python /home/admin/mtr/script_for_cron.py -j default -m 20 -a 'python3 ~/workarea/git/Velours/python/prod/datou.py -j batch_current -a 3995 -P 5486001' -s traitement_sts -M 0 -S 0 -U 100,80,95 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/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', '/home/admin/workarea/git/apy', '/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 : 4027910 load datou : 3995 # 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 ! 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 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 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 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 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 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 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 ? 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 ? [ (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 ? [ (photo_id, hashtag_id_0, score_0), (photo_id, hashtag_id_1, score_1), ...] was removed should we ? output optionnel thcl was removed should we ? id de la photo (peut être local ou global) was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? [ (photo_id, hashtag_id_0, score_0), (photo_id, hashtag_id_1, score_1), ...] was removed should we ? id de la photo (peut être local ou global) was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] 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 ? [ (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 ? 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 ? 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 : (5359, 'Mask_Limeil_Label_PEHD_080621', 16384, 25088, 'Mask_Limeil_Label_PEHD_080621', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 6, 9, 7, 22, 34), datetime.datetime(2021, 6, 9, 7, 22, 34)) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5557, 'learn_generique_01122021_6000_v2', 2048, 2048, 'learn_generique_01122021_6000_v2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 1, 19, 59, 17), datetime.datetime(2021, 12, 1, 19, 59, 17)) load thcls load THCL from format json or kwargs add thcl : 2976 in CacheModelConfig load THCL from format json or kwargs add thcl : 3233 in CacheModelConfig load pdts add pdt : 5359 in CacheModelConfig add pdt : 5557 in CacheModelConfig Running datou job : batch_current updating current state to 1 list_input_json: [] Current got : datou_id : 3995, datou_cur_ids : ['2596594'] with mtr_portfolio_ids : ['5486001'] and first list_photo_ids : [] new path : /proc/4027910/ 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 ! 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 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 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 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 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 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, blur_detection, brightness, crop_condition, thcl, merge_mask_thcl_custom, rle_unique_nms_with_priority, crop_condition, ventilate_hashtags_in_portfolio, final, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 20 list_input_json : [] origin We have 1 , BFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 5 ; length of list_pids : 5 ; length of list_args : 5 time to download the photos : 3.4754366874694824 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 : 13 step1:mask_detect Tue Feb 18 14:54:51 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 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 : 10593 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-18 14:54:54.974258: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-18 14:54:55.011270: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-18 14:54:55.013017: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9364000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-18 14:54:55.013041: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-18 14:54:55.018465: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-18 14:54:55.276751: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xf3bdb40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-18 14:54:55.276810: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-18 14:54:55.278453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-18 14:54:55.280450: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-18 14:54:55.289450: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-18 14:54:55.306497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-18 14:54:55.309092: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-18 14:54:55.332066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-18 14:54:55.336489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-18 14:54:55.380994: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-18 14:54:55.383382: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-18 14:54:55.383497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-18 14:54:55.384744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-18 14:54:55.384769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-18 14:54:55.384785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-18 14:54:55.387198: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-18 14:54:55.767420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-18 14:54:55.767586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-18 14:54:55.767611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-18 14:54:55.767630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-18 14:54:55.767650: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-18 14:54:55.767668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-18 14:54:55.767686: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-18 14:54:55.767705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-18 14:54:55.769283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-18 14:54:55.770842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-18 14:54:55.770888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-18 14:54:55.770942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-18 14:54:55.770964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-18 14:54:55.770984: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-18 14:54:55.771003: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-18 14:54:55.771023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-18 14:54:55.771043: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-18 14:54:55.772478: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-18 14:54:55.772514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-18 14:54:55.772522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-18 14:54:55.772529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-18 14:54:55.773732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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 : thcl2976 thcls : [{'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5359 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5359, 'Mask_Limeil_Label_PEHD_080621', 16384, 25088, 'Mask_Limeil_Label_PEHD_080621', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 6, 9, 7, 22, 34), datetime.datetime(2021, 6, 9, 7, 22, 34)) {'thcl': {'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'environnement', 'fibreux_cont', 'metal_cont', 'pet_clair_cont', 'pet_fonce_cont', 'pet_opaque_cont', 'barquette', 'film_plastique', 'autre_contaminant', 'etiquette_detachee'], 'list_hashtags_csv': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'svm_hashtag_type_desc': 5359, 'photo_desc_type': 5359, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'environnement', 'fibreux_cont', 'metal_cont', 'pet_clair_cont', 'pet_fonce_cont', 'pet_opaque_cont', 'barquette', 'film_plastique', 'autre_contaminant', 'etiquette_detachee'] 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 Mask_Limeil_Label_PEHD_080621 NUM_CLASSES 11 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 : Mask_Limeil_Label_PEHD_080621 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-18 14:55:07.774136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-18 14:55:08.005877: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/Mask_Limeil_Label_PEHD_080621 /data/models_weight/Mask_Limeil_Label_PEHD_080621/mask_model.h5 size_local : 256052544 size in s3 : 256052544 create time local : 2021-08-11 19:43:15 create time in s3 : 2021-08-06 17:21:30 mask_model.h5 already exist and didn't need to update list_images length : 5 NEW PHOTO Processing 1 images image shape: (2464, 3280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3280.00000 nb d'objets trouves : 77 NEW PHOTO Processing 1 images image shape: (2464, 3280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3280.00000 nb d'objets trouves : 73 NEW PHOTO Processing 1 images image shape: (2464, 3280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3280.00000 nb d'objets trouves : 71 NEW PHOTO Processing 1 images image shape: (2464, 3280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3280.00000 nb d'objets trouves : 74 NEW PHOTO Processing 1 images image shape: (2464, 3280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3280.00000 nb d'objets trouves : 79 Detection mask done ! Trying to reset tf kernel 4028207 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1306 tf kernel not reseted sub process len(results) : 5 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 5 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 6595 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2976 Catched exception ! Connect or reconnect ! thcls : [{'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2976, 'mtr_user_id': 31, 'name': 'Mask_Limeil_Label_PEHD_080621', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,environnement,fibreux_cont,metal_cont,pet_clair_cont,pet_fonce_cont,pet_opaque_cont,barquette,film_plastique,autre_contaminant,etiquette_detachee', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3760, 'photo_desc_type': 5359, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5359 ['background', 'environnement', 'fibreux_cont', 'metal_cont', 'pet_clair_cont', 'pet_fonce_cont', 'pet_opaque_cont', 'barquette', 'film_plastique', 'autre_contaminant', 'etiquette_detachee'] time for calcul the mask position with numpy : 0.2783622741699219 nb_pixel_total : 263167 time to create 1 rle with new method : 0.025328397750854492 length of segment : 539 time for calcul the mask position with numpy : 0.012853145599365234 nb_pixel_total : 169256 time to create 1 rle with new method : 0.022641658782958984 length of segment : 820 time for calcul the mask position with numpy : 0.0056304931640625 nb_pixel_total : 75569 time to create 1 rle with old method : 0.09954357147216797 length of segment : 259 time for calcul the mask position with numpy : 0.0046350955963134766 nb_pixel_total : 72926 time to create 1 rle with old method : 0.09990477561950684 length of segment : 391 time for calcul the mask position with numpy : 0.001483917236328125 nb_pixel_total : 27186 time to create 1 rle with old method : 0.0350346565246582 length of segment : 182 time for calcul the mask position with numpy : 0.17083048820495605 nb_pixel_total : 337035 time to create 1 rle with new method : 0.038976192474365234 length of segment : 826 time for calcul the mask position with numpy : 0.01259756088256836 nb_pixel_total : 239115 time to create 1 rle with new method : 0.018075227737426758 length of segment : 678 time for calcul the mask position with numpy : 0.057982683181762695 nb_pixel_total : 383610 time to create 1 rle with new method : 0.022837162017822266 length of segment : 760 time for calcul the mask position with numpy : 0.005333900451660156 nb_pixel_total : 96364 time to create 1 rle with old method : 0.10914778709411621 length of segment : 520 time for calcul the mask position with numpy : 0.007383584976196289 nb_pixel_total : 163551 time to create 1 rle with new method : 0.010206222534179688 length of segment : 479 time for calcul the mask position with numpy : 0.0200040340423584 nb_pixel_total : 323183 time to create 1 rle with new method : 0.10435843467712402 length of segment : 875 time for calcul the mask position with numpy : 0.012253284454345703 nb_pixel_total : 308318 time to create 1 rle with new method : 0.019326448440551758 length of segment : 655 time for calcul the mask position with numpy : 0.003445148468017578 nb_pixel_total : 40559 time to create 1 rle with old method : 0.06511068344116211 length of segment : 343 time for calcul the mask position with numpy : 0.0005681514739990234 nb_pixel_total : 21104 time to create 1 rle with old method : 0.027691125869750977 length of segment : 118 time for calcul the mask position with numpy : 0.0041506290435791016 nb_pixel_total : 74705 time to create 1 rle with old method : 0.09587454795837402 length of segment : 357 time for calcul the mask position with numpy : 0.011601924896240234 nb_pixel_total : 252125 time to create 1 rle with new method : 0.031438350677490234 length of segment : 537 time for calcul the mask position with numpy : 0.01921367645263672 nb_pixel_total : 316942 time to create 1 rle with new method : 0.5253970623016357 length of segment : 745 time for calcul the mask position with numpy : 0.0024900436401367188 nb_pixel_total : 36404 time to create 1 rle with old method : 0.05910468101501465 length of segment : 332 time for calcul the mask position with numpy : 0.0016634464263916016 nb_pixel_total : 19621 time to create 1 rle with old method : 0.028870582580566406 length of segment : 177 time for calcul the mask position with numpy : 0.015160322189331055 nb_pixel_total : 201181 time to create 1 rle with new method : 0.026170015335083008 length of segment : 559 time for calcul the mask position with numpy : 0.0005545616149902344 nb_pixel_total : 6954 time to create 1 rle with old method : 0.00855875015258789 length of segment : 69 time for calcul the mask position with numpy : 0.01786208152770996 nb_pixel_total : 326680 time to create 1 rle with new method : 0.10769009590148926 length of segment : 1599 time for calcul the mask position with numpy : 0.0025696754455566406 nb_pixel_total : 36168 time to create 1 rle with old method : 0.04530525207519531 length of segment : 308 time for calcul the mask position with numpy : 0.0025177001953125 nb_pixel_total : 52034 time to create 1 rle with old method : 0.06075930595397949 length of segment : 461 time for calcul the mask position with numpy : 0.005170583724975586 nb_pixel_total : 87550 time to create 1 rle with old method : 0.11269640922546387 length of segment : 600 time for calcul the mask position with numpy : 0.0029010772705078125 nb_pixel_total : 40117 time to create 1 rle with old method : 0.04589104652404785 length of segment : 323 time for calcul the mask position with numpy : 0.0012679100036621094 nb_pixel_total : 10601 time to create 1 rle with old method : 0.013365507125854492 length of segment : 184 time for calcul the mask position with numpy : 0.001993417739868164 nb_pixel_total : 20904 time to create 1 rle with old method : 0.025035858154296875 length of segment : 214 time for calcul the mask position with numpy : 0.0004963874816894531 nb_pixel_total : 4505 time to create 1 rle with old method : 0.005835294723510742 length of segment : 62 time for calcul the mask position with numpy : 0.0015716552734375 nb_pixel_total : 24971 time to create 1 rle with old method : 0.030138731002807617 length of segment : 255 time for calcul the mask position with numpy : 0.002679586410522461 nb_pixel_total : 40842 time to create 1 rle with old method : 0.04824113845825195 length of segment : 214 time for calcul the mask position with numpy : 0.0027229785919189453 nb_pixel_total : 39890 time to create 1 rle with old method : 0.05001425743103027 length of segment : 281 time for calcul the mask position with numpy : 0.0015168190002441406 nb_pixel_total : 17860 time to create 1 rle with old method : 0.02218794822692871 length of segment : 198 time for calcul the mask position with numpy : 0.0010433197021484375 nb_pixel_total : 11002 time to create 1 rle with old method : 0.015409708023071289 length of segment : 81 time for calcul the mask position with numpy : 0.012528181076049805 nb_pixel_total : 247020 time to create 1 rle with new method : 0.019066572189331055 length of segment : 585 time for calcul the mask position with numpy : 0.0007894039154052734 nb_pixel_total : 8255 time to create 1 rle with old method : 0.009718179702758789 length of segment : 138 time for calcul the mask position with numpy : 0.0025281906127929688 nb_pixel_total : 37403 time to create 1 rle with old method : 0.04568290710449219 length of segment : 423 time for calcul the mask position with numpy : 0.010895252227783203 nb_pixel_total : 246612 time to create 1 rle with new method : 0.017106056213378906 length of segment : 969 time for calcul the mask position with numpy : 0.013840913772583008 nb_pixel_total : 266788 time to create 1 rle with new method : 0.021788597106933594 length of segment : 1057 time for calcul the mask position with numpy : 0.0017290115356445312 nb_pixel_total : 29759 time to create 1 rle with old method : 0.034282684326171875 length of segment : 216 time for calcul the mask position with numpy : 0.013741016387939453 nb_pixel_total : 111249 time to create 1 rle with old method : 0.1246328353881836 length of segment : 560 time for calcul the mask position with numpy : 0.011332273483276367 nb_pixel_total : 166326 time to create 1 rle with new method : 0.022661685943603516 length of segment : 791 time for calcul the mask position with numpy : 0.004642009735107422 nb_pixel_total : 91912 time to create 1 rle with old method : 0.10703873634338379 length of segment : 337 time for calcul the mask position with numpy : 0.005401611328125 nb_pixel_total : 105041 time to create 1 rle with old method : 0.12340712547302246 length of segment : 413 time for calcul the mask position with numpy : 0.001873016357421875 nb_pixel_total : 27299 time to create 1 rle with old method : 0.035672664642333984 length of segment : 292 time for calcul the mask position with numpy : 0.0024127960205078125 nb_pixel_total : 47068 time to create 1 rle with old method : 0.05605936050415039 length of segment : 256 time for calcul the mask position with numpy : 0.0003390312194824219 nb_pixel_total : 12617 time to create 1 rle with old method : 0.015452861785888672 length of segment : 144 time for calcul the mask position with numpy : 0.01725149154663086 nb_pixel_total : 291136 time to create 1 rle with new method : 0.04831743240356445 length of segment : 1147 time for calcul the mask position with numpy : 0.011207103729248047 nb_pixel_total : 186609 time to create 1 rle with new method : 0.019164085388183594 length of segment : 796 time for calcul the mask position with numpy : 0.0019114017486572266 nb_pixel_total : 22716 time to create 1 rle with old method : 0.04097390174865723 length of segment : 389 time for calcul the mask position with numpy : 0.006354808807373047 nb_pixel_total : 65322 time to create 1 rle with old method : 0.07535862922668457 length of segment : 402 time for calcul the mask position with numpy : 0.008432388305664062 nb_pixel_total : 88453 time to create 1 rle with old method : 0.09853649139404297 length of segment : 457 time for calcul the mask position with numpy : 0.0020096302032470703 nb_pixel_total : 34718 time to create 1 rle with old method : 0.03970026969909668 length of segment : 270 time for calcul the mask position with numpy : 0.0013413429260253906 nb_pixel_total : 21485 time to create 1 rle with old method : 0.02512955665588379 length of segment : 269 time for calcul the mask position with numpy : 0.0006985664367675781 nb_pixel_total : 19735 time to create 1 rle with old method : 0.023322105407714844 length of segment : 255 time for calcul the mask position with numpy : 0.0026099681854248047 nb_pixel_total : 39726 time to create 1 rle with old method : 0.047434091567993164 length of segment : 332 time for calcul the mask position with numpy : 0.0025644302368164062 nb_pixel_total : 38039 time to create 1 rle with old method : 0.046239376068115234 length of segment : 286 time for calcul the mask position with numpy : 0.003217458724975586 nb_pixel_total : 64188 time to create 1 rle with old method : 0.07183599472045898 length of segment : 368 time for calcul the mask position with numpy : 0.001430511474609375 nb_pixel_total : 17357 time to create 1 rle with old method : 0.02165508270263672 length of segment : 236 time for calcul the mask position with numpy : 0.0020568370819091797 nb_pixel_total : 79483 time to create 1 rle with old method : 0.09154629707336426 length of segment : 488 time for calcul the mask position with numpy : 0.0009760856628417969 nb_pixel_total : 13491 time to create 1 rle with old method : 0.01620006561279297 length of segment : 169 time for calcul the mask position with numpy : 0.0026140213012695312 nb_pixel_total : 43661 time to create 1 rle with old method : 0.05093955993652344 length of segment : 311 time for calcul the mask position with numpy : 0.0029060840606689453 nb_pixel_total : 38741 time to create 1 rle with old method : 0.044846296310424805 length of segment : 500 time for calcul the mask position with numpy : 0.0009329319000244141 nb_pixel_total : 17877 time to create 1 rle with old method : 0.02025604248046875 length of segment : 180 time for calcul the mask position with numpy : 0.002030611038208008 nb_pixel_total : 33896 time to create 1 rle with old method : 0.04086470603942871 length of segment : 334 time for calcul the mask position with numpy : 0.010215520858764648 nb_pixel_total : 219001 time to create 1 rle with new method : 0.014817237854003906 length of segment : 767 time for calcul the mask position with numpy : 0.0015306472778320312 nb_pixel_total : 25990 time to create 1 rle with old method : 0.02962803840637207 length of segment : 158 time for calcul the mask position with numpy : 0.01211857795715332 nb_pixel_total : 240972 time to create 1 rle with new method : 0.01839900016784668 length of segment : 791 time for calcul the mask position with numpy : 0.002717256546020508 nb_pixel_total : 50199 time to create 1 rle with old method : 0.05866360664367676 length of segment : 237 time for calcul the mask position with numpy : 0.0016324520111083984 nb_pixel_total : 25037 time to create 1 rle with old method : 0.028606653213500977 length of segment : 268 time for calcul the mask position with numpy : 0.016193866729736328 nb_pixel_total : 300954 time to create 1 rle with new method : 0.035888671875 length of segment : 1146 time for calcul the mask position with numpy : 0.0017027854919433594 nb_pixel_total : 22533 time to create 1 rle with old method : 0.02640843391418457 length of segment : 166 time for calcul the mask position with numpy : 0.011230230331420898 nb_pixel_total : 233189 time to create 1 rle with new method : 0.015691041946411133 length of segment : 1122 time for calcul the mask position with numpy : 0.009453296661376953 nb_pixel_total : 180788 time to create 1 rle with new method : 0.014003992080688477 length of segment : 723 time for calcul the mask position with numpy : 0.002293825149536133 nb_pixel_total : 28699 time to create 1 rle with old method : 0.03377509117126465 length of segment : 274 time for calcul the mask position with numpy : 0.004817008972167969 nb_pixel_total : 30195 time to create 1 rle with old method : 0.046651601791381836 length of segment : 270 time for calcul the mask position with numpy : 0.0067310333251953125 nb_pixel_total : 135795 time to create 1 rle with old method : 0.1519937515258789 length of segment : 461 time for calcul the mask position with numpy : 0.0013430118560791016 nb_pixel_total : 21893 time to create 1 rle with old method : 0.025341272354125977 length of segment : 181 time for calcul the mask position with numpy : 0.0017485618591308594 nb_pixel_total : 32641 time to create 1 rle with old method : 0.03925895690917969 length of segment : 334 time for calcul the mask position with numpy : 0.0005803108215332031 nb_pixel_total : 9231 time to create 1 rle with old method : 0.011333227157592773 length of segment : 113 time for calcul the mask position with numpy : 0.000759124755859375 nb_pixel_total : 11757 time to create 1 rle with old method : 0.014383554458618164 length of segment : 109 time for calcul the mask position with numpy : 0.0007824897766113281 nb_pixel_total : 10605 time to create 1 rle with old method : 0.012475013732910156 length of segment : 143 time for calcul the mask position with numpy : 0.0018630027770996094 nb_pixel_total : 35628 time to create 1 rle with old method : 0.04107165336608887 length of segment : 249 time for calcul the mask position with numpy : 0.0021305084228515625 nb_pixel_total : 28254 time to create 1 rle with old method : 0.031797170639038086 length of segment : 282 time for calcul the mask position with numpy : 0.0007073879241943359 nb_pixel_total : 31653 time to create 1 rle with old method : 0.036261558532714844 length of segment : 268 time for calcul the mask position with numpy : 0.0036411285400390625 nb_pixel_total : 50702 time to create 1 rle with old method : 0.05717301368713379 length of segment : 328 time for calcul the mask position with numpy : 0.007338047027587891 nb_pixel_total : 135231 time to create 1 rle with old method : 0.1513063907623291 length of segment : 541 time for calcul the mask position with numpy : 0.005133390426635742 nb_pixel_total : 98686 time to create 1 rle with old