numpy : 0.028032541275024414 nb_pixel_total : 12875 time to create 1 rle with old method : 0.01376795768737793 time for calcul the mask position with numpy : 0.0289766788482666 nb_pixel_total : 266739 time to create 1 rle with new method : 0.6718146800994873 time for calcul the mask position with numpy : 0.034361839294433594 nb_pixel_total : 46071 time to create 1 rle with old method : 0.06046342849731445 time for calcul the mask position with numpy : 0.03452134132385254 nb_pixel_total : 90751 time to create 1 rle with old method : 0.09864473342895508 time for calcul the mask position with numpy : 0.028338193893432617 nb_pixel_total : 345 time to create 1 rle with old method : 0.0006263256072998047 time for calcul the mask position with numpy : 0.02971363067626953 nb_pixel_total : 20676 time to create 1 rle with old method : 0.022861480712890625 time for calcul the mask position with numpy : 0.031091690063476562 nb_pixel_total : 13473 time to create 1 rle with old method : 0.014902353286743164 time for calcul the mask position with numpy : 0.028434276580810547 nb_pixel_total : 17315 time to create 1 rle with old method : 0.019916772842407227 create new chi : 4.228958368301392 time to delete rle : 0.0021157264709472656 batch 1 Loaded 89 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17868 TO DO : save crop sub photo not yet done ! save time : 0.9991199970245361 nb_obj : 21 nb_hashtags : 6 time to prepare the origin masks : 3.5550625324249268 time for calcul the mask position with numpy : 0.3655972480773926 nb_pixel_total : 5910723 time to create 1 rle with new method : 0.8978290557861328 time for calcul the mask position with numpy : 0.026753664016723633 nb_pixel_total : 16522 time to create 1 rle with old method : 0.017236948013305664 time for calcul the mask position with numpy : 0.02742290496826172 nb_pixel_total : 62640 time to create 1 rle with old method : 0.06653165817260742 time for calcul the mask position with numpy : 0.026652097702026367 nb_pixel_total : 27139 time to create 1 rle with old method : 0.02702808380126953 time for calcul the mask position with numpy : 0.027581453323364258 nb_pixel_total : 47747 time to create 1 rle with old method : 0.0511782169342041 time for calcul the mask position with numpy : 0.027809858322143555 nb_pixel_total : 190966 time to create 1 rle with new method : 0.5971353054046631 time for calcul the mask position with numpy : 0.028257369995117188 nb_pixel_total : 25433 time to create 1 rle with old method : 0.027838945388793945 time for calcul the mask position with numpy : 0.027655363082885742 nb_pixel_total : 48284 time to create 1 rle with old method : 0.050287485122680664 time for calcul the mask position with numpy : 0.029427051544189453 nb_pixel_total : 240070 time to create 1 rle with new method : 0.6658015251159668 time for calcul the mask position with numpy : 0.027078866958618164 nb_pixel_total : 30410 time to create 1 rle with old method : 0.032811880111694336 time for calcul the mask position with numpy : 0.028744220733642578 nb_pixel_total : 6558 time to create 1 rle with old method : 0.006858110427856445 time for calcul the mask position with numpy : 0.027709007263183594 nb_pixel_total : 58570 time to create 1 rle with old method : 0.059786319732666016 time for calcul the mask position with numpy : 0.028860807418823242 nb_pixel_total : 86924 time to create 1 rle with old method : 0.08983564376831055 time for calcul the mask position with numpy : 0.02761387825012207 nb_pixel_total : 6540 time to create 1 rle with old method : 0.007135868072509766 time for calcul the mask position with numpy : 0.027467012405395508 nb_pixel_total : 8423 time to create 1 rle with old method : 0.008758783340454102 time for calcul the mask position with numpy : 0.027399778366088867 nb_pixel_total : 38385 time to create 1 rle with old method : 0.048779964447021484 time for calcul the mask position with numpy : 0.0305635929107666 nb_pixel_total : 42239 time to create 1 rle with old method : 0.04760599136352539 time for calcul the mask position with numpy : 0.07483768463134766 nb_pixel_total : 19825 time to create 1 rle with old method : 0.04265284538269043 time for calcul the mask position with numpy : 0.03832840919494629 nb_pixel_total : 31323 time to create 1 rle with old method : 0.06851053237915039 time for calcul the mask position with numpy : 0.05762028694152832 nb_pixel_total : 94744 time to create 1 rle with old method : 0.11844563484191895 time for calcul the mask position with numpy : 0.030060768127441406 nb_pixel_total : 3280 time to create 1 rle with old method : 0.0040225982666015625 time for calcul the mask position with numpy : 0.02978682518005371 nb_pixel_total : 53495 time to create 1 rle with old method : 0.06178712844848633 create new chi : 4.113804340362549 time to delete rle : 0.0022134780883789062 batch 1 Loaded 45 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 13518 TO DO : save crop sub photo not yet done ! save time : 0.7735302448272705 nb_obj : 26 nb_hashtags : 4 time to prepare the origin masks : 5.04308819770813 time for calcul the mask position with numpy : 0.8628935813903809 nb_pixel_total : 5320962 time to create 1 rle with new method : 0.9151320457458496 time for calcul the mask position with numpy : 0.03045964241027832 nb_pixel_total : 28097 time to create 1 rle with old method : 0.031847476959228516 time for calcul the mask position with numpy : 0.0293581485748291 nb_pixel_total : 118713 time to create 1 rle with old method : 0.1340928077697754 time for calcul the mask position with numpy : 0.029604196548461914 nb_pixel_total : 55356 time to create 1 rle with old method : 0.06310749053955078 time for calcul the mask position with numpy : 0.029114961624145508 nb_pixel_total : 4213 time to create 1 rle with old method : 0.004886627197265625 time for calcul the mask position with numpy : 0.029396533966064453 nb_pixel_total : 28598 time to create 1 rle with old method : 0.03257393836975098 time for calcul the mask position with numpy : 0.029242277145385742 nb_pixel_total : 66229 time to create 1 rle with old method : 0.07150506973266602 time for calcul the mask position with numpy : 0.03212714195251465 nb_pixel_total : 367836 time to create 1 rle with new method : 1.3561015129089355 time for calcul the mask position with numpy : 0.031212806701660156 nb_pixel_total : 25913 time to create 1 rle with old method : 0.030738353729248047 time for calcul the mask position with numpy : 0.030698060989379883 nb_pixel_total : 7110 time to create 1 rle with old method : 0.008003473281860352 time for calcul the mask position with numpy : 0.03719186782836914 nb_pixel_total : 14364 time to create 1 rle with old method : 0.01867389678955078 time for calcul the mask position with numpy : 0.030864715576171875 nb_pixel_total : 115203 time to create 1 rle with old method : 0.13297581672668457 time for calcul the mask position with numpy : 0.030689001083374023 nb_pixel_total : 30919 time to create 1 rle with old method : 0.05016183853149414 time for calcul the mask position with numpy : 0.03320765495300293 nb_pixel_total : 1223 time to create 1 rle with old method : 0.0020036697387695312 time for calcul the mask position with numpy : 0.0324704647064209 nb_pixel_total : 153804 time to create 1 rle with new method : 1.0985870361328125 time for calcul the mask position with numpy : 0.03036212921142578 nb_pixel_total : 37567 time to create 1 rle with old method : 0.059735774993896484 time for calcul the mask position with numpy : 0.03392434120178223 nb_pixel_total : 74401 time to create 1 rle with old method : 0.09122252464294434 time for calcul the mask position with numpy : 0.02913951873779297 nb_pixel_total : 20204 time to create 1 rle with old method : 0.022598743438720703 time for calcul the mask position with numpy : 0.029239416122436523 nb_pixel_total : 12040 time to create 1 rle with old method : 0.013017416000366211 time for calcul the mask position with numpy : 0.028992652893066406 nb_pixel_total : 1743 time to create 1 rle with old method : 0.0021941661834716797 time for calcul the mask position with numpy : 0.03303670883178711 nb_pixel_total : 58487 time to create 1 rle with old method : 0.06587815284729004 time for calcul the mask position with numpy : 0.029639482498168945 nb_pixel_total : 8719 time to create 1 rle with old method : 0.009871244430541992 time for calcul the mask position with numpy : 0.029872417449951172 nb_pixel_total : 246359 time to create 1 rle with new method : 0.8348031044006348 time for calcul the mask position with numpy : 0.02968764305114746 nb_pixel_total : 6180 time to create 1 rle with old method : 0.007659435272216797 time for calcul the mask position with numpy : 0.030591487884521484 nb_pixel_total : 188035 time to create 1 rle with new method : 0.8938310146331787 time for calcul the mask position with numpy : 0.029459714889526367 nb_pixel_total : 13814 time to create 1 rle with old method : 0.015626907348632812 time for calcul the mask position with numpy : 0.0295562744140625 nb_pixel_total : 44151 time to create 1 rle with old method : 0.047446250915527344 create new chi : 7.844801664352417 time to delete rle : 0.002354860305786133 batch 1 Loaded 57 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 15938 TO DO : save crop sub photo not yet done ! save time : 0.9363226890563965 map_output_result : {1333266340: (0.0, 'Should be the crop_list due to order', 0.0), 1333266339: (0.0, 'Should be the crop_list due to order', 0.0), 1333266334: (0.0, 'Should be the crop_list due to order', 0.0), 1333266329: (0.0, 'Should be the crop_list due to order', 0.0), 1333266314: (0.0, 'Should be the crop_list due to order', 0.0)} 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 [1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 5 /1333266340.Didn't retrieve data . /1333266339.Didn't retrieve data . /1333266334.Didn't retrieve data . /1333266329.Didn't retrieve data . /1333266314.