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 : 125964 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 : ['2899936'] with mtr_portfolio_ids : ['23140986'] and first list_photo_ids : [] new path : /proc/125964/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 12 ; length of list_pids : 12 ; length of list_args : 12 time to download the photos : 2.0014028549194336 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 Tue May 20 01:10:31 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 : 4182 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-20 01:10:34.364879: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-20 01:10:34.395055: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-20 01:10:34.397193: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0174000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-20 01:10:34.397238: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-20 01:10:34.401136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-20 01:10:34.643187: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x346ce310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-20 01:10:34.643241: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-20 01:10:34.644407: 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 2025-05-20 01:10:34.644806: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-20 01:10:34.647853: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-20 01:10:34.650544: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-20 01:10:34.651106: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-20 01:10:34.653692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-20 01:10:34.654959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-20 01:10:34.659785: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-20 01:10:34.661137: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-20 01:10:34.661240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-20 01:10:34.662094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-20 01:10:34.662115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-20 01:10:34.662127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-20 01:10:34.663678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3730 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. 2025-05-20 01:10:34.937243: 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 2025-05-20 01:10:34.937316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-20 01:10:34.937337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-20 01:10:34.937356: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-20 01:10:34.937373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-20 01:10:34.937391: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-20 01:10:34.937409: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-20 01:10:34.937427: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-20 01:10:34.938538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-20 01:10:34.939679: 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 2025-05-20 01:10:34.939716: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-20 01:10:34.939735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-20 01:10:34.939752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-20 01:10:34.939769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-20 01:10:34.939786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-20 01:10:34.939802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-20 01:10:34.939819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-20 01:10:34.940921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-20 01:10:34.940952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-20 01:10:34.940962: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-20 01:10:34.940972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-20 01:10:34.942124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3730 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 : [] 2025-05-20 01:10:45.365166: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-20 01:10:45.551288: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-20 01:10:46.873751: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.14G (3376283648 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-20 01:10:47.650492: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-20 01:10:47.650560: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 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 : 12 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 65.00000 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 : 13 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 : 30 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 17.00000 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 : 35 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 34.00000 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 : 9 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 24.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 19.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 40.00000 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 : 14 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 : 33 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 26.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 37.00000 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 : 30 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 7.00000 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 : 33 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 17.00000 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 : 36 Detection mask done ! Trying to reset tf kernel 126467 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 91 tf kernel not reseted sub process len(results) : 12 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 12 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 : 4182 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 : 0.013990402221679688 nb_pixel_total : 345089 time to create 1 rle with new method : 0.03239107131958008 length of segment : 769 time for calcul the mask position with numpy : 0.00037360191345214844 nb_pixel_total : 19151 time to create 1 rle with old method : 0.022080421447753906 length of segment : 185 time for calcul the mask position with numpy : 0.00024318695068359375 nb_pixel_total : 18316 time to create 1 rle with old method : 0.020734071731567383 length of segment : 225 time for calcul the mask position with numpy : 0.00021719932556152344 nb_pixel_total : 14068 time to create 1 rle with old method : 0.019411563873291016 length of segment : 159 time for calcul the mask position with numpy : 0.0004930496215820312 nb_pixel_total : 37067 time to crInside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 135 chid ids of type : 3594 Number RLEs to save : 38933 save missing photos in datou_result : time spend for datou_step_exec : 60.4810152053833 time spend to save output : 35.381831645965576 total time spend for step 1 : 95.86284685134888 step2:crop_condition Tue May 20 01:12:07 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 : 12 ! batch 1 Loaded 135 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 87 About to insert : list_path_to_insert length 87 new photo from crops ! About to upload 87 photos upload in portfolio : 3736932 init cache_photo without model_param we have 87 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1747696350_125964 we have uploaded 87 photos in the portfolio 3736932 time of upload the photos Elapsed time : 23.662347555160522 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 ! 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/1747696377_125964 we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.1617536544799805 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 37 About to insert : list_path_to_insert length 37 new photo from crops ! About to upload 37 photos upload in portfolio : 3736932 init cache_photo without model_param we have 37 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1747696396_125964 we have uploaded 37 photos in the portfolio 3736932 time of upload the photos Elapsed time : 17.942622661590576 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 ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1747696415_125964 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6276159286499023 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1747696417_125964 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.5870590209960938 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1359488377, 1359379376, 1359379369, 1359379148, 1359379146, 1359379143, 1359378929, 1359378926, 1359378923, 1359378920, 1359378917, 1359378900] Looping around the photos to save general results len do output : 135 /1359683833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359683900Didn't retrieve data .Didn't retrieve data .Didn't 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('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359488377', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379376', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379369', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379148', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379146', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379143', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378929', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378926', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378923', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378920', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378917', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378900', None, None, None, None, None, '2899936') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 