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 : 0.19566059112548828 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 Mar 13 15:35:27 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 2025-03-13 15:35:29.892830: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-13 15:35:29.923300: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-13 15:35:29.924951: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f25cc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:35:29.925030: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-13 15:35:29.927769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-13 15:35:30.168569: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2a8a2990 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:35:30.168624: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-13 15:35:30.169716: 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 2025-03-13 15:35:30.170024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:30.172406: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:30.174623: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:35:30.175052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:35:30.177394: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:35:30.178537: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:35:30.183786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:35:30.185333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:35:30.185437: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:30.186228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:35:30.186245: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:35:30.186254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:35:30.187663: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 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 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 2025-03-13 15:35:30.948313: 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 2025-03-13 15:35:30.948520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:30.948548: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:30.948570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:35:30.948593: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:35:30.948615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:35:30.948653: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:35:30.948674: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:35:30.950493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:35:30.952164: 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 2025-03-13 15:35:30.952252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:30.952277: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:30.952298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:35:30.952319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:35:30.952341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:35:30.952362: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:35:30.952386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:35:30.953720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:35:30.953763: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:35:30.953772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:35:30.953782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:35:30.955238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. 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 : [] 2025-03-13 15:35:38.416147: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:38.578540: 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: (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 3346944 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5263 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 : 10178 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 : 0.0005590915679931641 nb_pixel_total : 15555 time to create 1 rle with old method : 0.018505096435546875 length of segment : 256 time for calcul the mask position with numpy : 0.002691030502319336 nb_pixel_total : 146499 time to create 1 rle with old method : 0.1676328182220459 length of segment : 374 time for calcul the mask position with numpy : 0.00022840499877929688 nb_pixel_total : 14254 time to create 1 rle with old method : 0.017120361328125 length of segment : 151 time for calcul the mask position with numpy : 0.00012922286987304688 nb_pixel_total : 5613 time to create 1 rle with old method : 0.00736689567565918 length of segment : 48 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1824 time to create 1 rle with old method : 0.002501964569091797 length of segment : 39 time spent for convertir_results : 0.9784023761749268 time spend for datou_step_exec : 18.293657064437866 time spend to save output : 4.00543212890625e-05 total time spend for step 1 : 18.293697118759155 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 3315 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 : 0.015696048736572266 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, 0.99549246, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (26, 32, 4), (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, 105), (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), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 46), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 42), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 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)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,29,32,30,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,42,162,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 28, 582, 19, 423, 0.9917287, [(302, 35, 37), (266, 36, 102), (250, 37, 129), (241, 38, 143), (200, 39, 189), (190, 40, 203), (186, 41, 217), (182, 42, 233), (178, 43, 243), (175, 44, 252), (172, 45, 262), (168, 46, 275), (164, 47, 289), (158, 48, 301), (155, 49, 310), (153, 50, 317), (151, 51, 323), (150, 52, 326), (148, 53, 329), (146, 54, 333), (145, 55, 335), (144, 