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, '2539826') ('3318', None, '1333266340', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266339', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266334', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266329', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266314', None, None, None, None, None, '2539826') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.012427806854248047 save_final save missing photos in datou_result : time spend for datou_step_exec : 53.57663822174072 time spend to save output : 0.012959957122802734 total time spend for step 3 : 53.589598178863525 step4:ventilate_hashtags_in_portfolio Mon Feb 3 14:04:41 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 20176487 get user id for portfolio 20176487 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`=20176487 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pet_fonce','pet_clair','autre','mal_croppe','metal','papier','carton','background','pehd','flou')) 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`=20176487 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pet_fonce','pet_clair','autre','mal_croppe','metal','papier','carton','background','pehd','flou')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20177829,20177830,20177831,20177832,20177833,20177834,20177835,20177836,20177837,20177838,20177839?tags=carton,pet_clair,mal_croppe,autre,pehd,flou,background,metal,pet_fonce,environnement,papier Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 1 /20176487. 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, '2539826') ('3318', None, '1333266340', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266339', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266334', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266329', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266314', None, None, None, None, None, '2539826') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012630462646484375 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.4845049381256104 time spend to save output : 0.013005256652832031 total time spend for step 4 : 1.4975101947784424 step5:final Mon Feb 3 14:04:42 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 : {1333266340: ('0.18771046092047955',), 1333266339: ('0.18771046092047955',), 1333266334: ('0.18771046092047955',), 1333266329: ('0.18771046092047955',), 1333266314: ('0.18771046092047955',)} new output for save of step final : {1333266340: ('0.18771046092047955',), 1333266339: ('0.18771046092047955',), 1333266334: ('0.18771046092047955',), 1333266329: ('0.18771046092047955',), 1333266314: ('0.18771046092047955',)} [1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 5 /1333266340.Didn't retrieve data . /1333266339.Didn't retrieve data . /1333266334.Didn't retrieve data . /1333266329.Didn't retrieve data . /1333266314.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, '2539826') ('3318', None, '1333266340', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266339', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266334', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266329', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266314', None, None, None, None, None, '2539826') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.012532472610473633 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.12188935279846191 time spend to save output : 0.012949228286743164 total time spend for step 5 : 0.13483858108520508 step6:blur_detection Mon Feb 3 14:04:42 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 toutes les photos sont déjà traitées, on saute les calculs 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 : 5 time used for this insertion : 0.008753061294555664 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 0.009525060653686523 save missing photos in datou_result : time spend for datou_step_exec : 0.0254056453704834 time spend to save output : 0.022702455520629883 total time spend for step 6 : 0.04810810089111328 step7:brightness Mon Feb 3 14:04:42 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 toutes les photos sont déjà traitées, on saute les calculs 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 : 5 time used for this insertion : 0.007996320724487305 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 0.008885383605957031 save missing photos in datou_result : time spend for datou_step_exec : 0.02732539176940918 time spend to save output : 0.02174997329711914 total time spend for step 7 : 0.04907536506652832 step8:velours_tree Mon Feb 3 14:04:42 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 : 1.0319552421569824 time spend to save output : 3.910064697265625e-05 total time spend for step 8 : 1.031994342803955 step9:send_mail_cod Mon Feb 3 14:04:43 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 complete output_args for input 1 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_P20176487_03-02-2025_14_04_43.pdf 20177829 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 .imagette201778291738587883 20177830 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 .imagette201778301738587886 20177831 imagette201778311738587888 20177832 