417 time used for this insertion : 0.42466139793395996 save_final save missing photos in datou_result : time spend for datou_step_exec : 90.23384642601013 time spend to save output : 0.42926621437072754 total time spend for step 2 : 90.66311264038086 step3:rle_unique_nms_with_priority Tue May 20 01:13:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 135 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 2.3402328491210938 time for calcul the mask position with numpy : 0.5683951377868652 nb_pixel_total : 6616549 time to create 1 rle with new method : 0.64212965965271 time for calcul the mask position with numpy : 0.02097940444946289 nb_pixel_total : 37067 time to create 1 rle with old method : 0.03947734832763672 time for calcul the mask position with numpy : 0.019776344299316406 nb_pixel_total : 14068 time to create 1 rle with old method : 0.014608383178710938 time for calcul the mask position with numpy : 0.019365310668945312 nb_pixel_total : 18316 time to create 1 rle with old method : 0.01949620246887207 time for calcul the mask position with numpy : 0.020628929138183594 nb_pixel_total : 19151 time to create 1 rle with old method : 0.020959854125976562 time for calcul the mask position with numpy : 0.02244710922241211 nb_pixel_total : 345089 time to create 1 rle with new method : 0.5500094890594482 create new chi : 2.009249687194824 time to delete rle : 0.021142959594726562 batch 1 Loaded 11 chid ids of type : 3594 +++++++Number RLEs to save : 5160 TO DO : save crop sub photo not yet done ! save time : 5.759074449539185 nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 3.3050179481506348 time for calcul the mask position with numpy : 0.6559083461761475 nb_pixel_total : 6587572 time to create 1 rle with new method : 0.3957400321960449 time for calcul the mask position with numpy : 0.020528554916381836 nb_pixel_total : 12331 time to create 1 rle with old method : 0.015219926834106445 time for calcul the mask position with numpy : 0.020366907119750977 nb_pixel_total : 57403 time to create 1 rle with old method : 0.06105399131774902 time for calcul the mask position with numpy : 0.02119755744934082 nb_pixel_total : 8258 time to create 1 rle with old method : 0.009016752243041992 time for calcul the mask position with numpy : 0.020895004272460938 nb_pixel_total : 21431 time to create 1 rle with old method : 0.02296304702758789 time for calcul the mask position with numpy : 0.019667387008666992 nb_pixel_total : 26059 time to create 1 rle with old method : 0.027493953704833984 time for calcul the mask position with numpy : 0.0199124813079834 nb_pixel_total : 40526 time to create 1 rle with old method : 0.04215812683105469 time for calcul the mask position with numpy : 0.020142316818237305 nb_pixel_total : 51713 time to create 1 rle with old method : 0.054837942123413086 time for calcul the mask position with numpy : 0.020201921463012695 nb_pixel_total : 32561 time to create 1 rle with old method : 0.03289318084716797 time for calcul the mask position with numpy : 0.019954442977905273 nb_pixel_total : 47181 time to create 1 rle with old method : 0.04790902137756348 time for calcul the mask position with numpy : 0.020188331604003906 nb_pixel_total : 165205 time to create 1 rle with new method : 0.7039275169372559 create new chi : 2.329493999481201 time to delete rle : 0.0009386539459228516 batch 1 Loaded 21 chid ids of type : 3594 +++++++++++Number RLEs to save : 6620 TO DO : save crop sub photo not yet done ! save time : 4.410024404525757 nb_obj : 17 nb_hashtags : 3 time to prepare the origin masks : 5.796410083770752 time for calcul the mask position with numpy : 0.5449166297912598 nb_pixel_total : 6284266 time to create 1 rle with new method : 0.34677934646606445 time for calcul the mask position with numpy : 0.020887136459350586 nb_pixel_total : 41219 time to create 1 rle with old method : 0.04439210891723633 time for calcul the mask position with numpy : 0.019917964935302734 nb_pixel_total : 23505 time to create 1 rle with old method : 0.025301218032836914 time for calcul the mask position with numpy : 0.020593643188476562 nb_pixel_total : 23018 time to create 1 rle with old method : 0.0252225399017334 time for calcul the mask position with numpy : 0.021674633026123047 nb_pixel_total : 25468 time to create 1 rle with old method : 0.02804732322692871 time for calcul the mask position with numpy : 0.023973464965820312 nb_pixel_total : 210667 time to create 1 rle with new method : 0.45052576065063477 time for calcul the mask position with numpy : 0.0219876766204834 nb_pixel_total : 20327 time to create 1 rle with old method : 0.023064613342285156 time for calcul the mask position with numpy : 0.02269577980041504 nb_pixel_total : 8285 time to create 1 rle with old method : 0.009119987487792969 time for calcul the mask position with numpy : 0.022658586502075195 nb_pixel_total : 39396 time to create 1 rle with old method : 0.04376721382141113 time for calcul the mask position with numpy : 0.022057771682739258 nb_pixel_total : 155947 time to create 1 rle with new method : 0.682213544845581 time for calcul the mask position with numpy : 0.019620418548583984 nb_pixel_total : 15098 time to create 1 rle with old method : 0.015722036361694336 time for calcul the mask position with numpy : 0.020589828491210938 nb_pixel_total : 4825 time to create 1 rle with old method : 0.0050868988037109375 time for calcul the mask position with numpy : 0.020783185958862305 nb_pixel_total : 16265 time to create 1 rle with old method : 0.01715874671936035 time for calcul the mask position with numpy : 0.02016162872314453 nb_pixel_total : 27803 time to create 1 rle with old method : 0.029341936111450195 time for calcul the mask position with numpy : 0.020737409591674805 nb_pixel_total : 21196 time to create 1 rle with old method : 0.02312159538269043 time for calcul the mask position with numpy : 0.02194046974182129 nb_pixel_total : 83895 time to create 1 rle with old method : 0.08796143531799316 time for calcul the mask position with numpy : 0.020691633224487305 nb_pixel_total : 31000 time to create 1 rle with old method : 0.03298377990722656 time for calcul the mask position with numpy : 0.019880294799804688 nb_pixel_total : 18060 time to create 1 rle with old method : 0.0183260440826416 create new chi : 2.8981878757476807 time to delete rle : 0.0014410018920898438 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++++Number RLEs to save : 11276 TO DO : save crop sub photo not yet done ! save time : 10.171252727508545 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 2.322892665863037 time for calcul the mask position with numpy : 0.6398839950561523 nb_pixel_total : 6724366 time to create 1 rle with new method : 0.6174836158752441 time for calcul the mask position with numpy : 0.01944756507873535 nb_pixel_total : 31299 time to create 1 rle with old method : 0.03243851661682129 time for calcul the mask position with numpy : 0.01986527442932129 nb_pixel_total : 19805 time to create 1 rle with old method : 0.020684242248535156 time for calcul the mask position with numpy : 0.019550085067749023 nb_pixel_total : 80868 time to create 1 rle with old method : 0.08559060096740723 time for calcul the mask position with numpy : 0.021151304244995117 nb_pixel_total : 167838 time to create 1 rle with new method : 0.5876777172088623 time for calcul the mask position with numpy : 0.020914316177368164 nb_pixel_total : 26064 time to create 1 rle with old method : 0.02727651596069336 create new chi : 2.1683948040008545 time to delete rle : 0.0006153583526611328 batch 1 Loaded 11 chid ids of type : 3594 ++++++Number RLEs to save : 4800 TO DO : save crop sub photo not yet done ! save time : 6.392610549926758 nb_obj : 9 nb_hashtags : 2 time to prepare the origin masks : 3.1515254974365234 time for calcul the mask position with numpy : 0.5261092185974121 nb_pixel_total : 6230196 time to create 1 rle with new method : 0.6443002223968506 time for calcul the mask position with numpy : 0.021142244338989258 nb_pixel_total : 219685 time to create 1 rle with new method : 0.4934957027435303 time for calcul the mask position with numpy : 0.020427703857421875 nb_pixel_total : 25417 time to create 1 rle with old method : 0.027721405029296875 time for calcul the mask position with numpy : 0.019912242889404297 nb_pixel_total : 97516 time to create 1 rle with old method : 0.1061854362487793 time for calcul the mask position with numpy : 0.02151942253112793 nb_pixel_total : 121838 time to create 1 rle with old method : 0.14356374740600586 time for calcul the mask position with numpy : 0.021395206451416016 nb_pixel_total : 25698 time to create 1 rle with old method : 0.02752828598022461 time for calcul the mask position with numpy : 0.021182775497436523 nb_pixel_total : 109379 time to create 1 rle with old method : 0.12118744850158691 time for calcul the mask position with numpy : 0.021626710891723633 nb_pixel_total : 66173 