python /home/admin/mtr/script_for_cron.py -j python_test3 -m 12 -a ' --short_python3 -v ' -s python_test3 -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data list_job_run_as_list : ['mask_detection', 'datou', 'CacheModelData_queries', 'CachePhotoData_queries', 'test_fork', 'prepare_maskdata', 'portfolio_queries', 'sla_mensuel'] python version used : 3 liste_fichiers : [('tests/mask_test', True, 'Test mask-detection ', 'mask_detection'), ('tests/datou_test', True, 'Datou All Test', 'datou', 'all'), ('mtr/database_queries/CacheModelData_queries', True, 'Test Cache Model Data', 'CacheModelData_queries'), ('tests/cache_photo_data_test', True, 'Test local_cache_photo ', 'CachePhotoData_queries'), ('mtr/mask_rcnn/prepare_maskdata', True, 'test prepare mask data', 'prepare_maskdata', 'all'), ('mtr/database_queries/portfolio_queries', True, 'test portfolio queries', 'portfolio_queries'), ('prod/memo/memo', True, 'SLA Mensuel', 'sla_mensuel', 'all')] #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10593 run mask_detect Inside batchDatouExec : verbose : False # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1540360450744629 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 : False number of steps : 1 step1:mask_detect Thu Apr 10 21:35:27 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 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-04-10 21:35:30.404121: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-10 21:35:30.435233: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-10 21:35:30.436796: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2380000blocal folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 173274 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5304 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 10428 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0005254745483398438 nb_pixel_total : 15552 time to create 1 rle with old method : 0.018932819366455078 length of segment : 256 time for calcul the mask position with numpy : 0.003077983856201172 nb_pixel_total : 145327 time to create 1 rle with old method : 0.16963744163513184 length of segment : 371 time for calcul the mask position with numpy : 0.00038695335388183594 nb_pixel_total : 14254 time to create 1 rle with old method : 0.01961970329284668 length of segment : 151 time for calcul the mask position with numpy : 0.00024771690368652344 nb_pixel_total : 5614 time to create 1 rle with old method : 0.01206064224243164 length of segment : 48 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 1824 time to create 1 rle with old method : 0.002307415008544922 length of segment : 39 time spent for convertir_results : 1.0259137153625488 time spend for datou_step_exec : 18.741201877593994 time spend to save output : 5.054473876953125e-05 total time spend for step 1 : 18.741252422332764 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3329 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 1.423107624053955 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.99548763, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 106), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), 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23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,109,76,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,226,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923804, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 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(474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1744313727_172844_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9400 ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2263031005859375 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 : False number of steps : 1 step1:mask_detect Thu Apr 10 21:35:56 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 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 : 5304 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-10 21:35:59.192492: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-10 21:35:59.219155: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-10 21:35:59.221449: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f237c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-10 21:35:59.221516: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-10 21:35:59.225695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-10 21:35:59.383025: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x206ecf70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-10 21:35:59.383096: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-10 21:35:59.384440: 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 2025-04-10 21:35:59.384926: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:35:59.392166: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:35:59.395660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:35:59.396341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:35:59.400698: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:35:59.402267: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:35:59.410973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:35:59.412701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:35:59.412868: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:35:59.413767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-10 21:35:59.413789: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-10 21:35:59.413803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-10 21:35:59.415306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4843 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. 2025-04-10 21:35:59.513282: 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 2025-04-10 21:35:59.513438: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:35:59.513465: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:35:59.513490: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:35:59.513513: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:35:59.513537: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:35:59.513559: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:35:59.513582: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:35:59.514821: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:35:59.516017: 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 2025-04-10 21:35:59.516059: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:35:59.516080: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:35:59.516099: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:35:59.516117: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:35:59.516136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:35:59.516155: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:35:59.516173: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:35:59.517340: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:35:59.517380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-10 21:35:59.517391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-10 21:35:59.517400: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-10 21:35:59.518634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4843 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 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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_coco_origin NUM_CLASSES 81 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_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-10 21:36:07.809246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:36:08.083269: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 174995 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 15 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 5304 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0006291866302490234 nb_pixel_total : 16902 time to create 1 rle with old method : 0.018521547317504883 length of segment : 107 time for calcul the mask position with numpy : 0.020443439483642578 nb_pixel_total : 480765 time to create 1 rle with new method : 0.05839872360229492 length of segment : 632 time for calcul the mask position with numpy : 0.0005707740783691406 nb_pixel_total : 36641 time to create 1 rle with old method : 0.04245138168334961 length of segment : 133 time for calcul the mask position with numpy : 0.0001747608184814453 nb_pixel_total : 4794 time to create 1 rle with old method : 0.0060176849365234375 length of segment : 51 time spent for convertir_results : 0.3290133476257324 time spend for datou_step_exec : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 412 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.018922805786132812 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.99883777, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.9977508, [(710, 22, 23), (924, 22, 49), (608, 23, 147), (892, 23, 105), (598, 24, 237), (846, 24, 162), (589, 25, 428), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (477, 56, 607), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (233, 148, 856), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (183, 157, 906), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (148, 175, 940), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), (107, 210, 979), (106, 211, 980), (105, 212, 981), (104, 213, 982), (103, 214, 983), (102, 215, 984), (101, 216, 985), (101, 217, 984), (100, 218, 985), (100, 219, 985), (99, 220, 986), (98, 221, 987), (98, 222, 987), (97, 223, 988), (97, 224, 987), (96, 225, 988), (96, 226, 988), (95, 227, 989), (95, 228, 989), (94, 229, 990), (94, 230, 990), (94, 231, 990), (93, 232, 990), (93, 233, 990), (92, 234, 991), (92, 235, 991), (92, 236, 991), (91, 237, 992), (91, 238, 991), (91, 239, 991), (91, 240, 991), (91, 241, 990), (90, 242, 991), (90, 243, 990), (90, 244, 990), (90, 245, 989), (90, 246, 989), (89, 247, 990), (89, 248, 989), (89, 249, 989), (89, 250, 988), (89, 251, 988), (88, 252, 988), (88, 253, 988), (88, 254, 987), (88, 255, 986), (88, 256, 986), (87, 257, 986), (87, 258, 986), (87, 259, 985), (87, 260, 984), (87, 261, 983), (86, 262, 983), (86, 263, 983), (86, 264, 982), (86, 265, 981), (85, 266, 981), (85, 267, 980), (85, 268, 980), (84, 269, 980), (84, 270, 979), (84, 271, 979), (84, 272, 978), (83, 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['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1744313756_172844_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']} ############################### TEST POLYGON ################################ Inside batchDatouExec : verbose : False # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.19737720489501953 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 : False number of steps : 1 step1:mask_detect Thu Apr 10 21:36:18 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 : 5304 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-10 21:36:20.963789: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-10 21:36:20.991289: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-10 21:36:20.993435: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f237c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-10 21:36:20.993467: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-10 21:36:20.997624: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-10 21:36:21.146837: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x20870eb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-10 21:36:21.146888: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-10 21:36:21.148180: 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 2025-04-10 21:36:21.148609: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:36:21.152282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:36:21.155343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:36:21.155885: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:36:21.158063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:36:21.159105: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:36:21.164878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:36:21.166074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:36:21.166162: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:36:21.166772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-10 21:36:21.166788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-10 21:36:21.166797: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-10 21:36:21.167811: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4843 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. 2025-04-10 21:36:21.244725: 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 2025-04-10 21:36:21.244849: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:36:21.244869: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:36:21.244885: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:36:21.244901: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:36:21.244916: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:36:21.244931: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:36:21.244947: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:36:21.245859: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:36:21.246865: 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 2025-04-10 21:36:21.246946: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:36:21.246967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:36:21.246983: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:36:21.246999: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:36:21.247015: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:36:21.247031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:36:21.247047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:36:21.248012: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:36:21.248047: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-10 21:36:21.248056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-10 21:36:21.248064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-10 21:36:21.249279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4843 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 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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_coco_origin NUM_CLASSES 81 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_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-10 21:36:31.654591: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:36:31.841576: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 176328 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5304 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 10593 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.3912386894226074 nb_pixel_total : 3697055 time to create 1 rle with new method : 0.4572272300720215 length of segment : 2044 time spent for convertir_results : 1.8964998722076416 time spend for datou_step_exec : 21.50011706352234 time spend to save output : 4.792213439941406e-05 total time spend for step 1 : 21.50016498565674 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 721 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.017590999603271484 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 0, 2279, 103, 2222, 0.9819166, [(1250, 110, 27), (652, 111, 289), (1203, 111, 134), (614, 112, 376), (1082, 112, 345), (525, 113, 909), (518, 114, 922), (511, 115, 936), (505, 116, 948), (499, 117, 960), (492, 118, 972), (487, 119, 983), (481, 120, 994), (476, 121, 1004), (470, 122, 1015), (465, 123, 1025), (460, 124, 1034), (456, 125, 1042), (451, 126, 1052), (447, 127, 1061), (442, 128, 1076), (438, 129, 1089), (434, 130, 1102), (430, 131, 1115), (427, 132, 1126), (423, 133, 1139), (420, 134, 1150), (416, 135, 1161), (413, 136, 1172), (409, 137, 1181), (406, 138, 1187), (403, 139, 1193), (400, 140, 1200), (397, 141, 1206), (394, 142, 1212), (391, 143, 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(827, 2114, 337), (830, 2115, 332), (833, 2116, 327), (836, 2117, 321), (839, 2118, 315), (842, 2119, 310), (845, 2120, 304), (848, 2121, 298), (851, 2122, 293), (855, 2123, 286), (859, 2124, 279), (863, 2125, 272), (867, 2126, 265), (871, 2127, 258), (876, 2128, 250), (880, 2129, 243), (884, 2130, 236), (889, 2131, 228), (893, 2132, 221), (898, 2133, 213), (903, 2134, 204), (907, 2135, 197), (912, 2136, 188), (917, 2137, 177), (921, 2138, 164), (926, 2139, 150), (931, 2140, 136), (937, 2141, 120), (955, 2142, 93), (973, 2143, 66), (992, 2144, 37), (1012, 2145, 8)], ['937,2141,851,2122,768,2090,665,2063,557,2021,367,1982,205,1964,122,1970,95,1928,51,1806,51,1725,40,1658,40,1437,29,1276,30,910,26,739,19,648,29,525,47,444,99,291,120,269,245,191,525,113,652,111,1426,112,1589,137,1747,171,1832,180,1910,204,2017,290,2059,369,2114,443,2154,597,2166,665,2152,822,2127,898,2119,974,2093,1050,2046,1108,1939,1350,1885,1428,1848,1654,1772,1887,1726,1963,1668,2011,1585,2014,1500,2050,1429,2054,1263,2079,1099,2136'])], 'temp/1744313777_172844_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3677650 proportion of common points : 0.996033632198944 [('test release memory', 'SUCCESS', True), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : True #&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_80aab10e0219982fef52d1eae72d1bddac86cb8e SQL :INSERT INTO MTRAdmin.monitor_sys (name, type, server, version_code, result_str, result_bool, lien , test_group ,test_name) VALUES ('python_test3','1','marlene','refs/heads/master_80aab10e0219982fef52d1eae72d1bddac86cb8e','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/April/10042025/python_test3//data_2/data_log/job/2025/April/10042025/python_test3/log-python3----short_python3--v--marlene-21:35:01.txt','mask_detection','unknown'); #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4573 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4573 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4573 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : sam list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1189321094) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1189321094 download finish for photo 1189321094 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.29563164710998535 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:sam Thu Apr 10 21:36:48 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313808_172844_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1744313808_172844_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}} map_subphoto_mainphoto : {} Beginning of datou step sam ! pht : 4677 Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0021948814392089844 nb_pixel_total : 3781 time to create 1 rle with old method : 0.006367921829223633 time for calcul the mask position with numpy : 0.001649618148803711 nb_pixel_total : 6647 time to create 1 rle with old method : 0.011142253875732422 time for calcul the mask position with numpy : 0.0016090869903564453 nb_pixel_total : 3926 time to create 1 rle with old method : 0.006587028503417969 time for calcul the mask position with numpy : 0.0016896724700927734 nb_pixel_total : 13947 time to create 1 rle with old method : 0.024908781051635742 time for calcul the mask position with numpy : 0.001504659652709961 nb_pixel_total : 5615 time to create 1 rle with old method : 0.0066986083984375 time for calcul the mask position with numpy : 0.0015223026275634766 nb_pixel_total : 10830 time to create 1 rle with old method : 0.014061689376831055 time for calcul the mask position with numpy : 0.0019032955169677734 nb_pixel_total : 83615 time to create 1 rle with old method : 0.09393715858459473 time for calcul the mask position with numpy : 0.0015387535095214844 nb_pixel_total : 5498 time to create 1 rle with old method : 0.00643467903137207 time for calcul the mask position with numpy : 0.001466989517211914 nb_pixel_total : 5650 time to create 1 rle with old method : 0.0069522857666015625 time for calcul the mask position with numpy : 0.00159454345703125 nb_pixel_total : 2780 time to create 1 rle with old method : 0.0037271976470947266 time for calcul the mask position with numpy : 0.001558065414428711 nb_pixel_total : 2949 time to create 1 rle with old method : 0.00397801399230957 time for calcul the mask position with numpy : 0.0015130043029785156 nb_pixel_total : 2079 time to create 1 rle with old method : 0.002858877182006836 time for calcul the mask position with numpy : 0.00157928466796875 nb_pixel_total : 4372 time to create 1 rle with old method : 0.005818605422973633 time for calcul the mask position with numpy : 0.001741170883178711 nb_pixel_total : 29426 time to create 1 rle with old method : 0.03764796257019043 time for calcul the mask position with numpy : 0.0017435550689697266 nb_pixel_total : 9506 time to create 1 rle with old method : 0.012649774551391602 time for calcul the mask position with numpy : 0.0016052722930908203 nb_pixel_total : 1226 time to create 1 rle with old method : 0.001646280288696289 time for calcul the mask position with numpy : 0.0017497539520263672 nb_pixel_total : 39002 time to create 1 rle with old method : 0.049907684326171875 time for calcul the mask position with numpy : 0.0016522407531738281 nb_pixel_total : 9878 time to create 1 rle with old method : 0.01227426528930664 time for calcul the mask position with numpy : 0.0015461444854736328 nb_pixel_total : 2452 time to create 1 rle with old method : 0.003103494644165039 time for calcul the mask position with numpy : 0.0015404224395751953 nb_pixel_total : 13090 time to create 1 rle with old method : 0.019437551498413086 time for calcul the mask position with numpy : 0.0015473365783691406 nb_pixel_total : 3097 time to create 1 rle with old method : 0.003894805908203125 time for calcul the mask position with numpy : 0.0017583370208740234 nb_pixel_total : 16201 time to create 1 rle with old method : 0.019307851791381836 time for calcul the mask position with numpy : 0.0015730857849121094 nb_pixel_total : 7494 time to create 1 rle with old method : 0.009542465209960938 time for calcul the mask position with numpy : 0.0018033981323242188 nb_pixel_total : 4305 time to create 1 rle with old method : 0.0062716007232666016 time for calcul the mask position with numpy : 0.0016884803771972656 nb_pixel_total : 16443 time to create 1 rle with old method : 0.020191431045532227 time for calcul the mask position with numpy : 0.0018858909606933594 nb_pixel_total : 27908 time to create 1 rle with old method : 0.03392148017883301 time for calcul the mask position with numpy : 0.0017480850219726562 nb_pixel_total : 2490 time to create 1 rle with old method : 0.0033721923828125 time for calcul the mask position with numpy : 0.0016705989837646484 nb_pixel_total : 8641 time to create 1 rle with old method : 0.010888814926147461 time for calcul the mask position with numpy : 0.0016758441925048828 nb_pixel_total : 12052 time to create 1 rle with old method : 0.015211820602416992 time for calcul the mask position with numpy : 0.0019681453704833984 nb_pixel_total : 3526 time to create 1 rle with old method : 0.005115985870361328 time for calcul the mask position with numpy : 0.0015804767608642578 nb_pixel_total : 3908 time to create 1 rle with old method : 0.005235195159912109 time for calcul the mask position with numpy : 0.0016481876373291016 nb_pixel_total : 2727 time to create 1 rle with old method : 0.003748655319213867 time for calcul the mask position with numpy : 0.0015943050384521484 nb_pixel_total : 13009 time to create 1 rle with old method : 0.017357826232910156 time for calcul the mask position with numpy : 0.001590728759765625 nb_pixel_total : 951 time to create 1 rle with old method : 0.0013546943664550781 time for calcul the mask position with numpy : 0.0015795230865478516 nb_pixel_total : 3330 time to create 1 rle with old method : 0.004212617874145508 time for calcul the mask position with numpy : 0.0014653205871582031 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0014221668243408203 time for calcul the mask position with numpy : 0.001920461654663086 nb_pixel_total : 14665 time to create 1 rle with old method : 0.01851201057434082 time for calcul the mask position with numpy : 0.0016450881958007812 nb_pixel_total : 12990 time to create 1 rle with old method : 0.016930580139160156 time for calcul the mask position with numpy : 0.0016605854034423828 nb_pixel_total : 1652 time to create 1 rle with old method : 0.002317190170288086 time for calcul the mask position with numpy : 0.0017313957214355469 nb_pixel_total : 343 time to create 1 rle with old method : 0.0005326271057128906 time for calcul the mask position with numpy : 0.0017309188842773438 nb_pixel_total : 4127 time to create 1 rle with old method : 0.005810976028442383 time for calcul the mask position with numpy : 0.0015988349914550781 nb_pixel_total : 1842 time to create 1 rle with old method : 0.0026679039001464844 time for calcul the mask position with numpy : 0.001661062240600586 nb_pixel_total : 1252 time to create 1 rle with old method : 0.0016236305236816406 time for calcul the mask position with numpy : 0.0016183853149414062 nb_pixel_total : 4179 time to create 1 rle with old method : 0.005832195281982422 time for calcul the mask position with numpy : 0.0018033981323242188 nb_pixel_total : 10572 time to create 1 rle with old method : 0.014158248901367188 time for calcul the mask position with numpy : 0.0016434192657470703 nb_pixel_total : 2344 time to create 1 rle with old method : 0.0032973289489746094 time for calcul the mask position with numpy : 0.0015876293182373047 nb_pixel_total : 874 time to create 1 rle with old method : 0.0012962818145751953 time for calcul the mask position with numpy : 0.0015003681182861328 nb_pixel_total : 595 time to create 1 rle with old method : 0.0008156299591064453 time for calcul the mask position with numpy : 0.0015246868133544922 nb_pixel_total : 2418 time to create 1 rle with old method : 0.0034630298614501953 time for calcul the mask position with numpy : 0.0016248226165771484 nb_pixel_total : 339 time to create 1 rle with old method : 0.0005431175231933594 time for calcul the mask position with numpy : 0.0016870498657226562 nb_pixel_total : 886 time to create 1 rle with old method : 0.0012445449829101562 time for calcul the mask position with numpy : 0.0015859603881835938 nb_pixel_total : 1209 time to create 1 rle with old method : 0.001787424087524414 time for calcul the mask position with numpy : 0.0017805099487304688 nb_pixel_total : 852 time to create 1 rle with old method : 0.0012354850769042969 time for calcul the mask position with numpy : 0.0018885135650634766 nb_pixel_total : 2529 time to create 1 rle with old method : 0.0033512115478515625 time for calcul the mask position with numpy : 0.0017123222351074219 nb_pixel_total : 2774 time to create 1 rle with old method : 0.003937244415283203 time for calcul the mask position with numpy : 0.0017628669738769531 nb_pixel_total : 8599 time to create 1 rle with old method : 0.011673450469970703 time for calcul the mask position with numpy : 0.0016880035400390625 nb_pixel_total : 577 time to create 1 rle with old method : 0.0008833408355712891 time for calcul the mask position with numpy : 0.0015604496002197266 nb_pixel_total : 693 time to create 1 rle with old method : 0.0010762214660644531 time for calcul the mask position with numpy : 0.0016660690307617188 nb_pixel_total : 1705 time to create 1 rle with old method : 0.0024268627166748047 time for calcul the mask position with numpy : 0.0016129016876220703 nb_pixel_total : 1057 time to create 1 rle with old method : 0.0015263557434082031 time for calcul the mask position with numpy : 0.0015728473663330078 nb_pixel_total : 1206 time to create 1 rle with old method : 0.0017807483673095703 time for calcul the mask position with numpy : 0.0017380714416503906 nb_pixel_total : 18559 time to create 1 rle with old method : 0.022005796432495117 time for calcul the mask position with numpy : 0.0016982555389404297 nb_pixel_total : 1656 time to create 1 rle with old method : 0.0021293163299560547 time for calcul the mask position with numpy : 0.0014312267303466797 nb_pixel_total : 1077 time to create 1 rle with old method : 0.0013511180877685547 time for calcul the mask position with numpy : 0.0015022754669189453 nb_pixel_total : 16641 time to create 1 rle with old method : 0.020707130432128906 time for calcul the mask position with numpy : 0.0014827251434326172 nb_pixel_total : 1746 time to create 1 rle with old method : 0.002103567123413086 time for calcul the mask position with numpy : 0.0014107227325439453 nb_pixel_total : 267 time to create 1 rle with old method : 0.0003497600555419922 time for calcul the mask position with numpy : 0.001428842544555664 nb_pixel_total : 884 time to create 1 rle with old method : 0.0011303424835205078 time for calcul the mask position with numpy : 0.001435995101928711 nb_pixel_total : 726 time to create 1 rle with old method : 0.0010526180267333984 time for calcul the mask position with numpy : 0.001470327377319336 nb_pixel_total : 9083 time to create 1 rle with old method : 0.010767459869384766 time for calcul the mask position with numpy : 0.0015635490417480469 nb_pixel_total : 8478 time to create 1 rle with old method : 0.009949922561645508 time for calcul the mask position with numpy : 0.0014791488647460938 nb_pixel_total : 1521 time to create 1 rle with old method : 0.001857757568359375 time for calcul the mask position with numpy : 0.0014202594757080078 nb_pixel_total : 1336 time to create 1 rle with old method : 0.0017094612121582031 time for calcul the mask position with numpy : 0.0014405250549316406 nb_pixel_total : 714 time to create 1 rle with old method : 0.0009551048278808594 time for calcul the mask position with numpy : 0.0015633106231689453 nb_pixel_total : 27850 time to create 1 rle with old method : 0.032828569412231445 time for calcul the mask position with numpy : 0.00148773193359375 nb_pixel_total : 968 time to create 1 rle with old method : 0.0011882781982421875 time for calcul the mask position with numpy : 0.0014739036560058594 nb_pixel_total : 3166 time to create 1 rle with old method : 0.003815174102783203 time for calcul the mask position with numpy : 0.001455545425415039 nb_pixel_total : 1634 time to create 1 rle with old method : 0.0020554065704345703 time for calcul the mask position with numpy : 0.0014519691467285156 nb_pixel_total : 618 time to create 1 rle with old method : 0.0008068084716796875 time for calcul the mask position with numpy : 0.0014653205871582031 nb_pixel_total : 583 time to create 1 rle with old method : 0.0007727146148681641 time for calcul the mask position with numpy : 0.0014197826385498047 nb_pixel_total : 246 time to create 1 rle with old method : 0.00036454200744628906 time for calcul the mask position with numpy : 0.0014271736145019531 nb_pixel_total : 973 time to create 1 rle with old method : 0.0012671947479248047 time for calcul the mask position with numpy : 0.001416921615600586 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003314018249511719 time for calcul the mask position with numpy : 0.0014600753784179688 nb_pixel_total : 1503 time to create 1 rle with old method : 0.0018773078918457031 time for calcul the mask position with numpy : 0.001482248306274414 nb_pixel_total : 5012 time to create 1 rle with old method : 0.006180763244628906 time for calcul the mask position with numpy : 0.001504659652709961 nb_pixel_total : 7485 time to create 1 rle with old method : 0.008813858032226562 time for calcul the mask position with numpy : 0.0014805793762207031 nb_pixel_total : 2375 time to create 1 rle with old method : 0.002942323684692383 time for calcul the mask position with numpy : 0.0014357566833496094 nb_pixel_total : 595 time to create 1 rle with old method : 0.0008008480072021484 time for calcul the mask position with numpy : 0.001422882080078125 nb_pixel_total : 1442 time to create 1 rle with old method : 0.00186920166015625 time for calcul the mask position with numpy : 0.0014629364013671875 nb_pixel_total : 1092 time to create 1 rle with old method : 0.0013310909271240234 time for calcul the mask position with numpy : 0.0014188289642333984 nb_pixel_total : 298 time to create 1 rle with old method : 0.000537872314453125 time for calcul the mask position with numpy : 0.0014255046844482422 nb_pixel_total : 1135 time to create 1 rle with old method : 0.0014047622680664062 time for calcul the mask position with numpy : 0.0014548301696777344 nb_pixel_total : 891 time to create 1 rle with old method : 0.0011298656463623047 time for calcul the mask position with numpy : 0.0014142990112304688 nb_pixel_total : 917 time to create 1 rle with old method : 0.0013275146484375 time for calcul the mask position with numpy : 0.0014219284057617188 nb_pixel_total : 2199 time to create 1 rle with old method : 0.0029485225677490234 time for calcul the mask position with numpy : 0.0014200210571289062 nb_pixel_total : 885 time to create 1 rle with old method : 0.0012288093566894531 time for calcul the mask position with numpy : 0.0014336109161376953 nb_pixel_total : 475 time to create 1 rle with old method : 0.0006747245788574219 time for calcul the mask position with numpy : 0.0014188289642333984 nb_pixel_total : 1614 time to create 1 rle with old method : 0.002168416976928711 time for calcul the mask position with numpy : 0.0014188289642333984 nb_pixel_total : 1203 time to create 1 rle with old method : 0.0016317367553710938 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 1438 time to create 1 rle with old method : 0.0020134449005126953 time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0016734600067138672 time for calcul the mask position with numpy : 0.001422882080078125 nb_pixel_total : 832 time to create 1 rle with old method : 0.001074075698852539 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 102 chid ids of type : 4677 Number RLEs to save : 9513 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3754890850', '201', '535', '9') ... last line : ('3754890951', '815', '44', '5') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] map_info['map_portfolio_photo'] : {} final : True mtd_id 4573 list_pids : [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn'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 ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4573', None, '1189321094', 'None', None, None, None, None, None)] time used for this insertion : 0.014861106872558594 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.16445779800415 time spend to save output : 0.015103340148925781 total time spend for step 1 : 11.179561138153076 