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 .imagette201778321738587888 20177833 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 .imagette201778331738587889 20177834 imagette201778341738587890 20177835 imagette201778351738587890 20177836 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 .imagette201778361738587890 20177837 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 .imagette201778371738587891 20177839 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 .imagette201778391738587893 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20176487 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20177829,20177830,20177831,20177832,20177833,20177834,20177835,20177836,20177837,20177838,20177839?tags=carton,pet_clair,mal_croppe,autre,pehd,flou,background,metal,pet_fonce,environnement,papier args[1333266340] : ((1333266340, -4.956128758846408, 492609224), (1333266340, -0.30826919034581396, 496442774), '0.18771046092047955') We are sending mail with results at report@fotonower.com args[1333266339] : ((1333266339, -4.650026136384781, 492609224), (1333266339, -0.14216069553099292, 496442774), '0.18771046092047955') We are sending mail with results at report@fotonower.com args[1333266334] : ((1333266334, -5.3013162617903795, 492609224), (1333266334, -0.03621191838470336, 2107752395), '0.18771046092047955') We are sending mail with results at report@fotonower.com args[1333266329] : ((1333266329, -4.38897844226345, 492609224), (1333266329, -0.3315296051552947, 496442774), '0.18771046092047955') We are sending mail with results at report@fotonower.com args[1333266314] : ((1333266314, -4.956128758846408, 492609224), (1333266314, -0.30826919034581396, 496442774), '0.18771046092047955') We are sending mail with results at report@fotonower.com refus_total : 0.18771046092047955 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=20176487 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1333267265,1333267252,1333644592,1333266652,1333266647,1333644028,1333266673,1333644023,1333643947,1333644222,1333266340,1333266314,1333266660,1333644279,1333643929,1333643935,1333644581,1333643678,1333643676,1333643945) Found this number of photos: 20 begin to download photo : 1333267265 begin to download photo : 1333644028 begin to download photo : 1333266340 begin to download photo : 1333643935 download finish for photo 1333644028 begin to download photo : 1333266673 download finish for photo 1333266340 begin to download photo : 1333266314 download finish for photo 1333267265 begin to download photo : 1333267252 download finish for photo 1333643935 begin to download photo : 1333644581 download finish for photo 1333266314 begin to download photo : 1333266660 download finish for photo 1333266673 begin to download photo : 1333644023 download finish for photo 1333267252 begin to download photo : 1333644592 download finish for photo 1333644581 begin to download photo : 1333643678 download finish for photo 1333266660 begin to download photo : 1333644279 download finish for photo 1333644023 begin to download photo : 1333643947 download finish for photo 1333644592 begin to download photo : 1333266652 download finish for photo 1333643678 begin to download photo : 1333643676 download finish for photo 1333644279 begin to download photo : 1333643929 download finish for photo 1333266652 begin to download photo : 1333266647 download finish for photo 1333643947 begin to download photo : 1333644222 download finish for photo 1333643676 begin to download photo : 1333643945 download finish for photo 1333266647 download finish for photo 1333643929 download finish for photo 1333644222 download finish for photo 1333643945 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176487_03-02-2025_14_04_43.pdf results_Auto_P20176487_03-02-2025_14_04_43.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176487_03-02-2025_14_04_43.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','20176487','results_Auto_P20176487_03-02-2025_14_04_43.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176487_03-02-2025_14_04_43.pdf','pdf','','1.56','0.18771046092047955') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20176487

https://www.fotonower.com/image?json=false&list_photos_id=1333266340
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266339
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266334
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266329
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266314
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 18.77%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/20177829?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20177830?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/20177832?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20177833?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/20177836?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20177837?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/20177839?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176487_03-02-2025_14_04_43.pdf.