time to create 1 rle with old method : 0.07018923759460449 time for calcul the mask position with numpy : 0.021367788314819336 nb_pixel_total : 65808 time to create 1 rle with old method : 0.07071924209594727 time for calcul the mask position with numpy : 0.020422935485839844 nb_pixel_total : 88530 time to create 1 rle with old method : 0.09538984298706055 create new chi : 2.5723795890808105 time to delete rle : 0.0012264251708984375 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++++Number RLEs to save : 8306 TO DO : save crop sub photo not yet done ! save time : 7.764642715454102 nb_obj : 12 nb_hashtags : 2 time to prepare the origin masks : 4.405773162841797 time for calcul the mask position with numpy : 0.5556459426879883 nb_pixel_total : 6075384 time to create 1 rle with new method : 0.7824954986572266 time for calcul the mask position with numpy : 0.020125150680541992 nb_pixel_total : 162005 time to create 1 rle with new method : 0.5818326473236084 time for calcul the mask position with numpy : 0.021544694900512695 nb_pixel_total : 42015 time to create 1 rle with old method : 0.04873514175415039 time for calcul the mask position with numpy : 0.020976543426513672 nb_pixel_total : 22583 time to create 1 rle with old method : 0.024388551712036133 time for calcul the mask position with numpy : 0.021352052688598633 nb_pixel_total : 46068 time to create 1 rle with old method : 0.04829740524291992 time for calcul the mask position with numpy : 0.02053689956665039 nb_pixel_total : 58400 time to create 1 rle with old method : 0.06278347969055176 time for calcul the mask position with numpy : 0.020175457000732422 nb_pixel_total : 54728 time to create 1 rle with old method : 0.0584256649017334 time for calcul the mask position with numpy : 0.0206301212310791 nb_pixel_total : 96386 time to create 1 rle with old method : 0.10039520263671875 time for calcul the mask position with numpy : 0.021013975143432617 nb_pixel_total : 104510 time to create 1 rle with old method : 0.1193239688873291 time for calcul the mask position with numpy : 0.022333621978759766 nb_pixel_total : 25204 time to create 1 rle with old method : 0.038478851318359375 time for calcul the mask position with numpy : 0.019924163818359375 nb_pixel_total : 9939 time to create 1 rle with old method : 0.01026153564453125 time for calcul the mask position with numpy : 0.02040576934814453 nb_pixel_total : 68689 time to create 1 rle with old method : 0.07187986373901367 time for calcul the mask position with numpy : 0.022078275680541992 nb_pixel_total : 284329 time to create 1 rle with new method : 0.541229248046875 create new chi : 3.3735222816467285 time to delete rle : 0.0014159679412841797 batch 1 Loaded 25 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 10142 TO DO : save crop sub photo not yet done ! save time : 12.92854642868042 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 2.611924648284912 time for calcul the mask position with numpy : 0.33005499839782715 nb_pixel_total : 6309243 time to create 1 rle with new method : 0.6530497074127197 time for calcul the mask position with numpy : 0.019932985305786133 nb_pixel_total : 18425 time to create 1 rle with old method : 0.019164323806762695 time for calcul the mask position with numpy : 0.02075052261352539 nb_pixel_total : 137213 time to create 1 rle with old method : 0.15174579620361328 time for calcul the mask position with numpy : 0.019984006881713867 nb_pixel_total : 53403 time to create 1 rle with old method : 0.05715632438659668 time for calcul the mask position with numpy : 0.020578622817993164 nb_pixel_total : 62671 time to create 1 rle with old method : 0.06794905662536621 time for calcul the mask position with numpy : 0.0216677188873291 nb_pixel_total : 119954 time to create 1 rle with old method : 0.1283416748046875 time for calcul the mask position with numpy : 0.02189493179321289 nb_pixel_total : 349331 time to create 1 rle with new method : 0.7494890689849854 create new chi : 2.337985038757324 time to delete rle : 0.0010528564453125 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 7362 TO DO : save crop sub photo not yet done ! save time : 9.408080101013184 nb_obj : 17 nb_hashtags : 2 time to prepare the origin masks : 5.782062292098999 time for calcul the mask position with numpy : 0.46802186965942383 nb_pixel_total : 5821930 time to create 1 rle with new method : 0.7009384632110596 time for calcul the mask position with numpy : 0.03209638595581055 nb_pixel_total : 31538 time to create 1 rle with old method : 0.03332352638244629 time for calcul the mask position with numpy : 0.032277822494506836 nb_pixel_total : 7497 time to create 1 rle with old method : 0.008037805557250977 time for calcul the mask position with numpy : 0.02369999885559082 nb_pixel_total : 59040 time to create 1 rle with old method : 0.06789302825927734 time for calcul the mask position with numpy : 0.027311325073242188 nb_pixel_total : 119859 time to create 1 rle with old method : 0.2771472930908203 time for calcul the mask position with numpy : 0.02901315689086914 nb_pixel_total : 245659 time to create 1 rle with new method : 0.6842210292816162 time for calcul the mask position with numpy : 0.024104595184326172 nb_pixel_total : 34790 time to create 1 rle with old method : 0.03964972496032715 time for calcul the mask position with numpy : 0.023431777954101562 nb_pixel_total : 61439 time to create 1 rle with old method : 0.06993699073791504 time for calcul the mask position with numpy : 0.02486133575439453 nb_pixel_total : 276415 time to create 1 rle with new method : 0.8798773288726807 time for calcul the mask position with numpy : 0.02233600616455078 nb_pixel_total : 72887 time to create 1 rle with old method : 0.0856621265411377 time for calcul the mask position with numpy : 0.02104783058166504 nb_pixel_total : 21292 time to create 1 rle with old method : 0.0230257511138916 time for calcul the mask position with numpy : 0.021307945251464844 nb_pixel_total : 66711 time to create 1 rle with old method : 0.06853175163269043 time for calcul the mask position with numpy : 0.01976776123046875 nb_pixel_total : 12512 time to create 1 rle with old method : 0.01272892951965332 time for calcul the mask position with numpy : 0.01971602439880371 nb_pixel_total : 48462 time to create 1 rle with old method : 0.04957246780395508 time for calcul the mask position with numpy : 0.01960134506225586 nb_pixel_total : 25426 time to create 1 rle with old method : 0.025882244110107422 time for calcul the mask position with numpy : 0.020132064819335938 nb_pixel_total : 75381 time to create 1 rle with old method : 0.08003091812133789 time for calcul the mask position with numpy : 0.020157814025878906 nb_pixel_total : 55239 time to create 1 rle with old method : 0.05858659744262695 time for calcul the mask position with numpy : 0.02022528648376465 nb_pixel_total : 14163 time to create 1 rle with old method : 0.014624595642089844 create new chi : 4.131344795227051 time to delete rle : 0.0019497871398925781 batch 1 Loaded 35 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 14597 TO DO : save crop sub photo not yet done ! save time : 14.960829257965088 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 1.5190846920013428 time for calcul the mask position with numpy : 0.378673791885376 nb_pixel_total : 6833560 time to create 1 rle with new method : 0.7109477519989014 time for calcul the mask position with numpy : 0.022307634353637695 nb_pixel_total : 189536 time to create 1 rle with new method : 1.1108860969543457 time for calcul the mask position with numpy : 0.02155137062072754 nb_pixel_total : 27144 time to create 1 rle with old method : 0.030237436294555664 create new chi : 2.3368046283721924 time to delete rle : 0.0005345344543457031 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 3868 TO DO : save crop sub photo not yet done ! save time : 4.446670770645142 nb_obj : 12 nb_hashtags : 3 time to prepare the origin masks : 7.866418123245239 time for calcul the mask position with numpy : 0.8574018478393555 nb_pixel_total : 5922122 time to create 1 rle with new method : 0.7082729339599609 time for calcul the mask position with numpy : 0.03969573974609375 nb_pixel_total : 177056 time to create 1 rle with new method : 0.5791919231414795 time for calcul the mask position with numpy : 0.035965919494628906 nb_pixel_total : 45522 time to create 1 rle with old method : 0.05172920227050781 time for calcul the mask position with numpy : 0.037255287170410156 nb_pixel_total : 7784 time to create 1 rle with old method : 0.009111881256103516 time for calcul the mask position with numpy : 0.0352480411529541 nb_pixel_total : 29345 time to create 1 rle with old method : 0.03170347213745117 time for calcul the mask position with numpy : 0.04305386543273926 nb_pixel_total : 37343 time to create 1 rle with old method : 0.04586672782897949 time for calcul the mask position with numpy : 0.03418111801147461 nb_pixel_total : 10531 time to create 1 rle with old method : 0.012017488479614258 time for calcul the mask position with numpy : 0.03598451614379883 nb_pixel_total : 28782 time to create 1 rle with old method : 0.03175711631774902 time for calcul the mask position with numpy : 0.0324559211730957 nb_pixel_total : 20991 time to create 1 rle with old method : 0.025537490844726562 time for calcul the mask