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1744313808_172844_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 102 ############################### TEST frcnn ################################ test frcnn Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4184 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4184 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4184 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4184 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : frcnn list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917754606) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917754606 download finish for photo 917754606 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.2717163562774658 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:frcnn Thu Apr 10 21:37:00 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313819_172844_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1744313819_172844_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! classes : ['background', 'plaque'] pht : 4370 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'} To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1744313819_172844_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.081s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.063840814 (374.12695, 293.9193, 430.81012, 317.8086) proba : 0.052218758 (382.17978, 297.18927, 552.36035, 344.65845) proba : 0.012271096 (345.35696, 272.42987, 468.85757, 320.7243) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 3.408022403717041 time spend to save output : 0.0001354217529296875 total time spend for step 1 : 3.4081578254699707 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True Inside saveFrcnn : final : True verbose : True threshold to save the result : 0.1 output flattener : [(0, 493029425, 4370, 374, 430, 293, 317, 0.063840814, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052218758, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271096, None)] Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)] time used for this insertion : 0.1334824562072754 [917754606] map_info['map_portfolio_photo'] : {} final : True mtd_id 4184 list_pids : [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', None, None, None, None, None, None)] time used for this insertion : 0.013636112213134766 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.063840814, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052218758, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271096, None)], 'temp/1744313819_172844_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=2 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 2 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=2 # 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) 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 : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (916235064) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 916235064 download finish for photo 916235064 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1645984649658203 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Thu Apr 10 21:37:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313823_172844_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1744313823_172844_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'355': 1} we are using the classfication for only one thcl 355 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (66, 66, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0058138370513916016 time to convert the images to numpy array : 0.0008726119995117188 total time to convert the images to numpy array : 0.006919145584106445 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (355) thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_50629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'506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6496 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) param : , param.caffemodel : car_360_1027 None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 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 : car_360_1027 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_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 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/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/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 : 6496 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.01440882682800293 time used to do the prediction : 0.06452417373657227 save descriptor for thcl : 355 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.05676555633544922 Testing : ['916235064'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (916235064) Catched exception ! Connect or reconnect ! result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}} list_photo_exists : [916235064] storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.7477240562438965 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True time used to find the portfolios of the photos select button_legend_list from MTRDatou.classification_theme where id = 355 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018815813, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011635749, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008157251, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011772892, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025849699, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.004169841, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034811702, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.0073665828, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021887252, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.0005797988, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.0045909495, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.003158411, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022509433, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.005320067, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0011000768, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.005402325, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.0039188736, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014788156, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019778463, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012442505, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015049975, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021695162, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.002708038, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.0047044186, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019419239, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.00095859036, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007700586, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019469152, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013726067, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016203304, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013924917, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.0100451065, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010793937, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.00084475137, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012522249, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025843873, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.001243126, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028651215, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012950449, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010338323, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018767349, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.001134387, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015757671, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.0039805598, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.002874299, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023354138, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012106727, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447067, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022338994, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017465837, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.004698964, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021914376, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022632203, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018174154, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.00090571336, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.002040207, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025335343, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.004488828, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034250668, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031356092, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010257678, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015354942, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.0005915371, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040292046, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011586723, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012157946, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.01496631, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011321916, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.0105451355, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011986169, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041860533, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023635954, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016322877, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015569031, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013960549, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021979536, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.008827126, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014614492, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008824846, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024714097, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010092801, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015962116, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015331553, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014916783, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014585776, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.005645543, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.00111662, 332, '355'), 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('916235064', 'viano_1027_gao__port_506211', 0.0026944713, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.0008319836, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037379842, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.00079196814, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030524135, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.00372322, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017740102, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.001530565, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004087181, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.001186351, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026951388, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011406856, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012529417, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.001514943, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031655391, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017349474, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.01672631, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018255333, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.002145864, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014152803, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020553728, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022963595, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013944036, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011967827, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.001054794, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020715236, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017870136, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057225926, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.002766456, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017145586, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.004825479, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018687354, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012581412, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012625853, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.0009245192, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.0074660894, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.000729113, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010386788, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.00069120474, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021416578, 332, '355')]]} begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 6.4373016357421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 6.724902629852295 time spend to save output : 1.6997308731079102 total time spend for step 1 : 8.424633502960205 step2:argmax Thu Apr 10 21:37:12 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313823_172844_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1744313823_172844_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 355 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.01771159, 332, '355'), 'temp/1744313823_172844_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('916235064', '2049863950', '332') ... last line : ('916235064', '2049863950', '332') time used for this insertion : 0.011729001998901367 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.016720294952392578 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.01771159', None)] time used for this insertion : 0.01613140106201172 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 1.5020370483398438e-05 save missing photos in datou_result : time spend for datou_step_exec : 0.00033092498779296875 time spend to save output : 0.044916629791259766 total time spend for step 2 : 0.045247554779052734 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.01771159, 332, '355'), 'temp/1744313823_172844_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4567 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4567 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4567 # 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) 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 : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171252784,1171252764,1171252487) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171252487 begin to download photo : 1171252764 begin to download photo : 1171252784 download finish for photo 1171252487 download finish for photo 1171252764 download finish for photo 1171252784 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.239912748336792 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Thu Apr 10 21:37:12 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784} map_photo_id_path_extension : {1171252487: {'path': 'temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3609': 1} we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-04-10 21:37:15.477599: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-10 21:37:15.478293: 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 2025-04-10 21:37:15.478368: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:37:15.478415: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:37:15.480465: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:37:15.480588: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:37:15.483528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:37:15.484538: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:37:15.489738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:37:15.490929: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:37:15.491288: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-10 21:37:15.523112: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-10 21:37:15.524964: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f20e4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-10 21:37:15.524993: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-10 21:37:15.528564: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2f45d530 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-10 21:37:15.528595: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-10 21:37:15.529555: 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 2025-04-10 21:37:15.529674: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:37:15.529700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-10 21:37:15.529793: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-10 21:37:15.529827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-10 21:37:15.529869: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-10 21:37:15.529914: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-10 21:37:15.529960: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-10 21:37:15.531266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-10 21:37:15.531330: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-10 21:37:15.531377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-10 21:37:15.531389: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-10 21:37:15.531402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-10 21:37:15.532725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 6496 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3609) thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5832) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 8.941192150115967 time used to load_weights : 0.16412854194641113 0it [00:00, ?it/s] 3it [00:00, 670.20it/s]2025-04-10 21:37:27.054172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.1965107917785645 ['temp/image000000000_1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'temp/image000000001_1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'temp/image000000002_1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 (3, 1280) Got the blobs of the net to insert : [0, 9, 0, 0, 0, 0, 1, 0, 0, 0] code_as_byte_string:b'0009000000'| Got the blobs of the net to insert : [0, 6, 0, 1, 0, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000100'| Got the blobs of the net to insert : [0, 6, 0, 0, 1, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000001'| time to traite the descriptors : 0.02495861053466797 Testing : ['1171252487', '1171252764', '1171252784'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171252487,1171252764,1171252784) result : {1171252487: {'photo_id': 1171252487, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5ebdd6b0a6bb39942a3808ed114806de.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_35_005998m0.jpg 0.4259977941513062 for time 6.000020980834961, id_amount 3 this amount prod time diff : 0.006000020980834961'}, 1171252764: {'photo_id': 1171252764, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/29d5179a892cc50aadc9d67245534b59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_41_005998m0.jpg 0.4319977941513062 for time 6.0, id_amount 3 this amount prod time diff : 0.006'}, 1171252784: {'photo_id': 1171252784, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_47_006033m0.jpg 0.4379978291988373 for time 6.000035047531128, id_amount 4 this amount prod time diff : 0.006000035047531128'}} list_photo_exists : [1171252487, 1171252764, 1171252784] storage_type for insertDescriptorsMulti : 3 To insert : 1171252487 To insert : 1171252764 To insert : 1171252784 time to insert the descriptors : 1.1234827041625977 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252487, 1171252764, 1171252784] map_info['map_portfolio_photo'] : {} final : False mtd_id 4567 list_pids : [1171252487, 1171252764, 1171252784] Looping around the photos to save general results len do output : 3 /1171252487Didn't retrieve data . /1171252764Didn't retrieve data . /1171252784Didn'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 ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4567', None, '1171252487', 'None', None, None, None, None, None), ('4567', None, '1171252764', 'None', None, None, None, None, None), ('4567', None, '1171252784', 'None', None, None, None, None, None)] time used for this insertion : 0.01979970932006836 save_final save missing photos in datou_result : time spend for datou_step_exec : 18.654077768325806 time spend to save output : 0.02014780044555664 total time spend for step 1 : 18.674225568771362 step2:argmax Thu Apr 10 21:37:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784} map_photo_id_path_extension : {1171252487: {'path': 'temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.9263117, 4674, '3609'), 'temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.985362, 4674, '3609'), 'temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.96774894, 4674, '3609'), 'temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171252487', '495916461', '4674') ... last line : ('1171252784', '495916461', '4674') time used for this insertion : 0.012489557266235352 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.01448512077331543 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.9263117', None), ('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.985362', None), ('4567', None, '1171252784', 'jrm', None, None, '495916461', '0.96774894', None)] time used for this insertion : 0.014956474304199219 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 5.4836273193359375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00018668174743652344 time spend to save output : 0.04664969444274902 total time spend for step 2 : 0.04683637619018555 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252487': [(1171252487, 'jrm', 0.9263117, 4674, '3609'), 'temp/1744313832_172844_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252764': [(1171252764, 'jrm', 0.985362, 4674, '3609'), 'temp/1744313832_172844_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252784': [(1171252784, 'jrm', 0.96774894, 4674, '3609'), 'temp/1744313832_172844_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4621 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4621 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4621 # 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) 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 : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171291875,1171275372,1171275314) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171275314 begin to download photo : 1171275372 begin to download photo : 1171291875 download finish for photo 1171275372 download finish for photo 1171291875 download finish for photo 1171275314 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 4.986576080322266 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Thu Apr 10 21:37:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372, 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314} map_photo_id_path_extension : {1171275372: {'path': 'temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3655': 1} we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory l 3637 free memory gpu now : 2791 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3655) thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5862) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 8.45096492767334 time used to load_weights : 0.13326239585876465 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.2017669677734375 ['temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 (3, 1280) Got the blobs of the net to insert : [0, 0, 0, 0, 14, 0, 1, 4, 0, 0] code_as_byte_string:b'000000000e'| Got the blobs of the net to insert : [0, 1, 0, 0, 11, 0, 2, 2, 0, 0] code_as_byte_string:b'000100000b'| Got the blobs of the net to insert : [0, 0, 0, 0, 8, 0, 0, 0, 3, 0] code_as_byte_string:b'0000000008'| time to traite the descriptors : 0.038518428802490234 Testing : ['1171275372', '1171291875', '1171275314'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171275372,1171291875,1171275314) result : {1171275314: {'photo_id': 1171275314, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/6e0a72c8fa00d5e4b018bd689b547133.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_54_22_6187.jpg'}, 1171275372: {'photo_id': 1171275372, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/76d81364ff7df843bff095f45c07ba35.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_56_46_6098.jpg'}, 1171291875: {'photo_id': 1171291875, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/b62cd9e0d976b143f86fe82d072798c0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_59_04_5803.jpg'}} list_photo_exists : [1171275314, 1171275372, 1171291875] storage_type for insertDescriptorsMulti : 3 To insert : 1171275372 To insert : 1171291875 To insert : 1171275314 time to insert the descriptors : 0.9660036563873291 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275372, 1171291875, 1171275314] map_info['map_portfolio_photo'] : {} final : False mtd_id 4621 list_pids : [1171275372, 1171291875, 1171275314] Looping around the photos to save general results len do output : 3 /1171275372Didn't retrieve data . /1171291875Didn't retrieve data . /1171275314Didn'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 ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171275372', 'None', None, None, None, None, None), ('4621', None, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275314', 'None', None, None, None, None, None)] time used for this insertion : 0.017366886138916016 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.540804386138916 time spend to save output : 0.02005290985107422 total time spend for step 1 : 22.56085729598999 step2:argmax Thu Apr 10 21:37:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372, 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314} map_photo_id_path_extension : {1171275372: {'path': 'temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96743816, 4723, '3655'), 'temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.97065896, 4723, '3655'), 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96514803, 4723, '3655'), 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171275372', '2107748999', '4723') ... last line : ('1171275314', '2107748999', '4723') time used for this insertion : 0.01714944839477539 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.01920914649963379 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96743816', None), ('4621', None, '1171291875', 'tapis_vide', None, None, '2107748999', '0.97065896', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.96514803', None)] time used for this insertion : 0.01617574691772461 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 2.1457672119140625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00015163421630859375 time spend to save output : 0.05708742141723633 total time spend for step 2 : 0.05723905563354492 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275372': [(1171275372, 'tapis_vide', 0.96743816, 4723, '3655'), 'temp/1744313851_172844_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.97065896, 4723, '3655'), 'temp/1744313851_172844_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96514803, 4723, '3655'), 'temp/1744313851_172844_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (358) thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('FirstUploadExperveo_vignette__port_505674','CAR_EXTERIEUR_Roue__port_503398','FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486','FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485','CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465','CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198','CAR_EXTERIEUR_Face_avant_axe_droit__port_504451','CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235','FirstUploadExperveo_vin__port_505675','CAR_EXTERIEUR_cote_droite__port_504108','CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565','FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483','CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201','cartegrise_orientation__port_505064','CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217','CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531','CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218','CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214','CAR_EXTERIEUR_Angle_avant_droit__port_504087','FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484','CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563','CAR_EXTERIEUR_Angle_arriere_droit__port_504160','CAR_EXTERIEUR_arriere__port_504184','CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562','INTERIEUR_Compteur_kilometrique__port_503644','CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494','CAR_EXTERIEUR_Angle_arriere_gauche__port_504170','CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226','CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202','CAR_EXTERIEUR_moteur__port_503704','FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487','CAR_INTERIEUR_siege_arriere_class_1__port_506551','CAR_EXTERIEUR_avant__port_504146','CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215','CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225','CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564','FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482','CAR_INTERIEUR_coffre__port_503412','FirstUploadExperveo_rouetranche__port_505677','UploadPhotoImmatBest_class_1__port_505051','CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532','CAR_EXTERIEUR_angle_avant_gauche__port_504098','CAR_EXTERIEUR_face_avant_axe_gauche__port_504236','CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540','CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233','CAR_EXTERIEUR_roue_de_secour__port_503763','CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199','CAR_EXTERIEUR_cote_gauche__port_504017','CAR_INTERIEUR_avant_volant_class_1__port_506503','CAR_INTERIEUR_avant_volant_class_2__port_506504','CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'); 51 dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051']) select photo_hashtag_type from MTRDatou.classification_theme where id = 358 thcl : 358 photo_hashtag_type : 337 SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi, MTRUser.mtr_portfolio_photos mp where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365; {510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]} ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=230 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 230 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=230 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1834421157836914 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:rotate Thu Apr 10 21:38:02 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 90 degree temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : False image_rotate : image_path : temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : False image_rotate : image_path : temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : False image_rotate : image_path : temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg image_rotate.mode : RGB About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744313882_172844 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.194303035736084 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1351306919, 'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1351306921, 'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1351306923} Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] After datou_step_exec type output : time spend for datou_step_exec : 1.438770055770874 time spend to save output : 5.245208740234375e-05 total time spend for step 1 : 1.4388225078582764 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 230 list_pids : [917849322] Looping around the photos to save general results len do output : 3 /1351306919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351306921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351306923Didn'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 ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('230', None, '1351306919', 'None', None, None, None, None, None), ('230', None, '1351306921', 'None', None, None, None, None, None), ('230', None, '1351306923', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.02109980583190918 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1351306919: ['917849322', 'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1351306921: ['917849322', 'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1351306923: ['917849322', 'temp/1744313882_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=233 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 233 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=233 # 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 ! 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 ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.17035341262817383 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:thcl Thu Apr 10 21:38:04 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'500': 1} we are using the classfication for only one thcl 500 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (2448, 3264, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0002117156982421875 time to convert the images to numpy array : 1.7281126976013184 total time to convert the images to numpy array : 1.728700876235962 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (500) thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) param : , param.caffemodel : orientation_carte_grise_all_2 None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 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 : orientation_carte_grise_all_2 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_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 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/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/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 : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 1.6496167182922363 time used to do the prediction : 0.11613202095031738 save descriptor for thcl : 500 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.04937410354614258 Testing : ['917849322'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (917849322) result : {917849322: {'photo_id': 917849322, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/9/13/2bd260e91e91df8378dde8bb8b8c4548.