56, 337), (143, 57, 339), (141, 58, 342), (140, 59, 345), (138, 60, 348), (136, 61, 352), (134, 62, 355), (132, 63, 359), (130, 64, 363), (128, 65, 366), (126, 66, 370), (124, 67, 373), (123, 68, 375), (121, 69, 378), (119, 70, 380), (117, 71, 383), (116, 72, 385), (115, 73, 387), (113, 74, 390), (112, 75, 392), (111, 76, 394), (110, 77, 396), (109, 78, 398), (109, 79, 399), (108, 80, 401), (107, 81, 403), (106, 82, 405), (105, 83, 407), (105, 84, 408), (104, 85, 410), (103, 86, 413), (102, 87, 415), (101, 88, 417), (100, 89, 419), (99, 90, 421), (98, 91, 422), (97, 92, 424), (95, 93, 427), (94, 94, 429), (93, 95, 431), (92, 96, 432), (91, 97, 434), (90, 98, 436), (90, 99, 436), (90, 100, 437), (90, 101, 438), (90, 102, 440), (91, 103, 440), (91, 104, 441), (91, 105, 443), (91, 106, 444), (91, 107, 445), (91, 108, 446), (92, 109, 446), (92, 110, 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(46, 173, 522), (46, 174, 522), (45, 175, 523), (45, 176, 522), (45, 177, 522), (44, 178, 523), (44, 179, 523), (43, 180, 524), (43, 181, 524), (43, 182, 524), (42, 183, 524), (42, 184, 524), (42, 185, 524), (41, 186, 525), (40, 187, 525), (40, 188, 525), (39, 189, 526), (38, 190, 526), (38, 191, 526), (37, 192, 527), (37, 193, 526), (36, 194, 527), (36, 195, 527), (35, 196, 527), (35, 197, 527), (34, 198, 528), (34, 199, 528), (34, 200, 527), (34, 201, 527), (34, 202, 526), (33, 203, 527), (33, 204, 526), (33, 205, 525), (33, 206, 525), (33, 207, 524), (33, 208, 523), (32, 209, 524), (32, 210, 523), (32, 211, 522), (32, 212, 521), (32, 213, 520), (32, 214, 519), (32, 215, 518), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 513), (32, 222, 512), (31, 223, 512), (31, 224, 511), (31, 225, 511), (31, 226, 510), (31, 227, 509), (31, 228, 509), (31, 229, 508), (31, 230, 507), (31, 231, 505), (31, 232, 504), (31, 233, 503), (31, 234, 501), (31, 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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/1741876526_3346697_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### 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 : 0.302781343460083 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 Mar 13 15:35:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 5525 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-13 15:35:58.005784: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-13 15:35:58.031178: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-13 15:35:58.033626: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f25cc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:35:58.033739: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-13 15:35:58.038377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-13 15:35:58.188867: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2b061340 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:35:58.188930: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-13 15:35:58.190254: 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 2025-03-13 15:35:58.190749: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:58.197279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:58.200326: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:35:58.200738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:35:58.203504: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:35:58.204818: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:35:58.210366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:35:58.211885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:35:58.211983: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:58.212746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:35:58.212762: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:35:58.212771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:35:58.213887: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 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. 2025-03-13 15:35:58.292624: 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 2025-03-13 15:35:58.292754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:58.292780: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:58.292801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:35:58.292822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:35:58.292842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:35:58.292862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:35:58.292900: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:35:58.294086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:35:58.295288: 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 2025-03-13 15:35:58.295329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:35:58.295351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:35:58.295371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:35:58.295391: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:35:58.295410: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:35:58.295430: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:35:58.295449: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:35:58.296479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:35:58.296514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:35:58.296523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:35:58.296531: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:35:58.297558: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 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 : [] 2025-03-13 15:36:06.410527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:36:06.630861: 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 3348876 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 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 : 10814 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 : 0.0005731582641601562 nb_pixel_total : 16902 time to create 1 rle with old method : 0.02639007568359375 length of segment : 107 time for calcul the mask position