position with numpy : 0.03783440589904785 nb_pixel_total : 257316 time to create 1 rle with new method : 0.3938589096069336 time for calcul the mask position with numpy : 0.0349884033203125 nb_pixel_total : 17490 time to create 1 rle with old method : 0.0229032039642334 time for calcul the mask position with numpy : 0.0415804386138916 nb_pixel_total : 21749 time to create 1 rle with old method : 0.02451801300048828 time for calcul the mask position with numpy : 0.03802967071533203 nb_pixel_total : 474209 time to create 1 rle with new method : 0.855736255645752 create new chi : 4.215389966964722 time to delete rle : 0.0017476081848144531 batch 1 Loaded 25 chid ids of type : 3594 +++++++++++++++++++++Number RLEs to save : 10050 TO DO : save crop sub photo not yet done ! save time : 6.428181409835815 nb_obj : 22 nb_hashtags : 3 time to prepare the origin masks : 9.292984008789062 time for calcul the mask position with numpy : 0.4968850612640381 nb_pixel_total : 6378208 time to create 1 rle with new method : 0.6176598072052002 time for calcul the mask position with numpy : 0.02213597297668457 nb_pixel_total : 18982 time to create 1 rle with old method : 0.021344900131225586 time for calcul the mask position with numpy : 0.0211029052734375 nb_pixel_total : 10948 time to create 1 rle with old method : 0.011973857879638672 time for calcul the mask position with numpy : 0.02042222023010254 nb_pixel_total : 12931 time to create 1 rle with old method : 0.014144182205200195 time for calcul the mask position with numpy : 0.02080845832824707 nb_pixel_total : 14944 time to create 1 rle with old method : 0.016887664794921875 time for calcul the mask position with numpy : 0.021231412887573242 nb_pixel_total : 23869 time to create 1 rle with old method : 0.026966333389282227 time for calcul the mask position with numpy : 0.022005558013916016 nb_pixel_total : 128913 time to create 1 rle with old method : 0.14933228492736816 time for calcul the mask position with numpy : 0.02063894271850586 nb_pixel_total : 12518 time to create 1 rle with old method : 0.013360023498535156 time for calcul the mask position with numpy : 0.02078986167907715 nb_pixel_total : 17864 time to create 1 rle with old method : 0.019983768463134766 time for calcul the mask position with numpy : 0.020985841751098633 nb_pixel_total : 23796 time to create 1 rle with old method : 0.02616143226623535 time for calcul the mask position with numpy : 0.021734237670898438 nb_pixel_total : 12251 time to create 1 rle with old method : 0.013542890548706055 time for calcul the mask position with numpy : 0.020936965942382812 nb_pixel_total : 42677 time to create 1 rle with old method : 0.04890084266662598 time for calcul the mask position with numpy : 0.020368099212646484 nb_pixel_total : 47777 time to create 1 rle with old method : 0.05184459686279297 time for calcul the mask position with numpy : 0.0308225154876709 nb_pixel_total : 10024 time to create 1 rle with old method : 0.016471385955810547 time for calcul the mask position with numpy : 0.03869009017944336 nb_pixel_total : 8471 time to create 1 rle with old method : 0.01390981674194336 time for calcul the mask position with numpy : 0.03566884994506836 nb_pixel_total : 19707 time to create 1 rle with old method : 0.021627187728881836 time for calcul the mask position with numpy : 0.03488731384277344 nb_pixel_total : 37052 time to create 1 rle with old method : 0.04050326347351074 time for calcul the mask position with numpy : 0.0327916145324707 nb_pixel_total : 121294 time to create 1 rle with old method : 0.13336682319641113 time for calcul the mask position with numpy : 0.030089378356933594 nb_pixel_total : 20823 time to create 1 rle with old method : 0.022982120513916016 time for calcul the mask position with numpy : 0.02768731117248535 nb_pixel_total : 29644 time to create 1 rle with old method : 0.03382134437561035 time for calcul the mask position with numpy : 0.02185201644897461 nb_pixel_total : 16391 time to create 1 rle with old method : 0.018010854721069336 time for calcul the mask position with numpy : 0.021480083465576172 nb_pixel_total : 9669 time to create 1 rle with old method : 0.010573625564575195 time for calcul the mask position with numpy : 0.020920991897583008 nb_pixel_total : 31487 time to create 1 rle with old method : 0.033933162689208984 create new chi : 2.4610612392425537 time to delete rle : 0.0017011165618896484 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 10570 TO DO : save crop sub photo not yet done ! save time : 14.91623830795288 nb_obj : 18 nb_hashtags : 5 time to prepare the origin masks : 8.0715970993042 time for calcul the mask position with numpy : 0.3642842769622803 nb_pixel_total : 6594477 time to create 1 rle with new method : 0.6109192371368408 time for calcul the mask position with numpy : 0.021432161331176758 nb_pixel_total : 49031 time to create 1 rle with old method : 0.05415678024291992 time for calcul the mask position with numpy : 0.020687580108642578 nb_pixel_total : 13784 time to create 1 rle with old method : 0.014569997787475586 time for calcul the mask position with numpy : 0.020002365112304688 nb_pixel_total : 18093 time to create 1 rle with old method : 0.01948261260986328 time for calcul the mask position with numpy : 0.02160501480102539 nb_pixel_total : 13144 time to create 1 rle with old method : 0.014273405075073242 time for calcul the mask position with numpy : 0.02223825454711914 nb_pixel_total : 80192 time to create 1 rle with old method : 0.09280586242675781 time for calcul the mask position with numpy : 0.023916244506835938 nb_pixel_total : 25515 time to create 1 rle with old method : 0.031157255172729492 time for calcul the mask position with numpy : 0.02200627326965332 nb_pixel_total : 8230 time to create 1 rle with old method : 0.00960087776184082 time for calcul the mask position with numpy : 0.022066354751586914 nb_pixel_total : 45815 time to create 1 rle with old method : 0.04939985275268555 time for calcul the mask position with numpy : 0.02093672752380371 nb_pixel_total : 2206 time to create 1 rle with old method : 0.0024900436401367188 time for calcul the mask position with numpy : 0.02079319953918457 nb_pixel_total : 19187 time to create 1 rle with old method : 0.02055811882019043 time for calcul the mask position with numpy : 0.020280122756958008 nb_pixel_total : 7416 time to create 1 rle with old method : 0.00792837142944336 time for calcul the mask position with numpy : 0.0202789306640625 nb_pixel_total : 44339 time to create 1 rle with old method : 0.047365427017211914 time for calcul the mask position with numpy : 0.021546125411987305 nb_pixel_total : 9149 time to create 1 rle with old method : 0.009653091430664062 time for calcul the mask position with numpy : 0.021322250366210938 nb_pixel_total : 19350 time to create 1 rle with old method : 0.021245479583740234 time for calcul the mask position with numpy : 0.022635698318481445 nb_pixel_total : 53109 time to create 1 rle with old method : 0.057297468185424805 time for calcul the mask position with numpy : 0.022896528244018555 nb_pixel_total : 11935 time to create 1 rle with old method : 0.01567864418029785 time for calcul the mask position with numpy : 0.021430492401123047 nb_pixel_total : 16415 time to create 1 rle with old method : 0.0178072452545166 time for calcul the mask position with numpy : 0.020163536071777344 nb_pixel_total : 18853 time to create 1 rle with old method : 0.020685672760009766 create new chi : 1.9075121879577637 time to delete rle : 0.0015223026275634766 batch 1 Loaded 37 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 10276 TO DO : save crop sub photo not yet done ! save time : 7.212052822113037 map_output_result : {1359488377: (0.0, 'Should be the crop_list due to order', 0), 1359379376: (0.0, 'Should be the crop_list due to order', 0), 1359379369: (0.0, 'Should be the crop_list due to order', 0), 1359379148: (0.0, 'Should be the crop_list due to order', 0), 1359379146: (0.0, 'Should be the crop_list due to order', 0), 1359379143: (0.0, 'Should be the crop_list due to order', 0), 1359378929: (0.0, 'Should be the crop_list due to order', 0), 1359378926: (0.0, 'Should be the crop_list due to order', 0), 1359378923: (0.0, 'Should be the crop_list due to order', 0), 1359378920: (0.0, 'Should be the crop_list due to order', 0), 1359378917: (0.0, 'Should be the crop_list due to order', 0), 1359378900: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1359488377, 1359379376, 1359379369, 1359379148, 1359379146, 1359379143, 1359378929, 1359378926, 1359378923, 1359378920, 1359378917, 1359378900] Looping around the photos to save general results len do output : 12 /1359488377.Didn't retrieve data . /1359379376.Didn't retrieve data . /1359379369.Didn't retrieve data . /1359379148.Didn't retrieve data . /1359379146.Didn't retrieve data . /1359379143.Didn't retrieve data . /1359378929.Didn't retrieve data . /1359378926.Didn't retrieve data . /1359378923.Didn't retrieve data . /1359378920.Didn't retrieve data . /1359378917.Didn't retrieve data . /1359378900.