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_13092022_12_32_19_5566.jpg'}} list_photo_exists : [917849322] storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.5214519500732422 After datou_step_exec type output : time spend for datou_step_exec : 9.162663459777832 time spend to save output : 0.00010466575622558594 total time spend for step 1 : 9.162768125534058 step2:argmax Thu Apr 10 21:38:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005036301, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003663997, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014809034, 507, '500')]]} input_args_next_step : {'917849322': ()} output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005036301, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003663997, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014809034, 507, '500')]]} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ([('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005036301, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003663997, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014809034, 507, '500')],) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 500 After datou_step_exec type output : time spend for datou_step_exec : 0.00034689903259277344 time spend to save output : 7.653236389160156e-05 total time spend for step 2 : 0.000423431396484375 step3:rotate Thu Apr 10 21:38:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ()} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 1 complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ('temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500'), 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.997649, 507, '500')) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 0 degree temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 0 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : False image_rotate : image_path : temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg image_rotate.mode : RGB About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744313893_172844 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6672570705413818 map_filename_photo_id : 1 map_filename_photo_id : {'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 1351306946} Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] After datou_step_exec type output : time spend for datou_step_exec : 0.780832052230835 time spend to save output : 8.463859558105469e-05 total time spend for step 3 : 0.780916690826416 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 233 list_pids : [917849322] Looping around the photos to save general results len do output : 1 /1351306946Didn'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 ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('233', None, '1351306946', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.019526958465576172 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1351306946: ['917849322', 'temp/1744313883_172844_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=937852786 AND `type`=520 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3754438262,3754438271,3754438301,3754438302) # 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 ! 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 : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! 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 ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=243 # 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 ! 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 : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (937852786) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 937852786 download finish for photo 937852786 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.14266633987426758 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:crop Thu Apr 10 21:38:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786} map_photo_id_path_extension : {937852786: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (407) Loaded 4 chid ids of type : 407 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 937852786) WARNING : margin is only used for type bib ! type of cropped photo chosen : we resize croppped photo by 1 on x axis and by 1 on y axis new_file_path_bib_crop : temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg new_file_path_bib_crop : temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg new_file_path_bib_crop : temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg new_file_path_bib_crop : temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg map_result returned by crop_photo_return_map_crop : length : 4 map_result after crop : {8165075: {'crop': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (426, 467, 312, 347), 'sub_photo_infos': (418, 475, 304, 355, 1, 1), 'same_chi': False}, 8165076: {'crop': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (411, 445, 443, 480), 'sub_photo_infos': (403, 453, 435, 480, 1, 1), 'same_chi': False}, 8165077: {'crop': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (103, 138, 358, 396), 'sub_photo_infos': (95, 146, 350, 404, 1, 1), 'same_chi': False}, 8165078: {'crop': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (104, 131, 256, 292), 'sub_photo_infos': (96, 139, 248, 300, 1, 1), 'same_chi': False}} Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22242905 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744313897_172844 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (22242905, 1351306950, 0, NOW()),(22242905, 1351306951, 0, NOW()),(22242905, 1351306952, 0, NOW()),(22242905, 1351306953, 0, NOW()) 4 we have uploaded 4 photos in the portfolio 22242905 time of upload the photos Elapsed time : 3.7611167430877686 {'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1351306950, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1351306951, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1351306952, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1351306953} list_errors : [] map_result_insert : {'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1351306950, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1351306951, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1351306952, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1351306953} Now we prepare data that will be used for ellipse search ! chi_id found to be used 8165075 path of cropped varroa found to be used to match on an ellipse temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg sub_photo_id found to be used 1351306950 chi_id found to be used 8165076 path of cropped varroa found to be used to match on an ellipse temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg sub_photo_id found to be used 1351306951 chi_id found to be used 8165077 path of cropped varroa found to be used to match on an ellipse temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg sub_photo_id found to be used 1351306952 chi_id found to be used 8165078 path of cropped varroa found to be used to match on an ellipse temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg sub_photo_id found to be used 1351306953 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '1351306950', 31), (8165076, '1351306951', 31), (8165077, '1351306952', 31), (8165078, '1351306953', 31)] map of cropped photos with some data : {'1351306950': ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', (426, 467, 312, 347)], '1351306951': ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', (411, 445, 443, 480)], '1351306952': ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', (103, 138, 358, 396)], '1351306953': ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', (104, 131, 256, 292)]} About to compute ellipse and record with type : 520 (54, 57) (51, 57) [54, 57] (54, 57) score : 5120 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [14.25, 42.75, 1.78125]), ('yc', [12.75, 38.25, 1.59375]), ('radius', [14.25, 42.75, 1.78125])] {0.5: 18351, 0.55: 16004, 0.6000000000000001: 14127, 0.65: 12575, 0.7000000000000001: 11300, 0.75: 10158, 0.8: 9208, 0.8500000000000001: 8418, 0.9: 7642, 0.9500000000000001: 6925, 1.0: 6364, 1.05: 5843, 1.1: 5353, 1.1500000000000001: 4868, 1.2000000000000002: 4553, 1.25: 4250, 1.3: 3947, 1.35: 3698, 1.4000000000000001: 3438, 1.4500000000000002: 3230, 1.5: 3012, 1.55: 2829, 1.6: 2636, 1.6500000000000001: 2535, 1.7000000000000002: 2460, 1.75: 2388, 1.8: 2372, 1.85: 2354, 1.9000000000000001: 2332, 1.9500000000000002: 2311} [(1.9500000000000002, 2311), (1.9000000000000001, 2332), (1.85, 2354), (1.8, 2372), (1.75, 2388), (1.7000000000000002, 2460), (1.6500000000000001, 2535), (1.6, 2636), (1.55, 2829), (1.5, 3012), (1.4500000000000002, 3230), (1.4000000000000001, 3438), (1.35, 3698), (1.3, 3947), (1.25, 4250), (1.2000000000000002, 4553), (1.1500000000000001, 4868), (1.1, 5353), (1.05, 5843), (1.0, 6364), (0.9500000000000001, 6925), (0.9, 7642), (0.8500000000000001, 8418), (0.8, 9208), (0.75, 10158), (0.7000000000000001, 11300), (0.65, 12575), (0.6000000000000001, 14127), (0.55, 16004), (0.5, 18351)] arg_min reach at : 1.9500000000000002 with value = 2311 | arg_min : 1.9500000000000002 min_score : 2311{-90.0: 3359, -85.0: 3372, -80.0: 3395, -75.0: 3380, -70.0: 3304, -65.0: 3180, -60.0: 2991, -55.0: 2793, -50.0: 2601, -45.0: 2377, -40.0: 2183, -35.0: 2078, -30.0: 1968, -25.0: 1968, -20.0: 2021, -15.0: 2036, -10.0: 2102, -5.0: 2188, 0.0: 2208, 5.0: 2265, 10.0: 2311, 15.0: 2333, 20.0: 2362, 25.0: 2375, 30.0: 2375, 35.0: 2397, 40.0: 2370, 45.0: 2421, 50.0: 2491, 55.0: 2661, 60.0: 2826, 65.0: 2993, 70.0: 3106, 75.0: 3182, 80.0: 3263, 85.0: 3306} [(-30.0, 1968), (-25.0, 1968), (-20.0, 2021), (-15.0, 2036), (-35.0, 2078), (-10.0, 2102), (-40.0, 2183), (-5.0, 2188), (0.0, 2208), (5.0, 2265), (10.0, 2311), (15.0, 2333), (20.0, 2362), (40.0, 2370), (25.0, 2375), (30.0, 2375), (-45.0, 2377), (35.0, 2397), (45.0, 2421), (50.0, 2491), (-50.0, 2601), (55.0, 2661), (-55.0, 2793), (60.0, 2826), (-60.0, 2991), (65.0, 2993), (70.0, 3106), (-65.0, 3180), (75.0, 3182), (80.0, 3263), (-70.0, 3304), (85.0, 3306), (-90.0, 3359), (-85.0, 3372), (-75.0, 3380), (-80.0, 3395)] arg_min reach at : -30.0 with value = 1968 | arg_min : -30.0 min_score : 1968{14.25: 1703, 16.03125: 1654, 17.8125: 1614, 19.59375: 1679, 21.375: 1702, 23.15625: 1712, 24.9375: 1730, 26.71875: 1847, 28.5: 1968, 30.28125: 2177, 32.0625: 2456, 33.84375: 2827, 35.625: 3294, 37.40625: 3723, 39.1875: 4161, 40.96875: 4643} [(17.8125, 1614), (16.03125, 1654), (19.59375, 1679), (21.375, 1702), (14.25, 1703), (23.15625, 1712), (24.9375, 1730), (26.71875, 1847), (28.5, 1968), (30.28125, 2177), (32.0625, 2456), (33.84375, 2827), (35.625, 3294), (37.40625, 3723), (39.1875, 4161), (40.96875, 4643)] arg_min reach at : 17.8125 with value = 1614 | arg_min : 17.8125 min_score : 1614{12.75: 5534, 14.34375: 5352, 15.9375: 5116, 17.53125: 4723, 19.125: 4111, 20.71875: 3506, 22.3125: 2799, 23.90625: 2194, 25.5: 1614, 27.09375: 1339, 28.6875: 1206, 30.28125: 1137, 31.875: 1105, 33.46875: 1339, 35.0625: 1659, 36.65625: 2022} [(31.875, 1105), (30.28125, 1137), (28.6875, 1206), (27.09375, 1339), (33.46875, 1339), (25.5, 1614), (35.0625, 1659), (36.65625, 2022), (23.90625, 2194), (22.3125, 2799), (20.71875, 3506), (19.125, 4111), (17.53125, 4723), (15.9375, 5116), (14.34375, 5352), (12.75, 5534)] arg_min reach at : 31.875 with value = 1105 | arg_min : 31.875 min_score : 1105{14.25: 1942, 16.03125: 1853, 17.8125: 1756, 19.59375: 1651, 21.375: 1533, 23.15625: 1424, 24.9375: 1309, 26.71875: 1199, 28.5: 1105, 30.28125: 1219, 32.0625: 1332, 33.84375: 1547, 35.625: 1881, 37.40625: 2378, 39.1875: 3042, 40.96875: 3824} [(28.5, 1105), (26.71875, 1199), (30.28125, 1219), (24.9375, 1309), (32.0625, 1332), (23.15625, 1424), (21.375, 1533), (33.84375, 1547), (19.59375, 1651), (17.8125, 1756), (16.03125, 1853), (35.625, 1881), (14.25, 1942), (37.40625, 2378), (39.1875, 3042), (40.96875, 3824)] arg_min reach at : 28.5 with value = 1105 | arg_min : 28.5 min_score : 1105{0.5: 17397, 0.55: 15138, 0.6000000000000001: 13352, 0.65: 11859, 0.7000000000000001: 10523, 0.75: 9463, 0.8: 8469, 0.8500000000000001: 7565, 0.9: 6551, 0.9500000000000001: 5592, 1.0: 4856, 1.05: 4291, 1.1: 3654, 1.1500000000000001: 3099, 1.2000000000000002: 2647, 1.25: 2309, 1.3: 1983, 1.35: 1755, 1.4000000000000001: 1594, 1.4500000000000002: 1447, 1.5: 1358, 1.55: 1323, 1.6: 1219, 1.6500000000000001: 1215, 1.7000000000000002: 1201, 1.75: 1131, 1.8: 1134, 1.85: 1142, 1.9000000000000001: 1137, 1.9500000000000002: 1105} [(1.9500000000000002, 1105), (1.75, 1131), (1.8, 1134), (1.9000000000000001, 1137), (1.85, 1142), (1.7000000000000002, 1201), (1.6500000000000001, 1215), (1.6, 1219), (1.55, 1323), (1.5, 1358), (1.4500000000000002, 1447), (1.4000000000000001, 1594), (1.35, 1755), (1.3, 1983), (1.25, 2309), (1.2000000000000002, 2647), (1.1500000000000001, 3099), (1.1, 3654), (1.05, 4291), (1.0, 4856), (0.9500000000000001, 5592), (0.9, 6551), (0.8500000000000001, 7565), (0.8, 8469), (0.75, 9463), (0.7000000000000001, 10523), (0.65, 11859), (0.6000000000000001, 13352), (0.55, 15138), (0.5, 17397)] arg_min reach at : 1.9500000000000002 with value = 1105 arg_min : 1.9500000000000002 min_score : 1105{-90.0: 3514, -85.0: 3396, -80.0: 3238, -75.0: 3017, -70.0: 2764, -65.0: 2549, -60.0: 2339, -55.0: 2105, -50.0: 1912, -45.0: 1701, -40.0: 1467, -35.0: 1266, -30.0: 1105, -25.0: 1110, -20.0: 1114, -15.0: 1111, -10.0: 1110, -5.0: 1121, 0.0: 1120, 5.0: 1118, 10.0: 1109, 15.0: 1109, 20.0: 1099, 25.0: 1088, 30.0: 1108, 35.0: 1355, 40.0: 1649, 45.0: 1925, 50.0: 2257, 55.0: 2542, 60.0: 2845, 65.0: 3137, 70.0: 3370, 75.0: 3500, 80.0: 3578, 85.0: 3540} [(25.0, 1088), (20.0, 1099), (-30.0, 1105), (30.0, 1108), (10.0, 1109), (15.0, 1109), (-25.0, 1110), (-10.0, 1110), (-15.0, 1111), (-20.0, 1114), (5.0, 1118), (0.0, 1120), (-5.0, 1121), (-35.0, 1266), (35.0, 1355), (-40.0, 1467), (40.0, 1649), (-45.0, 1701), (-50.0, 1912), (45.0, 1925), (-55.0, 2105), (50.0, 2257), (-60.0, 2339), (55.0, 2542), (-65.0, 2549), (-70.0, 2764), (60.0, 2845), (-75.0, 3017), (65.0, 3137), (-80.0, 3238), (70.0, 3370), (-85.0, 3396), (75.0, 3500), (-90.0, 3514), (85.0, 3540), (80.0, 3578)] arg_min reach at : 25.0 with value = 1088 arg_min : 25.0 min_score : 1088{14.25: 1174, 16.03125: 1133, 17.8125: 1088, 19.59375: 1051, 21.375: 1026, 23.15625: 997, 24.9375: 979, 26.71875: 1139, 28.5: 1344, 30.28125: 1564, 32.0625: 1949, 33.84375: 2422, 35.625: 2930, 37.40625: 3453, 39.1875: 4006, 40.96875: 4592} [(24.9375, 979), (23.15625, 997), (21.375, 1026), (19.59375, 1051), (17.8125, 1088), (16.03125, 1133), (26.71875, 1139), (14.25, 1174), (28.5, 1344), (30.28125, 1564), (32.0625, 1949), (33.84375, 2422), (35.625, 2930), (37.40625, 3453), (39.1875, 4006), (40.96875, 4592)] arg_min reach at : 24.9375 with value = 979 arg_min : 24.9375 min_score : 979{12.75: 5539, 14.34375: 5239, 15.9375: 4911, 17.53125: 4425, 19.125: 3749, 20.71875: 3181, 22.3125: 2575, 23.90625: 2124, 25.5: 1808, 27.09375: 1478, 28.6875: 1290, 30.28125: 1104, 31.875: 979, 33.46875: 1070, 35.0625: 1368, 36.65625: 1872} [(31.875, 979), (33.46875, 1070), (30.28125, 1104), (28.6875, 1290), (35.0625, 1368), (27.09375, 1478), (25.5, 1808), (36.65625, 1872), (23.90625, 2124), (22.3125, 2575), (20.71875, 3181), (19.125, 3749), (17.53125, 4425), (15.9375, 4911), (14.34375, 5239), (12.75, 5539)] arg_min reach at : 31.875 with value = 979 arg_min : 31.875 min_score : 979{14.25: 1939, 16.03125: 1852, 17.8125: 1755, 19.59375: 1643, 21.375: 1531, 23.15625: 1397, 24.9375: 1264, 26.71875: 1119, 28.5: 979, 30.28125: 1023, 32.0625: 1319, 33.84375: 1771, 35.625: 2432, 37.40625: 3324, 39.1875: 4299, 40.96875: 5405} [(28.5, 979), (30.28125, 1023), (26.71875, 1119), (24.9375, 1264), (32.0625, 1319), (23.15625, 1397), (21.375, 1531), (19.59375, 1643), (17.8125, 1755), (33.84375, 1771), (16.03125, 1852), (14.25, 1939), (35.625, 2432), (37.40625, 3324), (39.1875, 4299), (40.96875, 5405)] arg_min reach at : 28.5 with value = 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 x0 : 426 y1 : 347 width : 41, height : 35, area : 1435, score : 1.0 x0 : 432 y1 : 355 width : 35, height : 52, area : 1820, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! (47, 50) (45, 50) [47, 50] (47, 50) score : 5362 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.5, 37.5, 1.5625]), ('yc', [11.25, 33.75, 1.40625]), ('radius', [12.5, 37.5, 1.5625])] {0.5: 15776, 0.55: 13889, 0.6000000000000001: 12446, 0.65: 11147, 0.7000000000000001: 10181, 0.75: 9228, 0.8: 8441, 0.8500000000000001: 7765, 0.9: 7258, 0.9500000000000001: 6684, 1.0: 6195, 1.05: 5920, 1.1: 5487, 1.1500000000000001: 5099, 1.2000000000000002: 4795, 1.25: 4511, 1.3: 4245, 1.35: 4042, 1.4000000000000001: 3828, 1.4500000000000002: 3629, 1.5: 3467, 1.55: 3235, 1.6: 3126, 1.6500000000000001: 2946, 1.7000000000000002: 2826, 1.75: 2700, 1.8: 2549, 1.85: 2472, 1.9000000000000001: 2381, 1.9500000000000002: 2281} [(1.9500000000000002, 2281), (1.9000000000000001, 2381), (1.85, 2472), (1.8, 2549), (1.75, 2700), (1.7000000000000002, 2826), (1.6500000000000001, 2946), (1.6, 3126), (1.55, 3235), (1.5, 3467), (1.4500000000000002, 3629), (1.4000000000000001, 3828), (1.35, 4042), (1.3, 4245), (1.25, 4511), (1.2000000000000002, 4795), (1.1500000000000001, 5099), (1.1, 5487), (1.05, 5920), (1.0, 6195), (0.9500000000000001, 6684), (0.9, 7258), (0.8500000000000001, 7765), (0.8, 8441), (0.75, 9228), (0.7000000000000001, 10181), (0.65, 11147), (0.6000000000000001, 12446), (0.55, 13889), (0.5, 15776)] arg_min reach at : 1.9500000000000002 with value = 2281 | arg_min : 1.9500000000000002 min_score : 2281{-90.0: 3295, -85.0: 3240, -80.0: 3290, -75.0: 3269, -70.0: 3287, -65.0: 3223, -60.0: 3095, -55.0: 3043, -50.0: 2985, -45.0: 2906, -40.0: 2796, -35.0: 2724, -30.0: 2625, -25.0: 2489, -20.0: 2330, -15.0: 2220, -10.0: 2127, -5.0: 2127, 0.0: 2143, 5.0: 2182, 10.0: 2281, 15.0: 2385, 20.0: 2506, 25.0: 2665, 30.0: 2768, 35.0: 2900, 40.0: 2939, 45.0: 2994, 50.0: 3007, 55.0: 3076, 60.0: 3095, 65.0: 3201, 70.0: 3243, 75.0: 3247, 80.0: 3279, 85.0: 3240} [(-10.0, 2127), (-5.0, 2127), (0.0, 2143), (5.0, 2182), (-15.0, 2220), (10.0, 2281), (-20.0, 2330), (15.0, 2385), (-25.0, 2489), (20.0, 2506), (-30.0, 2625), (25.0, 2665), (-35.0, 2724), (30.0, 2768), (-40.0, 2796), (35.0, 2900), (-45.0, 2906), (40.0, 2939), (-50.0, 2985), (45.0, 2994), (50.0, 3007), (-55.0, 3043), (55.0, 3076), (-60.0, 3095), (60.0, 3095), (65.0, 3201), (-65.0, 3223), (-85.0, 3240), (85.0, 3240), (70.0, 3243), (75.0, 3247), (-75.0, 3269), (80.0, 3279), (-70.0, 3287), (-80.0, 3290), (-90.0, 3295)] arg_min reach at : -10.0 with value = 2127 | arg_min : -10.0 min_score : 2127{12.5: 2171, 14.0625: 2288, 15.625: 2314, 17.1875: 2360, 18.75: 2347, 20.3125: 2300, 21.875: 2249, 23.4375: 2188, 25.0: 2127, 26.5625: 2163, 28.125: 2315, 29.6875: 2476, 31.25: 2756, 32.8125: 2992, 34.375: 3319, 35.9375: 3672} [(25.0, 2127), (26.5625, 2163), (12.5, 2171), (23.4375, 2188), (21.875, 2249), (14.0625, 2288), (20.3125, 2300), (15.625, 2314), (28.125, 2315), (18.75, 2347), (17.1875, 2360), (29.6875, 2476), (31.25, 2756), (32.8125, 2992), (34.375, 3319), (35.9375, 3672)] arg_min reach at : 25.0 with value = 2127 | arg_min : 25.0 min_score : 2127{11.25: 6815, 12.65625: 6505, 14.0625: 5928, 15.46875: 5326, 16.875: 4772, 18.28125: 4095, 19.6875: 3413, 21.09375: 2728, 22.5: 2127, 23.90625: 1694, 25.3125: 1345, 26.71875: 1048, 28.125: 823, 29.53125: 728, 30.9375: 714, 32.34375: 1027} [(30.9375, 714), (29.53125, 728), (28.125, 823), (32.34375, 1027), (26.71875, 1048), (25.3125, 1345), (23.90625, 1694), (22.5, 2127), (21.09375, 2728), (19.6875, 3413), (18.28125, 4095), (16.875, 4772), (15.46875, 5326), (14.0625, 5928), (12.65625, 6505), (11.25, 6815)] arg_min reach at : 30.9375 with value = 714 | arg_min : 30.9375 min_score : 714{12.5: 1275, 14.0625: 1207, 15.625: 1132, 17.1875: 1049, 18.75: 966, 20.3125: 866, 21.875: 784, 23.4375: 729, 25.0: 714, 26.5625: 976, 28.125: 1546, 29.6875: 2147, 31.25: 2981, 32.8125: 3767, 34.375: 4761, 35.9375: 5732} [(25.0, 714), (23.4375, 729), (21.875, 784), (20.3125, 866), (18.75, 966), (26.5625, 976), (17.1875, 1049), (15.625, 1132), (14.0625, 1207), (12.5, 1275), (28.125, 1546), (29.6875, 2147), (31.25, 2981), (32.8125, 3767), (34.375, 4761), (35.9375, 5732)] arg_min reach at : 25.0 with value = 714 | arg_min : 25.0 min_score : 714{0.5: 20107, 0.55: 18155, 0.6000000000000001: 16503, 0.65: 15058, 0.7000000000000001: 13673, 0.75: 12474, 0.8: 11100, 0.8500000000000001: 9667, 0.9: 8374, 0.9500000000000001: 7344, 1.0: 6274, 1.05: 5570, 1.1: 4846, 1.1500000000000001: 4170, 1.2000000000000002: 3623, 1.25: 3211, 1.3: 2832, 1.35: 2475, 1.4000000000000001: 2182, 1.4500000000000002: 1949, 1.5: 1655, 1.55: 1513, 1.6: 1333, 1.6500000000000001: 1182, 1.7000000000000002: 1038, 1.75: 921, 1.8: 841, 1.85: 732, 1.9000000000000001: 733, 1.9500000000000002: 714} [(1.9500000000000002, 714), (1.85, 732), (1.9000000000000001, 733), (1.8, 841), (1.75, 921), (1.7000000000000002, 1038), (1.6500000000000001, 1182), (1.6, 1333), (1.55, 1513), (1.5, 1655), (1.4500000000000002, 1949), (1.4000000000000001, 2182), (1.35, 2475), (1.3, 2832), (1.25, 3211), (1.2000000000000002, 3623), (1.1500000000000001, 4170), (1.1, 4846), (1.05, 5570), (1.0, 6274), (0.9500000000000001, 7344), (0.9, 8374), (0.8500000000000001, 9667), (0.8, 11100), (0.75, 12474), (0.7000000000000001, 13673), (0.65, 15058), (0.6000000000000001, 16503), (0.55, 18155), (0.5, 20107)] arg_min reach at : 1.9500000000000002 with value = 714 arg_min : 1.9500000000000002 min_score : 714{-90.0: 2866, -85.0: 3006, -80.0: 3063, -75.0: 3168, -70.0: 3193, -65.0: 3193, -60.0: 3149, -55.0: 3022, -50.0: 2885, -45.0: 2656, -40.0: 2413, -35.0: 2119, -30.0: 1819, -25.0: 1500, -20.0: 1171, -15.0: 865, -10.0: 714, -5.0: 668, 0.0: 689, 5.0: 734, 10.0: 824, 15.0: 898, 20.0: 1116, 25.0: 1368, 30.0: 1610, 35.0: 1866, 40.0: 2105, 45.0: 2304, 50.0: 2467, 55.0: 2604, 60.0: 2731, 65.0: 2753, 70.0: 2753, 75.0: 2761, 80.0: 2722, 85.0: 2786} [(-5.0, 668), (0.0, 689), (-10.0, 714), (5.0, 734), (10.0, 824), (-15.0, 865), (15.0, 898), (20.0, 1116), (-20.0, 1171), (25.0, 1368), (-25.0, 1500), (30.0, 1610), (-30.0, 1819), (35.0, 1866), (40.0, 2105), (-35.0, 2119), (45.0, 2304), (-40.0, 2413), (50.0, 2467), (55.0, 2604), (-45.0, 2656), (80.0, 2722), (60.0, 2731), (65.0, 2753), (70.0, 2753), (75.0, 2761), (85.0, 2786), (-90.0, 2866), (-50.0, 2885), (-85.0, 3006), (-55.0, 3022), (-80.0, 3063), (-60.0, 3149), (-75.0, 3168), (-70.0, 3193), (-65.0, 3193)] arg_min reach at : -5.0 with value = 668 arg_min : -5.0 min_score : 668{12.5: 1164, 14.0625: 1082, 15.625: 1019, 17.1875: 933, 18.75: 866, 20.3125: 765, 21.875: 713, 23.4375: 655, 25.0: 668, 26.5625: 845, 28.125: 1107, 29.6875: 1374, 31.25: 1691, 32.8125: 2036, 34.375: 2375, 35.9375: 2731} [(23.4375, 655), (25.0, 668), (21.875, 713), (20.3125, 765), (26.5625, 845), (18.75, 866), (17.1875, 933), (15.625, 1019), (14.0625, 1082), (28.125, 1107), (12.5, 1164), (29.6875, 1374), (31.25, 1691), (32.8125, 2036), (34.375, 2375), (35.9375, 2731)] arg_min reach at : 23.4375 with value = 655 arg_min : 23.4375 min_score : 655{11.25: 7209, 12.65625: 6826, 14.0625: 6195, 15.46875: 5638, 16.875: 4941, 18.28125: 4177, 19.6875: 3484, 21.09375: 2750, 22.5: 2183, 23.90625: 1709, 25.3125: 1327, 26.71875: 1008, 28.125: 779, 29.53125: 631, 30.9375: 655, 32.34375: 920} [(29.53125, 631), (30.9375, 655), (28.125, 779), (32.34375, 920), (26.71875, 1008), (25.3125, 1327), (23.90625, 1709), (22.5, 2183), (21.09375, 2750), (19.6875, 3484), (18.28125, 4177), (16.875, 4941), (15.46875, 5638), (14.0625, 6195), (12.65625, 6826), (11.25, 7209)] arg_min reach at : 29.53125 with value = 631 arg_min : 29.53125 min_score : 631{12.5: 1282, 14.0625: 1211, 15.625: 1130, 17.1875: 1050, 18.75: 960, 20.3125: 871, 21.875: 772, 23.4375: 672, 25.0: 631, 26.5625: 666, 28.125: 947, 29.6875: 1472, 31.25: 2184, 32.8125: 3032, 34.375: 3932, 35.9375: 5005} [(25.0, 631), (26.5625, 666), (23.4375, 672), (21.875, 772), (20.3125, 871), (28.125, 947), (18.75, 960), (17.1875, 1050), (15.625, 1130), (14.0625, 1211), (12.5, 1282), (29.6875, 1472), (31.25, 2184), (32.8125, 3032), (34.375, 3932), (35.9375, 5005)] arg_min reach at : 25.0 with value = 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 x0 : 411 y1 : 480 width : 34, height : 37, area : 1258, score : 1.0 x0 : 419 y1 : 483 width : 26, height : 49, area : 1274, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! (55, 54) (54, 51) [55, 54] (55, 54) score : 4603 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.75, 38.25, 1.59375]), ('yc', [13.5, 40.5, 1.6875]), ('radius', [13.5, 40.5, 1.6875])] {0.5: 14831, 0.55: 12756, 0.6000000000000001: 10989, 0.65: 9469, 0.7000000000000001: 8158, 0.75: 7251, 0.8: 6254, 0.8500000000000001: 5559, 0.9: 5119, 0.9500000000000001: 4564, 1.0: 4072, 1.05: 3783, 1.1: 3496, 1.1500000000000001: 3342, 1.2000000000000002: 3228, 1.25: 3161, 1.3: 3127, 1.35: 3087, 1.4000000000000001: 3047, 1.4500000000000002: 3050, 1.5: 2990, 1.55: 2992, 1.6: 2992, 1.6500000000000001: 2985, 1.7000000000000002: 2988, 1.75: 2999, 1.8: 2994, 1.85: 2981, 1.9000000000000001: 3002, 1.9500000000000002: 2995} [(1.85, 2981), (1.6500000000000001, 2985), (1.7000000000000002, 2988), (1.5, 2990), (1.55, 2992), (1.6, 2992), (1.8, 2994), (1.9500000000000002, 2995), (1.75, 2999), (1.9000000000000001, 3002), (1.4000000000000001, 3047), (1.4500000000000002, 3050), (1.35, 3087), (1.3, 3127), (1.25, 3161), (1.2000000000000002, 3228), (1.1500000000000001, 3342), (1.1, 3496), (1.05, 3783), (1.0, 4072), (0.9500000000000001, 4564), (0.9, 5119), (0.8500000000000001, 5559), (0.8, 6254), (0.75, 7251), (0.7000000000000001, 8158), (0.65, 9469), (0.6000000000000001, 10989), (0.55, 12756), (0.5, 14831)] arg_min reach at : 1.85 with value = 2981 | arg_min : 1.85 min_score : 2981{-90.0: 1442, -85.0: 1411, -80.0: 1411, -75.0: 1423, -70.0: 1409, -65.0: 1428, -60.0: 1417, -55.0: 1385, -50.0: 1356, -45.0: 1374, -40.0: 1401, -35.0: 1501, -30.0: 1635, -25.0: 1772, -20.0: 1988, -15.0: 2183, -10.0: 2376, -5.0: 2552, 0.0: 2691, 5.0: 2860, 10.0: 2981, 15.0: 3063, 20.0: 3044, 25.0: 2938, 30.0: 2889, 35.0: 2865, 40.0: 2787, 45.0: 2694, 50.0: 2566, 55.0: 2452, 60.0: 2275, 65.0: 2055, 70.0: 1849, 75.0: 1731, 80.0: 1532, 85.0: 1466} [(-50.0, 1356), (-45.0, 1374), (-55.0, 1385), (-40.0, 1401), (-70.0, 1409), (-85.0, 1411), (-80.0, 1411), (-60.0, 1417), (-75.0, 1423), (-65.0, 1428), (-90.0, 1442), (85.0, 1466), (-35.0, 1501), (80.0, 1532), (-30.0, 1635), (75.0, 1731), (-25.0, 1772), (70.0, 1849), (-20.0, 1988), (65.0, 2055), (-15.0, 2183), (60.0, 2275), (-10.0, 2376), (55.0, 2452), (-5.0, 2552), (50.0, 2566), (0.0, 2691), (45.0, 2694), (40.0, 2787), (5.0, 2860), (35.0, 2865), (30.0, 2889), (25.0, 2938), (10.0, 2981), (20.0, 3044), (15.0, 3063)] arg_min reach at : -50.0 with value = 1356 | arg_min : -50.0 min_score : 1356{12.75: 3483, 14.34375: 3154, 15.9375: 2818, 17.53125: 2499, 19.125: 2212, 20.71875: 1992, 22.3125: 1750, 23.90625: 1554, 25.5: 1356, 27.09375: 1213, 28.6875: 1123, 30.28125: 1079, 31.875: 1312, 33.46875: 1586, 35.0625: 1971, 36.65625: 2464} [(30.28125, 1079), (28.6875, 1123), (27.09375, 1213), (31.875, 1312), (25.5, 1356), (23.90625, 1554), (33.46875, 1586), (22.3125, 1750), (35.0625, 1971), (20.71875, 1992), (19.125, 2212), (36.65625, 2464), (17.53125, 2499), (15.9375, 2818), (14.34375, 3154), (12.75, 3483)] arg_min reach at : 30.28125 with value = 1079 | arg_min : 30.28125 min_score : 1079{13.5: 1299, 15.1875: 1190, 16.875: 1129, 18.5625: 1073, 20.25: 1041, 21.9375: 1025, 23.625: 995, 25.3125: 1002, 27.0: 1079, 28.6875: 1280, 30.375: 1506, 32.0625: 1818, 33.75: 2218, 35.4375: 2606, 37.125: 3013, 38.8125: 3545} [(23.625, 995), (25.3125, 1002), (21.9375, 1025), (20.25, 1041), (18.5625, 1073), (27.0, 1079), (16.875, 1129), (15.1875, 1190), (28.6875, 1280), (13.5, 1299), (30.375, 1506), (32.0625, 1818), (33.75, 2218), (35.4375, 2606), (37.125, 3013), (38.8125, 3545)] arg_min reach at : 23.625 with value = 995 | arg_min : 23.625 min_score : 995{13.5: 1907, 15.1875: 1827, 16.875: 1734, 18.5625: 1635, 20.25: 1520, 21.9375: 1400, 23.625: 1268, 25.3125: 1139, 27.0: 995, 28.6875: 1081, 30.375: 1376, 32.0625: 1837, 33.75: 2365, 35.4375: 3034, 37.125: 3644, 38.8125: 4482} [(27.0, 995), (28.6875, 1081), (25.3125, 1139), (23.625, 1268), (30.375, 1376), (21.9375, 1400), (20.25, 1520), (18.5625, 1635), (16.875, 1734), (15.1875, 1827), (32.0625, 1837), (13.5, 1907), (33.75, 2365), (35.4375, 3034), (37.125, 3644), (38.8125, 4482)] arg_min reach at : 27.0 with value = 995 | arg_min : 27.0 min_score : 995{0.5: 16358, 0.55: 14291, 0.6000000000000001: 12544, 0.65: 11117, 0.7000000000000001: 9800, 0.75: 8586, 0.8: 7533, 0.8500000000000001: 6488, 0.9: 5599, 0.9500000000000001: 4779, 1.0: 4075, 1.05: 3520, 1.1: 3021, 1.1500000000000001: 2582, 1.2000000000000002: 2150, 1.25: 1820, 1.3: 1534, 1.35: 1347, 1.4000000000000001: 1219, 1.4500000000000002: 1130, 1.5: 1073, 1.55: 1037, 1.6: 998, 1.6500000000000001: 961, 1.7000000000000002: 981, 1.75: 984, 1.8: 995, 1.85: 995, 1.9000000000000001: 1023, 1.9500000000000002: 1058} [(1.6500000000000001, 961), (1.7000000000000002, 981), (1.75, 984), (1.8, 995), (1.85, 995), (1.6, 998), (1.9000000000000001, 1023), (1.55, 1037), (1.9500000000000002, 1058), (1.5, 1073), (1.4500000000000002, 1130), (1.4000000000000001, 1219), (1.35, 1347), (1.3, 1534), (1.25, 1820), (1.2000000000000002, 2150), (1.1500000000000001, 2582), (1.1, 3021), (1.05, 3520), (1.0, 4075), (0.9500000000000001, 4779), (0.9, 5599), (0.8500000000000001, 6488), (0.8, 7533), (0.75, 8586), (0.7000000000000001, 9800), (0.65, 11117), (0.6000000000000001, 12544), (0.55, 14291), (0.5, 16358)] arg_min reach at : 1.6500000000000001 with value = 961 arg_min : 1.6500000000000001 min_score : 961{-90.0: 906, -85.0: 871, -80.0: 858, -75.0: 866, -70.0: 852, -65.0: 864, -60.0: 855, -55.0: 853, -50.0: 961, -45.0: 1180, -40.0: 1412, -35.0: 1690, -30.0: 1959, -25.0: 2243, -20.0: 2484, -15.0: 2677, -10.0: 2820, -5.0: 2976, 0.0: 3039, 5.0: 3049, 10.0: 3098, 15.0: 3122, 20.0: 3061, 25.0: 2954, 30.0: 2845, 35.0: 2631, 40.0: 2417, 45.0: 2130, 50.0: 1863, 55.0: 1564, 60.0: 1439, 65.0: 1326, 70.0: 1220, 75.0: 1142, 80.0: 1056, 85.0: 986} [(-70.0, 852), (-55.0, 853), (-60.0, 855), (-80.0, 858), (-65.0, 864), (-75.0, 866), (-85.0, 871), (-90.0, 906), (-50.0, 961), (85.0, 986), (80.0, 1056), (75.0, 1142), (-45.0, 1180), (70.0, 1220), (65.0, 1326), (-40.0, 1412), (60.0, 1439), (55.0, 1564), (-35.0, 1690), (50.0, 1863), (-30.0, 1959), (45.0, 2130), (-25.0, 2243), (40.0, 2417), (-20.0, 2484), (35.0, 2631), (-15.0, 2677), (-10.0, 2820), (30.0, 2845), (25.0, 2954), (-5.0, 2976), (0.0, 3039), (5.0, 3049), (20.0, 3061), (10.0, 3098), (15.0, 3122)] arg_min reach at : -70.0 with value = 852 arg_min : -70.0 min_score : 852{12.75: 4966, 14.34375: 4695, 15.9375: 4364, 17.53125: 3945, 19.125: 3465, 20.71875: 2958, 22.3125: 2415, 23.90625: 1841, 25.5: 1295, 27.09375: 936, 28.6875: 847, 30.28125: 852, 31.875: 883, 33.46875: 997, 35.0625: 1322, 36.65625: 1859} [(28.6875, 847), (30.28125, 852), (31.875, 883), (27.09375, 936), (33.46875, 997), (25.5, 1295), (35.0625, 1322), (23.90625, 1841), (36.65625, 1859), (22.3125, 2415), (20.71875, 2958), (19.125, 3465), (17.53125, 3945), (15.9375, 4364), (14.34375, 4695), (12.75, 4966)] arg_min reach at : 28.6875 with value = 847 arg_min : 28.6875 min_score : 847{13.5: 1489, 15.1875: 1374, 16.875: 1244, 18.5625: 1126, 20.25: 996, 21.9375: 893, 23.625: 847, 25.3125: 933, 27.0: 1153, 28.6875: 1446, 30.375: 1827, 32.0625: 2217, 33.75: 2658, 35.4375: 3137, 37.125: 3653, 38.8125: 4142} [(23.625, 847), (21.9375, 893), (25.3125, 933), (20.25, 996), (18.5625, 1126), (27.0, 1153), (16.875, 1244), (15.1875, 1374), (28.6875, 1446), (13.5, 1489), (30.375, 1827), (32.0625, 2217), (33.75, 2658), (35.4375, 3137), (37.125, 3653), (38.8125, 4142)] arg_min reach at : 23.625 with value = 847 arg_min : 23.625 min_score : 847{13.5: 1870, 15.1875: 1783, 16.875: 1676, 18.5625: 1564, 20.25: 1442, 21.9375: 1302, 23.625: 1153, 25.3125: 997, 27.0: 847, 28.6875: 864, 30.375: 1163, 32.0625: 1613, 33.75: 2300, 35.4375: 3191, 37.125: 4251, 38.8125: 5356} [(27.0, 847), (28.6875, 864), (25.3125, 997), (23.625, 1153), (30.375, 1163), (21.9375, 1302), (20.25, 1442), (18.5625, 1564), (32.0625, 1613), (16.875, 1676), (15.1875, 1783), (13.5, 1870), (33.75, 2300), (35.4375, 3191), (37.125, 4251), (38.8125, 5356)] arg_min reach at : 27.0 with value = 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 x0 : 103 y1 : 396 width : 35, height : 38, area : 1330, score : 1.0 x0 : 93 y1 : 396 width : 51, height : 36, area : 1836, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! (57, 52) (52, 43) [57, 52] (57, 52) score : 7970 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [10.75, 32.25, 1.34375]), ('yc', [13.0, 39.0, 1.625]), ('radius', [13.0, 39.0, 1.625])] {0.5: 16167, 0.55: 14430, 0.6000000000000001: 12864, 0.65: 11529, 0.7000000000000001: 10346, 0.75: 9256, 0.8: 8418, 0.8500000000000001: 7609, 0.9: 6862, 0.9500000000000001: 6277, 1.0: 5482, 1.05: 4840, 1.1: 4264, 1.1500000000000001: 3773, 1.2000000000000002: 3275, 1.25: 2948, 1.3: 2727, 1.35: 2514, 1.4000000000000001: 2254, 1.4500000000000002: 2155, 1.5: 1986, 1.55: 1892, 1.6: 1846, 1.6500000000000001: 1782, 1.7000000000000002: 1717, 1.75: 1656, 1.8: 1641, 1.85: 1602, 1.9000000000000001: 1587, 1.9500000000000002: 1576} [(1.9500000000000002, 1576), (1.9000000000000001, 1587), (1.85, 1602), (1.8, 1641), (1.75, 1656), (1.7000000000000002, 1717), (1.6500000000000001, 1782), (1.6, 1846), (1.55, 1892), (1.5, 1986), (1.4500000000000002, 2155), (1.4000000000000001, 2254), (1.35, 2514), (1.3, 2727), (1.25, 2948), (1.2000000000000002, 3275), (1.1500000000000001, 3773), (1.1, 4264), (1.05, 4840), (1.0, 5482), (0.9500000000000001, 6277), (0.9, 6862), (0.8500000000000001, 7609), (0.8, 8418), (0.75, 9256), (0.7000000000000001, 10346), (0.65, 11529), (0.6000000000000001, 12864), (0.55, 14430), (0.5, 16167)] arg_min reach at : 1.9500000000000002 with value = 1576 | arg_min : 1.9500000000000002 min_score : 1576{-90.0: 2291, -85.0: 2369, -80.0: 2390, -75.0: 2401, -70.0: 2341, -65.0: 2390, -60.0: 2418, -55.0: 2445, -50.0: 2599, -45.0: 2701, -40.0: 2755, -35.0: 2787, -30.0: 2753, -25.0: 2678, -20.0: 2611, -15.0: 2478, -10.0: 2313, -5.0: 2098, 0.0: 1870, 5.0: 1702, 10.0: 1576, 15.0: 1422, 20.0: 1258, 25.0: 1061, 30.0: 872, 35.0: 