with numpy : 0.07521700859069824 nb_pixel_total : 480749 time to create 1 rle with new method : 0.030134916305541992 length of segment : 632 time for calcul the mask position with numpy : 0.0005316734313964844 nb_pixel_total : 36583 time to create 1 rle with old method : 0.04269981384277344 length of segment : 132 time for calcul the mask position with numpy : 0.00011134147644042969 nb_pixel_total : 4793 time to create 1 rle with old method : 0.005866289138793945 length of segment : 51 time spent for convertir_results : 0.37084507942199707 time spend for datou_step_exec : 17.7619686126709 time spend to save output : 7.486343383789062e-05 total time spend for step 1 : 17.762043476104736 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 403 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 : 0.014518499374389648 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, 0.99883646, [(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, 0.9977422, [(711, 22, 21), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (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), (495, 44, 578), (493, 45, 582), (490, 46, 586), (488, 47, 590), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (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, 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[(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1741876555_3346697_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 : 0.14667820930480957 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 Mar 13 15:36:15 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 : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-13 15:36:18.166126: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-13 15:36:18.191230: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-13 15:36:18.192879: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f25d0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:36:18.192932: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-13 15:36:18.195987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-13 15:36:18.418128: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2be5a900 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:36:18.418240: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-13 15:36:18.419488: 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 2025-03-13 15:36:18.420002: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:36:18.426032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:36:18.428864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:36:18.429394: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:36:18.432992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:36:18.434253: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:36:18.440275: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:36:18.442301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:36:18.442464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:36:18.447209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:36:18.447235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:36:18.447247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:36:18.449081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 2025-03-13 15:36:18.575168: 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 2025-03-13 15:36:18.575336: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:36:18.575366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:36:18.575393: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:36:18.575418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:36:18.575444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:36:18.575468: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:36:18.575494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:36:18.577124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:36:18.578519: 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 2025-03-13 15:36:18.578576: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:36:18.578600: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:36:18.578621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:36:18.578642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:36:18.578663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:36:18.578684: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:36:18.578705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:36:18.580333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:36:18.580372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:36:18.580383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:36:18.580393: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:36:18.582060: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-03-13 15:36:28.404644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:36:28.599194: 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 3350876 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 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 : 10661 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 : 0.31961870193481445 nb_pixel_total : 3697083 time to create 1 rle with new method : 0.4890928268432617 length of segment : 2044 time spent for convertir_results : 2.1008169651031494 time spend for datou_step_exec : 21.615912199020386 time spend to save output : 2.3365020751953125e-05 total time spend for step 1 : 21.615935564041138 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 : 0.013848304748535156 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, 0.98191833, [(1250, 110, 27), (652, 111, 289), (1203, 111, 135), (614, 112, 376), (1082, 112, 345), (525, 113, 909), (518, 114, 922), (511, 115, 936), (505, 116, 948), (499, 117, 960), (493, 118, 971), (487, 119, 983), (481, 120, 994), (476, 121, 1004), (470, 122, 1015), (465, 123, 1025), (461, 124, 1033), (456, 125, 1043), (451, 126, 1052), (447, 127, 1062), (443, 128, 1075), (438, 129, 1090), (434, 130, 1103), (431, 131, 1114), (427, 132, 1127), (423, 133, 