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, '2899936') ('3318', '23140986', '1359488377', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379376', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379369', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379148', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379146', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379143', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378929', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378926', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378923', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378920', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378917', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378900', None, None, None, None, None, '2899936') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.028656959533691406 save_final save missing photos in datou_result : time spend for datou_step_exec : 195.5035424232483 time spend to save output : 0.029158830642700195 total time spend for step 3 : 195.532701253891 step4:ventilate_hashtags_in_portfolio Tue May 20 01:16:53 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 : 23140986 get user id for portfolio 23140986 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`=23140986 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pehd','pet_clair','mal_croppe','metal','autre','background','pet_fonce','papier','flou','carton')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=23140986 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pehd','pet_clair','mal_croppe','metal','autre','background','pet_fonce','papier','flou','carton')) AND mptpi.`min_score`=0.5 To do 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`=23140986 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','pehd','pet_clair','mal_croppe','metal','autre','background','pet_fonce','papier','flou','carton')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/23147666,23147667,23147668,23147669,23147670,23147671,23147672,23147673,23147674,23147675,23147676?tags=environnement,pehd,pet_clair,mal_croppe,metal,autre,background,pet_fonce,papier,flou,carton Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1359488377, 1359379376, 1359379369, 1359379148, 1359379146, 1359379143, 1359378929, 1359378926, 1359378923, 1359378920, 1359378917, 1359378900] Looping around the photos to save general results len do output : 1 /23140986. 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, '2899936') ('3318', '23140986', '1359488377', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379376', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379369', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379148', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379146', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379143', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378929', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378926', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378923', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378920', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378917', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378900', None, None, None, None, None, '2899936') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.0711066722869873 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.1068291664123535 time spend to save output : 0.07138586044311523 total time spend for step 4 : 1.1782150268554688 step5:final Tue May 20 01:16:55 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 : {1359488377: ('0.09721899538171748',), 1359379376: ('0.09721899538171748',), 1359379369: ('0.09721899538171748',), 1359379148: ('0.09721899538171748',), 1359379146: ('0.09721899538171748',), 1359379143: ('0.09721899538171748',), 1359378929: ('0.09721899538171748',), 1359378926: ('0.09721899538171748',), 1359378923: ('0.09721899538171748',), 1359378920: ('0.09721899538171748',), 1359378917: ('0.09721899538171748',), 1359378900: ('0.09721899538171748',)} new output for save of step final : {1359488377: ('0.09721899538171748',), 1359379376: ('0.09721899538171748',), 1359379369: ('0.09721899538171748',), 1359379148: ('0.09721899538171748',), 1359379146: ('0.09721899538171748',), 1359379143: ('0.09721899538171748',), 1359378929: ('0.09721899538171748',), 1359378926: ('0.09721899538171748',), 1359378923: ('0.09721899538171748',), 1359378920: ('0.09721899538171748',), 1359378917: ('0.09721899538171748',), 1359378900: ('0.09721899538171748',)} [1359488377, 1359379376, 1359379369, 1359379148, 1359379146, 1359379143, 1359378929, 1359378926, 1359378923, 1359378920, 1359378917, 1359378900] Looping around the photos to save general results len do output : 12 /1359488377.Didn't retrieve data . /1359379376.Didn't retrieve data . /1359379369.Didn't retrieve data . /1359379148.Didn't retrieve data . /1359379146.Didn't retrieve data . /1359379143.Didn't retrieve data . /1359378929.Didn't retrieve data . /1359378926.Didn't retrieve data . /1359378923.Didn't retrieve data . /1359378920.Didn't retrieve data . /1359378917.Didn't retrieve data . /1359378900.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, '2899936') ('3318', '23140986', '1359488377', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379376', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379369', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379148', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379146', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379143', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378929', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378926', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378923', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378920', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378917', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378900', None, None, None, None, None, '2899936') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.1255338191986084 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.15714716911315918 time spend to save output : 0.12602639198303223 total time spend for step 5 : 0.2831735610961914 step6:blur_detection Tue May 20 01:16:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d.jpg resize: (2160, 3264) 1359488377 -1.0019157523357631 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0.jpg resize: (2160, 3264) 1359379376 -2.3442139824007646 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4.jpg resize: (2160, 3264) 1359379369 -2.013814386702721 treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c.jpg resize: (2160, 3264) 1359379148 -0.5001471978490393 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d.jpg resize: (2160, 3264) 1359379146 -0.14015828876632408 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c.jpg resize: (2160, 3264) 1359379143 -0.9376112070263483 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d.jpg resize: (2160, 3264) 1359378929 -0.8511557030960285 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6.jpg resize: (2160, 3264) 1359378926 -0.7244995745304244 treat image : temp/1747696229_125964_1359378923_5ddd14eca1da115bda9ff6305c2a2d97.jpg resize: (2160, 3264) 1359378923 2.157841761948703 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311.jpg resize: (2160, 3264) 1359378920 -1.901906358368387 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701.jpg resize: (2160, 3264) 1359378917 -3.1109723633952573 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843.jpg resize: (2160, 3264) 1359378900 -1.4966353962444743 treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d_rle_crop_3805668805_0.png resize: (769, 529) 1359683833 0.2348630286064176 treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d_rle_crop_3805668809_0.png resize: (157, 347) 1359683834 -2.4390264134086315 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668813_0.png resize: (262, 337) 1359683836 -1.5797174178409332 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668810_0.png resize: (521, 435) 1359683838 -0.9131645549612966 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668817_0.png resize: (64, 149) 1359683839 3.7717646667846494 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668815_0.png resize: (158, 232) 1359683842 -2.354848287559282 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668819_0.png resize: (143, 113) 1359683843 -1.2411947162312642 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668814_0.png resize: (283, 199) 1359683846 -0.6720673099464987 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668816_0.png resize: (115, 233) 1359683847 -1.0987259923624642 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668832_0.png resize: (584, 427) 1359683850 -1.1555364104178305 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668826_0.png resize: (152, 53) 1359683851 -2.244512119541416 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668828_0.png resize: (586, 537) 1359683854 -2.375700013553403 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668820_0.png resize: (201, 208) 1359683855 -1.213207109365905 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668825_0.png resize: (169, 130) 1359683856 -1.302719654369216 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668834_0.png resize: (192, 188) 1359683859 -1.1307690974234659 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668822_0.png resize: (446, 358) 1359683860 -1.2173240269857524 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668830_0.png resize: (94, 103) 1359683863 3.3362924780466057 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668823_0.png