752, 40.0: 632, 45.0: 677, 50.0: 872, 55.0: 1070, 60.0: 1274, 65.0: 1477, 70.0: 1637, 75.0: 1895, 80.0: 2049, 85.0: 2193} [(40.0, 632), (45.0, 677), (35.0, 752), (30.0, 872), (50.0, 872), (25.0, 1061), (55.0, 1070), (20.0, 1258), (60.0, 1274), (15.0, 1422), (65.0, 1477), (10.0, 1576), (70.0, 1637), (5.0, 1702), (0.0, 1870), (75.0, 1895), (80.0, 2049), (-5.0, 2098), (85.0, 2193), (-90.0, 2291), (-10.0, 2313), (-70.0, 2341), (-85.0, 2369), (-80.0, 2390), (-65.0, 2390), (-75.0, 2401), (-60.0, 2418), (-55.0, 2445), (-15.0, 2478), (-50.0, 2599), (-20.0, 2611), (-25.0, 2678), (-45.0, 2701), (-30.0, 2753), (-40.0, 2755), (-35.0, 2787)] arg_min reach at : 40.0 with value = 632 | arg_min : 40.0 min_score : 632{10.75: 982, 12.09375: 841, 13.4375: 751, 14.78125: 672, 16.125: 624, 17.46875: 599, 18.8125: 575, 20.15625: 561, 21.5: 632, 22.84375: 895, 24.1875: 1257, 25.53125: 1708, 26.875: 2192, 28.21875: 2686, 29.5625: 3202, 30.90625: 3701} [(20.15625, 561), (18.8125, 575), (17.46875, 599), (16.125, 624), (21.5, 632), (14.78125, 672), (13.4375, 751), (12.09375, 841), (22.84375, 895), (10.75, 982), (24.1875, 1257), (25.53125, 1708), (26.875, 2192), (28.21875, 2686), (29.5625, 3202), (30.90625, 3701)] arg_min reach at : 20.15625 with value = 561 | arg_min : 20.15625 min_score : 561{13.0: 3254, 14.625: 2871, 16.25: 2436, 17.875: 1977, 19.5: 1474, 21.125: 1071, 22.75: 835, 24.375: 629, 26.0: 561, 27.625: 716, 29.25: 937, 30.875: 1286, 32.5: 1738, 34.125: 2226, 35.75: 2782, 37.375: 3291} [(26.0, 561), (24.375, 629), (27.625, 716), (22.75, 835), (29.25, 937), (21.125, 1071), (30.875, 1286), (19.5, 1474), (32.5, 1738), (17.875, 1977), (34.125, 2226), (16.25, 2436), (35.75, 2782), (14.625, 2871), (13.0, 3254), (37.375, 3291)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{13.0: 1371, 14.625: 1301, 16.25: 1217, 17.875: 1127, 19.5: 1028, 21.125: 926, 22.75: 811, 24.375: 683, 26.0: 561, 27.625: 624, 29.25: 1026, 30.875: 1508, 32.5: 2147, 34.125: 2981, 35.75: 3949, 37.375: 5069} [(26.0, 561), (27.625, 624), (24.375, 683), (22.75, 811), (21.125, 926), (29.25, 1026), (19.5, 1028), (17.875, 1127), (16.25, 1217), (14.625, 1301), (13.0, 1371), (30.875, 1508), (32.5, 2147), (34.125, 2981), (35.75, 3949), (37.375, 5069)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{0.5: 13327, 0.55: 12420, 0.6000000000000001: 11540, 0.65: 10560, 0.7000000000000001: 9652, 0.75: 8782, 0.8: 7834, 0.8500000000000001: 7061, 0.9: 6294, 0.9500000000000001: 5633, 1.0: 4916, 1.05: 4424, 1.1: 3865, 1.1500000000000001: 3323, 1.2000000000000002: 2817, 1.25: 2401, 1.3: 1935, 1.35: 1600, 1.4000000000000001: 1218, 1.4500000000000002: 1019, 1.5: 834, 1.55: 693, 1.6: 614, 1.6500000000000001: 568, 1.7000000000000002: 549, 1.75: 529, 1.8: 520, 1.85: 521, 1.9000000000000001: 534, 1.9500000000000002: 561} [(1.8, 520), (1.85, 521), (1.75, 529), (1.9000000000000001, 534), (1.7000000000000002, 549), (1.9500000000000002, 561), (1.6500000000000001, 568), (1.6, 614), (1.55, 693), (1.5, 834), (1.4500000000000002, 1019), (1.4000000000000001, 1218), (1.35, 1600), (1.3, 1935), (1.25, 2401), (1.2000000000000002, 2817), (1.1500000000000001, 3323), (1.1, 3865), (1.05, 4424), (1.0, 4916), (0.9500000000000001, 5633), (0.9, 6294), (0.8500000000000001, 7061), (0.8, 7834), (0.75, 8782), (0.7000000000000001, 9652), (0.65, 10560), (0.6000000000000001, 11540), (0.55, 12420), (0.5, 13327)] arg_min reach at : 1.8 with value = 520 arg_min : 1.8 min_score : 520{-90.0: 2114, -85.0: 2268, -80.0: 2343, -75.0: 2405, -70.0: 2409, -65.0: 2435, -60.0: 2467, -55.0: 2514, -50.0: 2590, -45.0: 2654, -40.0: 2632, -35.0: 2610, -30.0: 2565, -25.0: 2526, -20.0: 2435, -15.0: 2330, -10.0: 2181, -5.0: 2013, 0.0: 1792, 5.0: 1584, 10.0: 1389, 15.0: 1208, 20.0: 1038, 25.0: 843, 30.0: 673, 35.0: 564, 40.0: 520, 45.0: 597, 50.0: 764, 55.0: 963, 60.0: 1158, 65.0: 1390, 70.0: 1573, 75.0: 1800, 80.0: 1969, 85.0: 2070} [(40.0, 520), (35.0, 564), (45.0, 597), (30.0, 673), (50.0, 764), (25.0, 843), (55.0, 963), (20.0, 1038), (60.0, 1158), (15.0, 1208), (10.0, 1389), (65.0, 1390), (70.0, 1573), (5.0, 1584), (0.0, 1792), (75.0, 1800), (80.0, 1969), (-5.0, 2013), (85.0, 2070), (-90.0, 2114), (-10.0, 2181), (-85.0, 2268), (-15.0, 2330), (-80.0, 2343), (-75.0, 2405), (-70.0, 2409), (-65.0, 2435), (-20.0, 2435), (-60.0, 2467), (-55.0, 2514), (-25.0, 2526), (-30.0, 2565), (-50.0, 2590), (-35.0, 2610), (-40.0, 2632), (-45.0, 2654)] arg_min reach at : 40.0 with value = 520 arg_min : 40.0 min_score : 520{10.75: 1004, 12.09375: 902, 13.4375: 775, 14.78125: 655, 16.125: 580, 17.46875: 515, 18.8125: 494, 20.15625: 520, 21.5: 692, 22.84375: 1028, 24.1875: 1466, 25.53125: 2001, 26.875: 2490, 28.21875: 3027, 29.5625: 3629, 30.90625: 4163} [(18.8125, 494), (17.46875, 515), (20.15625, 520), (16.125, 580), (14.78125, 655), (21.5, 692), (13.4375, 775), (12.09375, 902), (10.75, 1004), (22.84375, 1028), (24.1875, 1466), (25.53125, 2001), (26.875, 2490), (28.21875, 3027), (29.5625, 3629), (30.90625, 4163)] arg_min reach at : 18.8125 with value = 494 arg_min : 18.8125 min_score : 494{13.0: 3121, 14.625: 2763, 16.25: 2397, 17.875: 1962, 19.5: 1409, 21.125: 1056, 22.75: 809, 24.375: 589, 26.0: 494, 27.625: 635, 29.25: 987, 30.875: 1406, 32.5: 1937, 34.125: 2442, 35.75: 2987, 37.375: 3537} [(26.0, 494), (24.375, 589), (27.625, 635), (22.75, 809), (29.25, 987), (21.125, 1056), (30.875, 1406), (19.5, 1409), (32.5, 1937), (17.875, 1962), (16.25, 2397), (34.125, 2442), (14.625, 2763), (35.75, 2987), (13.0, 3121), (37.375, 3537)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494{13.0: 1347, 14.625: 1266, 16.25: 1182, 17.875: 1086, 19.5: 979, 21.125: 865, 22.75: 737, 24.375: 616, 26.0: 494, 27.625: 559, 29.25: 954, 30.875: 1524, 32.5: 2347, 34.125: 3350, 35.75: 4468, 37.375: 5635} [(26.0, 494), (27.625, 559), (24.375, 616), (22.75, 737), (21.125, 865), (29.25, 954), (19.5, 979), (17.875, 1086), (16.25, 1182), (14.625, 1266), (13.0, 1347), (30.875, 1524), (32.5, 2347), (34.125, 3350), (35.75, 4468), (37.375, 5635)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 x0 : 104 y1 : 292 width : 27, height : 36, area : 972, score : 1.0 x0 : 102 y1 : 288 width : 39, height : 43, area : 1677, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22242923 Result OK ! uploaded one batch 0 Elapsed time : 20.71440076828003 After datou_step_exec type output : time spend for datou_step_exec : 26.045247316360474 time spend to save output : 2.9325485229492188e-05 total time spend for step 1 : 26.045276641845703 step2:tile Thu Apr 10 21:38:40 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 : ['temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'] We expect there is only one output and this part is used while all output are not tuple or array 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 input_args_next_step, len :1, first value : [('temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1351306950, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1351306951, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1351306952, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1351306953} map_photo_id_path_extension : {937852786: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1351306950: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1351306951: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1351306952: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1351306953: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}} map_subphoto_mainphoto : {1351306950: 937852786, 1351306951: 937852786, 1351306952: 937852786, 1351306953: 937852786} verbose : True param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786,937852786) and `type` in (520) Loaded 4 chid ids of type : 520 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3754894674,3754894675,3754894676,3754894677) ++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3754894674,3754894675,3754894676,3754894677) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3754894674,3754894675,3754894676,3754894677) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22242924 with name tile_taggage_varroa feed_id_new_photos : 22242924 filename : temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] calcul des nouveaux crops pour le tile x0:0,x1:480,y0:0,y1:480 calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale chi selectionnes : [, , , ] new_crops_tiles : 1 crop_transformed : 4 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(937852786, 2090988864, 17, 0, 480, 0, 480, 1.0)] list_photo_ids_cropped : [937852786] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (17) Loaded 1 chid ids of type : 17 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165084) treat the image : temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.009954214096069336 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744313927_172844 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (22242924, 1351306989, 0, NOW()) 1 we have uploaded 1 photos in the portfolio 22242924 Importing ! upload mediasElapsed time : 0.8639824390411377 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1351306989, 0)] Saving 4 CHIs. list_chi_tile : [": {'photo_id': 1351306989, 'hashtag_id': 2087736828, 'type': 521, 'x0': 432, 'x1': 467, 'y0': 303, 'y1': 355, 'score': 1.0, 'id': 0, 'points': ['463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346', '463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1351306989, 'hashtag_id': 2087736828, 'type': 521, 'x0': 419, 'x1': 445, 'y0': 434, 'y1': 480, 'score': 1.0, 'id': 0, 'points': ['451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455', '451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1351306989, 'hashtag_id': 2087736828, 'type': 521, 'x0': 93, 'x1': 144, 'y0': 360, 'y1': 396, 'score': 1.0, 'id': 0, 'points': ['112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372', '112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1351306989, 'hashtag_id': 2087736828, 'type': 521, 'x0': 102, 'x1': 141, 'y0': 245, 'y1': 288, 'score': 1.0, 'id': 0, 'points': ['124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292', '124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}"] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 4 chid ids of type : 521 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.9424550533294678 map_pid_results : {'1351306989': ['temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 7.823584794998169 time spend to save output : 6.0558319091796875e-05 total time spend for step 2 : 7.823645353317261 step3:rotate Thu Apr 10 21:38:48 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 : {'1351306989': ['temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} input_args_next_step : {'1351306989': ()} output_args : {'1351306989': ['temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} args : 1351306989 depend.output_id : 0 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 input_args_next_step, len :1, first value : ('temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1351306950, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1351306951, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1351306952, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1351306953, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1351306989} map_photo_id_path_extension : {937852786: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1351306950: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1351306951: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1351306952: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1351306953: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1351306989: {'path': 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}} map_subphoto_mainphoto : {1351306950: 937852786, 1351306951: 937852786, 1351306952: 937852786, 1351306953: 937852786, 1351306989: 937852786} Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1351306989) and `type` in (521) Loaded 4 chid ids of type : 521 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3754896583,3754896584,3754896582,3754896581) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3754896581. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3754896582. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3754896583. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3754896584. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3754896583,3754896584,3754896582,3754896581) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3754896583,3754896584,3754896582,3754896581) map_chi : {1351306989: [, , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 22242925 photo_id in download_rotate_and_save : 1351306989 list_chi_loc : 4 Use all angle ! Rotation of photo 1351306989 of 0 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 0 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004444122314453125 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0022716522216796875 .time for calcul the mask position with numpy : 0.00046563148498535156 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002373218536376953 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 25, 'x1': 61, 'y0': 268, 'y1': 319, 'score': 1.0, 'id': None, 'points': ['32,279,40,270,50,268,57,271,61,281,60,294,54,307,46,316,37,319,29,315,25,305,26,292'], 'sub_photo_id': 0, 'rles': [(-1, 48, 268, 4), (-1, 43, 269, 11), (-1, 40, 270, 16), (-1, 39, 271, 19), (-1, 38, 272, 20), (-1, 37, 273, 22), (-1, 36, 274, 23), (-1, 36, 275, 24), (-1, 35, 276, 25), (-1, 34, 277, 26), (-1, 33, 278, 28), (-1, 32, 279, 29), (-1, 32, 280, 30), (-1, 31, 281, 31), (-1, 31, 282, 31), (-1, 30, 283, 32), (-1, 30, 284, 32), (-1, 29, 285, 33), (-1, 29, 286, 33), (-1, 28, 287, 34), (-1, 28, 288, 33), (-1, 27, 289, 34), (-1, 27, 290, 34), (-1, 26, 291, 35), (-1, 26, 292, 35), (-1, 26, 293, 35), (-1, 26, 294, 35), (-1, 26, 295, 35), (-1, 26, 296, 34), (-1, 26, 297, 34), (-1, 26, 298, 33), (-1, 25, 299, 34), (-1, 25, 300, 33), (-1, 25, 301, 33), (-1, 25, 302, 32), (-1, 25, 303, 32), (-1, 25, 304, 31), (-1, 25, 305, 31), (-1, 25, 306, 30), (-1, 26, 307, 29), (-1, 26, 308, 28), (-1, 27, 309, 26), (-1, 27, 310, 25), (-1, 27, 311, 24), (-1, 28, 312, 23), (-1, 28, 313, 22), (-1, 29, 314, 20), (-1, 29, 315, 19), (-1, 31, 316, 16), (-1, 33, 317, 12), (-1, 35, 318, 7), (-1, 37, 319, 2)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 13, 'x1': 56, 'y0': 174, 'y1': 213, 'score': 1.0, 'id': None, 'points': ['44,213,32,210,22,201,15,191,13,182,16,175,25,174,36,177,47,186,54,196,56,205,52,212'], 'sub_photo_id': 0, 'rles': [(-1, 21, 174, 6), (-1, 16, 175, 15), (-1, 16, 176, 19), (-1, 15, 177, 22), (-1, 15, 178, 23), (-1, 14, 179, 26), (-1, 14, 180, 27), (-1, 13, 181, 29), (-1, 13, 182, 30), (-1, 13, 183, 31), (-1, 13, 184, 33), (-1, 14, 185, 33), (-1, 14, 186, 34), (-1, 14, 187, 35), (-1, 14, 188, 35), (-1, 15, 189, 35), (-1, 15, 190, 36), (-1, 15, 191, 36), (-1, 16, 192, 36), (-1, 16, 193, 37), (-1, 17, 194, 37), (-1, 18, 195, 36), (-1, 18, 196, 37), (-1, 19, 197, 36), (-1, 20, 198, 35), (-1, 21, 199, 35), (-1, 21, 200, 35), (-1, 22, 201, 34), (-1, 23, 202, 33), (-1, 24, 203, 33), (-1, 25, 204, 32), (-1, 26, 205, 31), (-1, 28, 206, 28), (-1, 29, 207, 27), (-1, 30, 208, 25), (-1, 31, 209, 24), (-1, 32, 210, 22), (-1, 35, 211, 19), (-1, 39, 212, 14), (-1, 43, 213, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 15 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 15 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] 15 [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003566741943359375 nb_pixel_total : 694 time to create 1 rle with old method : 0.0011272430419921875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003590583801269531 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0019683837890625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -2, 'hashtag_id': 2087736828, 'type': 529, 'x0': 24, 'x1': 72, 'y0': 209, 'y1': 242, 'score': 1.0, 'id': None, 'points': ['62,241,50,242,38,236,28,228,24,220,25,212,34,209,45,209,58,215,67,222,72,231,70,238'], 'sub_photo_id': 0, 'rles': [(-1, 33, 209, 3), (-1, 37, 209, 9), (-1, 30, 210, 18), (-1, 49, 210, 1), (-1, 30, 211, 21), (-1, 26, 212, 27), (-1, 26, 213, 29), (-1, 26, 214, 32), (-1, 25, 215, 35), (-1, 25, 216, 36), (-1, 25, 217, 37), (-1, 25, 218, 38), (-1, 24, 219, 40), (-1, 25, 220, 40), (-1, 25, 221, 42), (-1, 25, 222, 43), (-1, 26, 223, 43), (-1, 27, 224, 42), (-1, 27, 225, 43), (-1, 28, 226, 43), (-1, 29, 227, 42), (-1, 29, 228, 42), (-1, 30, 229, 43), (-1, 30, 230, 43), (-1, 32, 231, 41), (-1, 33, 232, 39), (-1, 34, 233, 39), (-1, 36, 234, 36), (-1, 37, 235, 35), (-1, 38, 236, 34), (-1, 40, 237, 32), (-1, 42, 238, 30), (-1, 43, 239, 28), (-1, 46, 240, 22), (-1, 48, 241, 19), (-1, 50, 242, 15)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 30 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 30 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 0.5 ] [-0.5 0.8660254]] 30 [[ 0.8660254 0.5 ] [-0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00040078163146972656 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003719329833984375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004241466522216797 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014455318450927734 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -3, 'hashtag_id': 2087736828, 'type': 529, 'x0': 44, 'x1': 93, 'y0': 238, 'y1': 268, 'score': 1.0, 'id': None, 'points': ['86,264,74,268,61,265,50,260,44,253,43,245,50,240,61,237,75,239,87,245,93,251,93,259'], 'sub_photo_id': 0, 'rles': [(-1, 59, 238, 9), (-1, 55, 239, 17), (-1, 73, 239, 1), (-1, 51, 240, 27), (-1, 49, 241, 30), (-1, 48, 242, 34), (-1, 48, 243, 36), (-1, 46, 244, 40), (-1, 45, 245, 43), (-1, 44, 246, 45), (-1, 44, 247, 46), (-1, 44, 248, 47), (-1, 44, 249, 48), (-1, 44, 250, 49), (-1, 44, 251, 50), (-1, 44, 252, 50), (-1, 44, 253, 50), (-1, 45, 254, 49), (-1, 45, 255, 49), (-1, 47, 256, 47), (-1, 48, 257, 46), (-1, 48, 258, 46), (-1, 50, 259, 44), (-1, 51, 260, 43), (-1, 52, 261, 41), (-1, 54, 262, 37), (-1, 56, 263, 35), (-1, 59, 264, 31), (-1, 60, 265, 28), (-1, 63, 266, 21), (-1, 67, 267, 2), (-1, 70, 267, 11), (-1, 74, 268, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 45 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 45 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] 45 [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003955364227294922 nb_pixel_total : 143 time to create 1 rle with old method : 0.0002777576446533203 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041675567626953125 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0014717578887939453 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -4, 'hashtag_id': 2087736828, 'type': 529, 'x0': 69, 'x1': 121, 'y0': 258, 'y1': 286, 'score': 1.0, 'id': None, 'points': ['115,279,105,285,91,286,79,284,72,279,69,272,74,265,84,259,98,258,110,260,118,265,120,273'], 'sub_photo_id': 0, 'rles': [(-1, 96, 258, 5), (-1, 88, 259, 17), (-1, 84, 260, 26), (-1, 111, 260, 1), (-1, 82, 261, 31), (-1, 81, 262, 34), (-1, 78, 263, 38), (-1, 77, 264, 42), (-1, 75, 265, 45), (-1, 74, 266, 46), (-1, 73, 267, 47), (-1, 72, 268, 48), (-1, 72, 269, 49), (-1, 71, 270, 50), (-1, 70, 271, 51), (-1, 69, 272, 53), (-1, 70, 273, 52), (-1, 70, 274, 51), (-1, 70, 275, 50), (-1, 71, 276, 48), (-1, 71, 277, 49), (-1, 72, 278, 47), (-1, 71, 279, 47), (-1, 72, 280, 45), (-1, 73, 281, 41), (-1, 76, 282, 37), (-1, 77, 283, 34), (-1, 79, 284, 31), (-1, 82, 285, 25), (-1, 85, 286, 21)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 60 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 60 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 0.8660254] [-0.8660254 0.5 ]] 60 [[ 0.5 0.8660254] [-0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00047016143798828125 nb_pixel_total : 414 time to create 1 rle with old method : 0.0005490779876708984 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00043082237243652344 nb_pixel_total : 1159 time to create 1 rle with old method : 0.00148773193359375 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -5, 'hashtag_id': 2087736828, 'type': 529, 'x0': 102, 'x1': 152, 'y0': 269, 'y1': 300, 'score': 1.0, 'id': None, 'points': ['148,286,140,295,127,299,115,300,106,297,101,291,105,283,113,275,126,270,139,268,147,271,151,278'], 'sub_photo_id': 0, 'rles': [(-1, 132, 269, 1), (-1, 135, 269, 6), (-1, 126, 270, 18), (-1, 124, 271, 24), (-1, 121, 272, 28), (-1, 118, 273, 32), (-1, 116, 274, 34), (-1, 113, 275, 38), (-1, 112, 276, 39), (-1, 111, 277, 41), (-1, 111, 278, 42), (-1, 109, 279, 44), (-1, 108, 280, 44), (-1, 108, 281, 44), (-1, 106, 282, 45), (-1, 105, 283, 47), (-1, 105, 284, 46), (-1, 104, 285, 46), (-1, 104, 286, 46), (-1, 104, 287, 45), (-1, 104, 288, 44), (-1, 103, 289, 44), (-1, 103, 290, 43), (-1, 102, 291, 43), (-1, 102, 292, 42), (-1, 103, 293, 41), (-1, 104, 294, 38), (-1, 104, 295, 37), (-1, 105, 296, 33), (-1, 106, 297, 28), (-1, 107, 298, 27), (-1, 111, 299, 19), (-1, 112, 300, 1), (-1, 114, 300, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 75 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 75 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] 75 [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004265308380126953 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0014872550964355469 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041294097900390625 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014603137969970703 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004146099090576172 nb_pixel_total : 264 time to create 1 rle with old method : 0.000453948974609375 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -6, 'hashtag_id': 2087736828, 'type': 529, 'x0': 138, 'x1': 183, 'y0': 271, 'y1': 308, 'score': 1.0, 'id': None, 'points': ['182,285,176,295,164,303,153,307,144,306,138,302,139,293,145,283,156,275,168,270,177,271,183,277'], 'sub_photo_id': 0, 'rles': [(-1, 168, 271, 10), (-1, 165, 272, 14), (-1, 162, 273, 18), (-1, 160, 274, 22), (-1, 157, 275, 25), (-1, 155, 276, 28), (-1, 154, 277, 30), (-1, 153, 278, 31), (-1, 152, 279, 32), (-1, 150, 280, 33), (-1, 148, 281, 36), (-1, 148, 282, 36), (-1, 146, 283, 38), (-1, 145, 284, 38), (-1, 144, 285, 39), (-1, 144, 286, 39), (-1, 143, 287, 39), (-1, 143, 288, 38), (-1, 142, 289, 39), (-1, 141, 290, 40), (-1, 141, 291, 38), (-1, 140, 292, 39), (-1, 140, 293, 39), (-1, 139, 294, 39), (-1, 139, 295, 38), (-1, 139, 296, 38), (-1, 139, 297, 36), (-1, 139, 298, 35), (-1, 139, 299, 33), (-1, 139, 300, 32), (-1, 138, 301, 32), (-1, 138, 302, 30), (-1, 139, 303, 27), (-1, 140, 304, 25), (-1, 142, 305, 19), (-1, 143, 306, 17), (-1, 143, 307, 14), (-1, 150, 308, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 90 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 90 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00043129920959472656 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0024836063385009766 .time for calcul the mask position with numpy : 0.0004215240478515625 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0019440650939941406 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 268, 'x1': 319, 'y0': 258, 'y1': 294, 'score': 1.0, 'id': None, 'points': ['279,287,270,279,268,269,271,262,281,258,294,259,307,265,316,273,319,282,315,290,305,294,292,293'], 'sub_photo_id': 0, 'rles': [(-1, 280, 258, 8), (-1, 278, 259, 18), (-1, 275, 260, 23), (-1, 273, 261, 27), (-1, 271, 262, 31), (-1, 271, 263, 33), (-1, 270, 264, 36), (-1, 270, 265, 38), (-1, 269, 266, 40), (-1, 269, 267, 41), (-1, 268, 268, 43), (-1, 268, 269, 45), (-1, 268, 270, 46), (-1, 268, 271, 47), (-1, 269, 272, 47), (-1, 269, 273, 48), (-1, 269, 274, 48), (-1, 269, 275, 49), (-1, 269, 276, 49), (-1, 270, 277, 48), (-1, 270, 278, 49), (-1, 270, 279, 49), (-1, 271, 280, 48), (-1, 272, 281, 48), (-1, 273, 282, 47), (-1, 274, 283, 45), (-1, 276, 284, 43), (-1, 277, 285, 41), (-1, 278, 286, 40), (-1, 279, 287, 38), (-1, 281, 288, 36), (-1, 283, 289, 33), (-1, 285, 290, 31), (-1, 287, 291, 27), (-1, 289, 292, 23), (-1, 291, 293, 18), (-1, 299, 294, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 174, 'x1': 213, 'y0': 263, 'y1': 306, 'score': 1.0, 'id': None, 'points': ['213,275,210,287,201,297,191,304,182,306,175,303,174,294,177,283,186,272,196,265,205,263,212,267'], 'sub_photo_id': 0, 'rles': [(-1, 203, 263, 3), (-1, 199, 264, 9), (-1, 196, 265, 14), (-1, 194, 266, 18), (-1, 193, 267, 20), (-1, 192, 268, 21), (-1, 190, 269, 23), (-1, 189, 270, 24), (-1, 187, 271, 27), (-1, 186, 272, 28), (-1, 185, 273, 29), (-1, 184, 274, 30), (-1, 184, 275, 30), (-1, 183, 276, 31), (-1, 182, 277, 31), (-1, 181, 278, 32), (-1, 180, 279, 33), (-1, 179, 280, 34), (-1, 179, 281, 33), (-1, 178, 282, 34), (-1, 177, 283, 35), (-1, 177, 284, 35), (-1, 176, 285, 35), (-1, 176, 286, 35), (-1, 176, 287, 35), (-1, 176, 288, 34), (-1, 175, 289, 34), (-1, 175, 290, 33), (-1, 175, 291, 32), (-1, 175, 292, 31), (-1, 174, 293, 32), (-1, 174, 294, 31), (-1, 174, 295, 30), (-1, 174, 296, 29), (-1, 174, 297, 28), (-1, 174, 298, 27), (-1, 175, 299, 24), (-1, 175, 300, 23), (-1, 175, 301, 22), (-1, 175, 302, 20), (-1, 175, 303, 19), (-1, 177, 304, 15), (-1, 179, 305, 10), (-1, 181, 306, 4)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 105 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 105 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] 105 [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004534721374511719 nb_pixel_total : 694 time to create 1 rle with old method : 0.0009503364562988281 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004527568817138672 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0014672279357910156 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -8, 'hashtag_id': 2087736828, 'type': 529, 'x0': 209, 'x1': 242, 'y0': 248, 'y1': 296, 'score': 1.0, 'id': None, 'points': ['241,257,242,269,236,281,228,291,220,295,212,294,209,285,209,274,215,261,222,252,231,247,238,249'], 'sub_photo_id': 0, 'rles': [(-1, 229, 248, 3), (-1, 233, 248, 1), (-1, 229, 249, 10), (-1, 226, 250, 14), (-1, 225, 251, 15), (-1, 223, 252, 17), (-1, 222, 253, 19), (-1, 221, 254, 21), (-1, 221, 255, 21), (-1, 220, 256, 23), (-1, 219, 257, 24), (-1, 218, 258, 25), (-1, 217, 259, 26), (-1, 216, 260, 27), (-1, 215, 261, 28), (-1, 215, 262, 28), (-1, 214, 263, 29), (-1, 214, 264, 29), (-1, 214, 265, 29), (-1, 213, 266, 30), (-1, 213, 267, 30), (-1, 212, 268, 31), (-1, 212, 269, 31), (-1, 211, 270, 32), (-1, 210, 271, 32), (-1, 211, 272, 31), (-1, 210, 273, 31), (-1, 210, 274, 31), (-1, 209, 275, 31), (-1, 209, 276, 31), (-1, 209, 277, 31), (-1, 209, 278, 30), (-1, 209, 279, 29), (-1, 209, 280, 29), (-1, 209, 281, 28), (-1, 209, 282, 28), (-1, 209, 283, 27), (-1, 210, 284, 25), (-1, 209, 285, 25), (-1, 209, 286, 25), (-1, 209, 287, 24), (-1, 210, 288, 22), (-1, 210, 289, 21), (-1, 210, 290, 21), (-1, 212, 291, 17), (-1, 212, 292, 15), (-1, 212, 293, 14), (-1, 212, 294, 12), (-1, 215, 295, 8), (-1, 219, 296, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 120 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 120 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 0.8660254] [-0.8660254 -0.5 ]] 120 [[-0.5 0.8660254] [-0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004284381866455078 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003533363342285156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004279613494873047 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0017540454864501953 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -9, 'hashtag_id': 2087736828, 'type': 529, 'x0': 238, 'x1': 268, 'y0': 227, 'y1': 276, 'score': 1.0, 'id': None, 'points': ['264,233,268,245,265,258,260,269,253,275,245,276,240,269,237,258,239,244,245,232,251,226,259,226'], 'sub_photo_id': 0, 'rles': [(-1, 251, 227, 10), (-1, 250, 228, 12), (-1, 249, 229, 13), (-1, 248, 230, 16), (-1, 247, 231, 18), (-1, 246, 232, 19), (-1, 245, 233, 21), (-1, 245, 234, 21), (-1, 244, 235, 22), (-1, 244, 236, 22), (-1, 243, 237, 24), (-1, 243, 238, 24), (-1, 242, 239, 25), (-1, 242, 240, 26), (-1, 242, 241, 26), (-1, 241, 242, 27), (-1, 240, 243, 28), (-1, 240, 244, 29), (-1, 240, 245, 29), (-1, 240, 246, 29), (-1, 239, 247, 29), (-1, 240, 248, 28), (-1, 239, 249, 29), (-1, 239, 250, 29), (-1, 239, 251, 28), (-1, 239, 252, 29), (-1, 238, 253, 30), (-1, 238, 254, 29), (-1, 238, 255, 29), (-1, 238, 256, 29), (-1, 238, 257, 29), (-1, 238, 258, 28), (-1, 238, 259, 28), (-1, 238, 260, 28), (-1, 238, 261, 27), (-1, 239, 262, 25), (-1, 239, 263, 25), (-1, 239, 264, 25), (-1, 239, 265, 24), (-1, 240, 266, 23), (-1, 240, 267, 22), (-1, 240, 268, 22), (-1, 240, 269, 21), (-1, 241, 270, 19), (-1, 241, 271, 18), (-1, 242, 272, 17), (-1, 244, 273, 13), (-1, 244, 274, 12), (-1, 245, 275, 11), (-1, 246, 276, 8)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 135 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 135 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] 135 [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004360675811767578 nb_pixel_total : 143 time to create 1 rle with old method : 0.00034499168395996094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00044989585876464844 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0014491081237792969 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -10, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 286, 'y0': 199, 'y1': 251, 'score': 1.0, 'id': None, 'points': ['279,204,285,214,286,228,284,240,279,247,272,250,265,245,259,235,258,221,260,209,265,201,273,199'], 'sub_photo_id': 0, 'rles': [(-1, 272, 199, 2), (-1, 269, 200, 6), (-1, 265, 201, 1), (-1, 267, 201, 9), (-1, 277, 201, 1), (-1, 264, 202, 15), (-1, 264, 203, 16), (-1, 264, 204, 17), (-1, 263, 205, 18), (-1, 262, 206, 19), (-1, 262, 207, 20), (-1, 261, 208, 22), (-1, 260, 209, 23), (-1, 261, 210, 23), (-1, 260, 211, 25), (-1, 260, 212, 25), (-1, 260, 213, 25), (-1, 260, 214, 26), (-1, 260, 215, 27), (-1, 259, 216, 28), (-1, 259, 217, 28), (-1, 259, 218, 28), (-1, 259, 219, 28), (-1, 258, 220, 29), (-1, 258, 221, 29), (-1, 258, 222, 29), (-1, 258, 223, 29), (-1, 258, 224, 29), (-1, 259, 225, 28), (-1, 259, 226, 28), (-1, 259, 227, 28), (-1, 259, 228, 28), (-1, 259, 229, 28), (-1, 259, 230, 28), (-1, 259, 231, 28), (-1, 259, 232, 28), (-1, 260, 233, 27), (-1, 260, 234, 27), (-1, 260, 235, 27), (-1, 260, 236, 26), (-1, 261, 237, 25), (-1, 261, 238, 25), (-1, 262, 239, 23), (-1, 263, 240, 22), (-1, 263, 241, 22), (-1, 263, 242, 21), (-1, 264, 243, 20), (-1, 265, 244, 18), (-1, 265, 245, 17), (-1, 266, 246, 16), (-1, 267, 247, 15), (-1, 268, 248, 13), (-1, 270, 249, 8), (-1, 279, 249, 1), (-1, 271, 250, 5), (-1, 272, 251, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 150 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 150 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 0.5 ] [-0.5 -0.8660254]] 150 [[-0.8660254 0.5 ] [-0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004241466522216797 nb_pixel_total : 414 time to create 1 rle with old method : 0.0005893707275390625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004684925079345703 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014376640319824219 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004239082336425781 nb_pixel_total : 1 time to create 1 rle with old method : 5.1975250244140625e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -11, 'hashtag_id': 2087736828, 'type': 529, 'x0': 269, 'x1': 300, 'y0': 168, 'y1': 218, 'score': 1.0, 'id': None, 'points': ['286,171,295,179,299,192,300,204,297,213,291,218,283,214,275,206,270,193,268,180,271,172,278,168'], 'sub_photo_id': 0, 'rles': [(-1, 278, 168, 2), (-1, 277, 169, 5), (-1, 283, 169, 1), (-1, 275, 170, 10), (-1, 273, 171, 14), (-1, 272, 172, 16), (-1, 271, 173, 18), (-1, 271, 174, 19), (-1, 271, 175, 20), (-1, 271, 176, 21), (-1, 270, 177, 24), (-1, 270, 178, 24), (-1, 270, 179, 25), (-1, 269, 180, 27), (-1, 269, 181, 27), (-1, 269, 182, 27), (-1, 269, 183, 28), (-1, 269, 184, 28), (-1, 269, 185, 28), (-1, 270, 186, 27), (-1, 270, 187, 29), (-1, 269, 188, 30), (-1, 270, 189, 29), (-1, 270, 190, 29), (-1, 270, 191, 30), (-1, 270, 192, 30), (-1, 270, 193, 30), (-1, 270, 194, 30), (-1, 271, 195, 29), (-1, 271, 196, 29), (-1, 272, 197, 28), (-1, 272, 198, 29), (-1, 272, 199, 29), (-1, 273, 200, 28), (-1, 273, 201, 28), (-1, 273, 202, 28), (-1, 274, 203, 27), (-1, 274, 204, 27), (-1, 275, 205, 26), (-1, 275, 206, 26), (-1, 275, 207, 25), (-1, 276, 208, 25), (-1, 277, 209, 23), (-1, 279, 210, 20), (-1, 279, 211, 20), (-1, 280, 212, 19), (-1, 282, 213, 17), (-1, 282, 214, 16), (-1, 283, 215, 14), (-1, 285, 216, 11), (-1, 289, 217, 5), (-1, 291, 218, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 165 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 165 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] 165 [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00043964385986328125 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0014841556549072266 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00043010711669921875 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0015187263488769531 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004055500030517578 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004181861877441406 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -12, 'hashtag_id': 2087736828, 'type': 529, 'x0': 271, 'x1': 308, 'y0': 137, 'y1': 182, 'score': 1.0, 'id': None, 'points': ['285,137,295,143,303,155,307,166,306,175,302,181,293,180,283,174,275,163,270,151,271,142,277,136'], 'sub_photo_id': 0, 'rles': [(-1, 276, 137, 4), (-1, 281, 137, 3), (-1, 276, 138, 11), (-1, 274, 139, 14), (-1, 274, 140, 17), (-1, 273, 141, 18), (-1, 272, 142, 22), (-1, 271, 143, 24), (-1, 271, 144, 26), (-1, 271, 145, 26), (-1, 271, 146, 27), (-1, 271, 147, 28), (-1, 271, 148, 28), (-1, 271, 149, 29), (-1, 271, 150, 30), (-1, 271, 151, 31), (-1, 271, 152, 31), (-1, 272, 153, 31), (-1, 272, 154, 31), (-1, 272, 155, 32), (-1, 273, 156, 32), (-1, 273, 157, 32), (-1, 273, 158, 32), (-1, 274, 159, 31), (-1, 274, 160, 32), (-1, 275, 161, 32), (-1, 275, 162, 32), (-1, 275, 163, 32), (-1, 276, 164, 32), (-1, 276, 165, 32), (-1, 277, 166, 31), (-1, 278, 167, 30), (-1, 279, 168, 29), (-1, 280, 169, 28), (-1, 280, 170, 29), (-1, 281, 171, 27), (-1, 281, 172, 27), (-1, 283, 173, 25), (-1, 283, 174, 25), (-1, 284, 175, 24), (-1, 285, 176, 23), (-1, 287, 177, 21), (-1, 289, 178, 17), (-1, 290, 179, 15), (-1, 292, 180, 13), (-1, 294, 181, 10), (-1, 301, 182, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 180 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 180 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003597736358642578 