method : 0.1111140251159668 length of segment : 486 time for calcul the mask position with numpy : 0.002384662628173828 nb_pixel_total : 36151 time to create 1 rle with old method : 0.04263114929199219 length of segment : 255 time for calcul the mask position with numpy : 0.002396106719970703 nb_pixel_total : 33616 time to create 1 rle with old method : 0.03882002830505371 length of segment : 238 time for calcul the mask position with numpy : 0.0016148090362548828 nb_pixel_total : 24973 time to create 1 rle with old method : 0.028941869735717773 length of segment : 175 time for calcul the mask position with numpy : 0.00622248649597168 nb_pixel_total : 132570 time to create 1 rle with old method : 0.1517946720123291 length of segment : 359 time for calcul the mask position with numpy : 0.0005137920379638672 nb_pixel_total : 5511 time to create 1 rle with old method : 0.006962299346923828 length of segment : 108 time for calcul the mask position with numpy : 0.0012142658233642578 nb_pixel_total : 11933 time to create 1 rle with old method : 0.014400482177734375 length of segment : 138 time for calcul the mask position with numpy : 0.0018301010131835938 nb_pixel_total : 20694 time to create 1 rle with old method : 0.028075456619262695 length of segment : 287 time for calcul the mask position with numpy : 0.0018429756164550781 nb_pixel_total : 39200 time to create 1 rle with old method : 0.05308198928833008 length of segment : 203 time for calcul the mask position with numpy : 0.018167734146118164 nb_pixel_total : 133149 time to create 1 rle with old method : 0.15338516235351562 length of segment : 1183 time for calcul the mask position with numpy : 0.01134037971496582 nb_pixel_total : 240909 time to create 1 rle with new method : 0.01666092872619629 length of segment : 838 time for calcul the mask position with numpy : 0.00953364372253418 nb_pixel_total : 202984 time to create 1 rle with new method : 0.010732412338256836 length of segment : 786 time for calcul the mask position with numpy : 0.012401103973388672 nb_pixel_total : 172871 time to create 1 rle with new method : 0.013724327087402344 length of segment : 864 time for calcul the mask position with numpy : 0.010451316833496094 nb_pixel_total : 206777 time to create 1 rle with new method : 0.01467132568359375 length of segment : 581 time for calcul the mask position with numpy : 0.004953145980834961 nb_pixel_total : 96116 time to create 1 rle with old method : 0.10832500457763672 length of segment : 296 time for calcul the mask position with numpy : 0.0012750625610351562 nb_pixel_total : 12888 time to create 1 rle with old method : 0.014973878860473633 length of segment : 167 time for calcul the mask position with numpy : 0.0030517578125 nb_pixel_total : 45547 time to create 1 rle with old method : 0.05245399475097656 length of segment : 259 time for calcul the mask position with numpy : 0.002313375473022461 nb_pixel_total : 33765 time to create 1 rle with old method : 0.03897547721862793 length of segment : 265 time for calcul the mask position with numpy : 0.004438161849975586 nb_pixel_total : 44950 time to create 1 rle with old method : 0.05298328399658203 length of segment : 392 time for calcul the mask position with numpy : 0.002264261245727539 nb_pixel_total : 56070 time to create 1 rle with old method : 0.06378483772277832 length of segment : 304 time for calcul the mask position with numpy : 0.004973411560058594 nb_pixel_total : 46975 time to create 1 rle with old method : 0.05366325378417969 length of segment : 384 time for calcul the mask position with numpy : 0.0019462108612060547 nb_pixel_total : 30938 time to create 1 rle with old method : 0.03513598442077637 length of segment : 242 time for calcul the mask position with numpy : 0.0026187896728515625 nb_pixel_total : 29694 time to create 1 rle with old method : 0.034332990646362305 length of segment : 324 time for calcul the mask position with numpy : 0.004288196563720703 nb_pixel_total : 65343 time to create 1 rle with old method : 0.07239484786987305 length of segment : 437 time for calcul the mask position with numpy : 0.0035758018493652344 nb_pixel_total : 58801 time to create 1 rle with old method : 0.06633996963500977 length of segment : 325 time for calcul the mask position with numpy : 0.0039234161376953125 nb_pixel_total : 25876 time to create 1 rle with old method : 0.030882596969604492 length of segment : 410 time for calcul the mask position with numpy : 0.0016274452209472656 nb_pixel_total : 20850 time to create 1 rle with old method : 0.02445507049560547 length of segment : 174 time for calcul the mask position with numpy : 0.0030057430267333984 nb_pixel_total : 83866 time to create 1 rle with old method : 0.09966802597045898 length of segment : 615 time for calcul the mask position with numpy : 0.0023005008697509766 nb_pixel_total : 14519 time to create 1 rle with old method : 0.022992610931396484 length of segment : 126 time for calcul the mask position with numpy : 0.0027723312377929688 nb_pixel_total : 38723 time to create 1 rle with old method : 0.04730343818664551 length of segment : 342 time for calcul the mask position with numpy : 0.0047740936279296875 nb_pixel_total : 97594 time to create 1 rle with old method : 0.10994958877563477 length of segment : 429 time for calcul the mask position with numpy : 0.011476516723632812 nb_pixel_total : 173083 time to create 1 rle with new method : 0.01801276206970215 length of segment : 1105 time for calcul the mask position with numpy : 0.002572774887084961 nb_pixel_total : 45854 time to create 1 rle with old method : 0.0553584098815918 length of segment : 187 time for calcul the mask position with numpy : 0.001398324966430664 nb_pixel_total : 11967 time to create 1 rle with old method : 0.015031576156616211 length of segment : 237 time for calcul the mask position with numpy : 0.0005347728729248047 nb_pixel_total : 11021 time to create 1 rle with old method : 0.012949943542480469 length of segment : 141 time for calcul the mask position with numpy : 0.004788637161254883 nb_pixel_total : 82467 time to create 1 rle with old method : 0.09629416465759277 length of segment : 252 time for calcul the mask position with numpy : 0.0018463134765625 nb_pixel_total : 14503 time to create 1 rle with old method : 0.01715993881225586 length of segment : 321 time for calcul the mask position with numpy : 0.0010631084442138672 nb_pixel_total : 24350 time to create 1 rle with old method : 0.028118610382080078 length of segment : 218 time for calcul the mask position with numpy : 0.0014424324035644531 nb_pixel_total : 30030 time to create 1 rle with old method : 0.034230709075927734 length of segment : 169 time for calcul the mask position with numpy : 0.001287221908569336 nb_pixel_total : 9874 time to create 1 rle with old method : 0.012146472930908203 length of segment : 185 time for calcul the mask position with numpy : 0.0025920867919921875 nb_pixel_total : 49202 time to create 1 rle with old method : 0.05638384819030762 length of segment : 307 time for calcul the mask position with numpy : 0.002583026885986328 nb_pixel_total : 42057 time to create 1 rle with old method : 0.04797768592834473 length of segment : 225 time for calcul the mask position with numpy : 0.008818864822387695 nb_pixel_total : 224431 time to create 1 rle with new method : 0.012096881866455078 length of segment : 687 time for calcul the mask position with numpy : 0.0017695426940917969 nb_pixel_total : 35380 time to create 1 rle with old method : 0.04086804389953613 length of segment : 167 time for calcul the mask position with numpy : 0.0031113624572753906 nb_pixel_total : 56021 time to create 1 rle with old method : 0.06497716903686523 length of segment : 303 time for calcul the mask position with numpy : 0.0029506683349609375 nb_pixel_total : 67607 time to create 1 rle with old method : 0.0778343677520752 length of segment : 234 time for calcul the mask position with numpy : 0.015627145767211914 nb_pixel_total : 389983 time to create 1 rle with new method : 0.021600961685180664 length of segment : 1208 time for calcul the mask position with numpy : 0.013765335083007812 nb_pixel_total : 281600 time to create 1 rle with new method : 0.02242588996887207 length of segment : 1133 time for calcul the mask position with numpy : 0.011734247207641602 nb_pixel_total : 136714 time to create 1 rle with old method : 0.15692448616027832 length of segment : 774 time for calcul the mask position with numpy : 0.0017580986022949219 nb_pixel_total : 29815 time to create 1 rle with old method : 0.037024497985839844 length of segment : 264 time for calcul the mask position with numpy : 0.0006923675537109375 nb_pixel_total : 9186 time to create 1 rle with old method : 0.011125564575195312 length of segment : 93 time for calcul the mask position with numpy : 0.010096549987792969 nb_pixel_total : 187151 time to create 1 rle with new method : 0.016318798065185547 length of segment : 999 time for calcul the mask position with numpy : 0.007761240005493164 nb_pixel_total : 193659 time to create 1 rle with new method : 0.016405344009399414 length of segment : 645 time for calcul the mask position with numpy : 0.0016736984252929688 nb_pixel_total : 23604 time to create 1 rle with old method : 0.026718616485595703 length of segment : 390 time for calcul the mask position with numpy : 0.0014786720275878906 nb_pixel_total : 50356 time to create 1 rle with old method : 0.06453394889831543 length of segment : 320 time for calcul the mask position with numpy : 0.010732173919677734 nb_pixel_total : 151477 time to create 1 rle with new method : 0.01260828971862793 length of segment : 706 time for calcul the mask position with numpy : 0.0017290115356445312 nb_pixel_total : 27060 time to create 1 rle with old method : 0.03106975555419922 length of segment : 300 time for calcul the mask position with numpy : 0.0016431808471679688 nb_pixel_total : 56559 time to create 1 rle with old method : 0.06323361396789551 length of segment : 276 time for calcul the mask position with numpy : 0.0027909278869628906 nb_pixel_total : 38471 time to create 1 rle with old method : 0.043257951736450195 length of segment : 508 time for calcul the mask position with numpy : 0.0015010833740234375 nb_pixel_total : 39774 time to create 1 rle with old method : 0.04507780075073242 length of segment : 165 time for calcul the mask position with numpy : 0.0048487186431884766 nb_pixel_total : 130434 time to create 1 rle with old method : 0.14559221267700195 length of segment : 446 time for calcul the mask position with numpy : 0.0031235218048095703 nb_pixel_total : 73199 time to create 1 rle with old method : 0.08216714859008789 length of segment : 364 time for calcul the mask position with numpy : 0.001943826675415039 nb_pixel_total : 41357 time to create 1 rle with old method : 0.050222158432006836 length of segment : 220 time for calcul the mask position with numpy : 0.0005867481231689453 nb_pixel_total : 13772 time to create 1 rle with old method : 0.016057491302490234 length of segment : 228 time for calcul the mask position with numpy : 0.0013661384582519531 nb_pixel_total : 16159 time to create 1 rle with old method : 0.019105911254882812 length of segment : 164 time for calcul the mask position with numpy : 0.0010297298431396484 nb_pixel_total : 14617 time to create 1 rle with old method : 0.017851829528808594 length of segment : 197 time for calcul the mask position with numpy : 0.0056455135345458984 nb_pixel_total : 121147 time to create 1 rle with old method : 0.1371936798095703 length of segment : 572 time for calcul the mask position with numpy : 0.0038797855377197266 nb_pixel_total : 32653 time to create 1 rle with old method : 0.03783726692199707 length of segment : 318 time for calcul the mask position with numpy : 0.0007047653198242188 nb_pixel_total : 8971 time to create 1 rle with old method : 0.010869741439819336 length of segment : 91 time for calcul the mask position with numpy : 0.0015158653259277344 nb_pixel_total : 28811 time to create 1 rle with old method : 0.033111572265625 length of segment : 191 time for calcul the mask position with numpy : 0.003137826919555664 nb_pixel_total : 12957 time to create 1 rle with old method : 0.015624046325683594 length of segment : 335 time for calcul the mask position with numpy : 0.001435995101928711 nb_pixel_total : 54824 time to create 1 rle with old method : 0.06409549713134766 length of segment : 316 time for calcul the mask position with numpy : 0.0005857944488525391 nb_pixel_total : 21596 time to create 1 rle with old method : 0.025114774703979492 length of segment : 204 time for calcul the mask position with numpy : 0.002150297164916992 nb_pixel_total : 26080 time to create 1 rle with old method : 0.03470206260681152 length of segment : 