resize: (156, 226) 1359683864 -1.4303409975713068 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668835_0.png resize: (179, 207) 1359683866 -1.2933565608962276 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668831_0.png resize: (220, 116) 1359683868 -4.3849403350747975 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668833_0.png resize: (200, 220) 1359683870 -0.13844054954687665 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668836_0.png resize: (216, 256) 1359683872 1.155728961503253 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668821_0.png resize: (252, 156) 1359683873 -1.2137071098411463 treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c_rle_crop_3805668840_0.png resize: (155, 183) 1359683876 -1.6064655048187313 treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c_rle_crop_3805668837_0.png resize: (238, 178) 1359683877 -0.18755149774656255 treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c_rle_crop_3805668841_0.png resize: (271, 152) 1359683881 0.05070822878983351 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668849_0.png resize: (267, 176) 1359683882 -0.1654107290628242 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668850_0.png resize: (548, 775) 1359683885 -0.11751034218027416 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668844_0.png resize: (392, 293) 1359683886 -0.3981864137441019 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668846_0.png resize: (193, 221) 1359683889 -0.551970061482332 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668842_0.png resize: (264, 537) 1359683890 -0.4878380276665506 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668843_0.png resize: (292, 383) 1359683892 0.15304162330437263 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668854_0.png resize: (332, 107) 1359683895 -1.0496927805501743 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668859_0.png resize: (193, 370) 1359683896 -1.264437423287311 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668853_0.png resize: (151, 89) 1359683899 -0.5259933290561911 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668856_0.png resize: (375, 359) 1359683900 -1.6235125122116494 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668861_0.png resize: (178, 369) 1359683903 -2.5160027366743742 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668851_0.png resize: (712, 724) 1359683904 -0.9734299333590868 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668860_0.png resize: (175, 196) 1359683906 -0.3047959957337916 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668862_0.png resize: (347, 663) 1359683908 -1.3004567315769118 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d_rle_crop_3805668868_0.png resize: (175, 194) 1359683909 -1.0069865294551972 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668878_0.png resize: (1063, 350) 1359683912 -1.6390512704537277 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668874_0.png resize: (134, 167) 1359683913 -1.6859974219661877 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668873_0.png resize: (264, 260) 1359683915 -0.05560144237150191 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668884_0.png resize: (136, 82) 1359683916 -1.3743642495123847 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668871_0.png resize: (484, 248) 1359683918 0.28296589551375406 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668870_0.png resize: (383, 197) 1359683919 -0.03246538096764602 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668876_0.png resize: (197, 160) 1359683920 -1.0309951078798654 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668885_0.png resize: (203, 242) 1359683922 -1.8811831038093014 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668869_0.png resize: (202, 100) 1359683923 -1.7672723527908258 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668872_0.png resize: (282, 140) 1359683925 -1.228471141446365 treat image : temp/1747696229_125964_1359378923_5ddd14eca1da115bda9ff6305c2a2d97_rle_crop_3805668886_0.png resize: (224, 221) 1359683926 0.5797386308126533 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668891_0.png resize: (619, 617) 1359683927 -0.3132146133570656 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668898_0.png resize: (256, 292) 1359683929 -1.3839149203852796 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668897_0.png resize: (84, 155) 1359683930 -1.5923665932501716 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668889_0.png resize: (215, 145) 1359683932 -0.27225492940039975 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668893_0.png resize: (272, 218) 1359683933 -0.9220304530640044 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668890_0.png resize: (161, 150) 1359683934 0.5289002348267019 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668892_0.png resize: (267, 154) 1359683936 -1.3504059138411038 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668894_0.png resize: (187, 157) 1359683937 -0.06666543127062481 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668896_0.png resize: (166, 248) 1359683938 -1.0393200437475798 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668919_0.png resize: (138, 137) 1359683940 -1.7256380510424538 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668914_0.png resize: (215, 191) 1359683942 -1.8518187159050394 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668917_0.png resize: (149, 231) 1359683944 -1.2715421351515304 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668915_0.png resize: (155, 99) 1359683945 -0.29344804385602563 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668913_0.png resize: (180, 183) 1359683946 -1.3617245611973106 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668902_0.png resize: (139, 181) 1359683948 -2.67225214211574 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668921_0.png resize: (157, 167) 1359683949 -0.9827214019786917 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668901_0.png resize: (145, 109) 1359683950 -1.5707099864356406 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668918_0.png resize: (181, 124) 1359683952 -1.765294114005119 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668907_0.png resize: (175, 176) 1359683954 -0.7969651543051358 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668908_0.png resize: (125, 85) 1359683955 0.8783963809307234 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668904_0.png resize: (137, 190) 1359683957 -0.09675475029615618 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668903_0.png resize: (175, 218) 1359683958 -0.8189055129049253 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668920_0.png resize: (129, 116) 1359683960 -1.0312647494431542 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668909_0.png resize: (171, 108) 1359683961 -2.3174586770488044 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668933_0.png resize: (200, 63) 1359683962 -2.4627770784548724 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668932_0.png resize: (239, 251) 1359683964 -1.18765388394814 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668923_0.png resize: (239, 107) 1359683965 -2.182816060285948 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668930_0.png resize: (184, 141) 1359683967 -1.2819978013244753 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668936_0.png resize: (156, 151) 1359683968 -1.5827552273704604 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668935_0.png resize: (426, 256) 1359683970 -0.7514376387590231 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668922_0.png resize: (189, 205) 1359683972 -2.5356380459277954 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668931_0.png resize: (74, 114) 1359683973 -0.8229044079127127 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668934_0.png resize: (184, 209) 1359683975 -1.4071828360121004 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668927_0.png resize: (105, 116) 1359683976 -1.6137770521153934 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668924_0.png resize: (162, 90) 1359683978 0.040621284493948064 treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d_rle_crop_3805668808_0.png resize: (159, 119) 1359684034 -0.015179854959252243 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668824_0.png resize: (198, 199) 1359684036 -0.30777627019819254 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d_rle_crop_3805668866_0.png resize: (266, 334) 1359684037 -0.8900365280741787 treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668888_0.png resize: (788, 1082) 1359684039 -0.0687960616173618 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668900_0.png resize: (199, 226) 1359684041 -1.2010513660795983 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668912_0.png resize: (181, 100) 1359684042 -1.6454373834907707 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668939_0.png resize: (197, 358) 1359684044 -1.5838833815117488 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668937_0.png resize: (155, 148) 1359684046 -0.11159713837902703 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668926_0.png resize: (236, 136) 1359684047 -1.3695429742074248 treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d_rle_crop_3805668807_0.png resize: (202, 