nb_pixel_total : 1389 time to create 1 rle with old method : 0.001714468002319336 .time for calcul the mask position with numpy : 0.0004062652587890625 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014560222625732422 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 294, 'y0': 1, 'y1': 52, 'score': 1.0, 'id': None, 'points': ['287,41,279,50,269,52,262,49,258,39,259,26,265,13,273,4,282,1,290,5,294,15,293,28'], 'sub_photo_id': 0, 'rles': [(-1, 281, 1, 2), (-1, 278, 2, 7), (-1, 275, 3, 12), (-1, 273, 4, 16), (-1, 272, 5, 19), (-1, 271, 6, 20), (-1, 270, 7, 22), (-1, 269, 8, 23), (-1, 269, 9, 24), (-1, 268, 10, 25), (-1, 267, 11, 26), (-1, 266, 12, 28), (-1, 265, 13, 29), (-1, 265, 14, 30), (-1, 264, 15, 31), (-1, 264, 16, 31), (-1, 263, 17, 32), (-1, 263, 18, 32), (-1, 262, 19, 33), (-1, 262, 20, 33), (-1, 261, 21, 34), (-1, 261, 22, 33), (-1, 260, 23, 34), (-1, 260, 24, 34), (-1, 259, 25, 35), (-1, 259, 26, 35), (-1, 259, 27, 35), (-1, 259, 28, 35), (-1, 259, 29, 35), (-1, 259, 30, 34), (-1, 259, 31, 34), (-1, 259, 32, 33), (-1, 258, 33, 34), (-1, 258, 34, 33), (-1, 258, 35, 33), (-1, 258, 36, 32), (-1, 258, 37, 32), (-1, 258, 38, 31), (-1, 258, 39, 31), (-1, 258, 40, 30), (-1, 259, 41, 29), (-1, 259, 42, 28), (-1, 260, 43, 26), (-1, 260, 44, 25), (-1, 260, 45, 24), (-1, 261, 46, 23), (-1, 261, 47, 22), (-1, 262, 48, 20), (-1, 262, 49, 19), (-1, 264, 50, 16), (-1, 266, 51, 11), (-1, 268, 52, 4)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 263, 'x1': 306, 'y0': 107, 'y1': 146, 'score': 1.0, 'id': None, 'points': ['275,107,287,110,297,119,304,129,306,138,303,145,294,146,283,143,272,134,265,124,263,115,267,108'], 'sub_photo_id': 0, 'rles': [(-1, 271, 107, 6), (-1, 267, 108, 14), (-1, 266, 109, 19), (-1, 266, 110, 22), (-1, 265, 111, 24), (-1, 265, 112, 25), (-1, 264, 113, 27), (-1, 264, 114, 28), (-1, 263, 115, 31), (-1, 263, 116, 32), (-1, 263, 117, 33), (-1, 264, 118, 33), (-1, 264, 119, 34), (-1, 264, 120, 35), (-1, 264, 121, 35), (-1, 265, 122, 35), (-1, 265, 123, 36), (-1, 265, 124, 37), (-1, 266, 125, 36), (-1, 266, 126, 37), (-1, 267, 127, 37), (-1, 268, 128, 36), (-1, 269, 129, 36), (-1, 269, 130, 36), (-1, 270, 131, 35), (-1, 271, 132, 35), (-1, 271, 133, 35), (-1, 272, 134, 34), (-1, 273, 135, 33), (-1, 274, 136, 33), (-1, 276, 137, 31), (-1, 277, 138, 30), (-1, 278, 139, 29), (-1, 279, 140, 27), (-1, 280, 141, 26), (-1, 282, 142, 23), (-1, 283, 143, 22), (-1, 285, 144, 19), (-1, 289, 145, 15), (-1, 293, 146, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 195 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 195 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] 195 [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042176246643066406 nb_pixel_total : 727 time to create 1 rle with old method : 0.0013966560363769531 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 1162 time to create 1 rle with old method : 0.001401662826538086 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -14, 'hashtag_id': 2087736828, 'type': 529, 'x0': 248, 'x1': 296, 'y0': 78, 'y1': 111, 'score': 1.0, 'id': None, 'points': ['257,78,269,77,281,83,291,91,295,99,294,107,285,110,274,110,261,104,252,97,247,88,249,81'], 'sub_photo_id': 0, 'rles': [(-1, 256, 78, 15), (-1, 254, 79, 19), (-1, 253, 80, 22), (-1, 250, 81, 28), (-1, 249, 82, 30), (-1, 249, 83, 32), (-1, 249, 84, 34), (-1, 249, 85, 35), (-1, 249, 86, 36), (-1, 248, 87, 39), (-1, 249, 88, 39), (-1, 248, 89, 41), (-1, 248, 90, 43), (-1, 248, 91, 43), (-1, 250, 92, 42), (-1, 250, 93, 42), (-1, 250, 94, 43), (-1, 251, 95, 43), (-1, 252, 96, 42), (-1, 252, 97, 43), (-1, 253, 98, 43), (-1, 254, 99, 42), (-1, 256, 100, 40), (-1, 257, 101, 40), (-1, 258, 102, 38), (-1, 259, 103, 37), (-1, 260, 104, 36), (-1, 261, 105, 35), (-1, 263, 106, 32), (-1, 266, 107, 29), (-1, 268, 108, 27), (-1, 270, 109, 21), (-1, 271, 110, 1), (-1, 273, 110, 18), (-1, 275, 111, 9), (-1, 285, 111, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 210 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 210 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] 210 [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00039124488830566406 nb_pixel_total : 250 time to create 1 rle with old method : 0.0005054473876953125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004355907440185547 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002171754837036133 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -15, 'hashtag_id': 2087736828, 'type': 529, 'x0': 227, 'x1': 276, 'y0': 52, 'y1': 82, 'score': 1.0, 'id': None, 'points': ['233,55,245,51,258,54,269,59,275,66,276,74,269,79,258,82,244,80,232,74,226,68,226,60'], 'sub_photo_id': 0, 'rles': [(-1, 244, 52, 3), (-1, 240, 53, 11), (-1, 252, 53, 2), (-1, 237, 54, 21), (-1, 233, 55, 28), (-1, 231, 56, 31), (-1, 230, 57, 35), (-1, 230, 58, 37), (-1, 228, 59, 41), (-1, 227, 60, 43), (-1, 227, 61, 44), (-1, 227, 62, 46), (-1, 227, 63, 46), (-1, 227, 64, 47), (-1, 227, 65, 49), (-1, 227, 66, 49), (-1, 227, 67, 50), (-1, 227, 68, 50), (-1, 227, 69, 50), (-1, 228, 70, 49), (-1, 229, 71, 48), (-1, 230, 72, 47), (-1, 231, 73, 46), (-1, 232, 74, 45), (-1, 233, 75, 43), (-1, 235, 76, 40), (-1, 237, 77, 36), (-1, 239, 78, 34), (-1, 242, 79, 30), (-1, 243, 80, 27), (-1, 247, 81, 1), (-1, 249, 81, 17), (-1, 253, 82, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 225 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 225 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] 225 [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00041294097900390625 nb_pixel_total : 169 time to create 1 rle with old method : 0.0003771781921386719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004086494445800781 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0022385120391845703 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -16, 'hashtag_id': 2087736828, 'type': 529, 'x0': 199, 'x1': 251, 'y0': 34, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['204,40,214,34,228,33,240,35,247,40,250,47,245,54,235,60,221,61,209,59,201,54,199,46'], 'sub_photo_id': 0, 'rles': [(-1, 215, 34, 21), (-1, 214, 35, 25), (-1, 211, 36, 31), (-1, 210, 37, 34), (-1, 208, 38, 37), (-1, 207, 39, 41), (-1, 204, 40, 45), (-1, 203, 41, 47), (-1, 202, 42, 47), (-1, 201, 43, 49), (-1, 202, 44, 48), (-1, 201, 45, 50), (-1, 200, 46, 51), (-1, 199, 47, 52), (-1, 199, 48, 53), (-1, 200, 49, 51), (-1, 200, 50, 50), (-1, 200, 51, 49), (-1, 201, 52, 48), (-1, 201, 53, 47), (-1, 201, 54, 46), (-1, 201, 55, 45), (-1, 202, 56, 42), (-1, 205, 57, 38), (-1, 206, 58, 34), (-1, 208, 59, 31), (-1, 209, 60, 1), (-1, 211, 60, 26), (-1, 216, 61, 17), (-1, 220, 62, 5)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 240 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 240 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] 240 [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004432201385498047 nb_pixel_total : 450 time to create 1 rle with old method : 0.0008711814880371094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002424955368041992 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004050731658935547 nb_pixel_total : 1 time to create 1 rle with old method : 3.123283386230469e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -17, 'hashtag_id': 2087736828, 'type': 529, 'x0': 168, 'x1': 218, 'y0': 20, 'y1': 51, 'score': 1.0, 'id': None, 'points': ['171,33,179,24,192,20,204,19,213,22,218,28,214,36,206,44,193,49,180,51,172,48,168,41'], 'sub_photo_id': 0, 'rles': [(-1, 198, 20, 9), (-1, 208, 20, 1), (-1, 191, 21, 19), (-1, 187, 22, 27), (-1, 187, 23, 28), (-1, 183, 24, 33), (-1, 180, 25, 37), (-1, 179, 26, 38), (-1, 177, 27, 41), (-1, 177, 28, 42), (-1, 176, 29, 43), (-1, 175, 30, 43), (-1, 174, 31, 44), (-1, 173, 32, 44), (-1, 172, 33, 45), (-1, 171, 34, 46), (-1, 171, 35, 46), (-1, 170, 36, 46), (-1, 169, 37, 47), (-1, 170, 38, 45), (-1, 169, 39, 44), (-1, 169, 40, 44), (-1, 168, 41, 44), (-1, 168, 42, 42), (-1, 169, 43, 41), (-1, 170, 44, 39), (-1, 170, 45, 38), (-1, 171, 46, 34), (-1, 171, 47, 32), (-1, 172, 48, 28), (-1, 173, 49, 24), (-1, 177, 50, 18), (-1, 180, 51, 6), (-1, 188, 51, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 255 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 255 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] 255 [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00043010711669921875 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0023555755615234375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004093647003173828 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0021812915802001953 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00039887428283691406 nb_pixel_total : 234 time to create 1 rle with old method : 0.00046372413635253906 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -18, 'hashtag_id': 2087736828, 'type': 529, 'x0': 137, 'x1': 182, 'y0': 12, 'y1': 49, 'score': 1.0, 'id': None, 'points': ['137,34,143,24,155,16,166,12,175,13,181,17,180,26,174,36,163,44,151,49,142,48,136,42'], 'sub_photo_id': 0, 'rles': [(-1, 170, 12, 1), (-1, 164, 13, 14), (-1, 161, 14, 17), (-1, 160, 15, 19), (-1, 156, 16, 25), (-1, 155, 17, 27), (-1, 153, 18, 30), (-1, 151, 19, 32), (-1, 150, 20, 32), (-1, 149, 21, 33), (-1, 147, 22, 35), (-1, 146, 23, 36), (-1, 144, 24, 38), (-1, 144, 25, 38), (-1, 143, 26, 39), (-1, 142, 27, 39), (-1, 142, 28, 39), (-1, 142, 29, 38), (-1, 140, 30, 40), (-1, 140, 31, 39), (-1, 140, 32, 38), (-1, 139, 33, 39), (-1, 138, 34, 39), (-1, 138, 35, 39), (-1, 138, 36, 38), (-1, 137, 37, 38), (-1, 137, 38, 36), (-1, 137, 39, 36), (-1, 138, 40, 33), (-1, 137, 41, 32), (-1, 137, 42, 31), (-1, 137, 43, 30), (-1, 137, 44, 29), (-1, 139, 45, 25), (-1, 139, 46, 22), (-1, 141, 47, 18), (-1, 142, 48, 14), (-1, 143, 49, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 270 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 270 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004589557647705078 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0026865005493164062 .time for calcul the mask position with numpy : 0.0004382133483886719 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0022134780883789062 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 1, 'x1': 52, 'y0': 26, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['41,32,50,40,52,50,49,57,39,61,26,60,13,54,4,46,1,37,5,29,15,25,28,26'], 'sub_photo_id': 0, 'rles': [(-1, 14, 26, 8), (-1, 12, 27, 18), (-1, 9, 28, 23), (-1, 7, 29, 27), (-1, 5, 30, 31), (-1, 5, 31, 33), (-1, 4, 32, 36), (-1, 4, 33, 38), (-1, 3, 34, 40), (-1, 3, 35, 41), (-1, 2, 36, 43), (-1, 2, 37, 45), (-1, 1, 38, 47), (-1, 1, 39, 48), (-1, 2, 40, 48), (-1, 2, 41, 49), (-1, 2, 42, 49), (-1, 3, 43, 48), (-1, 3, 44, 49), (-1, 3, 45, 49), (-1, 4, 46, 48), (-1, 4, 47, 48), (-1, 5, 48, 47), (-1, 6, 49, 47), (-1, 7, 50, 46), (-1, 8, 51, 45), (-1, 10, 52, 43), (-1, 11, 53, 41), (-1, 12, 54, 40), (-1, 13, 55, 38), (-1, 15, 56, 36), (-1, 17, 57, 33), (-1, 19, 58, 31), (-1, 21, 59, 27), (-1, 23, 60, 23), (-1, 25, 61, 18), (-1, 33, 62, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 107, 'x1': 146, 'y0': 14, 'y1': 57, 'score': 1.0, 'id': None, 'points': ['107,44,110,32,119,22,129,15,138,13,144,16,145,25,143,36,134,47,124,54,115,56,108,52'], 'sub_photo_id': 0, 'rles': [(-1, 136, 14, 4), (-1, 132, 15, 10), (-1, 129, 16, 15), (-1, 127, 17, 19), (-1, 126, 18, 20), (-1, 124, 19, 22), (-1, 123, 20, 23), (-1, 122, 21, 24), (-1, 120, 22, 27), (-1, 119, 23, 28), (-1, 118, 24, 29), (-1, 117, 25, 30), (-1, 116, 26, 31), (-1, 115, 27, 32), (-1, 115, 28, 31), (-1, 114, 29, 32), (-1, 113, 30, 33), (-1, 112, 31, 34), (-1, 111, 32, 34), (-1, 110, 33, 35), (-1, 110, 34, 35), (-1, 110, 35, 35), (-1, 109, 36, 35), (-1, 109, 37, 35), (-1, 109, 38, 34), (-1, 109, 39, 33), (-1, 108, 40, 34), (-1, 108, 41, 33), (-1, 108, 42, 32), (-1, 108, 43, 31), (-1, 107, 44, 31), (-1, 107, 45, 30), (-1, 107, 46, 30), (-1, 107, 47, 29), (-1, 107, 48, 28), (-1, 107, 49, 27), (-1, 108, 50, 24), (-1, 108, 51, 23), (-1, 108, 52, 21), (-1, 108, 53, 20), (-1, 109, 54, 18), (-1, 111, 55, 14), (-1, 113, 56, 9), (-1, 115, 57, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 285 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 285 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] 285 [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0005099773406982422 nb_pixel_total : 727 time to create 1 rle with old method : 0.001402139663696289 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004744529724121094 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0022525787353515625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -20, 'hashtag_id': 2087736828, 'type': 529, 'x0': 78, 'x1': 111, 'y0': 24, 'y1': 72, 'score': 1.0, 'id': None, 'points': ['78,62,77,50,83,38,91,28,99,24,107,25,110,34,110,45,104,58,97,67,88,72,81,70'], 'sub_photo_id': 0, 'rles': [(-1, 101, 24, 1), (-1, 98, 25, 8), (-1, 97, 26, 12), (-1, 95, 27, 14), (-1, 94, 28, 15), (-1, 92, 29, 17), (-1, 90, 30, 21), (-1, 90, 31, 21), (-1, 89, 32, 22), (-1, 88, 33, 24), (-1, 87, 34, 25), (-1, 87, 35, 25), (-1, 86, 36, 25), (-1, 85, 37, 27), (-1, 84, 38, 28), (-1, 84, 39, 28), (-1, 83, 40, 29), (-1, 83, 41, 29), (-1, 82, 42, 30), (-1, 81, 43, 31), (-1, 81, 44, 31), (-1, 81, 45, 31), (-1, 80, 46, 31), (-1, 80, 47, 31), (-1, 79, 48, 31), (-1, 79, 49, 32), (-1, 78, 50, 32), (-1, 78, 51, 31), (-1, 78, 52, 31), (-1, 78, 53, 30), (-1, 78, 54, 30), (-1, 78, 55, 29), (-1, 78, 56, 29), (-1, 78, 57, 29), (-1, 78, 58, 28), (-1, 78, 59, 28), (-1, 78, 60, 27), (-1, 78, 61, 26), (-1, 78, 62, 25), (-1, 78, 63, 24), (-1, 78, 64, 23), (-1, 79, 65, 21), (-1, 79, 66, 21), (-1, 80, 67, 19), (-1, 81, 68, 17), (-1, 81, 69, 15), (-1, 81, 70, 14), (-1, 82, 71, 10), (-1, 87, 72, 1), (-1, 89, 72, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 300 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 300 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] 300 [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004317760467529297 nb_pixel_total : 250 time to create 1 rle with old method : 0.0005145072937011719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00042366981506347656 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0021524429321289062 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -21, 'hashtag_id': 2087736828, 'type': 529, 'x0': 52, 'x1': 82, 'y0': 44, 'y1': 93, 'score': 1.0, 'id': None, 'points': ['55,86,51,74,54,61,59,50,66,44,74,43,79,50,82,61,80,75,74,87,68,93,60,93'], 'sub_photo_id': 0, 'rles': [(-1, 67, 44, 8), (-1, 65, 45, 11), (-1, 65, 46, 12), (-1, 64, 47, 13), (-1, 62, 48, 17), (-1, 62, 49, 18), (-1, 61, 50, 19), (-1, 60, 51, 21), (-1, 59, 52, 22), (-1, 59, 53, 22), (-1, 58, 54, 23), (-1, 58, 55, 24), (-1, 57, 56, 25), (-1, 57, 57, 25), (-1, 57, 58, 25), (-1, 56, 59, 27), (-1, 55, 60, 28), (-1, 55, 61, 28), (-1, 55, 62, 28), (-1, 54, 63, 29), (-1, 54, 64, 29), (-1, 54, 65, 29), (-1, 54, 66, 29), (-1, 53, 67, 30), (-1, 53, 68, 29), (-1, 54, 69, 28), (-1, 53, 70, 29), (-1, 53, 71, 29), (-1, 53, 72, 28), (-1, 53, 73, 29), (-1, 52, 74, 29), (-1, 52, 75, 29), (-1, 52, 76, 29), (-1, 53, 77, 28), (-1, 53, 78, 27), (-1, 53, 79, 26), (-1, 53, 80, 26), (-1, 54, 81, 25), (-1, 54, 82, 24), (-1, 54, 83, 24), (-1, 55, 84, 22), (-1, 55, 85, 22), (-1, 55, 86, 21), (-1, 55, 87, 21), (-1, 56, 88, 19), (-1, 56, 89, 18), (-1, 57, 90, 16), (-1, 59, 91, 13), (-1, 59, 92, 12), (-1, 60, 93, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 315 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 315 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] 315 [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004496574401855469 nb_pixel_total : 169 time to create 1 rle with old method : 0.00033283233642578125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00042319297790527344 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0021047592163085938 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -22, 'hashtag_id': 2087736828, 'type': 529, 'x0': 34, 'x1': 62, 'y0': 69, 'y1': 121, 'score': 1.0, 'id': None, 'points': ['40,115,34,105,33,91,35,79,40,72,47,69,54,74,60,84,61,98,59,110,54,118,46,120'], 'sub_photo_id': 0, 'rles': [(-1, 48, 69, 1), (-1, 45, 70, 5), (-1, 41, 71, 1), (-1, 43, 71, 8), (-1, 40, 72, 13), (-1, 39, 73, 15), (-1, 39, 74, 16), (-1, 39, 75, 17), (-1, 38, 76, 18), (-1, 37, 77, 20), (-1, 37, 78, 21), (-1, 36, 79, 22), (-1, 36, 80, 22), (-1, 36, 81, 23), (-1, 35, 82, 25), (-1, 35, 83, 25), (-1, 35, 84, 26), (-1, 34, 85, 27), (-1, 34, 86, 27), (-1, 34, 87, 27), (-1, 34, 88, 28), (-1, 34, 89, 28), (-1, 34, 90, 28), (-1, 34, 91, 28), (-1, 34, 92, 28), (-1, 34, 93, 28), (-1, 34, 94, 28), (-1, 34, 95, 28), (-1, 34, 96, 29), (-1, 34, 97, 29), (-1, 34, 98, 29), (-1, 34, 99, 29), (-1, 34, 100, 29), (-1, 34, 101, 28), (-1, 34, 102, 28), (-1, 34, 103, 28), (-1, 34, 104, 28), (-1, 34, 105, 27), (-1, 35, 106, 26), (-1, 36, 107, 25), (-1, 36, 108, 25), (-1, 36, 109, 25), (-1, 37, 110, 23), (-1, 38, 111, 23), (-1, 38, 112, 22), (-1, 39, 113, 20), (-1, 40, 114, 19), (-1, 40, 115, 18), (-1, 40, 116, 17), (-1, 41, 117, 16), (-1, 42, 118, 15), (-1, 43, 119, 1), (-1, 45, 119, 11), (-1, 46, 120, 6), (-1, 47, 121, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 330 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 330 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] 330 [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042438507080078125 nb_pixel_total : 450 time to create 1 rle with old method : 0.0008265972137451172 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000392913818359375 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0021088123321533203 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -23, 'hashtag_id': 2087736828, 'type': 529, 'x0': 20, 'x1': 51, 'y0': 102, 'y1': 152, 'score': 1.0, 'id': None, 'points': ['33,148,24,140,20,127,19,115,22,106,28,101,36,105,44,113,49,126,51,139,48,147,41,151'], 'sub_photo_id': 0, 'rles': [(-1, 28, 102, 2), (-1, 27, 103, 5), (-1, 25, 104, 11), (-1, 24, 105, 14), (-1, 23, 106, 16), (-1, 22, 107, 17), (-1, 22, 108, 19), (-1, 22, 109, 20), (-1, 22, 110, 20), (-1, 21, 111, 23), (-1, 20, 112, 25), (-1, 21, 113, 25), (-1, 20, 114, 26), (-1, 20, 115, 26), (-1, 20, 116, 27), (-1, 20, 117, 27), (-1, 20, 118, 28), (-1, 20, 119, 28), (-1, 20, 120, 28), (-1, 20, 121, 29), (-1, 20, 122, 29), (-1, 21, 123, 28), (-1, 21, 124, 29), (-1, 21, 125, 29), (-1, 21, 126, 30), (-1, 21, 127, 30), (-1, 21, 128, 30), (-1, 21, 129, 30), (-1, 22, 130, 29), (-1, 22, 131, 29), (-1, 22, 132, 30), (-1, 22, 133, 29), (-1, 24, 134, 27), (-1, 24, 135, 28), (-1, 24, 136, 28), (-1, 24, 137, 28), (-1, 25, 138, 27), (-1, 25, 139, 27), (-1, 25, 140, 27), (-1, 26, 141, 25), (-1, 27, 142, 24), (-1, 27, 143, 24), (-1, 29, 144, 21), (-1, 30, 145, 20), (-1, 31, 146, 19), (-1, 32, 147, 18), (-1, 33, 148, 16), (-1, 34, 149, 14), (-1, 36, 150, 10), (-1, 37, 151, 1), (-1, 39, 151, 5), (-1, 41, 152, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1351306989 of 345 degree temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 345 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] 345 [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004019737243652344 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0019183158874511719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003669261932373047 nb_pixel_total : 1157 time to create 1 rle with old method : 0.017731428146362305 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003254413604736328 nb_pixel_total : 234 time to create 1 rle with old method : 0.00040984153747558594 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -24, 'hashtag_id': 2087736828, 'type': 529, 'x0': 12, 'x1': 49, 'y0': 138, 'y1': 183, 'score': 1.0, 'id': None, 'points': ['34,182,24,176,16,164,12,153,13,144,17,138,26,139,36,145,44,156,49,168,48,177,42,183'], 'sub_photo_id': 0, 'rles': [(-1, 18, 138, 2), (-1, 17, 139, 10), (-1, 16, 140, 13), (-1, 16, 141, 15), (-1, 15, 142, 17), (-1, 13, 143, 21), (-1, 13, 144, 23), (-1, 13, 145, 24), (-1, 13, 146, 25), (-1, 13, 147, 25), (-1, 13, 148, 27), (-1, 13, 149, 27), (-1, 12, 150, 29), (-1, 13, 151, 28), (-1, 13, 152, 29), (-1, 13, 153, 30), (-1, 13, 154, 31), (-1, 13, 155, 32), (-1, 13, 156, 32), (-1, 14, 157, 32), (-1, 14, 158, 32), (-1, 14, 159, 32), (-1, 15, 160, 32), (-1, 16, 161, 31), (-1, 16, 162, 32), (-1, 16, 163, 32), (-1, 16, 164, 32), (-1, 17, 165, 32), (-1, 18, 166, 31), (-1, 18, 167, 31), (-1, 19, 168, 31), (-1, 19, 169, 31), (-1, 20, 170, 30), (-1, 21, 171, 29), (-1, 22, 172, 28), (-1, 22, 173, 28), (-1, 23, 174, 27), (-1, 24, 175, 26), (-1, 24, 176, 26), (-1, 26, 177, 24), (-1, 27, 178, 22), (-1, 30, 179, 18), (-1, 30, 180, 17), (-1, 33, 181, 14), (-1, 34, 182, 11), (-1, 37, 183, 3), (-1, 41, 183, 3)], 'hashtag': '', 'sum_segment': 0} About to upload 24 photos upload in portfolio : 22242925 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744313930_172844 we have uploaded 24 photos in the portfolio 22242925 time of upload the photos Elapsed time : 6.529512643814087 map_filename_photo_id : 24 map_filename_photo_id : {'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 1351306996, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 1351306997, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 1351306998, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 1351306999, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 1351307000, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 1351307001, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 1351307002, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 1351307003, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 1351307004, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 1351307005, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 1351307006, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 1351307007, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 1351307008, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 1351307009, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 1351307010, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 1351307011, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 1351307012, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 1351307013, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 1351307014, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 1351307015, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 1351307016, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 1351307017, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 1351307018, 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 1351307020} Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 list_new_chi_with_photo_id : [, , , , , , , , , , , , , , , , , , , , , , , , , , , ] batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 13.698645114898682 time spend to save output : 0.00010156631469726562 total time spend for step 3 : 13.698746681213379 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1351306989'] map_info['map_portfolio_photo'] : {} final : True mtd_id 243 list_pids : [937852786, 937852786, '1351306989'] Looping around the photos to save general results len do output : 24 /1351306996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351306997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351306998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351306999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307020Didn'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 ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1351306989', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('243', None, '1351306996', 'None', None, None, None, None, None), ('243', None, '1351306997', 'None', None, None, None, None, None), ('243', None, '1351306998', 'None', None, None, None, None, None), ('243', None, '1351306999', 'None', None, None, None, None, None), ('243', None, '1351307000', 'None', None, None, None, None, None), ('243', None, '1351307001', 'None', None, None, None, None, None), ('243', None, '1351307002', 'None', None, None, None, None, None), ('243', None, '1351307003', 'None', None, None, None, None, None), ('243', None, '1351307004', 'None', None, None, None, None, None), ('243', None, '1351307005', 'None', None, None, None, None, None), ('243', None, '1351307006', 'None', None, None, None, None, None), ('243', None, '1351307007', 'None', None, None, None, None, None), ('243', None, '1351307008', 'None', None, None, None, None, None), ('243', None, '1351307009', 'None', None, None, None, None, None), ('243', None, '1351307010', 'None', None, None, None, None, None), ('243', None, '1351307011', 'None', None, None, None, None, None), ('243', None, '1351307012', 'None', None, None, None, None, None), ('243', None, '1351307013', 'None', None, None, None, None, None), ('243', None, '1351307014', 'None', None, None, None, None, None), ('243', None, '1351307015', 'None', None, None, None, None, None), ('243', None, '1351307016', 'None', None, None, None, None, None), ('243', None, '1351307017', 'None', None, None, None, None, None), ('243', None, '1351307018', 'None', None, None, None, None, None), ('243', None, '1351307020', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1351306989', None, None, None, None, None, None)] time used for this insertion : 0.6211056709289551 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1351306996: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1351306997: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1351306998: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1351306999: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1351307000: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1351307001: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1351307002: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1351307003: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1351307004: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1351307005: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1351307006: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1351307007: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1351307008: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1351307009: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1351307010: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1351307011: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1351307012: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1351307013: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1351307014: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1351307015: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1351307016: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1351307017: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1351307018: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1351307020: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} ret_da : {1351306996: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1351306997: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1351306998: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1351306999: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1351307000: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1351307001: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1351307002: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1351307003: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1351307004: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1351307005: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1351307006: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1351307007: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1351307008: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1351307009: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1351307010: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1351307011: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1351307012: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1351307013: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1351307014: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1351307015: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1351307016: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1351307017: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1351307018: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1351307020: ['937852786', 'temp/1744313894_172844_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=571 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=571 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 571 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=571 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : flip list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (911785586) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 911785586 download finish for photo 911785586 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.12363743782043457 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:flip Thu Apr 10 21:39:04 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586} map_photo_id_path_extension : {911785586: {'path': 'temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 911785586) and `type` in (741) Loaded 6 chid ids of type : 741 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points +SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) map_chi_objs : {911785586: [, , , , , ]} photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 version de PIL : 9.5.0 vertically flipped image is saved in temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg Horizontal flip of photo 911785586 version de PIL : 9.5.0 horizontally flipped image is saved in temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744313945_172844 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8839395046234131 map_filename_photo_id : 2 map_filename_photo_id : {'temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 1351307024, 'temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 1351307025} Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 list_new_chi_with_photo_id : [, , , , , , , , , , , ] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 12 chid ids of type : 741 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 1.0144493579864502 time spend to save output : 9.5367431640625e-05 total time spend for step 1 : 1.0145447254180908 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] map_info['map_portfolio_photo'] : {} final : True mtd_id 571 list_pids : [911785586] Looping around the photos to save general results len do output : 2 /1351307024 /1351307025 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('571', None, '911785586', None, None, None, None, None, None)] time used for this insertion : 0.02792048454284668 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1351307024': ['911785586', 'temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1351307025': ['911785586', 'temp/1744313944_172844_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=686 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=686 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 686 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=686 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (950103132) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 950103132 download finish for photo 950103132 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.15275835990905762 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:crop Thu Apr 10 21:39:06 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132} map_photo_id_path_extension : {950103132: {'path': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 950103132) and `type` in (755) and score>0.3 Loaded 8 chid ids of type : 755 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) ++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 950103132) WARNING : margin is only used for type bib ! type of cropped photo chosen : rle we resize croppped photo by 1 on x axis and by 1 on y axis we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! map_result returned by crop_photo_return_map_crop : length : 8 map_result after crop : {1947670931: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670938_0.jpg', 'coordonates': (131, 155, 181, 195), 'sub_photo_id': -1, 'same_chi': False}} Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22242927 in upload media Upload medias : ['temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg after data_to_send, before sending request after request b'{"photo_ids":["1351307038","1351307032","1351307035","1351307034","1351307039","1351307027","1351307036","1351307030"],"photo_ids_order":["1351307027","1351307030","1351307032","1351307034","1351307035","1351307036","1351307038","1351307039"],"photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/0d6e4fcc87be4c0d5d58a90d2c635d2c.jpg","text":"TemporaryFile(/tmp/multipartBody4050853328495131372asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/9607c6a24679c40fb8ee89b0353f7a10.jpg","text":"TemporaryFile(/tmp/multipartBody4677023788396314144asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/e4147bbe1efdc4eeae5d18d9f664019d.jpg","text":"TemporaryFile(/tmp/multipartBody8679890219992515246asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/0c94d2e1518cd79c4951e5ecbcabc3a9.jpg","text":"TemporaryFile(/tmp/multipartBody8019692848256552536asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/76aba3b67a827ebee6a9ac304c1202ca.jpg","text":"TemporaryFile(/tmp/multipartBody7232985110039060024asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/5ea42f5ce2edc5c55d57fa2f6be66543.jpg","text":"TemporaryFile(/tmp/multipartBody7906815820181254944asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/f9577256401899153c3e067e3badbb65.jpg","text":"TemporaryFile(/tmp/multipartBody4968569490083863013asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/10/9bde3950351d18aac8588f65a5069d36.jpg","text":"TemporaryFile(/tmp/multipartBody5556505108572942510asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744313948210,"filename":"1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg","height":0,"width":0}],"map_files_photo_id":{"file2":"1351307032","file6":"1351307038","file1":"1351307030","file7":"1351307039","file0":"1351307027","file4":"1351307035","file5":"1351307036","file3":"1351307034"},"map_files_photo_id_array":[{"photo_id":"1351307036","filename":"file5"},{"photo_id":"1351307032","filename":"file2"},{"photo_id":"1351307035","filename":"file4"},{"photo_id":"1351307039","filename":"file7"},{"photo_id":"1351307030","filename":"file1"},{"photo_id":"1351307027","filename":"file0"},{"photo_id":"1351307034","filename":"file3"},{"photo_id":"1351307038","filename":"file6"}],"portfolio_id":22242927,"hashtag_by_photo_ids":[{"1351307038":["hashtag1","hashtag2"]},{"1351307032":["hashtag1","hashtag2"]},{"1351307035":["hashtag1","hashtag2"]},{"1351307034":["hashtag1","hashtag2"]},{"1351307039":["hashtag1","hashtag2"]},{"1351307027":["hashtag1","hashtag2"]},{"1351307036":["hashtag1","hashtag2"]},{"1351307030":["hashtag1","hashtag2"]}],"comms":"Portfolio 