174 time for calcul the mask position with numpy : 0.003808736801147461 nb_pixel_total : 40269 time to create 1 rle with old method : 0.07973790168762207 length of segment : 523 time for calcul the mask position with numpy : 0.00019598007202148438 nb_pixel_total : 4072 time to create 1 rle with old method : 0.0050051212310791016 length of segment : 67 time for calcul the mask position with numpy : 0.0006678104400634766 nb_pixel_total : 27683 time to create 1 rle with old method : 0.03140449523925781 length of segment : 158 time for calcul the mask position with numpy : 0.0015277862548828125 nb_pixel_total : 42590 time to create 1 rle with old method : 0.05189800262451172 length of segment : 457 time spent for convertir_results : 20.137293100357056 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 165 chid ids of type : 3760 Number RLEs to save : 66838 save missing photos in datou_result : time spend for datou_step_exec : 112.05727624893188 time spend to save output : 3.883209705352783 total time spend for step 1 : 115.94048595428467 step2:blur_detection Tue Feb 18 14:56:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection toutes les photos sont déjà traitées, on saute les calculs Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 5 time used for this insertion : 0.009001016616821289 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 0.012842178344726562 save missing photos in datou_result : time spend for datou_step_exec : 0.027364730834960938 time spend to save output : 0.026994705200195312 total time spend for step 2 : 0.05435943603515625 step3:brightness Tue Feb 18 14:56:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness toutes les photos sont déjà traitées, on saute les calculs Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 5 time used for this insertion : 0.008925914764404297 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 0.007915735244750977 save missing photos in datou_result : time spend for datou_step_exec : 0.02579808235168457 time spend to save output : 0.021069765090942383 total time spend for step 3 : 0.04686784744262695 step4:crop_condition Tue Feb 18 14:56:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 3760 Loading chi in step crop for list_pids : 5 ! batch 1 Loaded 165 chid ids of type : 3760 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : barquette param for this class : {'min_score': 0.7} filtre for class : barquette hashtag_id of this class : 492787675 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 12 About to insert : list_path_to_insert length 12 new photo from crops ! we have finished the crop for the class : barquette begin to crop the class : fibreux_cont param for this class : {'min_score': 0.7} filtre for class : fibreux_cont hashtag_id of this class : 2107756748 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 17 About to insert : list_path_to_insert length 17 new photo from crops ! we have finished the crop for the class : fibreux_cont begin to crop the class : film_plastique param for this class : {'min_score': 0.7} filtre for class : film_plastique hashtag_id of this class : 2107756122 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 26 About to insert : list_path_to_insert length 26 new photo from crops ! we have finished the crop for the class : film_plastique begin to crop the class : autre_contaminant param for this class : {'min_score': 0.7} filtre for class : autre_contaminant hashtag_id of this class : 2107756781 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 35 About to insert : list_path_to_insert length 35 new photo from crops ! we have finished the crop for the class : autre_contaminant begin to crop the class : etiquette_detachee param for this class : {'min_score': 0.7} filtre for class : etiquette_detachee hashtag_id of this class : 2107756860 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 22 About to insert : list_path_to_insert length 22 new photo from crops ! we have finished the crop for the class : etiquette_detachee begin to crop the class : pet_clair_cont param for this class : {'min_score': 0.7} filtre for class : pet_clair_cont hashtag_id of this class : 2107758154 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 11 About to insert : list_path_to_insert length 11 new photo from crops ! we have finished the crop for the class : pet_clair_cont begin to crop the class : metal_cont param for this class : {'min_score': 0.7} filtre for class : metal_cont hashtag_id of this class : 2107756749 begin to crop the class : pet_fonce_cont param for this class : {'min_score': 0.7} filtre for class : pet_fonce_cont hashtag_id of this class : 2107758155 begin to crop the class : pet_opaque_cont param for this class : {'min_score': 0.7} filtre for class : pet_opaque_cont hashtag_id of this class : 2107758156 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! we have finished the crop for the class : pet_opaque_cont delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 127 /-3679027596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3679027663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 386 time used for this insertion : 0.04280710220336914 save_final save missing photos in datou_result : time spend for datou_step_exec : 33.743587017059326 time spend to save output : 0.04540514945983887 total time spend for step 4 : 33.788992166519165 step5:thcl Tue Feb 18 14:57:20 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 Beginning of datou step Thcl ! we are using the classfication for only one thcl 3233 time to import caffe and check if the image exist : 0.014388084411621094 time to convert the images to numpy array : 0.09766101837158203 time to import caffe and check if the image exist : 0.02356553077697754 time to convert the images to numpy array : 0.09318995475769043 time to import caffe and check if the image exist : 0.01501154899597168 time to convert the images to numpy array : 0.10567259788513184 time to import caffe and check if the image exist : 0.017919540405273438 time to convert the images to numpy array : 0.11600017547607422 time to import caffe and check if the image exist : 0.018314599990844727 time to convert the images to numpy array : 0.11969113349914551 time to import caffe and check if the image exist : 0.018071413040161133 time to convert the images to numpy array : 0.12349200248718262 time to import caffe and check if the image exist : 0.019020795822143555 time to convert the images to numpy array : 0.12445569038391113 time to import caffe and check if the image exist : 0.02410578727722168 time to convert the images to numpy array : 0.11821889877319336 time to import caffe and check if the image exist : 0.025370121002197266 time to convert the images to numpy array : 0.11817383766174316 time to import caffe and check if the image exist : 0.02214503288269043 time to convert the images to numpy array : 0.15152335166931152 total time to convert the images to numpy array : 0.6695351600646973 list photo_ids error: [] list photo_ids correct : [-3679027671, -3679027673, -3679027667, -3679027685, -3679027666, -3679027696, -3679027586, -3679027612, -3679027599, -3679027663, -3679027676, -3679027681, -3679027662, -3679027721, -3679027711, -3679027720, -3679027712, -3679027719, -3679027587, -3679027590, -3679027637, -3679027687, -3679027675, -3679027589, -3679027564, -3679027580, -3679027593, -3679027588, -3679027614, -3679027605, -3679027609, -3679027604, -3679027655, -3679027628, -3679027664, -3679027688, -3679027595, -3679027603, -3679027615, -3679027616, -3679027621, -3679027620, -3679027622, -3679027654, -3679027631, -3679027643, -3679027679, -3679027724, -3679027722, -3679027647, -3679027645, -3679027653, -3679027641, -3679027638, -3679027626, -3679027644, -3679027670, -3679027669, -3679027684, -3679027683, -3679027701, -3679027691, -3679027652, -3679027648, -3679027634, -3679027629, -3679027646, -3679027650, -3679027651, -3679027640, -3679027635, -3679027677, -3679027680, -3679027665, -3679027686, -3679027706, -3679027690, -3679027710, -3679027582, -3679027568, -3679027592, -3679027572, -3679027563, -3679027610, -3679027624, -3679027618, -3679027611, -3679027649, -3679027661, -3679027668, -3679027708, -3679027718, -3679027705, -3679027695, -3679027716, -3679027715, -3679027692, -3679027700, -3679027723, -3679027703, -3679027577, -3679027713, -3679027709, -3679027704, -3679027562, -3679027591, -3679027578, -3679027573, -3679027613, -3679027617, -3679027606, -3679027623, -3679027642, -3679027639, -3679027596, -3679027574, -3679027569, -3679027585, -3679027584, -3679027602, -3679027619, -3679027636, -3679027682, -3679027674, -3679027672, -3679027707, -3679027583] number of photos to traite : 127 try to delete the photos incorrect in DB tagging for thcl : 3233 To do loadFromThcl(), then load ParamDescType : thcl3233 thcls : [{'id': 3233, 'mtr_user_id': 31, 'name': 'learn_generique_01122021_6000_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'barquette_opaque,carton,ela,environnement,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_fonce,pet_opaque,textiles_sanitaires', 'svm_portfolios_learning': '4825680,4829009,4825557,4824709,4824714,4824733,4825676,4824737,4824753,4829002,4824848,4824863,4825667,4825677', 'photo_hashtag_type': 4151, 'photo_desc_type': 5557, 'type_classification': 'caffe', 'hashtag_id_list': '2107760128,492774966,492741797,493012381,492636447,2107756122,493202403,492628673,492668766,628944319,2107755846,2107755900,2107759152,2107760129'}] thcl {'id': 3233, 'mtr_user_id': 31, 'name': 'learn_generique_01122021_6000_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'barquette_opaque,carton,ela,environnement,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_fonce,pet_opaque,textiles_sanitaires', 'svm_portfolios_learning': '4825680,4829009,4825557,4824709,4824714,4824733,4825676,4824737,4824753,4829002,4824848,4824863,4825667,4825677', 'photo_hashtag_type': 4151, 'photo_desc_type': 5557, 'type_classification': 'caffe', 'hashtag_id_list': '2107760128,492774966,492741797,493012381,492636447,2107756122,493202403,492628673,492668766,628944319,2107755846,2107755900,2107759152,2107760129'} Update svm_hashtag_type_desc : 5557 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5557, 'learn_generique_01122021_6000_v2', 2048, 2048, 'learn_generique_01122021_6000_v2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 1, 19, 59, 17), datetime.datetime(2021, 12, 1, 19, 59, 17)) To loadFromThcl() : net_5557 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10372 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5557, 'learn_generique_01122021_6000_v2', 2048, 2048, 'learn_generique_01122021_6000_v2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 1, 19, 59, 17), datetime.datetime(2021, 12, 1, 19, 59, 17)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_generique_01122021_6000_v2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_generique_01122021_6000_v2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_generique_01122021_6000_v2 /data/models_weight/learn_generique_01122021_6000_v2/caffemodel size_local : 94383085 size in s3 : 94383085 create time local : 2021-12-09 17:03:46 create time in s3 : 2021-12-01 18:45:54 caffemodel already exist and didn't need to update /data/models_weight/learn_generique_01122021_6000_v2/deploy.prototxt size_local : 32544 size in s3 : 32544 create time local : 2021-12-09 17:03:46 create time in s3 : 2021-12-01 18:45:53 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_generique_01122021_6000_v2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-12-09 17:03:47 create time in s3 : 2021-12-01 18:59:02 mean.npy already exist and didn't need to update /data/models_weight/learn_generique_01122021_6000_v2/synset_words.txt size_local : 454 size in s3 : 454 create time local : 2021-12-09 17:03:47 create time in s3 : 2021-12-01 18:59:15 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /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/:/home/admin/workarea/git/apy/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_generique_01122021_6000_v2/deploy.prototxt caffemodel_filename : /data/models_weight/learn_generique_01122021_6000_v2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10153 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 1.3569862842559814 time used to do the prediction : 0.8472957611083984 save descriptor for thcl : 3233 time to traite the descriptors : 0.8341631889343262 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 3 Missing photo l117 : -3679027671 Missing photo l117 : -3679027673 Missing photo l117 : -3679027667 Missing photo l117 : -3679027685 Missing photo l117 : -3679027666 Missing photo l117 : -3679027696 Missing photo l117 : -3679027586 Missing photo l117 : -3679027612 Missing photo l117 : -3679027599 Missing