111) 1359684327 -2.3203531493540988 treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d_rle_crop_3805668806_0.png resize: (162, 258) 1359684329 -2.888873259314661 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668812_0.png resize: (186, 244) 1359684330 0.08714777420919645 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668811_0.png resize: (198, 298) 1359684331 -1.332301715007345 treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0_rle_crop_3805668818_0.png resize: (146, 552) 1359684333 -1.6930187371264056 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668829_0.png resize: (214, 230) 1359684334 -3.96795515957328 treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4_rle_crop_3805668827_0.png resize: (118, 164) 1359684335 -1.4665812676869616 treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c_rle_crop_3805668839_0.png resize: (335, 286) 1359684337 -1.3489238763820632 treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c_rle_crop_3805668838_0.png resize: (248, 878) 1359684338 0.5530142960992246 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668848_0.png resize: (277, 495) 1359684339 -0.04076583020698073 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668845_0.png resize: (282, 449) 1359684341 -2.111404125984597 treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d_rle_crop_3805668847_0.png resize: (408, 410) 1359684342 -1.2841535482968094 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668852_0.png resize: (316, 254) 1359684343 -0.23491376433578648 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668855_0.png resize: (403, 362) 1359684345 -1.5348975310201722 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668858_0.png resize: (235, 293) 1359684346 -1.539384818693896 treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c_rle_crop_3805668857_0.png resize: (261, 332) 1359684347 -2.5409539618266597 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d_rle_crop_3805668867_0.png resize: (630, 330) 1359684349 -0.5538226618715892 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d_rle_crop_3805668865_0.png resize: (274, 296) 1359684350 -1.3161902930728582 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d_rle_crop_3805668863_0.png resize: (835, 688) 1359684351 -1.9354170931024681 treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d_rle_crop_3805668864_0.png resize: (365, 426) 1359684352 -0.33314205097178073 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668880_0.png resize: (179, 271) 1359684354 -1.9522382857778675 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668883_0.png resize: (194, 361) 1359684355 -0.6142793636152932 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668879_0.png resize: (295, 269) 1359684356 0.21783149725792084 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668882_0.png resize: (349, 521) 1359684358 -0.600575404743972 treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6_rle_crop_3805668877_0.png resize: (495, 178) 1359684359 -1.4494745222846233 treat image : 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temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668916_0.png resize: (312, 767) 1359684368 -1.5560728554471566 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668905_0.png resize: (258, 577) 1359684370 -4.186794455596378 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668911_0.png resize: (220, 312) 1359684371 -2.9822308467691183 treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668906_0.png resize: (237, 241) 1359684372 -3.1967119693215666 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668925_0.png resize: (287, 300) 1359684374 -2.605017766028772 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668928_0.png resize: (333, 199) 1359684375 -1.3549391698259483 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668929_0.png resize: (88, 109) 1359684387 0.21600031177417217 treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668938_0.png resize: (101, 188) 1359684400 -1.1100874414004995 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 : 147 time used for this insertion : 0.0174560546875 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 147 time used for this insertion : 0.8597440719604492 save missing photos in datou_result : time spend for datou_step_exec : 42.92519474029541 time spend to save output : 0.8826041221618652 total time spend for step 6 : 43.807798862457275 step7:brightness Tue May 20 01:17:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1747696229_125964_1359488377_200735cbabbd9e90d00c5d7c7bda422d.jpg treat image : temp/1747696229_125964_1359379376_9b9f037936a6f4a1937e092adaff73d0.jpg treat image : temp/1747696229_125964_1359379369_9f0a013e9421512bd96742c1cef164f4.jpg treat image : temp/1747696229_125964_1359379148_d7a6e5ce6b0a2c9745fc187caf340f1c.jpg treat image : temp/1747696229_125964_1359379146_70b4ff75e44b706c64531790b3d5b32d.jpg treat image : temp/1747696229_125964_1359379143_f4912ac62902db0cccc3d5d5df4d9d2c.jpg treat image : temp/1747696229_125964_1359378929_ec6a33f2b2342bc72558dfb3993ff53d.jpg treat image : temp/1747696229_125964_1359378926_a0f92d7761882aef04e08b527dc6f5d6.jpg treat image : temp/1747696229_125964_1359378923_5ddd14eca1da115bda9ff6305c2a2d97.jpg treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311.jpg treat image : 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temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668895_0.png treat image : temp/1747696229_125964_1359378920_fd5500c9af3fdea36893d3a1ba77a311_rle_crop_3805668899_0.png treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668910_0.png treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668916_0.png treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668905_0.png treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668911_0.png treat image : temp/1747696229_125964_1359378917_0caf3ff96466b9b56d819a6a84b02701_rle_crop_3805668906_0.png treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668925_0.png treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668928_0.png treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668929_0.png treat image : temp/1747696229_125964_1359378900_5288ddd9bb3d97cc87912975fe917843_rle_crop_3805668938_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 147 time used for this insertion : 0.020061016082763672 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 147 time used for this insertion : 0.453688383102417 save missing photos in datou_result : time spend for datou_step_exec : 11.78969955444336 time spend to save output : 0.4789900779724121 total time spend for step 7 : 12.268689632415771 step8:velours_tree Tue May 20 01:17:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.19802284240722656 time spend to save output : 6.222724914550781e-05 total time spend for step 8 : 0.19808506965637207 step9:send_mail_cod Tue May 20 01:17:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P23140986_20-05-2025_01_17_51.pdf 23147667 imagette231476671747696671 23147668 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 .imagette231476681747696671 23147669 imagette231476691747696673 23147670 imagette231476701747696673 23147671 change filename to text .imagette231476711747696673 23147672 imagette231476721747696673 23147673 change filename to text .imagette231476731747696673 23147674 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 .imagette231476741747696673 23147675 imagette231476751747696674 23147676 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 .imagette231476761747696674 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=23140986 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/23147666,23147667,23147668,23147669,23147670,23147671,23147672,23147673,23147674,23147675,23147676?tags=environnement,pehd,pet_clair,mal_croppe,metal,autre,background,pet_fonce,papier,flou,carton args[1359488377] : ((1359488377, -1.0019157523357631, 492688767), (1359488377, 0.823096350777072, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359379376] : ((1359379376, -2.3442139824007646, 492609224), (1359379376, 0.31353429324838483, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359379369] : ((1359379369, -2.013814386702721, 492609224), (1359379369, -0.07925666414448931, 496442774), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359379148] : ((1359379148, -0.5001471978490393, 492688767), (1359379148, 0.3137820731279017, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359379146] : ((1359379146, -0.14015828876632408, 492688767), (1359379146, 0.4223978792232053, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359379143] : ((1359379143, -0.9376112070263483, 492688767), (1359379143, 0.10031438582103365, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359378929] : ((1359378929, -0.8511557030960285, 492688767), (1359378929, 0.6321560786316748, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359378926] : ((1359378926, -0.7244995745304244, 492688767), (1359378926, 0.1907559788871085, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359378923] : ((1359378923, 2.157841761948703, 492688767), (1359378923, 0.015384700502983635, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359378920] : ((1359378920, -1.901906358368387, 492688767), (1359378920, 0.4831240692638515, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359378917] : ((1359378917, -3.1109723633952573, 492609224), (1359378917, 0.06546758821589048, 2107752395), '0.09721899538171748') We are sending mail with results at report@fotonower.com args[1359378900] : ((1359378900, -1.4966353962444743, 492688767), (1359378900, -0.10323498016250002, 496442774), '0.09721899538171748') We are sending mail with results at report@fotonower.com refus_total : 0.09721899538171748 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=23140986 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140986_20-05-2025_01_17_51.pdf results_Auto_P23140986_20-05-2025_01_17_51.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140986_20-05-2025_01_17_51.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','23140986','results_Auto_P23140986_20-05-2025_01_17_51.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140986_20-05-2025_01_17_51.pdf','pdf','','0.67','0.09721899538171748') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/23140986