22242927 used, photo_id : ArrayBuffer(1351307038, 1351307032, 1351307035, 1351307034, 1351307039, 1351307027, 1351307036, 1351307030)","result":[],"list_datou_current":[]}' Result OK ! uploaded one batch 0 Elapsed time : 19.825502157211304 map_result_insert : {'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1351307032, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1351307038, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1351307030, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1351307039, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1351307027, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1351307035, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1351307036, 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1351307034} Now we prepare data that will be used for ellipse search ! chi_id found to be used 1947670931 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg sub_photo_id found to be used 1351307027 chi_id found to be used 1947670932 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg sub_photo_id found to be used 1351307030 chi_id found to be used 1947670933 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg sub_photo_id found to be used 1351307032 chi_id found to be used 1947670934 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg sub_photo_id found to be used 1351307034 chi_id found to be used 1947670935 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg sub_photo_id found to be used 1351307035 chi_id found to be used 1947670936 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg sub_photo_id found to be used 1351307036 chi_id found to be used 1947670937 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg sub_photo_id found to be used 1351307038 chi_id found to be used 1947670938 path of cropped varroa found to be used to match on an ellipse temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg sub_photo_id found to be used 1351307039 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1351307027', 31), (1947670932, '1351307030', 31), (1947670933, '1351307032', 31), (1947670934, '1351307034', 31), (1947670935, '1351307035', 31), (1947670936, '1351307036', 31), (1947670937, '1351307038', 31), (1947670938, '1351307039', 31)] map of cropped photos with some data : {'1351307027': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1351307030': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1351307032': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1351307034': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1351307035': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1351307036': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1351307038': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1351307039': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} After datou_step_exec type output : time spend for datou_step_exec : 20.05116629600525 time spend to save output : 6.985664367675781e-05 total time spend for step 1 : 20.051236152648926 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] map_info['map_portfolio_photo'] : {} final : True mtd_id 686 list_pids : [950103132] Looping around the photos to save general results len do output : 8 /1351307027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1351307039Didn'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 ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('686', None, '1351307027', 'None', None, None, None, None, None), ('686', None, '1351307030', 'None', None, None, None, None, None), ('686', None, '1351307032', 'None', None, None, None, None, None), ('686', None, '1351307034', 'None', None, None, None, None, None), ('686', None, '1351307035', 'None', None, None, None, None, None), ('686', None, '1351307036', 'None', None, None, None, None, None), ('686', None, '1351307038', 'None', None, None, None, None, None), ('686', None, '1351307039', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)] time used for this insertion : 0.01744842529296875 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1351307027': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1351307030': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1351307032': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1351307034': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1351307035': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1351307036': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1351307038': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1351307039': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} ret_da : {'1351307027': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1351307030': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1351307032': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1351307034': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1351307035': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1351307036': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1351307038': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1351307039': ['950103132', 'temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 Found filename_to_hash : temp/1744313946_172844_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=852 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=852 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 852 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=852 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : angular_coeff list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (932296368) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 932296368 download finish for photo 932296368 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.16496634483337402 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:angular_coeff Thu Apr 10 21:39:26 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313966_172844_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368} map_photo_id_path_extension : {932296368: {'path': 'temp/1744313966_172844_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 932296368) and `type` in (846) Loaded 19 chid ids of type : 846 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 treating photo 932296368 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 After datou_step_exec type output : time spend for datou_step_exec : 0.07600235939025879 time spend to save output : 0.0006041526794433594 total time spend for step 1 : 0.07660651206970215 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=708 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=708 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 708 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=708 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 946711423 download finish for photo 946711423 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1708376407623291 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detection_filter_by_crop Thu Apr 10 21:39:26 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313966_172844_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423} map_photo_id_path_extension : {946711423: {'path': 'temp/1744313966_172844_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) treating photo 946711423 SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, surface aoutside conditional crop After datou_step_exec type output : time spend for datou_step_exec : 0.7735555171966553 time spend to save output : 4.57763671875e-05 total time spend for step 1 : 0.7736012935638428 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, 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'519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=946711423 AND `type`=816 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3754441067,3754441066,3754441065,3754441074,3754441073,3754441072,3754441071,3754441070,3754441079,3754441082,3754441068,3754441069,3754441078,3754441077,3754441083,3754441084,3754441085,3754441087,3754441075,3754441081,3754441080,3754441086,3754441076) Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=672 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=672 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 672 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=672 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.00425267219543457 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:detection_filter_by_classif Thu Apr 10 21:39:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) treating photo 946711423 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 list of crops kept {'retroviseur': 2, 'roue': 2, 'capot': 1, 'pare-brise': 1, 'vitre': 10, 'phare': 2, 'feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'plaque-immatriculation': 1, 'essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop not duplicated for hashtag coffre : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 crop duplicated for hashtag retroviseur : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 crop not duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 crop duplicated for hashtag capot : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop duplicated for hashtag pare-brise : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated for hashtag poignee : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated for hashtag logo-marque : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop not duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop not duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop duplicated for hashtag pare-choc : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 list of crops kept {'pare-choc': ('pare-chocs-avant',), 'phare': ('phare-droite', 'a-gauche-de', 'phare-gauche'), 'porte': ('porte-avant', 'a-droite-de', 'porte-arriere')} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 batch 1 Loaded 0 chid ids of type : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 23 chid ids of type : 816 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 1600 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3754897482', '117', '95', '16') ... last line : ('3754897504', '70', '147', '1') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 1.453857660293579 time spend to save output : 0.00014138221740722656 total time spend for step 1 : 1.4539990425109863 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1243 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : blur_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (930729675) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 930729675 download finish for photo 930729675 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.11475396156311035 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:blur_detection Thu Apr 10 21:39:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313969_172844_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675} map_photo_id_path_extension : {930729675: {'path': 'temp/1744313969_172844_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blur_detection methode: ratio et variance treat image : temp/1744313969_172844_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 score_blur_detection : {930729675: [(930729675, 12.961859636534896, 492688767)]} After datou_step_exec type output : time spend for datou_step_exec : 0.2006855010986328 time spend to save output : 4.220008850097656e-05 total time spend for step 1 : 0.2007277011871338 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1908 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1908 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1908 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1908 # 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) 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 : step 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515187,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515207,987515208,987515209,987515211,987515212,987515213,987515215,987515216,987515217,987515219,987515220,987515222,987515223,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515239,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515248,987515249,987515250) Found this number of photos: 64 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515175 begin to download photo : 987515188 begin to download photo : 987515207 begin to download photo : 987515224 begin to download photo : 987515239 download finish for photo 987515239 begin to download photo : 987515240 download finish for photo 987515188 begin to download photo : 987515189 download finish for photo 987515224 begin to download photo : 987515226 download finish for photo 987515207 begin to download photo : 987515208 download finish for photo 987515175 begin to download photo : 987515176 download finish for photo 987515240 begin to download photo : 987515241 download finish for photo 987515226 begin to download photo : 987515227 download finish for photo 987515189 begin to download photo : 987515190 download finish for photo 987515176 begin to download photo : 987515177 download finish for photo 987515208 begin to download photo : 987515209 download finish for photo 987515227 begin to download photo : 987515228 download finish for photo 987515190 begin to download photo : 987515192 download finish for photo 987515241 begin to download photo : 987515242 download finish for photo 987515177 begin to download photo : 987515178 download finish for photo 987515192 begin to download photo : 987515193 download finish for photo 987515228 begin to download photo : 987515230 download finish for photo 987515209 begin to download photo : 987515211 download finish for photo 987515242 begin to download photo : 987515243 download finish for photo 987515193 begin to download photo : 987515195 download finish for photo 987515230 begin to download photo : 987515231 download finish for photo 987515178 begin to download photo : 987515179 download finish for photo 987515243 begin to download photo : 987515244 download finish for photo 987515211 begin to download photo : 987515212 download finish for photo 987515195 begin to download photo : 987515196 download finish for photo 987515231 begin to download photo : 987515232 download finish for photo 987515212 begin to download photo : 987515213 download finish for photo 987515179 begin to download photo : 987515180 download finish for photo 987515244 begin to download photo : 987515245 download finish for photo 987515196 begin to download photo : 987515198 download finish for photo 987515213 begin to download photo : 987515215 download finish for photo 987515232 begin to download photo : 987515233 download finish for photo 987515198 begin to download photo : 987515200 download finish for photo 987515180 begin to download photo : 987515181 download finish for photo 987515245 begin to download photo : 987515246 download finish for photo 987515233 begin to download photo : 987515234 download finish for photo 987515215 begin to download photo : 987515216 download finish for photo 987515234 begin to download photo : 987515235 download finish for photo 987515200 begin to download photo : 987515201 download finish for photo 987515181 begin to download photo : 987515182 download finish for photo 987515216 begin to download photo : 987515217 download finish for photo 987515235 begin to download photo : 987515236 download finish for photo 987515201 begin to download photo : 987515202 download finish for photo 987515246 begin to download photo : 987515247 download finish for photo 987515217 begin to download photo : 987515219 download finish for photo 987515182 begin to download photo : 987515183 download finish for photo 987515247 begin to download photo : 987515248 download finish for photo 987515202 begin to download photo : 987515204 download finish for photo 987515219 begin to download photo : 987515220 download finish for photo 987515183 begin to download photo : 987515184 download finish for photo 987515236 begin to download photo : 987515237 download finish for photo 987515204 begin to download photo : 987515205 download finish for photo 987515248 begin to download photo : 987515249 download finish for photo 987515220 begin to download photo : 987515222 download finish for photo 987515184 begin to download photo : 987515185 download finish for photo 987515249 begin to download photo : 987515250 download finish for photo 987515205 download finish for photo 987515237 begin to download photo : 987515238 download finish for photo 987515222 begin to download photo : 987515223 download finish for photo 987515185 begin to download photo : 987515186 download finish for photo 987515238 download finish for photo 987515250 download finish for photo 987515223 download finish for photo 987515186 begin to download photo : 987515187 download finish for photo 987515187 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 ##### After load_data_input time to download the photos : 1.7507398128509521 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Thu Apr 10 21:39:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313969_172844_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1744313969_172844_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1744313969_172844_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1744313969_172844_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1744313969_172844_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1744313969_172844_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1744313969_172844_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1744313969_172844_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1744313969_172844_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1744313969_172844_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1744313969_172844_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1744313969_172844_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1744313969_172844_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1744313969_172844_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1744313969_172844_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1744313969_172844_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1744313969_172844_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1744313969_172844_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1744313969_172844_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1744313969_172844_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1744313969_172844_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1744313969_172844_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1744313969_172844_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1744313969_172844_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1744313969_172844_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1744313969_172844_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1744313969_172844_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1744313969_172844_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1744313969_172844_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1744313969_172844_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1744313969_172844_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1744313969_172844_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1744313969_172844_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1744313969_172844_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1744313969_172844_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1744313969_172844_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1744313969_172844_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1744313969_172844_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1744313969_172844_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1744313969_172844_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1744313969_172844_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1744313969_172844_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1744313969_172844_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1744313969_172844_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1744313969_172844_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1744313969_172844_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1744313969_172844_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1744313969_172844_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1744313969_172844_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1744313969_172844_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1744313969_172844_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1744313969_172844_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1744313969_172844_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1744313969_172844_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1744313969_172844_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1744313969_172844_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1744313969_172844_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1744313969_172844_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1744313969_172844_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1744313969_172844_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1744313969_172844_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1744313969_172844_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1744313969_172844_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1744313969_172844_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187} map_photo_id_path_extension : {987515188: {'path': 'temp/1744313969_172844_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1744313969_172844_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1744313969_172844_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1744313969_172844_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1744313969_172844_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1744313969_172844_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1744313969_172844_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1744313969_172844_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1744313969_172844_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1744313969_172844_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1744313969_172844_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1744313969_172844_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1744313969_172844_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1744313969_172844_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1744313969_172844_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1744313969_172844_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1744313969_172844_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1744313969_172844_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1744313969_172844_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1744313969_172844_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1744313969_172844_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1744313969_172844_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1744313969_172844_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1744313969_172844_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1744313969_172844_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1744313969_172844_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1744313969_172844_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1744313969_172844_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1744313969_172844_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1744313969_172844_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1744313969_172844_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1744313969_172844_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1744313969_172844_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1744313969_172844_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1744313969_172844_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1744313969_172844_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1744313969_172844_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1744313969_172844_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1744313969_172844_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1744313969_172844_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1744313969_172844_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1744313969_172844_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1744313969_172844_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1744313969_172844_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1744313969_172844_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1744313969_172844_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1744313969_172844_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1744313969_172844_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1744313969_172844_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1744313969_172844_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1744313969_172844_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515175: {'path': 'temp/1744313969_172844_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1744313969_172844_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1744313969_172844_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1744313969_172844_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1744313969_172844_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1744313969_172844_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1744313969_172844_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1744313969_172844_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1744313969_172844_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1744313969_172844_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1744313969_172844_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1744313969_172844_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1744313969_172844_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'1528': 1} we are using the classfication for only one thcl 1528 In convert_file_to_np l 337 : 1 l343 1 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0006492137908935547 time to convert the images to numpy array : 0.004819631576538086 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.005377769470214844 time to convert the images to numpy array : 0.03724408149719238 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.010406255722045898 time to convert the images to numpy array : 0.033499717712402344 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.008724689483642578 time to convert the images to numpy array : 0.03683328628540039 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.005264997482299805 time to convert the images to numpy array : 0.04123187065124512 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.005353212356567383 time to convert the images to numpy array : 0.040653228759765625 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.007004737854003906 time to convert the images to numpy array : 0.0419766902923584 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0070037841796875 time to convert the images to numpy array : 0.04218697547912598 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.010938644409179688 time to convert the images to numpy array : 0.03650522232055664 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.015080451965332031 time to convert the images to numpy array : 0.0340275764465332 total time to convert the images to numpy array : 0.051169633865356445 list photo_ids error: [] list photo_ids correct : [987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515248, 987515249, 987515250, 987515207, 987515208, 987515209, 987515211, 987515234, 987515235, 987515236, 987515237, 987515238, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515222, 987515223, 987515175, 987515176, 987515177, 987515178, 987515179, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (1528) thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (4421) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (4421) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) param : , param.caffemodel : learn_refus_upm_blanches_1924 None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 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_refus_upm_blanches_1924 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_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 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/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/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 : 3165 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.0627753734588623 time used to do the prediction : 0.25650811195373535 save descriptor for thcl : 1528 (64, 512, 7, 7) Got the blobs of the net to insert : [3, 1, 1, 1, 1, 1, 2, 1, 1, 2] code_as_byte_string:b'0301010101'| Got the blobs of the net to insert : [1, 1, 1, 2, 0, 3, 4, 3, 4, 5] code_as_byte_string:b'0101010200'| Got the blobs of the net to insert : [3, 2, 1, 4, 5, 7, 6, 5, 6, 5] code_as_byte_string:b'0302010405'| Got the blobs of the net to insert : [3, 4, 5, 5, 9, 9, 9, 9, 11, 7] code_as_byte_string:b'0304050509'| Got the blobs of the net to insert : [5, 3, 3, 6, 8, 12, 9, 9, 3, 5] code_as_byte_string:b'0503030608'| Got the blobs of the net to insert : [6, 3, 3, 0, 1, 2, 2, 5, 2, 4] code_as_byte_string:b'0603030001'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [3, 3, 1, 4, 7, 7, 9, 6, 2, 1] code_as_byte_string:b'0303010407'| Got the blobs of the net to insert : [4, 2, 1, 2, 3, 1, 0, 1, 0, 1] code_as_byte_string:b'0402010203'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 2, 1, 1, 0, 0] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [1, 0, 0, 1, 1, 1, 0, 1, 1, 3] code_as_byte_string:b'0100000101'| Got the blobs of the net to insert : [1, 1, 2, 3, 4, 4, 1, 2, 8, 7] code_as_byte_string:b'0101020304'| Got the blobs of the net to insert : [6, 8, 3, 6, 6, 6, 5, 10, 10, 3] code_as_byte_string:b'0608030606'| Got the blobs of the net to insert : [1, 2, 1, 5, 7, 10, 8, 2, 2, 1] code_as_byte_string:b'0102010507'| Got the blobs of the net to insert : [0, 0, 0, 1, 5, 4, 6, 3, 1, 3] code_as_byte_string:b'0000000105'| Got the blobs of the net to insert : [0, 2, 1, 0, 0, 0, 0, 0, 4, 2] code_as_byte_string:b'0002010000'| Got the blobs of the net to insert : [0, 2, 0, 1, 1, 0, 0, 0, 1, 3] code_as_byte_string:b'0002000101'| Got the blobs of the net to insert : [1, 1, 0, 0, 0, 0, 0, 3, 5, 1] code_as_byte_string:b'0101000000'| Got the blobs of the net to insert : [0, 3, 0, 1, 1, 6, 7, 5, 5, 3] code_as_byte_string:b'0003000101'| Got the blobs of the net to insert : [6, 6, 3, 3, 8, 8, 6, 6, 2, 0] code_as_byte_string:b'0606030308'| Got the blobs of the net to insert : [5, 5, 2, 4, 6, 5, 9, 9, 4, 2] code_as_byte_string:b'0505020406'| Got the blobs of the net to insert : [2, 1, 3, 5, 7, 5, 3, 4, 1, 3] code_as_byte_string:b'0201030507'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 3, 2, 1, 0, 2] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 1, 3, 1, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 1, 0, 0, 0, 0, 1, 3] code_as_byte_string:b'0000000100'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 0, 1, 4, 0, 2] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [1, 0, 1, 2, 1, 0, 3, 3, 3, 8] code_as_byte_string:b'0100010201'| Got the blobs of the net to insert : [5, 5, 6, 4, 3, 6, 9, 7, 7, 7] code_as_byte_string:b'0505060403'| Got the blobs of the net to insert : [4, 6, 6, 4, 7, 8, 8, 8, 12, 8] code_as_byte_string:b'0406060407'| Got the blobs of the net to insert : [11, 8, 5, 9, 12, 14, 13, 14, 12, 6] code_as_byte_string:b'0b0805090c'| Got the blobs of the net to insert : [8, 7, 6, 4, 2, 1, 2, 4, 4, 4] code_as_byte_string:b'0807060402'| Got the blobs of the net to insert : [2, 3, 5, 5, 2, 2, 3, 0, 1, 4] code_as_byte_string:b'0203050502'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 1, 3, 3, 0, 0] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [1, 1, 0, 0, 1, 2, 0, 0, 0, 1] code_as_byte_string:b'0101000001'| Got the blobs of the net to insert : [0, 1, 0, 1, 3, 4, 2, 1, 3, 5] code_as_byte_string:b'0001000103'| Got the blobs of the net to insert : [3, 3, 3, 8, 7, 5, 5, 4, 5, 3] code_as_byte_string:b'0303030807'| Got the blobs of the net to insert : [2, 5, 4, 6, 9, 9, 10, 9, 11, 5] code_as_byte_string:b'0205040609'| Got the blobs of the net to insert : [5, 2, 3, 7, 5, 7, 6, 2, 4, 3] code_as_byte_string:b'0502030705'| Got the blobs of the net to insert : [5, 7, 3, 2, 1, 2, 2, 2, 0, 0] code_as_byte_string:b'0507030201'| Got the blobs of the net to insert : [7, 7, 3, 8, 9, 6, 7, 10, 11, 6] code_as_byte_string:b'0707030809'| Got the blobs of the net to insert : [3, 3, 3, 1, 6, 7, 10, 5, 3, 5] code_as_byte_string:b'0303030106'| Got the blobs of the net to insert : [3, 2, 2, 1, 1, 2, 2, 3, 2, 5] code_as_byte_string:b'0302020101'| Got the blobs of the net to insert : [1, 0, 3, 1, 0, 0, 0, 0, 0, 5] code_as_byte_string:b'0100030100'| Got the blobs of the net to insert : [0, 0, 2, 2, 0, 0, 0, 0, 0, 3] code_as_byte_string:b'0000020200'| Got the blobs of the net to insert : [1, 1, 2, 1, 0, 0, 0, 3, 6, 7] code_as_byte_string:b'0101020100'| Got the blobs of the net to insert : [1, 3, 3, 3, 0, 1, 5, 6, 7, 4] code_as_byte_string:b'0103030300'| Got the blobs of the net to insert : [5, 7, 3, 2, 5, 9, 10, 3, 5, 0] code_as_byte_string:b'0507030205'| Got the blobs of the net to insert : [0, 1, 1, 4, 2, 1, 3, 7, 9, 9] code_as_byte_string:b'0001010402'| Got the blobs of the net to insert : [7, 10, 10, 2, 4, 7, 8, 5, 5, 2] code_as_byte_string:b'070a0a0204'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [2, 2, 6, 7, 8, 6, 4, 3, 1, 8] code_as_byte_string:b'0202060708'| Got the blobs of the net to insert : [1, 2, 2, 1, 0, 1, 1, 2, 1, 1] code_as_byte_string:b'0102020100'| Got the blobs of the net to insert : [2, 2, 1, 3, 2, 3, 2, 0, 0, 1] code_as_byte_string:b'0202010302'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [5, 5, 4, 3, 5, 6, 3, 4, 3, 3] code_as_byte_string:b'0505040305'| Got the blobs of the net to insert : [5, 3, 3, 1, 1, 1, 2, 2, 2, 4] code_as_byte_string:b'0503030101'| Got the blobs of the net to insert : [2, 1, 2, 2, 2, 0, 0, 1, 0, 3] code_as_byte_string:b'0201020202'| Got the blobs of the net to insert : [0, 0, 2, 3, 3, 1, 0, 0, 0, 0] code_as_byte_string:b'0000020303'| Got the blobs of the net to insert : [1, 0, 0, 1, 0, 0, 0, 3, 4, 5] code_as_byte_string:b'0100000100'| time to traite the descriptors : 3.2782249450683594 Testing : ['987515187', '987515188', '987515189', '987515190', '987515192', '987515193', '987515195', '987515196', '987515248', '987515249', '987515250', '987515207', '987515208', '987515209', '987515211', '987515234', '987515235', '987515236', '987515237', '987515238', '987515239', '987515240', '987515241', '987515242', '987515243', '987515244', '987515245', '987515246', '987515247', '987515180', '987515181', '987515182', '987515183', '987515184', '987515185', '987515186', '987515198', '987515200', '987515201', '987515202', '987515204', '987515205', '987515224', '987515226', '987515227', '987515228', '987515230', '987515231', '987515232', '987515233', '987515222', '987515223', '987515175', '987515176', '987515177', '987515178', '987515179', '987515212', '987515213', '987515215', '987515216', '987515217', '987515219', '987515220'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (987515187,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515248,987515249,987515250,987515207,987515208,987515209,987515211,987515234,987515235,987515236,987515237,987515238,987515239,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515198,987515200,987515201,987515202,987515204,987515205,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515222,987515223,987515175,987515176,987515177,987515178,987515179,987515212,987515213,987515215,987515216,987515217,987515219,987515220) result : {987515175: {'photo_id': 987515175, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0001.jpg'}, 987515176: {'photo_id': 987515176, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_144.jpg'}, 987515177: {'photo_id': 987515177, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4a54e9967227806219ddf45d256539d8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_176.jpg'}, 987515178: {'photo_id': 987515178, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/298b3d2bfe0fda6787b59a78e2e68867.