Lien vers velours :https://www.fotonower.com/velours/20177829,20177830,20177831,20177832,20177833,20177834,20177835,20177836,20177837,20177838,20177839?tags=carton,pet_clair,mal_croppe,autre,pehd,flou,background,metal,pet_fonce,environnement,papier.


L'équipe Fotonower 202 b'' Server: nginx Date: Mon, 03 Feb 2025 13:05:00 GMT Content-Length: 0 Connection: close X-Message-Id: _E7f7qxiRWqZyOG0vPNvgw 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 [1333266340, 1333266339, 1333266334, 1333266329, 1333266314] 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, '2539826') ('3318', None, '1333266340', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266339', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266334', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266329', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266314', None, None, None, None, None, '2539826') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 5 time used for this insertion : 0.014007806777954102 save_final save missing photos in datou_result : time spend for datou_step_exec : 16.19771432876587 time spend to save output : 0.014284610748291016 total time spend for step 9 : 16.21199893951416 step10:split_time_score Mon Feb 3 14:05:00 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('09', 45),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31012025 20176487 Nombre de photos uploadées : 45 / 23040 (0%) 31012025 20176487 Nombre de photos taguées (types de déchets): 0 / 45 (0%) 31012025 20176487 Nombre de photos taguées (volume) : 0 / 45 (0%) elapsed_time : load_data_split_time_score 4.0531158447265625e-06 elapsed_time : order_list_meta_photo_and_scores 7.867813110351562e-06 ????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.00200653076171875 elapsed_time : insert_dashboard_record_day_entry 0.02191305160522461 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 = 20176483 order by id desc limit 1 Qualite : 0.18765544939683718 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176487_03-02-2025_14_04_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20176487 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`=20176487 AND mptpi.`type`=3594 To do Qualite : 0.17372436966684815 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176513_03-02-2025_13_11_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20176513 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`=20176513 AND mptpi.`type`=3594 To do Qualite : 0.20109579764144703 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176517_03-02-2025_13_00_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20176517 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`=20176517 AND mptpi.`type`=3594 To do Qualite : 0.21289584725935826 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176518_03-02-2025_12_51_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20176518 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`=20176518 AND mptpi.`type`=3594 To do Qualite : 0.11710340391431627 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20176521_03-02-2025_12_39_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20176521 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20176521 AND mptpi.`type`=3726 To do Qualite : 0.22578661033761485 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20135989 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`=20135989 AND mptpi.`type`=3594 To do Qualite : 0.20874959339445653 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128161_01-02-2025_01_24_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128161 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`=20128161 AND mptpi.`type`=3594 To do Qualite : 0.07726311033869263 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128169_01-02-2025_01_28_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128169 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20128169 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'31012025': {'nb_upload': 45, '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 [1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 1 /20176487Didn'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, '2539826') ('3318', None, '1333266340', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266339', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266334', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266329', None, None, None, None, None, '2539826') ('3318', None, None, None, None, None, None, None, '2539826') ('3318', None, '1333266314', None, None, None, None, None, '2539826') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.01247859001159668 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.186300039291382 time spend to save output : 0.012680530548095703 total time spend for step 10 : 2.1989805698394775 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 5 set_done_treatment 112.90user 69.16system 4:37.24elapsed 65%CPU (0avgtext+0avgdata 5973512maxresident)k 2658768inputs+189416outputs (65462major+11127926minor)pagefaults 0swaps