https://www.fotonower.com/image?json=false&list_photos_id=1359488377
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359379376
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359379369
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359379148
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359379146
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359379143
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359378929
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359378926
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359378923
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.157841761948703)
https://www.fotonower.com/image?json=false&list_photos_id=1359378920
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359378917
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1359378900
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 9.72%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/23147668?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/23147671?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/23147673?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/23147674?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/23147676?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140986_20-05-2025_01_17_51.pdf.

Lien vers velours :https://www.fotonower.com/velours/23147666,23147667,23147668,23147669,23147670,23147671,23147672,23147673,23147674,23147675,23147676?tags=environnement,pehd,pet_clair,mal_croppe,metal,autre,background,pet_fonce,papier,flou,carton.


L'équipe Fotonower 202 b'' Server: nginx Date: Mon, 19 May 2025 23:17:58 GMT Content-Length: 0 Connection: close X-Message-Id: mcAezP_hTUyOtxOLHykJAw 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 [1359488377, 1359379376, 1359379369, 1359379148, 1359379146, 1359379143, 1359378929, 1359378926, 1359378923, 1359378920, 1359378917, 1359378900] 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, '2899936') ('3318', '23140986', '1359488377', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379376', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379369', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379148', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379146', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379143', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378929', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378926', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378923', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378920', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378917', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378900', None, None, None, None, None, '2899936') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.10342717170715332 save_final save missing photos in datou_result : time spend for datou_step_exec : 6.448836326599121 time spend to save output : 0.10365414619445801 total time spend for step 9 : 6.552490472793579 step10:split_time_score Tue May 20 01:17:58 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'}] (('14', 12),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 19052025 23140986 Nombre de photos uploadées : 12 / 23040 (0%) 19052025 23140986 Nombre de photos taguées (types de déchets): 0 / 12 (0%) 19052025 23140986 Nombre de photos taguées (volume) : 0 / 12 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 4.76837158203125e-06 ???????????? elapsed_time : fill_and_build_computed_from_old_data 0.0007841587066650391 elapsed_time : insert_dashboard_record_day_entry 0.036269426345825195 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.14282205050229974 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23110032_19-05-2025_11_58_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23110032 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`=23110032 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23104389 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23104390 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23110081 order by id desc limit 1 Qualite : 0.12968882744229468 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140955_20-05-2025_00_46_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23140955 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`=23140955 AND mptpi.`type`=3726 To do Qualite : 0.13723545766434592 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140956_19-05-2025_23_23_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23140956 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`=23140956 AND mptpi.`type`=3594 To do Qualite : 0.09290547883148983 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140964_19-05-2025_23_06_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23140964 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`=23140964 AND mptpi.`type`=3594 To do Qualite : 0.23833715240819672 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140984_19-05-2025_23_40_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23140984 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`=23140984 AND mptpi.`type`=3726 To do Qualite : 0.09721899538171748 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23140986_20-05-2025_01_17_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23140986 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`=23140986 AND mptpi.`type`=3594 To do Qualite : 0.11220033257410708 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23141006_19-05-2025_22_45_41.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23141006 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`=23141006 AND mptpi.`type`=3594 To do Qualite : 0.1102060097502806 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23141018_19-05-2025_22_36_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23141018 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`=23141018 AND mptpi.`type`=3594 To do Qualite : 0.1278078983329443 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23141021_19-05-2025_22_27_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23141021 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`=23141021 AND mptpi.`type`=3594 To do Qualite : 0.05480124245730098 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23141024_19-05-2025_23_02_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23141024 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`=23141024 AND mptpi.`type`=3726 To do Qualite : 0.17062256036673928 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P23141034_19-05-2025_22_07_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 23141034 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`=23141034 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'19052025': {'nb_upload': 12, '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 [1359488377, 1359379376, 1359379369, 1359379148, 1359379146, 1359379143, 1359378929, 1359378926, 1359378923, 1359378920, 1359378917, 1359378900] Looping around the photos to save general results len do output : 1 /23140986Didn'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, '2899936') ('3318', '23140986', '1359488377', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379376', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379369', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379148', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379146', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359379143', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378929', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378926', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378923', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378920', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378917', None, None, None, None, None, '2899936') ('3318', None, None, None, None, None, None, None, '2899936') ('3318', '23140986', '1359378900', None, None, None, None, None, '2899936') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.03131365776062012 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.7312614917755127 time spend to save output : 0.031591176986694336 total time spend for step 10 : 3.762852668762207 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 12 set_done_treatment 138.35user 125.11system 7:35.76elapsed 57%CPU (0avgtext+0avgdata 5249780maxresident)k 598368inputs+83248outputs (386major+13166997minor)pagefaults 0swaps