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_208.jpg'}, 987515179: {'photo_id': 987515179, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_240.jpg'}, 987515180: {'photo_id': 987515180, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_272.jpg'}, 987515181: {'photo_id': 987515181, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1738c2798fb31152809ecb443ac286d6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_304.jpg'}, 987515182: {'photo_id': 987515182, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fe7f29bf6d13e08c3e985f91b5232178.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_336.jpg'}, 987515183: {'photo_id': 987515183, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/6aab9ca0421398b4899892c10c2594c6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_112.jpg'}, 987515184: {'photo_id': 987515184, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/19c8c2177209a285df6014d95fe53f2c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_144.jpg'}, 987515185: {'photo_id': 987515185, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e172d54457cabee9d7f02ee1300f3ae9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_176.jpg'}, 987515186: {'photo_id': 987515186, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/797def426440b544aa80dbd63a19234a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_208.jpg'}, 987515187: {'photo_id': 987515187, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f62f98efd3caca0b9c17d27f5c70440.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_240.jpg'}, 987515188: {'photo_id': 987515188, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4116f9906657a69bb76c2fda982037b9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_272.jpg'}, 987515189: {'photo_id': 987515189, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8e8590a26f72249d4c2116dffd0cf668.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_304.jpg'}, 987515190: {'photo_id': 987515190, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/d56932bfc6ba2a8c974c691108755017.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_336.jpg'}, 987515192: {'photo_id': 987515192, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b661073b218f5f056833d6af1c617153.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_112.jpg'}, 987515193: {'photo_id': 987515193, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_144.jpg'}, 987515195: {'photo_id': 987515195, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_17_37_58_622227.jpg'}, 987515196: {'photo_id': 987515196, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_208.jpg'}, 987515198: {'photo_id': 987515198, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/599e80f444c876f407e94b533c89360b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_240.jpg'}, 987515200: {'photo_id': 987515200, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/978964436b5d5fb0eeda17e3bfafe889.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_272.jpg'}, 987515201: {'photo_id': 987515201, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_304.jpg'}, 987515202: {'photo_id': 987515202, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/3314bd90d1404f31b827d8925abf2d62.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_336.jpg'}, 987515204: {'photo_id': 987515204, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9779c4f9d44360a9c80499e3b01e8a09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_112.jpg'}, 987515205: {'photo_id': 987515205, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_144.jpg'}, 987515207: {'photo_id': 987515207, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/de216ddb041e249524b0fb2b949064a5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_176.jpg'}, 987515208: {'photo_id': 987515208, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_208.jpg'}, 987515209: {'photo_id': 987515209, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/02dfe1ae39f51994652f4a8538844aea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_240.jpg'}, 987515211: {'photo_id': 987515211, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/72cc7664d45bd40477351b9b764f1500.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_272.jpg'}, 987515212: {'photo_id': 987515212, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_09_32_14_525625.jpg'}, 987515213: {'photo_id': 987515213, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_336.jpg'}, 987515215: {'photo_id': 987515215, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/902ef348a7eebb9a8b87f42927347936.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_112.jpg'}, 987515216: {'photo_id': 987515216, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_144.jpg'}, 987515217: {'photo_id': 987515217, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/78877bb2c5760be28518d17f77d1c609.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_176.jpg'}, 987515219: {'photo_id': 987515219, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_208.jpg'}, 987515220: {'photo_id': 987515220, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e729f316c4c3b32049adfbaaa336d95c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_240.jpg'}, 987515222: {'photo_id': 987515222, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/067a027bc7402f969b6277d0dcb47eaa.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_272.jpg'}, 987515223: {'photo_id': 987515223, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/ebb57f09941cd11d7ee45a9368a883c1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_304.jpg'}, 987515224: {'photo_id': 987515224, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e8747b400e713ecbd08d5b75db4d7568.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_336.jpg'}, 987515226: {'photo_id': 987515226, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a18048dca1a77ae086b62cf07759f704.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_112.jpg'}, 987515227: {'photo_id': 987515227, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_144.jpg'}, 987515228: {'photo_id': 987515228, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_176.jpg'}, 987515230: {'photo_id': 987515230, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/846ad925884264181565c81d152a2e94.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_208.jpg'}, 987515231: {'photo_id': 987515231, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_240.jpg'}, 987515232: {'photo_id': 987515232, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/38db7950cdb3c674ee0ad65915b021f3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_272.jpg'}, 987515233: {'photo_id': 987515233, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_304.jpg'}, 987515234: {'photo_id': 987515234, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/2eca3480aed0f8b876242675ad99b666.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_336.jpg'}, 987515235: {'photo_id': 987515235, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/87075955a2f76b3948b47ffe1825ecd9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_112.jpg'}, 987515236: {'photo_id': 987515236, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b44a98b1aceadad73ed000d65836a9a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_144.jpg'}, 987515237: {'photo_id': 987515237, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1183dfa371a457f11ce2b622c7cf9467.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_176.jpg'}, 987515238: {'photo_id': 987515238, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_208.jpg'}, 987515239: {'photo_id': 987515239, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b3fa6f29636080b5138c8d8c33fea309.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_240.jpg'}, 987515240: {'photo_id': 987515240, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_272.jpg'}, 987515241: {'photo_id': 987515241, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/073420d938f5f010ffd5b4353c064e09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_304.jpg'}, 987515242: {'photo_id': 987515242, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/327abb5215d6fd1f0aad51f53ed8c324.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_336.jpg'}, 987515243: {'photo_id': 987515243, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_112.jpg'}, 987515244: {'photo_id': 987515244, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/419530eaef5ef868f75c758b94eea4b4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_144.jpg'}, 987515245: {'photo_id': 987515245, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/757d9d208d5bd4375c5f21f68b699148.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_176.jpg'}, 987515246: {'photo_id': 987515246, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/671a708f67f2efa19004b8257fc7b9c8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_208.jpg'}, 987515247: {'photo_id': 987515247, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e47b65403df916ba909bc9c439b0af73.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_240.jpg'}, 987515248: {'photo_id': 987515248, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0002.jpg'}, 987515249: {'photo_id': 987515249, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_304.jpg'}, 987515250: {'photo_id': 987515250, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b2827c9639df69656f23abcc7f2f82d9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_336.jpg'}} list_photo_exists : [987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515249, 987515250] storage_type for insertDescriptorsMulti : 1 To insert : 987515187 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515222 To insert : 987515223 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 time to insert the descriptors : 18.543190479278564 After datou_step_exec type output : time spend for datou_step_exec : 25.941901922225952 time spend to save output : 9.226799011230469e-05 total time spend for step 1 : 25.941994190216064 step2:argmax Thu Apr 10 21:39:57 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313969_172844_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 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'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 After datou_step_exec type output : time spend for datou_step_exec : 0.0012996196746826172 time spend to save output : 7.390975952148438e-05 total time spend for step 2 : 0.0013735294342041016 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515187': [('987515187', 'Carton', 0.9811291, 1927, '1528'), 'temp/1744313969_172844_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515188': [('987515188', 'Carton', 0.9956605, 1927, '1528'), 'temp/1744313969_172844_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977888, 1927, '1528'), 'temp/1744313969_172844_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9764213, 1927, '1528'), 'temp/1744313969_172844_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991107, 1927, '1528'), 'temp/1744313969_172844_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939525, 1927, '1528'), 'temp/1744313969_172844_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846528, 1927, '1528'), 'temp/1744313969_172844_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98464173, 1927, '1528'), 'temp/1744313969_172844_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515248': [('987515248', 'Carton', 0.9813168, 1927, '1528'), 'temp/1744313969_172844_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813085, 1927, '1528'), 'temp/1744313969_172844_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808019, 1927, '1528'), 'temp/1744313969_172844_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87426394, 1927, '1528'), 'temp/1744313969_172844_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99171656, 1927, '1528'), 'temp/1744313969_172844_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96786433, 1927, '1528'), 'temp/1744313969_172844_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.973558, 1927, '1528'), 'temp/1744313969_172844_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515234': [('987515234', 'Carton', 0.9448212, 1927, '1528'), 'temp/1744313969_172844_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89175063, 1927, '1528'), 'temp/1744313969_172844_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53700703, 1927, '1528'), 'temp/1744313969_172844_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7699708, 1927, '1528'), 'temp/1744313969_172844_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995746, 1927, '1528'), 'temp/1744313969_172844_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1744313969_172844_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.99952114, 1927, '1528'), 'temp/1744313969_172844_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9820855, 1927, '1528'), 'temp/1744313969_172844_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.93595695, 1927, '1528'), 'temp/1744313969_172844_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8743113, 1927, '1528'), 'temp/1744313969_172844_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81744045, 1927, '1528'), 'temp/1744313969_172844_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86575246, 1927, '1528'), 'temp/1744313969_172844_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992317, 1927, '1528'), 'temp/1744313969_172844_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966884, 1927, '1528'), 'temp/1744313969_172844_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515180': [('987515180', 'Carton', 0.9900051, 1927, '1528'), 'temp/1744313969_172844_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977817, 1927, '1528'), 'temp/1744313969_172844_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99243116, 1927, '1528'), 'temp/1744313969_172844_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1744313969_172844_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997315, 1927, '1528'), 'temp/1744313969_172844_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7982181, 1927, '1528'), 'temp/1744313969_172844_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847506, 1927, '1528'), 'temp/1744313969_172844_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515198': [('987515198', 'Carton', 0.96620196, 1927, '1528'), 'temp/1744313969_172844_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98594636, 1927, '1528'), 'temp/1744313969_172844_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954536, 1927, '1528'), 'temp/1744313969_172844_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911103, 1927, '1528'), 'temp/1744313969_172844_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99508274, 1927, '1528'), 'temp/1744313969_172844_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908584, 1927, '1528'), 'temp/1744313969_172844_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.90853775, 1927, '1528'), 'temp/1744313969_172844_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869882, 1927, '1528'), 'temp/1744313969_172844_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.90058357, 1927, '1528'), 'temp/1744313969_172844_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.522527, 1927, '1528'), 'temp/1744313969_172844_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940646, 1927, '1528'), 'temp/1744313969_172844_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.999421, 1927, '1528'), 'temp/1744313969_172844_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992471, 1927, '1528'), 'temp/1744313969_172844_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.9834345, 1927, '1528'), 'temp/1744313969_172844_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515222': [('987515222', 'Carton', 0.99747545, 1927, '1528'), 'temp/1744313969_172844_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.99206734, 1927, '1528'), 'temp/1744313969_172844_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998134, 1927, '1528'), 'temp/1744313969_172844_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.999814, 1927, '1528'), 'temp/1744313969_172844_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771345, 1927, '1528'), 'temp/1744313969_172844_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.85760355, 1927, '1528'), 'temp/1744313969_172844_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92691964, 1927, '1528'), 'temp/1744313969_172844_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515212': [('987515212', 'Carton', 0.986924, 1927, '1528'), 'temp/1744313969_172844_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.9869175, 1927, '1528'), 'temp/1744313969_172844_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939195, 1927, '1528'), 'temp/1744313969_172844_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774509, 1927, '1528'), 'temp/1744313969_172844_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52852786, 1927, '1528'), 'temp/1744313969_172844_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99937004, 1927, '1528'), 'temp/1744313969_172844_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963767, 1927, '1528'), 'temp/1744313969_172844_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg']} Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1879 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1879 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1879 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1879 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detect_points list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515173) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515173 download finish for photo 987515173 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.15084600448608398 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detect_points Thu Apr 10 21:39:57 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313997_172844_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173} map_photo_id_path_extension : {987515173: {'path': 'temp/1744313997_172844_987515173_91fa471b1a04f95b356afdbaf021f623.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 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_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1744313997_172844_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) time to do the preprocess : 0.04456615447998047 time to do a prediction : 0.3469550609588623 dict_keys(['prob']) shape of output (64, 10, 1, 1) shape of the out_put heatmap (10, 8, 8) number of sub_photos vertical and horizon 8 8 size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) After datou_step_exec type output : time spend for datou_step_exec : 1.7058520317077637 time spend to save output : 3.886222839355469e-05 total time spend for step 1 : 1.7058908939361572 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.216826966415345e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4251912977835133e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0663353755546723e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.452804205357097e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9239303128415486e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.776797893806361e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012300458911340684), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.950095040432643e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.38041213407314e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.214608407768992e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3764812933914072e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4734163187313243e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1282112609478645e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015760325186420232), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.00044386909576132894), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.530356768053025e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3329489547686535e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.623444290999032e-06), (987515173, 1982, 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'Autre_Environement', 304, -1, 208, -1, 4.531184004008537e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.815197361400351e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.0959605434618425e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6447031612187857e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.961603402378387e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4360219893205795e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.85613065090729e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2837356734962668e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.28779445530381e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1644324078806676e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.832102720480179e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.546874384279363e-06), (987515173, 1982, 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-1, 304, -1, 3.166973692714237e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1663197255984414e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8796274162014015e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002464556018821895), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004703543381765485), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003352791245561093), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.0002365721156820655), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010663174180081114), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.56042786128819e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013111857697367668), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.00073155079735443)]} result thcl : {'987515187': [('987515187', 'Carton', 0.9811291, 1927, '1528'), 'temp/1744313969_172844_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515188': [('987515188', 'Carton', 0.9956605, 1927, '1528'), 'temp/1744313969_172844_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977888, 1927, '1528'), 'temp/1744313969_172844_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9764213, 1927, '1528'), 'temp/1744313969_172844_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991107, 1927, '1528'), 'temp/1744313969_172844_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939525, 1927, '1528'), 'temp/1744313969_172844_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846528, 1927, '1528'), 'temp/1744313969_172844_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98464173, 1927, '1528'), 'temp/1744313969_172844_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515248': [('987515248', 'Carton', 0.9813168, 1927, '1528'), 'temp/1744313969_172844_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813085, 1927, '1528'), 'temp/1744313969_172844_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808019, 1927, '1528'), 'temp/1744313969_172844_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87426394, 1927, '1528'), 'temp/1744313969_172844_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99171656, 1927, '1528'), 'temp/1744313969_172844_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96786433, 1927, '1528'), 'temp/1744313969_172844_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.973558, 1927, '1528'), 'temp/1744313969_172844_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515234': [('987515234', 'Carton', 0.9448212, 1927, '1528'), 'temp/1744313969_172844_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89175063, 1927, '1528'), 'temp/1744313969_172844_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53700703, 1927, '1528'), 'temp/1744313969_172844_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7699708, 1927, '1528'), 'temp/1744313969_172844_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995746, 1927, '1528'), 'temp/1744313969_172844_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1744313969_172844_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.99952114, 1927, '1528'), 'temp/1744313969_172844_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9820855, 1927, '1528'), 'temp/1744313969_172844_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.93595695, 1927, '1528'), 'temp/1744313969_172844_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8743113, 1927, '1528'), 'temp/1744313969_172844_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81744045, 1927, '1528'), 'temp/1744313969_172844_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86575246, 1927, '1528'), 'temp/1744313969_172844_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992317, 1927, '1528'), 'temp/1744313969_172844_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966884, 1927, '1528'), 'temp/1744313969_172844_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515180': [('987515180', 'Carton', 0.9900051, 1927, '1528'), 'temp/1744313969_172844_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977817, 1927, '1528'), 'temp/1744313969_172844_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99243116, 1927, '1528'), 'temp/1744313969_172844_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1744313969_172844_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997315, 1927, '1528'), 'temp/1744313969_172844_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7982181, 1927, '1528'), 'temp/1744313969_172844_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847506, 1927, '1528'), 'temp/1744313969_172844_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515198': [('987515198', 'Carton', 0.96620196, 1927, '1528'), 'temp/1744313969_172844_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98594636, 1927, '1528'), 'temp/1744313969_172844_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954536, 1927, '1528'), 'temp/1744313969_172844_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911103, 1927, '1528'), 'temp/1744313969_172844_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99508274, 1927, '1528'), 'temp/1744313969_172844_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908584, 1927, '1528'), 'temp/1744313969_172844_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.90853775, 1927, '1528'), 'temp/1744313969_172844_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869882, 1927, '1528'), 'temp/1744313969_172844_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.90058357, 1927, '1528'), 'temp/1744313969_172844_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.522527, 1927, '1528'), 'temp/1744313969_172844_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940646, 1927, '1528'), 'temp/1744313969_172844_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.999421, 1927, '1528'), 'temp/1744313969_172844_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992471, 1927, '1528'), 'temp/1744313969_172844_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.9834345, 1927, '1528'), 'temp/1744313969_172844_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515222': [('987515222', 'Carton', 0.99747545, 1927, '1528'), 'temp/1744313969_172844_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.99206734, 1927, '1528'), 'temp/1744313969_172844_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998134, 1927, '1528'), 'temp/1744313969_172844_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.999814, 1927, '1528'), 'temp/1744313969_172844_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771345, 1927, '1528'), 'temp/1744313969_172844_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.85760355, 1927, '1528'), 'temp/1744313969_172844_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92691964, 1927, '1528'), 'temp/1744313969_172844_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515212': [('987515212', 'Carton', 0.986924, 1927, '1528'), 'temp/1744313969_172844_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.9869175, 1927, '1528'), 'temp/1744313969_172844_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939195, 1927, '1528'), 'temp/1744313969_172844_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774509, 1927, '1528'), 'temp/1744313969_172844_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52852786, 1927, '1528'), 'temp/1744313969_172844_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99937004, 1927, '1528'), 'temp/1744313969_172844_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963767, 1927, '1528'), 'temp/1744313969_172844_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg']} result detect_point : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.216826966415345e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4251912977835133e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0663353755546723e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.452804205357097e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9239303128415486e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.776797893806361e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012300458911340684), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.950095040432643e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.38041213407314e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.214608407768992e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3764812933914072e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4734163187313243e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1282112609478645e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015760325186420232), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.00044386909576132894), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.530356768053025e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3329489547686535e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.623444290999032e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5247018129448406e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6137178135977592e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.276176918618148e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.609376527601853e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032613836810924113), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003052568936254829), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.855775735748466e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.927797014417592e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.6992122002411634e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.8017417460214347e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.348324233025778e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.698890446277801e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.531184004008537e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.815197361400351e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.0959605434618425e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6447031612187857e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.961603402378387e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4360219893205795e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 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'autre_refus', 112, -1, 304, -1, 0.00011542911670403555), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021742853277828544), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.00042741376091726124), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.0004272920486982912), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.586615199921653e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.166973692714237e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1663197255984414e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8796274162014015e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002464556018821895), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004703543381765485), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003352791245561093), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.0002365721156820655), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010663174180081114), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.56042786128819e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013111857697367668), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.00073155079735443)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1848 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1848 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1848 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1848 # 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 ! 