photo l117 : -3679027663 Missing photo l117 : -3679027676 Missing photo l117 : -3679027681 Missing photo l117 : -3679027662 Missing photo l117 : -3679027721 Missing photo l117 : -3679027711 Missing photo l117 : -3679027720 Missing photo l117 : -3679027712 Missing photo l117 : -3679027719 Missing photo l117 : -3679027587 Missing photo l117 : -3679027590 Missing photo l117 : -3679027637 Missing photo l117 : -3679027687 Missing photo l117 : -3679027675 Missing photo l117 : -3679027589 Missing photo l117 : -3679027564 Missing photo l117 : -3679027580 Missing photo l117 : -3679027593 Missing photo l117 : -3679027588 Missing photo l117 : -3679027614 Missing photo l117 : -3679027605 Missing photo l117 : -3679027609 Missing photo l117 : -3679027604 Missing photo l117 : -3679027655 Missing photo l117 : -3679027628 Missing photo l117 : -3679027664 Missing photo l117 : -3679027688 Missing photo l117 : -3679027595 Missing photo l117 : -3679027603 Missing photo l117 : -3679027615 Missing photo l117 : -3679027616 Missing photo l117 : -3679027621 Missing photo l117 : -3679027620 Missing photo l117 : -3679027622 Missing photo l117 : -3679027654 Missing photo l117 : -3679027631 Missing photo l117 : -3679027643 Missing photo l117 : -3679027679 Missing photo l117 : -3679027724 Missing photo l117 : -3679027722 Missing photo l117 : -3679027647 Missing photo l117 : -3679027645 Missing photo l117 : -3679027653 Missing photo l117 : -3679027641 Missing photo l117 : -3679027638 Missing photo l117 : -3679027626 Missing photo l117 : -3679027644 Missing photo l117 : -3679027670 Missing photo l117 : -3679027669 Missing photo l117 : -3679027684 Missing photo l117 : -3679027683 Missing photo l117 : -3679027701 Missing photo l117 : -3679027691 Missing photo l117 : -3679027652 Missing photo l117 : -3679027648 Missing photo l117 : -3679027634 Missing photo l117 : -3679027629 Missing photo l117 : -3679027646 Missing photo l117 : -3679027650 Missing photo l117 : -3679027651 Missing photo l117 : -3679027640 Missing photo l117 : -3679027635 Missing photo l117 : -3679027677 Missing photo l117 : -3679027680 Missing photo l117 : -3679027665 Missing photo l117 : -3679027686 Missing photo l117 : -3679027706 Missing photo l117 : -3679027690 Missing photo l117 : -3679027710 Missing photo l117 : -3679027582 Missing photo l117 : -3679027568 Missing photo l117 : -3679027592 Missing photo l117 : -3679027572 Missing photo l117 : -3679027563 Missing photo l117 : -3679027610 Missing photo l117 : -3679027624 Missing photo l117 : -3679027618 Missing photo l117 : -3679027611 Missing photo l117 : -3679027649 Missing photo l117 : -3679027661 Missing photo l117 : -3679027668 Missing photo l117 : -3679027708 Missing photo l117 : -3679027718 Missing photo l117 : -3679027705 Missing photo l117 : -3679027695 Missing photo l117 : -3679027716 Missing photo l117 : -3679027715 Missing photo l117 : -3679027692 Missing photo l117 : -3679027700 Missing photo l117 : -3679027723 Missing photo l117 : -3679027703 Missing photo l117 : -3679027577 Missing photo l117 : -3679027713 Missing photo l117 : -3679027709 Missing photo l117 : -3679027704 Missing photo l117 : -3679027562 Missing photo l117 : -3679027591 Missing photo l117 : -3679027578 Missing photo l117 : -3679027573 Missing photo l117 : -3679027613 Missing photo l117 : -3679027617 Missing photo l117 : -3679027606 Missing photo l117 : -3679027623 Missing photo l117 : -3679027642 Missing photo l117 : -3679027639 Missing photo l117 : -3679027596 Missing photo l117 : -3679027574 Missing photo l117 : -3679027569 Missing photo l117 : -3679027585 Missing photo l117 : -3679027584 Missing photo l117 : -3679027602 Missing photo l117 : -3679027619 Missing photo l117 : -3679027636 Missing photo l117 : -3679027682 Missing photo l117 : -3679027674 Missing photo l117 : -3679027672 Missing photo l117 : -3679027707 Missing photo l117 : -3679027583 To insert : -3679027671 Missing photo l134 : -3679027671 To insert : -3679027673 Missing photo l134 : -3679027673 To insert : -3679027667 Missing photo l134 : -3679027667 To insert : -3679027685 Missing photo l134 : -3679027685 To insert : -3679027666 Missing photo l134 : -3679027666 To insert : -3679027696 Missing photo l134 : -3679027696 To insert : -3679027586 Missing photo l134 : -3679027586 To insert : -3679027612 Missing photo l134 : -3679027612 To insert : -3679027599 Missing photo l134 : -3679027599 To insert : -3679027663 Missing photo l134 : -3679027663 To insert : -3679027676 Missing photo l134 : -3679027676 To insert : -3679027681 Missing photo l134 : -3679027681 To insert : -3679027662 Missing photo l134 : -3679027662 To insert : -3679027721 Missing photo l134 : -3679027721 To insert : -3679027711 Missing photo l134 : -3679027711 To insert : -3679027720 Missing photo l134 : -3679027720 To insert : -3679027712 Missing photo l134 : -3679027712 To insert : -3679027719 Missing photo l134 : -3679027719 To insert : -3679027587 Missing photo l134 : -3679027587 To insert : -3679027590 Missing photo l134 : -3679027590 To insert : -3679027637 Missing photo l134 : -3679027637 To insert : -3679027687 Missing photo l134 : -3679027687 To insert : -3679027675 Missing photo l134 : -3679027675 To insert : -3679027589 Missing photo l134 : -3679027589 To insert : -3679027564 Missing photo l134 : -3679027564 To insert : -3679027580 Missing photo l134 : -3679027580 To insert : -3679027593 Missing photo l134 : -3679027593 To insert : -3679027588 Missing photo l134 : -3679027588 To insert : -3679027614 Missing photo l134 : -3679027614 To insert : -3679027605 Missing photo l134 : -3679027605 To insert : -3679027609 Missing photo l134 : -3679027609 To insert : -3679027604 Missing photo l134 : -3679027604 To insert : -3679027655 Missing photo l134 : -3679027655 To insert : -3679027628 Missing photo l134 : -3679027628 To insert : -3679027664 Missing photo l134 : -3679027664 To insert : -3679027688 Missing photo l134 : -3679027688 To insert : -3679027595 Missing photo l134 : -3679027595 To insert : -3679027603 Missing photo l134 : -3679027603 To insert : -3679027615 Missing photo l134 : -3679027615 To insert : -3679027616 Missing photo l134 : -3679027616 To insert : -3679027621 Missing photo l134 : -3679027621 To insert : -3679027620 Missing photo l134 : -3679027620 To insert : -3679027622 Missing photo l134 : -3679027622 To insert : -3679027654 Missing photo l134 : -3679027654 To insert : -3679027631 Missing photo l134 : -3679027631 To insert : -3679027643 Missing photo l134 : -3679027643 To insert : -3679027679 Missing photo l134 : -3679027679 To insert : -3679027724 Missing photo l134 : -3679027724 To insert : -3679027722 Missing photo l134 : -3679027722 To insert : -3679027647 Missing photo l134 : -3679027647 To insert : -3679027645 Missing photo l134 : -3679027645 To insert : -3679027653 Missing photo l134 : -3679027653 To insert : -3679027641 Missing photo l134 : -3679027641 To insert : -3679027638 Missing photo l134 : -3679027638 To insert : -3679027626 Missing photo l134 : -3679027626 To insert : -3679027644 Missing photo l134 : -3679027644 To insert : -3679027670 Missing photo l134 : -3679027670 To insert : -3679027669 Missing photo l134 : -3679027669 To insert : -3679027684 Missing photo l134 : -3679027684 To insert : -3679027683 Missing photo l134 : -3679027683 To insert : -3679027701 Missing photo l134 : -3679027701 To insert : -3679027691 Missing photo l134 : -3679027691 To insert : -3679027652 Missing photo l134 : -3679027652 To insert : -3679027648 Missing photo l134 : -3679027648 To insert : -3679027634 Missing photo l134 : -3679027634 To insert : -3679027629 Missing photo l134 : -3679027629 To insert : -3679027646 Missing photo l134 : -3679027646 To insert : -3679027650 Missing photo l134 : -3679027650 To insert : -3679027651 Missing photo l134 : -3679027651 To insert : -3679027640 Missing photo l134 : -3679027640 To insert : -3679027635 Missing photo l134 : -3679027635 To insert : -3679027677 Missing photo l134 : -3679027677 To insert : -3679027680 Missing photo l134 : -3679027680 To insert : -3679027665 Missing photo l134 : -3679027665 To insert : -3679027686 Missing photo l134 : -3679027686 To insert : -3679027706 Missing photo l134 : -3679027706 To insert : -3679027690 Missing photo l134 : -3679027690 To insert : -3679027710 Missing photo l134 : -3679027710 To insert : -3679027582 Missing photo l134 : -3679027582 To insert : -3679027568 Missing photo l134 : -3679027568 To insert : -3679027592 Missing photo l134 : -3679027592 To insert : -3679027572 Missing photo l134 : -3679027572 To insert : -3679027563 Missing photo l134 : -3679027563 To insert : -3679027610 Missing photo l134 : -3679027610 To insert : -3679027624 Missing photo l134 : -3679027624 To insert : -3679027618 Missing photo l134 : -3679027618 To insert : -3679027611 Missing photo l134 : -3679027611 To insert : -3679027649 Missing photo l134 : -3679027649 To insert : -3679027661 Missing photo l134 : -3679027661 To insert : -3679027668 Missing photo l134 : -3679027668 To insert : -3679027708 Missing photo l134 : -3679027708 To insert : -3679027718 Missing photo l134 : -3679027718 To insert : -3679027705 Missing photo l134 : -3679027705 To insert : -3679027695 Missing photo l134 : -3679027695 To insert : -3679027716 Missing photo l134 : -3679027716 To insert : -3679027715 Missing photo l134 : -3679027715 To insert : -3679027692 Missing photo l134 : -3679027692 To insert : -3679027700 Missing photo l134 : -3679027700 To insert : -3679027723 Missing photo l134 : -3679027723 To insert : -3679027703 Missing photo l134 : -3679027703 To insert : -3679027577 Missing photo l134 : -3679027577 To insert : -3679027713 Missing photo l134 : -3679027713 To insert : -3679027709 Missing photo l134 : -3679027709 To insert : -3679027704 Missing photo l134 : -3679027704 To insert : -3679027562 Missing photo l134 : -3679027562 To insert : -3679027591 Missing photo l134 : -3679027591 To insert : -3679027578 Missing photo l134 : -3679027578 To insert : -3679027573 Missing photo l134 : -3679027573 To insert : -3679027613 Missing photo l134 : -3679027613 To insert : -3679027617 Missing photo l134 : -3679027617 To insert : -3679027606 Missing photo l134 : -3679027606 To insert : -3679027623 Missing photo l134 : -3679027623 To insert : -3679027642 Missing photo l134 : -3679027642 To insert : -3679027639 Missing photo l134 : -3679027639 To insert : -3679027596 Missing photo l134 : -3679027596 To insert : -3679027574 Missing photo l134 : -3679027574 To insert : -3679027569 Missing photo l134 : -3679027569 To insert : -3679027585 Missing photo l134 : -3679027585 To insert : -3679027584 Missing photo l134 : -3679027584 To insert : -3679027602 Missing photo l134 : -3679027602 To insert : -3679027619 Missing photo l134 : -3679027619 To insert : -3679027636 Missing photo l134 : -3679027636 To insert : -3679027682 Missing photo l134 : -3679027682 To insert : -3679027674 Missing photo l134 : -3679027674 To insert : -3679027672 Missing photo l134 : -3679027672 To insert : -3679027707 Missing photo l134 : -3679027707 To insert : -3679027583 Missing photo l134 : -3679027583 time to insert the descriptors : 28.386930227279663 Inside saveOutput : final : False verbose : 0 time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 3.814697265625e-06 save missing photos in datou_result : time spend for datou_step_exec : 35.94995450973511 time spend to save output : 0.058045387268066406 total time spend for step 5 : 36.007999897003174 step6:merge_mask_thcl_custom Tue Feb 18 14:57:56 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 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step merge_mask_thcl_custom batch 1 Loaded 165 chid ids of type : 3760 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++As expected we have just one thcl present End of step merge_mask_thcl_custom Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : merge_mask_thcl_custom we use saveGeneral [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 5 /1065568816Didn't retrieve data .Didn't retrieve data . /1065568708Didn't retrieve data .Didn't retrieve data . /1065568705Didn't retrieve data .Didn't retrieve data . /1065568698Didn't retrieve data .Didn't retrieve data . /1065568694Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.013851404190063477 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.052744388580322266 time spend to save output : 0.01421499252319336 total time spend for step 6 : 0.06695938110351562 step7:rle_unique_nms_with_priority Tue Feb 18 14:57:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms nb_obj : 25 nb_hashtags : 7 time to prepare the origin masks : 23.691189765930176 time for calcul the mask position with numpy : 0.7284693717956543 nb_pixel_total : 7029536 time to create 1 rle with new method : 1.2779412269592285 