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 ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 Catched exception ! Connect or reconnect ! We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 987321136, 'portfolio_id': 1902940}] map_portfolio_id_photo_id: {1902940: [987321136]} ##### Call download_photos : nb_thread : 5 begin to download photo : 987321136 download finish for photo 987321136 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.25623011589050293 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 7 step1:init_dechet Thu Apr 10 21:39:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} debut step init detect dechets input : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn'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 ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('1848', '1902940', '987321136', 'None', None, None, None, None, None)] time used for this insertion : 0.013951301574707031 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0001811981201171875 time spend to save output : 0.01418304443359375 total time spend for step 1 : 0.014364242553710938 step2:tile Thu Apr 10 21:39:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 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 input_args_next_step, len :1, first value : ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {0: 987321136} verbose : True param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 987321136,987321136,987321136) and `type` in (406) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22242928 with name tile_correct_upm feed_id_new_photos : 22242928 filename : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] calcul des nouveaux crops pour le tile x0:0,x1:562,y0:0,y1:439 chi selectionnes : [] new_crops_tiles : 1 crop_transformed : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(987321136, 2090988864, 1522, 0, 562, 0, 439, 1.0)] list_photo_ids_cropped : [987321136] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 987321136) and `type` in (1522) Loaded 1 chid ids of type : 1522 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1608847328) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1608847328) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1608847328) treat the image : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010227680206298828 About to upload 1 photos upload in portfolio : 22242928 Result OK ! uploaded one batch 0 Elapsed time : 5.046832323074341 upload mediasElapsed time : 5.057136297225952 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1608847328, 1351307191, 0)] Saving 0 CHIs. list_chi_tile : [] end of tileElapsed time : 5.097596168518066 map_pid_results : {'1351307191': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1351307191'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136, 987321136, '1351307191'] Looping around the photos to save general results len do output : 1 /1351307191Didn'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 ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1351307191', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('1848', None, '1351307191', 'None', None, None, None, None, None), ('1848', '1902940', '987321136', None, None, None, None, None, None)] time used for this insertion : 0.013941526412963867 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.005307912826538 time spend to save output : 0.014134645462036133 total time spend for step 2 : 12.019442558288574 step3:detect_points Thu Apr 10 21:40:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'1351307191': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'1351307191': ()} output_args : {'1351307191': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1351307191 depend.output_id : 0 complete output_args for input 1 : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'1351307191': ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',), '987321136': ()} output_args : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 2 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1351307191} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1351307191: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1351307191: 987321136} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 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_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.05097055435180664 time to do a prediction : 15.959320068359375 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True Inside savePoints : final : False verbose : True threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 output flattener 5 example : {1351307191: [(1351307191, 1945, 'Autre_Environement', 185, -1, 118, -1, 1.8337410438107327e-05), (1351307191, 1945, 'Autre_Environement', 320, -1, 118, -1, 0.0001291327498620376), (1351307191, 1945, 'Autre_Environement', 388, -1, 151, -1, 1.3445685908664018e-05), (1351307191, 1945, 'Autre_Environement', 286, -1, 185, -1, 0.00013466527161654085), (1351307191, 1945, 'Autre_Environement', 118, -1, 219, -1, 9.86390750767896e-06), (1351307191, 1945, 'Autre_Environement', 219, -1, 219, -1, 0.00038494516047649086), (1351307191, 1945, 'Autre_Environement', 354, -1, 219, -1, 0.0003431888180784881), (1351307191, 1945, 'Autre_Environement', 421, -1, 253, -1, 1.494663450785083e-07), (1351307191, 1945, 'Autre_Environement', 185, -1, 286, -1, 1.814520658172114e-07), (1351307191, 1945, 'Autre_Environement', 320, -1, 286, -1, 4.115241154067917e-06), (1351307191, 1945, 'Autre_Environement', 118, -1, 320, -1, 2.5274063397695556e-10), (1351307191, 1945, 'Autre_Environement', 253, -1, 320, -1, 1.0447603199237321e-10), (1351307191, 1945, 'Autre_Environement', 388, -1, 320, -1, 4.5192118136583304e-07), (1351307191, 1945, 'Carton', 151, -1, 118, -1, 0.9897055625915527), (1351307191, 1945, 'Carton', 286, -1, 118, -1, 0.8210961222648621), (1351307191, 1945, 'Carton', 421, -1, 118, -1, 0.4851985573768616), (1351307191, 1945, 'Carton', 219, -1, 151, -1, 0.9841372966766357), (1351307191, 1945, 'Carton', 118, -1, 185, -1, 0.9978604912757874), (1351307191, 1945, 'Carton', 185, -1, 219, -1, 0.7996194958686829), (1351307191, 1945, 'Carton', 354, -1, 219, -1, 0.13047637045383453), (1351307191, 1945, 'Carton', 286, -1, 253, -1, 0.05070793256163597), (1351307191, 1945, 'Carton', 118, -1, 286, -1, 0.31989040970802307), (1351307191, 1945, 'Carton', 219, -1, 286, -1, 0.0034983144141733646), (1351307191, 1945, 'Carton', 421, -1, 286, -1, 0.01660206913948059), (1351307191, 1945, 'Carton', 354, -1, 320, -1, 0.0016258988762274384), (1351307191, 1945, 'Kraft', 185, -1, 118, -1, 0.004793279338628054), (1351307191, 1945, 'Kraft', 286, -1, 118, -1, 0.0023909551091492176), (1351307191, 1945, 'Kraft', 421, -1, 118, -1, 0.002102428814396262), (1351307191, 1945, 'Kraft', 354, -1, 151, -1, 0.05212335288524628), (1351307191, 1945, 'Kraft', 118, -1, 185, -1, 0.0017166697653010488), (1351307191, 1945, 'Kraft', 253, -1, 185, -1, 0.04599893465638161), (1351307191, 1945, 'Kraft', 185, -1, 219, -1, 0.021099306643009186), (1351307191, 1945, 'Kraft', 388, -1, 219, -1, 6.061792737455107e-05), (1351307191, 1945, 'Kraft', 286, -1, 253, -1, 0.0037522290367633104), (1351307191, 1945, 'Kraft', 118, -1, 286, -1, 0.010319208726286888), (1351307191, 1945, 'Kraft', 219, -1, 286, -1, 5.351284926291555e-05), (1351307191, 1945, 'Kraft', 421, -1, 286, -1, 9.467339623370208e-06), (1351307191, 1945, 'Kraft', 320, -1, 320, -1, 0.00017124204896390438), (1351307191, 1945, 'Lointain_Papier_Magazine', 185, -1, 118, -1, 2.352082447032444e-06), (1351307191, 1945, 'Lointain_Papier_Magazine', 320, -1, 118, -1, 1.991412864299491e-05), (1351307191, 1945, 'Lointain_Papier_Magazine', 253, -1, 151, -1, 1.1851832823595032e-05), (1351307191, 1945, 'Lointain_Papier_Magazine', 354, -1, 185, -1, 8.877575601218268e-05), (1351307191, 1945, 'Lointain_Papier_Magazine', 118, -1, 219, -1, 2.1015976017224602e-06), (1351307191, 1945, 'Lointain_Papier_Magazine', 219, -1, 219, -1, 7.86672972026281e-05), (1351307191, 1945, 'Lointain_Papier_Magazine', 421, -1, 219, -1, 5.019426794206083e-07), (1351307191, 1945, 'Lointain_Papier_Magazine', 320, -1, 253, -1, 8.220934978453442e-05), (1351307191, 1945, 'Lointain_Papier_Magazine', 185, -1, 286, -1, 3.6812457437918056e-07), (1351307191, 1945, 'Lointain_Papier_Magazine', 388, -1, 286, -1, 3.3782250739022857e-06), (1351307191, 1945, 'Lointain_Papier_Magazine', 118, -1, 320, -1, 3.5824958555252806e-09), (1351307191, 1945, 'Lointain_Papier_Magazine', 286, -1, 320, -1, 1.2866975396264024e-07), (1351307191, 1945, 'Metal', 185, -1, 118, -1, 7.473808364011347e-05), (1351307191, 1945, 'Metal', 286, -1, 118, -1, 3.432366065680981e-05), (1351307191, 1945, 'Metal', 118, -1, 151, -1, 1.1519473446242046e-06), (1351307191, 1945, 'Metal', 354, -1, 151, -1, 0.001217490527778864), (1351307191, 1945, 'Metal', 253, -1, 185, -1, 0.001836584648117423), (1351307191, 1945, 'Metal', 421, -1, 185, -1, 3.084278432652354e-05), (1351307191, 1945, 'Metal', 185, -1, 219, -1, 0.0011127882171422243), (1351307191, 1945, 'Metal', 118, -1, 253, -1, 2.7215228328714147e-05), (1351307191, 1945, 'Metal', 354, -1, 253, -1, 0.0005037166411057115), (1351307191, 1945, 'Metal', 219, -1, 286, -1, 1.657771281315945e-05), (1351307191, 1945, 'Metal', 421, -1, 286, -1, 2.2439740860136226e-05), (1351307191, 1945, 'Metal', 151, -1, 320, -1, 1.393576087860282e-10), (1351307191, 1945, 'Metal', 320, -1, 320, -1, 3.1560623028781265e-05), (1351307191, 1945, 'Papier_Magazine', 118, -1, 118, -1, 0.001665871823206544), (1351307191, 1945, 'Papier_Magazine', 253, -1, 118, -1, 0.28050497174263), (1351307191, 1945, 'Papier_Magazine', 185, -1, 151, -1, 0.003942570183426142), (1351307191, 1945, 'Papier_Magazine', 421, -1, 151, -1, 0.9828073978424072), (1351307191, 1945, 'Papier_Magazine', 354, -1, 185, -1, 0.7690255045890808), (1351307191, 1945, 'Papier_Magazine', 286, -1, 219, -1, 0.9288092851638794), (1351307191, 1945, 'Papier_Magazine', 118, -1, 253, -1, 0.04313919320702553), (1351307191, 1945, 'Papier_Magazine', 219, -1, 253, -1, 0.8978631496429443), (1351307191, 1945, 'Papier_Magazine', 388, -1, 253, -1, 0.9886879324913025), (1351307191, 1945, 'Papier_Magazine', 151, -1, 320, -1, 0.9999996423721313), (1351307191, 1945, 'Papier_Magazine', 253, -1, 320, -1, 0.9999960660934448), (1351307191, 1945, 'Papier_Magazine', 354, -1, 320, -1, 0.9942159056663513), (1351307191, 1945, 'Plastique', 118, -1, 118, -1, 1.236031312146224e-05), (1351307191, 1945, 'Plastique', 219, -1, 118, -1, 0.00030184732167981565), (1351307191, 1945, 'Plastique', 320, -1, 118, -1, 0.0002493929350748658), (1351307191, 1945, 'Plastique', 253, -1, 185, -1, 0.007031592074781656), (1351307191, 1945, 'Plastique', 354, -1, 185, -1, 0.032503753900527954), (1351307191, 1945, 'Plastique', 185, -1, 219, -1, 0.050375860184431076), (1351307191, 1945, 'Plastique', 421, -1, 219, -1, 0.00012226666149217635), (1351307191, 1945, 'Plastique', 118, -1, 253, -1, 0.003930176142603159), (1351307191, 1945, 'Plastique', 286, -1, 253, -1, 0.0025490771513432264), (1351307191, 1945, 'Plastique', 219, -1, 286, -1, 6.120907346485183e-05), (1351307191, 1945, 'Plastique', 354, -1, 286, -1, 0.00538312504068017), (1351307191, 1945, 'Plastique', 151, -1, 320, -1, 1.8926894773674263e-10), (1351307191, 1945, 'Plastique', 421, -1, 320, -1, 0.00020204381144139916), (1351307191, 1945, 'Sol_Environement', 185, -1, 118, -1, 9.372543900099117e-06), (1351307191, 1945, 'Sol_Environement', 320, -1, 118, -1, 2.7338848667568527e-05), (1351307191, 1945, 'Sol_Environement', 118, -1, 151, -1, 2.5881729470711434e-07), (1351307191, 1945, 'Sol_Environement', 253, -1, 185, -1, 0.00011454988998593763), (1351307191, 1945, 'Sol_Environement', 354, -1, 185, -1, 0.00020838991622440517), (1351307191, 1945, 'Sol_Environement', 185, -1, 219, -1, 9.349620813736692e-05), (1351307191, 1945, 'Sol_Environement', 421, -1, 219, -1, 1.1348908657282664e-07), (1351307191, 1945, 'Sol_Environement', 118, -1, 253, -1, 7.004572921687213e-07), (1351307191, 1945, 'Sol_Environement', 320, -1, 253, -1, 5.724640504922718e-05), (1351307191, 1945, 'Sol_Environement', 219, -1, 286, -1, 3.869550369017816e-08), (1351307191, 1945, 'Sol_Environement', 388, -1, 286, -1, 6.792529802623903e-06), (1351307191, 1945, 'Sol_Environement', 151, -1, 320, -1, 2.6225014184994705e-14), (1351307191, 1945, 'Sol_Environement', 286, -1, 320, -1, 1.5886031690115487e-07), (1351307191, 1945, 'Teint_Dans_La_Masse', 185, -1, 118, -1, 0.002272234996780753), (1351307191, 1945, 'Teint_Dans_La_Masse', 286, -1, 118, -1, 0.001813237671740353), (1351307191, 1945, 'Teint_Dans_La_Masse', 388, -1, 118, -1, 0.04538803920149803), (1351307191, 1945, 'Teint_Dans_La_Masse', 118, -1, 151, -1, 0.00010940170614048839), (1351307191, 1945, 'Teint_Dans_La_Masse', 253, -1, 185, -1, 0.0056123328395187855), (1351307191, 1945, 'Teint_Dans_La_Masse', 354, -1, 185, -1, 0.15166763961315155), (1351307191, 1945, 'Teint_Dans_La_Masse', 185, -1, 219, -1, 0.0013750138459727168), (1351307191, 1945, 'Teint_Dans_La_Masse', 118, -1, 253, -1, 6.252125604078174e-05), (1351307191, 1945, 'Teint_Dans_La_Masse', 286, -1, 253, -1, 0.0018639108166098595), (1351307191, 1945, 'Teint_Dans_La_Masse', 388, -1, 253, -1, 0.001438076258637011), (1351307191, 1945, 'Teint_Dans_La_Masse', 219, -1, 286, -1, 1.016586884361459e-05), (1351307191, 1945, 'Teint_Dans_La_Masse', 151, -1, 320, -1, 3.425693932967988e-07), (1351307191, 1945, 'Teint_Dans_La_Masse', 320, -1, 320, -1, 0.0020001486409455538), (1351307191, 1945, 'Teint_Dans_La_Masse', 421, -1, 320, -1, 8.311669807881117e-05), (1351307191, 1945, 'autre_refus', 185, -1, 118, -1, 0.028516046702861786), (1351307191, 1945, 'autre_refus', 354, -1, 118, -1, 0.0014720888575538993), (1351307191, 1945, 'autre_refus', 118, -1, 151, -1, 3.824138184427284e-05), (1351307191, 1945, 'autre_refus', 253, -1, 185, -1, 0.07516990602016449), (1351307191, 1945, 'autre_refus', 185, -1, 219, -1, 0.010234796442091465), (1351307191, 1945, 'autre_refus', 354, -1, 219, -1, 0.04460597038269043), (1351307191, 1945, 'autre_refus', 118, -1, 253, -1, 0.00015751634782645851), (1351307191, 1945, 'autre_refus', 286, -1, 253, -1, 0.01393966656178236), (1351307191, 1945, 'autre_refus', 219, -1, 286, -1, 4.3672833271557465e-05), (1351307191, 1945, 'autre_refus', 388, -1, 286, -1, 0.0030952864326536655), (1351307191, 1945, 'autre_refus', 151, -1, 320, -1, 1.5080686699420198e-10), (1351307191, 1945, 'autre_refus', 320, -1, 320, -1, 0.0030849112663418055)]} hashtag or score ? = 0.9897055625915527 hashtag or score ? = 0.8210961222648621 hashtag or score ? = 0.4851985573768616 hashtag or score ? = 0.9841372966766357 hashtag or score ? = 0.9978604912757874 hashtag or score ? = 0.7996194958686829 hashtag or score ? = 0.13047637045383453 hashtag or score ? = 0.05070793256163597 hashtag or score ? = 0.31989040970802307 hashtag or score ? = 0.05212335288524628 hashtag or score ? = 0.28050497174263 hashtag or score ? = 0.9828073978424072 hashtag or score ? = 0.7690255045890808 hashtag or score ? = 0.9288092851638794 hashtag or score ? = 0.8978631496429443 hashtag or score ? = 0.9886879324913025 hashtag or score ? = 0.9999996423721313 hashtag or score ? = 0.9999960660934448 hashtag or score ? = 0.9942159056663513 hashtag or score ? = 0.050375860184431076 hashtag or score ? = 0.15166763961315155 hashtag or score ? = 0.07516990602016449 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [('1351307191', '492774966', '1945', '151', '-1', '118', '-1', '0.9897055625915527'), ('1351307191', '492774966', '1945', '286', '-1', '118', '-1', '0.8210961222648621'), ('1351307191', '492774966', '1945', '421', '-1', '118', '-1', '0.4851985573768616'), ('1351307191', '492774966', '1945', '219', '-1', '151', '-1', '0.9841372966766357'), ('1351307191', '492774966', '1945', '118', '-1', '185', '-1', '0.9978604912757874'), ('1351307191', '492774966', '1945', '185', '-1', '219', '-1', '0.7996194958686829'), ('1351307191', '492774966', '1945', '354', '-1', '219', '-1', '0.13047637045383453'), ('1351307191', '492774966', '1945', '286', '-1', '253', '-1', '0.05070793256163597'), ('1351307191', '492774966', '1945', '118', '-1', '286', '-1', '0.31989040970802307'), ('1351307191', '493202403', '1945', '354', '-1', '151', '-1', '0.05212335288524628'), ('1351307191', '2107752386', '1945', '253', '-1', '118', '-1', '0.28050497174263'), ('1351307191', '2107752386', '1945', '421', '-1', '151', '-1', '0.9828073978424072'), ('1351307191', '2107752386', '1945', '354', '-1', '185', '-1', '0.7690255045890808'), ('1351307191', '2107752386', '1945', '286', '-1', '219', '-1', '0.9288092851638794'), ('1351307191', '2107752386', '1945', '219', '-1', '253', '-1', '0.8978631496429443'), ('1351307191', '2107752386', '1945', '388', '-1', '253', '-1', '0.9886879324913025'), ('1351307191', '2107752386', '1945', '151', '-1', '320', '-1', '0.9999996423721313'), ('1351307191', '2107752386', '1945', '253', '-1', '320', '-1', '0.9999960660934448'), ('1351307191', '2107752386', '1945', '354', '-1', '320', '-1', '0.9942159056663513'), ('1351307191', '492725882', '1945', '185', '-1', '219', '-1', '0.050375860184431076'), ('1351307191', '2107752385', '1945', '354', '-1', '185', '-1', '0.15166763961315155'), ('1351307191', '2107752406', '1945', '253', '-1', '185', '-1', '0.07516990602016449')] final : False save missing photos in datou_result : time spend for datou_step_exec : 17.114928722381592 time spend to save output : 0.06483960151672363 total time spend for step 3 : 17.179768323898315 step4:count_percent_refus Thu Apr 10 21:40:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 1 complete output_args for input 1 : {'1351307191': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'987321136': (987321136,), '1351307191': ()} output_args : {'1351307191': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1351307191 depend.output_id : 0 complete output_args for input 2 : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': (987321136,), '1351307191': ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} output_args : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 2 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : (987321136, 0.9481481481481482) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1351307191} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1351307191: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1351307191: 987321136} debut step count percent refus args : {'987321136': (987321136, 0.9481481481481482), '1351307191': ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} (987321136, 0.9481481481481482) ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) on trouve le portfolio_id = 1902940 list_photo : [987321136] list_photo_correc : [1351307191] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True map_info[mapportfolio_photo] : {1902940: [987321136]} dans le for photo id : 987321136 output[photo_id] : [({'carton': 3, 'Papier_Magazine': 7}, [1351307191], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,30.0,'refus_total',1945) on duplicate key update value= 30.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,61.64383561643836,'refus_total',1945) on duplicate key update value= 61.64383561643836 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,30.0,'carton',1945) on duplicate key update value= 30.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,61.64383561643836,'carton',1945) on duplicate key update value= 61.64383561643836 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,70.0,'Papier_Magazine',1945) on duplicate key update value= 70.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945], ['0', 'qualipapia_surface', 1902940, 70.0, 'Papier_Magazine', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,38.35616438356164,'Papier_Magazine',1945) on duplicate key update value= 38.35616438356164 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945], ['0', 'qualipapia_surface', 1902940, 70.0, 'Papier_Magazine', 1945], ['0', 'qualipapia_gravi', 1902940, 38.35616438356164, 'Papier_Magazine', 1945]] time used for this insertion : 0.05095243453979492 save missing photos in datou_result : time spend for datou_step_exec : 0.016599655151367188 time spend to save output : 0.05117464065551758 total time spend for step 4 : 0.06777429580688477 step5:brightness Thu Apr 10 21:40:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1351307191} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1351307191: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1351307191: 987321136} inside step calcul brightness treat image : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg pour la photo_id : -0.39870825574700136, le score de luminosite est de 987321136 brightness_score : {987321136: [(987321136, -0.39870825574700136, 496442774)]} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True select photo_hashtag_type from MTRDatou.classification_theme where id = 1154 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.023683547973632812 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('987321136', '496442774', '1426') ... last line : ('987321136', '496442774', '1426') time used for this insertion : 0.01195383071899414 save missing photos in datou_result : time spend for datou_step_exec : 0.05906796455383301 time spend to save output : 0.040853023529052734 total time spend for step 5 : 0.09992098808288574 step6:blur_detection Thu Apr 10 21:40:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1351307191} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1351307191: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1351307191: 987321136} inside step blur_detection score_blur_detection : {} methode: ratio et variance treat image : temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 score_blur_detection : {987321136: [(987321136, -5.392404060312662, 492609224)]} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True select photo_hashtag_type from MTRDatou.classification_theme where id = 1055 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.007749795913696289 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('987321136', '492609224', '1294') ... last line : ('987321136', '492609224', '1294') time used for this insertion : 0.011978864669799805 save missing photos in datou_result : time spend for datou_step_exec : 0.1012258529663086 time spend to save output : 0.024636030197143555 total time spend for step 6 : 0.12586188316345215 step7:send_mail_dechet Thu Apr 10 21:40:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {987321136: [(987321136, -5.392404060312662, 492609224)]} input_args_next_step : {987321136: ()} output_args : {987321136: [(987321136, -5.392404060312662, 492609224)]} args : 987321136 depend.output_id : 0 complete output_args for input 1 : {987321136: [(987321136, -0.39870825574700136, 496442774)]} input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224),)} output_args : {987321136: [(987321136, -0.39870825574700136, 496442774)]} args : 987321136 depend.output_id : 0 complete output_args for input 2 : {987321136: [({'carton': 3, 'Papier_Magazine': 7}, [1351307191], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)]} input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774))} output_args : {987321136: [({'carton': 3, 'Papier_Magazine': 7}, [1351307191], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)]} args : 987321136 depend.output_id : 0 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 input_args_next_step, len :1, first value : ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774), ({'carton': 3, 'Papier_Magazine': 7}, [1351307191], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1351307191} map_photo_id_path_extension : {987321136: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1351307191: {'path': 'temp/1744313999_172844_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1351307191: 987321136} dans la step send mail dechet list_name : ['one', 'sample', 'debug', 'board', 'détect', 'port'] corps du mail : La photo est trop sombre et nette, merci de reprendre la photo
Lien affichage photo


Dans ces conditions de prise de photo, les résultats sur le tas sont les suivants :
Le pourcentage de matière impropre est de 61.64 %.

Pour plus de détails:

Teint Dans La Masse: 0%.

carton: 61.64%.

metal: 0%.

plastique: 0%.

senders@fotonower.com retour de l'envoi du mail : None After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1351307191'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : True mtd_id 1848 list_pids : [987321136, 987321136, '1351307191'] Looping around the photos to save general results len do output : 1 /987321136. 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 ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1351307191', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('1848', '1902940', '987321136', "{'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}", None, None, None, None, None), ('1848', None, '1351307191', None, None, None, None, None, None)] time used for this insertion : 1.588148832321167 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.3633716106414795 time spend to save output : 1.5884974002838135 total time spend for step 7 : 1.951869010925293 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1807 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1807 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1807 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1807 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : image_temperature_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (984484223) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 984484223 download finish for photo 984484223 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.17028355598449707 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:image_temperature_detection Thu Apr 10 21:40:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744314031_172844_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg': 984484223} map_photo_id_path_extension : {984484223: {'path': 'temp/1744314031_172844_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blanche_jaune_detection treat image : temp/1744314031_172844_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 After datou_step_exec type output : time spend for datou_step_exec : 0.18848633766174316 time spend to save output : 8.463859558105469e-05 total time spend for step 1 : 0.18857097625732422 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4041 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4041 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4041 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4041 # 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) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : split_time_score list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1064921404, 'portfolio_id': 5205529}, {'photo_id': 1064921402, 'portfolio_id': 5205529}, {'photo_id': 1064921401, 'portfolio_id': 5205529}, {'photo_id': 1064921201, 'portfolio_id': 5205529}, {'photo_id': 1064921196, 'portfolio_id': 5205529}, {'photo_id': 1064919876, 'portfolio_id': 5205529}, {'photo_id': 1064919873, 'portfolio_id': 5205529}, {'photo_id': 1064919869, 'portfolio_id': 5205529}, {'photo_id': 1064919862, 'portfolio_id': 5205529}, {'photo_id': 1064919858, 'portfolio_id': 5205529}, {'photo_id': 1064919856, 'portfolio_id': 5205529}, {'photo_id': 1064919752, 'portfolio_id': 5205529}, {'photo_id': 1064919748, 'portfolio_id': 5205529}, {'photo_id': 1064919745, 'portfolio_id': 5205529}, {'photo_id': 1064919741, 'portfolio_id': 5205529}, {'photo_id': 1064919737, 'portfolio_id': 5205529}, {'photo_id': 1064919730, 'portfolio_id': 5205529}, {'photo_id': 1064919660, 'portfolio_id': 5205529}] map_portfolio_id_photo_id: {5205529: [1064921404, 1064921402, 1064921401, 1064921201, 1064921196, 1064919876, 1064919873, 1064919869, 1064919862, 1064919858, 1064919856, 1064919752, 1064919748, 1064919745, 1064919741, 1064919737, 1064919730, 1064919660]} ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.018918514251708984 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:split_time_score Thu Apr 10 21:40:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} split portfolio by speed calcul order for each photo with time calcul time for a portfolio query : SELECT photo_id, text FROM MTRBack.photos where photo_id in (SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5205529); result : ((1064919660, 'image_01122021_10_11_30_014389.jpg'), (1064919730, 'image_01122021_10_12_17_665202.jpg'), (1064919737, 'image_01122021_10_11_40_031052.jpg'), (1064919741, 'image_01122021_10_11_34_021658.jpg'), (1064919745, 'image_01122021_10_11_32_018001.jpg'), (1064919748, 'image_01122021_10_12_27_027057.jpg'), (1064919752, 'image_01122021_10_12_24_005017.jpg'), (1064919856, 'image_01122021_10_13_13_399843.jpg'), (1064919858, 'image_01122021_10_13_04_729164.jpg'), (1064919862, 'image_01122021_10_12_56_581019.jpg'), (1064919869, 'image_01122021_10_12_29_030603.jpg'), (1064919873, 'image_01122021_10_13_30_005720.jpg'), (1064919876, 'image_01122021_10_13_22_147712.jpg'), (1064921196, 'image_01122021_10_16_18_114975.jpg'), (1064921201, 'image_01122021_10_16_14_925132.jpg'), (1064921401, 'image_01122021_10_16_57_981306.jpg'), (1064921402, 'image_01122021_10_16_53_913663.jpg'), (1064921404, 'image_01122021_10_16_47_889875.jpg')) INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) VALUES (%s, %s, %s) on duplicate key update `order`=VALUES(`order`); first line : (5205529, 1064919660, 1098136690) ... last line : (5205529, 1064921404, 1098137007) Catched exception ! Connect or reconnect ! 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} len of dic_time_useful : 2 get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; select cs_nb_photo / nb_photo, om_nb_photo / nb_photo from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo from MTRBack.photos where photo_id in ()) t1; select cs_nb_photo / nb_photo, om_nb_photo / nb_photo from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo from MTRBack.photos where photo_id in (1064919660, 1064919745, 1064919741, 1064919737, 1064919730, 1064919752, 1064919748, 1064919869, 1064919862, 1064919858)) t1; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 (22242930, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529) After datou_step_exec type output : time spend for datou_step_exec : 7.671144008636475 time spend to save output : 0.00013065338134765625 total time spend for step 1 : 7.671274662017822 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(22242930, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=719 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=719 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 719 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=719 # 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) 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 : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 950003838, 'portfolio_id': 1981316}, {'photo_id': 950003813, 'portfolio_id': 1981316}, {'photo_id': 950003812, 'portfolio_id': 1981316}, {'photo_id': 950003696, 'portfolio_id': 1981316}, {'photo_id': 950003695, 'portfolio_id': 1981316}, {'photo_id': 926687666, 'portfolio_id': 1981316}] map_portfolio_id_photo_id: {1981316: [950003838, 950003813, 950003812, 950003696, 950003695, 926687666]} ##### Call download_photos : nb_thread : 5 begin to download photo : 950003838 begin to download photo : 950003812 begin to download photo : 950003695 download finish for photo 950003838 begin to download photo : 950003813 download finish for photo 950003695 begin to download photo : 926687666 download finish for photo 950003812 begin to download photo : 950003696 download finish for photo 926687666 download finish for photo 950003813 download finish for photo 950003696 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 ##### After load_data_input time to download the photos : 0.35662364959716797 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:frcnn Thu Apr 10 21:40:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744314039_172844_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1744314039_172844_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1744314039_172844_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1744314039_172844_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1744314039_172844_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1744314039_172844_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696} map_photo_id_path_extension : {950003695: {'path': 'temp/1744314039_172844_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1744314039_172844_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003838: {'path': 'temp/1744314039_172844_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1744314039_172844_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1744314039_172844_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1744314039_172844_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! Inside try reload ! classes : ['background', 'retroviseur', 'roue', 'capot', 'pare-brise', 'vitre', 'phare', 'feu-antibrouillard', 'feu-arriere', 'poignee', 'porte', 'radiateur', 'logo-marque', 'cache-reservoir', 'plaque-immatriculation', 'pot-echappement', 'info-modele', 'essuie-glace', 'pare-choc', 'coffre', 'carrosserie-autre', 'toit', 'logo-roue', 'aile-avant', 'aile-arriere', 'autre'] pht : 757 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 685, 'mtr_user_id': 31, 'name': 'learn_piece_voiture_0808_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,retroviseur,roue,capot,pare-brise,vitre,phare,feu-antibrouillard,feu-arriere,poignee,porte,radiateur,logo-marque,cache-reservoir,plaque-immatriculation,pot-echappement,info-modele,essuie-glace,pare-choc,coffre,carrosserie-autre,toit,logo-roue,aile-avant,aile-arriere,autre', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 757, 'photo_desc_type': 3800, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] WARNING: Logging before InitGoogleLogging() is written to STDERR F0410 21:40:42.264537 172844 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 92.96user 48.57system 5:18.07elapsed 44%CPU (0avgtext+0avgdata 6517480maxresident)k 6086944inputs+44112outputs (6292major+6679035minor)pagefaults 0swaps