time for calcul the mask position with numpy : 0.0332334041595459 nb_pixel_total : 40489 time to create 1 rle with old method : 0.045813798904418945 time for calcul the mask position with numpy : 0.03813481330871582 nb_pixel_total : 21065 time to create 1 rle with old method : 0.026141643524169922 time for calcul the mask position with numpy : 0.028308629989624023 nb_pixel_total : 39832 time to create 1 rle with old method : 0.05189824104309082 time for calcul the mask position with numpy : 0.04709911346435547 nb_pixel_total : 20249 time to create 1 rle with old method : 0.026924610137939453 time for calcul the mask position with numpy : 0.05211353302001953 nb_pixel_total : 39988 time to create 1 rle with old method : 0.06042790412902832 time for calcul the mask position with numpy : 0.08256745338439941 nb_pixel_total : 40731 time to create 1 rle with old method : 0.051697731018066406 time for calcul the mask position with numpy : 0.04635882377624512 nb_pixel_total : 8249 time to create 1 rle with old method : 0.010362863540649414 time for calcul the mask position with numpy : 0.0422060489654541 nb_pixel_total : 10555 time to create 1 rle with old method : 0.013298511505126953 time for calcul the mask position with numpy : 0.04271697998046875 nb_pixel_total : 36113 time to create 1 rle with old method : 0.04364776611328125 time for calcul the mask position with numpy : 0.04422402381896973 nb_pixel_total : 75183 time to create 1 rle with old method : 0.09286069869995117 time for calcul the mask position with numpy : 0.04210090637207031 nb_pixel_total : 37380 time to create 1 rle with old method : 0.048583030700683594 time for calcul the mask position with numpy : 0.04021286964416504 nb_pixel_total : 60390 time to create 1 rle with old method : 0.07072782516479492 time for calcul the mask position with numpy : 0.04116344451904297 nb_pixel_total : 56304 time to create 1 rle with old method : 0.07307171821594238 time for calcul the mask position with numpy : 0.049852609634399414 nb_pixel_total : 163378 time to create 1 rle with new method : 1.0901424884796143 time for calcul the mask position with numpy : 0.07079696655273438 nb_pixel_total : 72802 time to create 1 rle with old method : 0.1086416244506836 time for calcul the mask position with numpy : 0.05119061470031738 nb_pixel_total : 36333 time to create 1 rle with old method : 0.045023441314697266 time for calcul the mask position with numpy : 0.046630144119262695 nb_pixel_total : 20883 time to create 1 rle with old method : 0.02324223518371582 time for calcul the mask position with numpy : 0.05063509941101074 nb_pixel_total : 105623 time to create 1 rle with old method : 0.12143635749816895 time for calcul the mask position with numpy : 0.04984712600708008 nb_pixel_total : 27120 time to create 1 rle with old method : 0.0324549674987793 time for calcul the mask position with numpy : 0.047265052795410156 nb_pixel_total : 4464 time to create 1 rle with old method : 0.0052776336669921875 time for calcul the mask position with numpy : 0.04686164855957031 nb_pixel_total : 17842 time to create 1 rle with old method : 0.02739572525024414 time for calcul the mask position with numpy : 0.05757284164428711 nb_pixel_total : 74614 time to create 1 rle with old method : 0.0883023738861084 time for calcul the mask position with numpy : 0.041728973388671875 nb_pixel_total : 10941 time to create 1 rle with old method : 0.012467145919799805 time for calcul the mask position with numpy : 0.042121171951293945 nb_pixel_total : 24949 time to create 1 rle with old method : 0.027441024780273438 time for calcul the mask position with numpy : 0.045848846435546875 nb_pixel_total : 6907 time to create 1 rle with old method : 0.007794380187988281 create new chi : 5.481809854507446 time to delete rle : 0.06264042854309082 batch 1 Loaded 26 chid ids of type : 4211 Number RLEs to save : 15222 TO DO : save crop sub photo not yet done ! save time : 2.7649707794189453 nb_obj : 22 nb_hashtags : 6 time to prepare the origin masks : 23.107974767684937 time for calcul the mask position with numpy : 0.6709554195404053 nb_pixel_total : 7189109 time to create 1 rle with new method : 1.1046969890594482 time for calcul the mask position with numpy : 0.040709495544433594 nb_pixel_total : 33894 time to create 1 rle with old method : 0.038626909255981445 time for calcul the mask position with numpy : 0.038089752197265625 nb_pixel_total : 3917 time to create 1 rle with old method : 0.004763364791870117 time for calcul the mask position with numpy : 0.02884507179260254 nb_pixel_total : 64078 time to create 1 rle with old method : 0.06943130493164062 time for calcul the mask position with numpy : 0.026240825653076172 nb_pixel_total : 20923 time to create 1 rle with old method : 0.02218317985534668 time for calcul the mask position with numpy : 0.02666950225830078 nb_pixel_total : 21417 time to create 1 rle with old method : 0.022347211837768555 time for calcul the mask position with numpy : 0.026826858520507812 nb_pixel_total : 34685 time to create 1 rle with old method : 0.03664898872375488 time for calcul the mask position with numpy : 0.0303647518157959 nb_pixel_total : 40794 time to create 1 rle with old method : 0.04468178749084473 time for calcul the mask position with numpy : 0.02762889862060547 nb_pixel_total : 27261 time to create 1 rle with old method : 0.02953028678894043 time for calcul the mask position with numpy : 0.03689885139465332 nb_pixel_total : 46987 time to create 1 rle with old method : 0.0680379867553711 time for calcul the mask position with numpy : 0.02743673324584961 nb_pixel_total : 91699 time to create 1 rle with old method : 0.10550427436828613 time for calcul the mask position with numpy : 0.02578878402709961 nb_pixel_total : 88270 time to create 1 rle with old method : 0.10510420799255371 time for calcul the mask position with numpy : 0.037122488021850586 nb_pixel_total : 17799 time to create 1 rle with old method : 0.020208120346069336 time for calcul the mask position with numpy : 0.040781259536743164 nb_pixel_total : 104912 time to create 1 rle with old method : 0.12818527221679688 time for calcul the mask position with numpy : 0.0385587215423584 nb_pixel_total : 29735 time to create 1 rle with old method : 0.03338289260864258 time for calcul the mask position with numpy : 0.04234957695007324 nb_pixel_total : 37980 time to create 1 rle with old method : 0.060610294342041016 time for calcul the mask position with numpy : 0.04460906982421875 nb_pixel_total : 13441 time to create 1 rle with old method : 0.016260385513305664 time for calcul the mask position with numpy : 0.03010272979736328 nb_pixel_total : 39220 time to create 1 rle with old method : 0.04422569274902344 time for calcul the mask position with numpy : 0.03310585021972656 nb_pixel_total : 43535 time to create 1 rle with old method : 0.06217694282531738 time for calcul the mask position with numpy : 0.03730273246765137 nb_pixel_total : 17129 time to create 1 rle with old method : 0.019812583923339844 time for calcul the mask position with numpy : 0.03427410125732422 nb_pixel_total : 12604 time to create 1 rle with old method : 0.014759063720703125 time for calcul the mask position with numpy : 0.04236292839050293 nb_pixel_total : 23203 time to create 1 rle with old method : 0.027377843856811523 time for calcul the mask position with numpy : 0.03866434097290039 nb_pixel_total : 79328 time to create 1 rle with old method : 0.08944272994995117 create new chi : 3.6378252506256104 time to delete rle : 0.0015156269073486328 batch 1 Loaded 23 chid ids of type : 4211 Number RLEs to save : 15464 TO DO : save crop sub photo not yet done ! save time : 0.8669495582580566 nb_obj : 26 nb_hashtags : 9 time to prepare the origin masks : 13.891708135604858 time for calcul the mask position with numpy : 0.9434144496917725 nb_pixel_total : 6994046 time to create 1 rle with new method : 1.487563133239746 time for calcul the mask position with numpy : 0.03768420219421387 nb_pixel_total : 25787 time to create 1 rle with old method : 0.03166675567626953 time for calcul the mask position with numpy : 0.033710479736328125 nb_pixel_total : 26061 time to create 1 rle with old method : 0.028675079345703125 time for calcul the mask position with numpy : 0.03283262252807617 nb_pixel_total : 32410 time to create 1 rle with old method : 0.03520321846008301 time for calcul the mask position with numpy : 0.03319144248962402 nb_pixel_total : 31668 time to create 1 rle with old method : 0.03561758995056152 time for calcul the mask position with numpy : 0.03383183479309082 nb_pixel_total : 11840 time to create 1 rle with old method : 0.013230323791503906 time for calcul the mask position with numpy : 0.036841392517089844 nb_pixel_total : 9196 time to create 1 rle with old method : 0.010924100875854492 time for calcul the mask position with numpy : 0.03840279579162598 nb_pixel_total : 24911 time to create 1 rle with old method : 0.039383649826049805 time for calcul the mask position with numpy : 0.0346074104309082 nb_pixel_total : 5502 time to create 1 rle with old method : 0.006099224090576172 time for calcul the mask position with numpy : 0.03333330154418945 nb_pixel_total : 33474 time to create 1 rle with old method : 0.04037642478942871 time for calcul the mask position with numpy : 0.033432722091674805 nb_pixel_total : 21832 time to create 1 rle with old method : 0.024387836456298828 time for calcul the mask position with numpy : 0.03318619728088379 nb_pixel_total : 10577 time to create 1 rle with old method : 0.01220703125 time for calcul the mask position with numpy : 0.03310394287109375 nb_pixel_total : 39140 time to create 1 rle with old method : 0.04387474060058594 time for calcul the mask position with numpy : 0.03349709510803223 nb_pixel_total : 28685 time to create 1 rle with old method : 0.03149747848510742 time for calcul the mask position with numpy : 0.03319716453552246 nb_pixel_total : 98686 time to create 1 rle with old method : 0.10401701927185059 time for calcul the mask position with numpy : 0.03358125686645508 nb_pixel_total : 50032 time to create 1 rle with old method : 0.06067657470703125 time for calcul the mask position with numpy : 0.03357982635498047 nb_pixel_total : 35932 time to create 1 rle with old method : 0.03998422622680664 time for calcul the mask position with numpy : 0.032628774642944336 nb_pixel_total : 50552 time to create 1 rle with old method : 0.056505680084228516 time for calcul the mask position with numpy : 0.03390336036682129 nb_pixel_total : 28218 time to create 1 rle with old method : 0.04467320442199707 time for calcul the mask position with numpy : 0.038847923278808594 nb_pixel_total : 135598 time to create 1 rle with old method : 0.1537015438079834 time for calcul the mask position with numpy : 0.033823251724243164 nb_pixel_total : 135155 time to create 1 rle with old method : 0.1480858325958252 time for calcul the mask position with numpy : 0.03308844566345215 nb_pixel_total : 132185 time to create 1 rle with old method : 0.14238834381103516 time for calcul the mask position with numpy : 0.03237748146057129 nb_pixel_total : 35588 time to create 1 rle with old method : 0.03822135925292969 time for calcul the mask position with numpy : 0.033423662185668945 nb_pixel_total : 22476 time to create 1 rle with old method : 0.026299476623535156 time for calcul the mask position with numpy : 0.03401899337768555 nb_pixel_total : 20616 time to create 1 rle with old method : 0.023515701293945312 time for calcul the mask position with numpy : 0.03315019607543945 nb_pixel_total : 30014 time to create 1 rle with old method : 0.03313946723937988 time for calcul the mask position with numpy : 0.032540321350097656 nb_pixel_total : 11739 time to create 1 rle with old method : 0.012632369995117188 create new chi : 4.595020294189453 time to delete rle : 0.0015017986297607422 batch 1 Loaded 27 chid ids of type : 4211 Number RLEs to save : 15674 TO DO : save crop sub photo not yet done ! save time : 0.8813285827636719 nb_obj : 27 nb_hashtags : 6 time to prepare the origin masks : 17.871417760849 time for calcul the mask position with numpy : 0.6086428165435791 nb_pixel_total : 7021549 time to create 1 rle with new method : 1.031177043914795 time for calcul the mask position with numpy : 0.03242230415344238 nb_pixel_total : 29636 time to create 1 rle with old method : 0.03394603729248047 time for calcul the mask position with numpy : 0.03228306770324707 nb_pixel_total : 14449 time to create 1 rle with old method : 0.01599431037902832 time for calcul the mask position with numpy : 0.030542373657226562 nb_pixel_total : 20757 time to create 1 rle with old method : 0.021785259246826172 time for calcul the mask position with numpy : 0.031157493591308594 nb_pixel_total : 24300 time to create 1 rle with old method : 0.02596306800842285 time for calcul the mask position with numpy : 0.031336069107055664 nb_pixel_total : 9837 time to create 1 rle with old method : 0.010915756225585938 time for calcul the mask position with numpy : 0.0342562198638916 nb_pixel_total : 49023 time to create 1 rle with old method : 0.06586050987243652 time for calcul the mask position with numpy : 0.03854084014892578 nb_pixel_total : 30823 time to create 1 rle with old method : 0.03298020362854004 time for calcul the mask position with numpy : 0.035138845443725586 nb_pixel_total : 46878 time to create 1 rle with old method : 0.05405235290527344 time for calcul the mask position with numpy : 0.03216123580932617 nb_pixel_total : 14492 time to create 1 rle with old method : 0.01590752601623535 time for calcul the mask position with numpy : 0.03493547439575195 nb_pixel_total : 95849 time to create 1 rle with old method : 0.11839532852172852 time for calcul the mask position with numpy : 0.03196597099304199 nb_pixel_total : 11007 time to create 1 rle with old method : 0.013186216354370117 time for calcul the mask position with numpy : 0.03157949447631836 nb_pixel_total : 14497 time to create 1 rle with old method : 0.015420913696289062 time for calcul the mask position with numpy : 0.032694101333618164 nb_pixel_total : 41881 time to create 1 rle with old method : 0.046382904052734375 time for calcul the mask position with numpy : 0.03332185745239258 nb_pixel_total : 33728 time to create 1 rle with old method : 0.04072761535644531 time for calcul the mask position with numpy : 0.0326390266418457 nb_pixel_total : 59637 time to create 1 rle with old method : 0.06517195701599121 time for calcul the mask position with numpy : 0.03354191780090332 nb_pixel_total : 38663 time to create 1 rle with old method : 0.044983863830566406 time for calcul the mask position with numpy : 0.03250408172607422 nb_pixel_total : 55984 time to create 1 rle with old method : 0.06042885780334473 time for calcul the mask position with numpy : 0.03176617622375488 nb_pixel_total : 44868 time to create 1 rle with old method : 0.04927849769592285 time for calcul the mask position with numpy : 0.0324554443359375 nb_pixel_total : 97482 time to create 1 rle with old method : 0.11117887496948242 time for calcul the mask position with numpy : 0.032427072525024414 nb_pixel_total : 83758 time to create 1 rle with old method : 0.08866739273071289 time for calcul the mask position with numpy : 0.03181648254394531 nb_pixel_total : 877 time to create 1 rle with old method : 0.0010993480682373047 time for calcul the mask position with numpy : 0.03189349174499512 nb_pixel_total : 45416 time to create 1 rle with old method : 0.04876828193664551 time for calcul the mask position with numpy : 0.03202533721923828 nb_pixel_total : 12769 time to create 1 rle with old method : 0.013932466506958008 time for calcul the mask position with numpy : 0.03145003318786621 nb_pixel_total : 45766 time to create 1 rle with old method : 0.04898858070373535 time for calcul the mask position with numpy : 0.03148078918457031 nb_pixel_total : 25769 time to create 1 rle with old method : 0.02713608741760254 time for calcul the mask position with numpy : 0.03166675567626953 nb_pixel_total : 82263 time to create 1 rle with old method : 0.08677911758422852 time for calcul the mask position with numpy : 0.031208276748657227 nb_pixel_total : 29962 time to create 1 rle with old method : 0.03109288215637207 create new chi : 3.7470383644104004 time to delete rle : 0.0014073848724365234 batch 1 Loaded 28 chid ids of type : 4211 Number RLEs to save : 16940 TO DO : save crop sub photo not yet done ! save time : 0.9486212730407715 nb_obj : 27 nb_hashtags : 8 time to prepare the origin masks : 15.893187999725342 time for calcul the mask position with numpy : 0.6049151420593262 nb_pixel_total : 6892690 time to create 1 rle with new method : 1.150575876235962 time for calcul the mask position with numpy : 0.03289318084716797 nb_pixel_total : 35268 time to create 1 rle with old method : 0.03798723220825195 time for calcul the mask position with numpy : 0.03210926055908203 nb_pixel_total : 55181 time to create 1 rle with old method : 0.06016826629638672 time for calcul the mask position with numpy : 0.03203415870666504 nb_pixel_total : 13714 time to create 1 rle with old method : 0.015455484390258789 time for calcul the mask position with numpy : 0.03160452842712402 nb_pixel_total : 1742 time to create 1 rle with old method : 0.002297639846801758 time for calcul the mask position with numpy : 0.03194713592529297 nb_pixel_total : 48896 time to create 1 rle with old method : 0.052513837814331055 time for calcul the mask position with numpy : 0.031470298767089844 nb_pixel_total : 8929 time to create 1 rle with old method : 0.010098934173583984 time for calcul the mask position with numpy : 0.03160572052001953 nb_pixel_total : 23412 time to create 1 rle with old method : 0.02625894546508789 time for calcul the mask position with numpy : 0.03209662437438965 nb_pixel_total : 26933 time to create 1 rle with old method : 0.02900218963623047 time for calcul the mask position with numpy : 0.03195762634277344 nb_pixel_total : 28730 time to create 1 rle with old method : 0.030863523483276367 time for calcul the mask position with numpy : 0.0321345329284668 nb_pixel_total : 136436 time to create 1 rle with old method : 0.14547109603881836 time for calcul the mask position with numpy : 0.032010555267333984 nb_pixel_total : 4057 time to create 1 rle with old method : 0.0047032833099365234 time for calcul the mask position with numpy : 0.03185558319091797 nb_pixel_total : 39557 time to create 1 rle with old method : 0.04387474060058594 time for calcul the mask position with numpy : 0.03235745429992676 nb_pixel_total : 67465 time to create 1 rle with old method : 0.0713808536529541 time for calcul the mask position with numpy : 0.031861305236816406 nb_pixel_total : 55897 time to create 1 rle with old method : 0.06022787094116211 time for calcul the mask position with numpy : 0.03216123580932617 nb_pixel_total : 56441 time to create 1 rle with old method : 0.06026744842529297 time for calcul the mask position with numpy : 0.031951189041137695 nb_pixel_total : 14552 time to create 1 rle with old method : 0.015975236892700195 time for calcul the mask position with numpy : 0.03194141387939453 nb_pixel_total : 29788 time to create 1 rle with old method : 0.03241372108459473 time for calcul the mask position with numpy : 0.03212094306945801 nb_pixel_total : 16090 time to create 1 rle with old method : 0.017844438552856445 time for calcul the mask position with numpy : 0.032160282135009766 nb_pixel_total : 47248 time to create 1 rle with old method : 0.05007314682006836 time for calcul the mask position with numpy : 0.031629323959350586 nb_pixel_total : 27645 time to create 1 rle with old method : 0.029921293258666992 time for calcul the mask position with numpy : 0.032291412353515625 nb_pixel_total : 41155 time to create 1 rle with old method : 0.04390120506286621 time for calcul the mask position with numpy : 0.03336596488952637 nb_pixel_total : 130069 time to create 1 rle with old method : 0.13806676864624023 time for calcul the mask position with numpy : 0.03198838233947754 nb_pixel_total : 21577 time to create 1 rle with old method : 0.024282455444335938 time for calcul the mask position with numpy : 0.03247213363647461 nb_pixel_total : 120970 time to create 1 rle with old method : 0.12752556800842285 time for calcul the mask position with numpy : 0.032384395599365234 nb_pixel_total : 38403 time to create 1 rle with old method : 0.04248762130737305 time for calcul the mask position with numpy : 0.03288078308105469 nb_pixel_total : 73135 time to create 1 rle with old method : 0.07931947708129883 time for calcul the mask position with numpy : 0.031900644302368164 nb_pixel_total : 25940 time to create 1 rle with old method : 0.027904272079467773 create new chi : 3.943016767501831 time to delete rle : 0.0016179084777832031 batch 1 Loaded 28 chid ids of type : 4211 Number RLEs to save : 17550 TO DO : save crop sub photo not yet done ! save time : 2.1294703483581543 map_output_result : {1065568816: (0.0, 'Should be the crop_list due to order', 0.0), 1065568708: (0.0, 'Should be the crop_list due to order', 0.0), 1065568705: (0.0, 'Should be the crop_list due to order', 0.0), 1065568698: (0.0, 'Should be the crop_list due to order', 0.0), 1065568694: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 5 /1065568816.Didn't retrieve data . /1065568708.Didn't retrieve data . /1065568705.Didn't retrieve data . /1065568698.Didn't retrieve data . /1065568694.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 ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.012316226959228516 save_final save missing photos in datou_result : time spend for datou_step_exec : 124.17990326881409 time spend to save output : 0.012756109237670898 total time spend for step 7 : 124.19265937805176 step8:crop_condition Tue Feb 18 15:00:01 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 some photos are not treated, begin crop_condition Loading chi in step crop with photo_hashtag_type : 4211 Loading chi in step crop for list_pids : 5 ! batch 1 Loaded 132 chid ids of type : 4211 begin to crop the class : barquette_opaque param for this class : {'min_score': 0.5} filtre for class : barquette_opaque hashtag_id of this class : 2107760128 Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 4869462 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887204_4027910 we have uploaded 4 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.3707470893859863 we have finished the crop for the class : barquette_opaque begin to crop the class : carton param for this class : {'min_score': 0.5} filtre for class : carton hashtag_id of this class : 492774966 Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 4869462 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887207_4027910 we have uploaded 3 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.6327145099639893 we have finished the crop for the class : carton begin to crop the class : ela param for this class : {'min_score': 0.5} filtre for class : ela hashtag_id of this class : 492741797 Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 4869462 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887210_4027910 we have uploaded 5 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.8175246715545654 we have finished the crop for the class : ela begin to crop the class : environnement param for this class : {'min_score': 0.5} filtre for class : environnement hashtag_id of this class : 493012381 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 : 4869462 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887213_4027910 we have uploaded 1 photos in the portfolio 4869462 time of upload the photos Elapsed time : 0.7694888114929199 we have finished the crop for the class : environnement begin to crop the class : etiquette param for this class : {'min_score': 0.5} filtre for class : etiquette hashtag_id of this class : 492636447 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 20 About to insert : list_path_to_insert length 20 new photo from crops ! About to upload 20 photos upload in portfolio : 4869462 init cache_photo without model_param we have 20 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887217_4027910 we have uploaded 20 photos in the portfolio 4869462 time of upload the photos Elapsed time : 5.917372703552246 we have finished the crop for the class : etiquette begin to crop the class : film_plastique param for this class : {'min_score': 0.5} filtre for class : film_plastique hashtag_id of this class : 2107756122 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! 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 : 4869462 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887229_4027910 we have uploaded 9 photos in the portfolio 4869462 time of upload the photos Elapsed time : 2.828835964202881 we have finished the crop for the class : film_plastique begin to crop the class : kraft param for this class : {'min_score': 0.5} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : metal param for this class : {'min_score': 0.5} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : pehd param for this class : {'min_score': 0.5} filtre for class : pehd hashtag_id of this class : 628944319 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 : 4869462 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887233_4027910 we have uploaded 1 photos in the portfolio 4869462 time of upload the photos Elapsed time : 0.7071046829223633 we have finished the crop for the class : pehd begin to crop the class : pet_clair param for this class : {'min_score': 0.5} filtre for class : pet_clair hashtag_id of this class : 2107755846 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 : 4869462 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887235_4027910 we have uploaded 1 photos in the portfolio 4869462 time of upload the photos Elapsed time : 0.5855753421783447 we have finished the crop for the class : pet_clair begin to crop the class : pet_opaque param for this class : {'min_score': 0.5} filtre for class : pet_opaque hashtag_id of this class : 2107759152 Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 4869462 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887238_4027910 we have uploaded 3 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.02768874168396 we have finished the crop for the class : pet_opaque begin to crop the class : textiles_sanitaires param for this class : {'min_score': 0.5} filtre for class : textiles_sanitaires hashtag_id of this class : 2107760129 begin to crop the class : pet_fonce param for this class : {'min_score': 0.5} filtre for class : pet_fonce hashtag_id of this class : 2107755900 begin to crop the class : papier param for this class : {'min_score': 0.5} filtre for class : papier hashtag_id of this class : 492668766 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 40 About to insert : list_path_to_insert length 40 new photo from crops ! About to upload 40 photos upload in portfolio : 4869462 init cache_photo without model_param we have 40 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739887248_4027910 we have uploaded 40 photos in the portfolio 4869462 time of upload the photos Elapsed time : 12.535522222518921 we have finished the crop for the class : papier delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 87 /1338345609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338345771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 266 time used for this insertion : 0.03487038612365723 save_final save missing photos in datou_result : time spend for datou_step_exec : 60.513166189193726 time spend to save output : 0.03701043128967285 total time spend for step 8 : 60.5501766204834 step9:ventilate_hashtags_in_portfolio Tue Feb 18 15:01:01 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 : 5486001 get user id for portfolio 5486001 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`=5486001 AND mptpi.`type`=4211 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','carton','flou','pet_clair','environnement','film_plastique','ela','metal','kraft','pet_opaque','autre','mal_croppe','textiles_sanitaires','barquette_opaque','pet_fonce','etiquette','papier')) 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`=5486001 AND mptpi.`type`=4211 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','carton','flou','pet_clair','environnement','film_plastique','ela','metal','kraft','pet_opaque','autre','mal_croppe','textiles_sanitaires','barquette_opaque','pet_fonce','etiquette','papier')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5486001 AND mptpi.`type`=4211 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pehd','carton','flou','pet_clair','environnement','film_plastique','ela','metal','kraft','pet_opaque','autre','mal_croppe','textiles_sanitaires','barquette_opaque','pet_fonce','etiquette','papier')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20639600,20639601,20639602,20639603,20639604,20639605,20639606,20639607,20639609,20639610,20639611,20639612,20639613,20639614,20639615,20639616,20639617?tags=pehd,carton,flou,pet_clair,environnement,film_plastique,ela,metal,kraft,pet_opaque,autre,mal_croppe,textiles_sanitaires,barquette_opaque,pet_fonce,etiquette,papier Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 1 /5486001. 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 ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012660503387451172 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.37146258354187 time spend to save output : 0.012865781784057617 total time spend for step 9 : 2.3843283653259277 step10:final Tue Feb 18 15:01:04 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 : {1065568816: ('0.04043459097389473',), 1065568708: ('0.04043459097389473',), 1065568705: ('0.04043459097389473',), 1065568698: ('0.04043459097389473',), 1065568694: ('0.04043459097389473',)} new output for save of step final : {1065568816: ('0.04043459097389473',), 1065568708: ('0.04043459097389473',), 1065568705: ('0.04043459097389473',), 1065568698: ('0.04043459097389473',), 1065568694: ('0.04043459097389473',)} [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 5 /1065568816.Didn't retrieve data . /1065568708.Didn't retrieve data . /1065568705.Didn't retrieve data . /1065568698.Didn't retrieve data . /1065568694.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 ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.012195110321044922 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10165596008300781 time spend to save output : 0.012510538101196289 total time spend for step 10 : 0.1141664981842041 step11:velours_tree Tue Feb 18 15:01:04 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 : 0.05422401428222656 time spend to save output : 4.291534423828125e-05 total time spend for step 11 : 0.054266929626464844 step12:send_mail_cod Tue Feb 18 15:01:04 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin in order to get the selector url, please entre the license of selector results_Auto_P5486001_18-02-2025_15_01_04.pdf 20639601 change filename to text .change filename to text .change filename to text .imagette206396011739887264 20639602 imagette206396021739887264 20639603 change filename to text .imagette206396031739887264 20639605 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 .imagette206396051739887264 20639606 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette206396061739887265 20639607 imagette206396071739887265 20639609 imagette206396091739887265 20639610 change filename to text .change filename to text .change filename to text .imagette206396101739887265 20639611 imagette206396111739887265 20639612 imagette206396121739887265 20639613 imagette206396131739887265 20639614 change filename to text .change filename to text .change filename to text .change filename to text .imagette206396141739887265 20639615 imagette206396151739887266 20639616 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 .imagette206396161739887266 20639617 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 .imagette206396171739887267 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=5486001 and hashtag_type = 4211 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20639600,20639601,20639602,20639603,20639604,20639605,20639606,20639607,20639609,20639610,20639611,20639612,20639613,20639614,20639615,20639616,20639617?tags=pehd,carton,flou,pet_clair,environnement,film_plastique,ela,metal,kraft,pet_opaque,autre,mal_croppe,textiles_sanitaires,barquette_opaque,pet_fonce,etiquette,papier your option no_mail is active, we will not send the real mail to your client args[1065568816] : ((1065568816, -0.8402069540962303, 492688767), (1065568816, -0.5797699732161256, 501862349), '0.04043459097389473') apple ((1065568816, -0.8402069540962303, 492688767), (1065568816, -0.5797699732161256, 501862349), '0.04043459097389473') We are sending mail with results at report@fotonower.com args[1065568708] : ((1065568708, -4.070521924534813, 492609224), (1065568708, -0.15785097409432802, 496442774), '0.04043459097389473') apple ((1065568708, -4.070521924534813, 492609224), (1065568708, -0.15785097409432802, 496442774), '0.04043459097389473') We are sending mail with results at report@fotonower.com args[1065568705] : ((1065568705, -2.9011072176121386, 492609224), (1065568705, -0.06353982647497829, 2107752395), '0.04043459097389473') apple ((1065568705, -2.9011072176121386, 492609224), (1065568705, -0.06353982647497829, 2107752395), '0.04043459097389473') We are sending mail with results at report@fotonower.com args[1065568698] : ((1065568698, -1.706155447051669, 492688767), (1065568698, 0.008376777865801007, 2107752395), '0.04043459097389473') apple ((1065568698, -1.706155447051669, 492688767), (1065568698, 0.008376777865801007, 2107752395), '0.04043459097389473') We are sending mail with results at report@fotonower.com args[1065568694] : ((1065568694, -2.374330514925937, 492609224), (1065568694, -0.31270551135448865, 496442774), '0.04043459097389473') apple ((1065568694, -2.374330514925937, 492609224), (1065568694, -0.31270551135448865, 496442774), '0.04043459097389473') We are sending mail with results at report@fotonower.com refus_total : 0.04043459097389473 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=5486001 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_P5486001_18-02-2025_15_01_04.pdf results_Auto_P5486001_18-02-2025_15_01_04.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5486001_18-02-2025_15_01_04.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3995','5486001','results_Auto_P5486001_18-02-2025_15_01_04.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5486001_18-02-2025_15_01_04.pdf','pdf','','0.59','0.04043459097389473') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] 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 ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 5 time used for this insertion : 0.011906147003173828 save_final save missing photos in datou_result : time spend for datou_step_exec : 6.018709421157837 time spend to save output : 0.012144088745117188 total time spend for step 12 : 6.030853509902954 step13:split_time_score Tue Feb 18 15:01:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('19', 5),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 25112021 5486001 Nombre de photos uploadées : 5 / 23040 (0%) 25112021 5486001 Nombre de photos taguées (types de déchets): 0 / 5 (0%) 25112021 5486001 Nombre de photos taguées (volume) : 0 / 5 (0%) elapsed_time : load_data_split_time_score 4.76837158203125e-06 elapsed_time : order_list_meta_photo_and_scores 8.106231689453125e-06 ????? elapsed_time : fill_and_build_computed_from_old_data 0.0004341602325439453 elapsed_time : insert_dashboard_record_day_entry 0.026366472244262695 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5181046_02-02-2022_12_15_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5181046 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5181067_01-02-2022_17_14_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5181067 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5485994 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5485999_19-02-2022_20_45_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5485999 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5181107_01-02-2022_18_24_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5181107 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5181110_01-02-2022_04_17_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5181110 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5486000 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5181144_01-02-2022_05_00_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5181144 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5181167_31-01-2022_21_17_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5181167 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5486004_19-02-2022_08_14_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5486004 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P5486005_19-02-2022_19_34_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 5486005 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'25112021': {'nb_upload': 5, '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 [1065568816, 1065568708, 1065568705, 1065568698, 1065568694] Looping around the photos to save general results len do output : 1 /5486001Didn'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 ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568816', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568708', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568705', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568698', None, None, None, None, None, '2596594') ('3995', None, None, None, None, None, None, None, '2596594') ('3995', '5486001', '1065568694', None, None, None, None, None, '2596594') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.013660669326782227 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.477810144424438 time spend to save output : 0.013908624649047852 total time spend for step 13 : 8.491718769073486 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 5 set_done_treatment 185.31user 137.31system 6:33.89elapsed 81%CPU (0avgtext+0avgdata 6471436maxresident)k 2269760inputs+109952outputs (22286major+19484803minor)pagefaults 0swaps