1139), (420, 134, 1150), (416, 135, 1162), (413, 136, 1172), (410, 137, 1180), (406, 138, 1187), (403, 139, 1194), (400, 140, 1200), (397, 141, 1206), (395, 142, 1211), (392, 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), (922, 2138, 163), (926, 2139, 150), (931, 2140, 136), (937, 2141, 120), (955, 2142, 93), (973, 2143, 66), (992, 2144, 37), (1012, 2145, 8)], ['937,2141,839,2118,774,2092,647,2057,582,2029,367,1982,204,1964,122,1970,88,1911,51,1806,51,1724,40,1658,44,1561,29,1276,29,888,19,648,29,525,47,444,99,291,213,208,280,178,416,135,525,113,652,111,1426,112,1584,136,1747,171,1832,180,1915,207,2018,292,2059,369,2114,443,2154,597,2166,665,2152,822,2127,898,2119,974,2093,1050,2062,1084,1939,1350,1886,1426,1852,1634,1772,1887,1727,1962,1668,2011,1585,2014,1506,2049,1428,2054,1263,2079,1099,2136'])], 'temp/1741876575_3346697_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3678759 proportion of common points : 0.9963339873980817 [('test release memory', 'FAILURE', False), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : False #&_# TEST FAILED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_6419193983607b8ec28e462733ffc29d3101ac80 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_6419193983607b8ec28e462733ffc29d3101ac80','{"mask_detection": "fail"}','0','http://marlene.fotonower-preprod.com/job/2025/March/13032025/python_test3//data_2/data_log/job/2025/March/13032025/python_test3/log-python3----short_python3--v--marlene-15:35:00.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 : 0.2399919033050537 #### 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 Mar 13 15:36:48 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/1741876607_3346697_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1741876607_3346697_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 : 0.04691791534423828 nb_pixel_total : 5615 time to create 1 rle with old method : 0.006680965423583984 time for calcul the mask position with numpy : 0.0014679431915283203 nb_pixel_total : 3762 time to create 1 rle with old method : 0.004266500473022461 time for calcul the mask position with numpy : 0.0015099048614501953 nb_pixel_total : 16249 time to create 1 rle with old method : 0.01900029182434082 time for calcul the mask position with numpy : 0.0014734268188476562 nb_pixel_total : 16458 time to create 1 rle with old method : 0.019524097442626953 time for calcul the mask position with numpy : 0.0013976097106933594 nb_pixel_total : 4274 time to create 1 rle with old method : 0.005053997039794922 time for calcul the mask position with numpy : 0.0017592906951904297 nb_pixel_total : 83474 time to create 1 rle with old method : 0.09899258613586426 time for calcul the mask position with numpy : 0.0015664100646972656 nb_pixel_total : 10801 time to create 1 rle with old method : 0.013351917266845703 time for calcul the mask position with numpy : 0.001529693603515625 nb_pixel_total : 6637 time to create 1 rle with old method : 0.008468389511108398 time for calcul the mask position with numpy : 0.001519918441772461 nb_pixel_total : 7582 time to create 1 rle with old method : 0.009080886840820312 time for calcul the mask position with numpy : 0.00145721435546875 nb_pixel_total : 2830 time to create 1 rle with old method : 0.003637075424194336 time for calcul the mask position with numpy : 0.0015411376953125 nb_pixel_total : 14664 time to create 1 rle with old method : 0.018381357192993164 time for calcul the mask position with numpy : 0.0015444755554199219 nb_pixel_total : 13916 time to create 1 rle with old method : 0.01714181900024414 time for calcul the mask position with numpy : 0.001485586166381836 nb_pixel_total : 2941 time to create 1 rle with old method : 0.003663778305053711 time for calcul the mask position with numpy : 0.0014910697937011719 nb_pixel_total : 1081 time to create 1 rle with old method : 0.0014028549194335938 time for calcul the mask position with numpy : 0.0016214847564697266 nb_pixel_total : 29473 time to create 1 rle with old method : 0.03484153747558594 time for calcul the mask position with numpy : 0.0015225410461425781 nb_pixel_total : 13055 time to create 1 rle with old method : 0.015490531921386719 time for calcul the mask position with numpy : 0.0014901161193847656 nb_pixel_total : 3952 time to create 1 rle with old method : 0.0047647953033447266 time for calcul the mask position with numpy : 0.0013484954833984375 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0015952587127685547 time for calcul the mask position with numpy : 0.001413583755493164 nb_pixel_total : 2452 time to create 1 rle with old method : 0.003086566925048828 time for calcul the mask position with numpy : 0.0014290809631347656 nb_pixel_total : 887 time to create 1 rle with old method : 0.001089334487915039 time for calcul the mask position with numpy : 0.0014252662658691406 nb_pixel_total : 9900 time to create 1 rle with old method : 0.01190948486328125 time for calcul the mask position with numpy : 0.0013499259948730469 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0013518333435058594 time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 1706 time to create 1 rle with old method : 0.002009868621826172 time for calcul the mask position with numpy : 0.0014238357543945312 nb_pixel_total : 4248 time to create 1 rle with old method : 0.005345344543457031 time for calcul the mask position with numpy : 0.0013451576232910156 nb_pixel_total : 4084 time to create 1 rle with old method : 0.005215883255004883 time for calcul the mask position with numpy : 0.001306295394897461 nb_pixel_total : 342 time to create 1 rle with old method : 0.00045013427734375 time for calcul the mask position with numpy : 0.0014476776123046875 nb_pixel_total : 2076 time to create 1 rle with old method : 0.0025870800018310547 time for calcul the mask position with numpy : 0.001495361328125 nb_pixel_total : 13014 time to create 1 rle with old method : 0.015289545059204102 time for calcul the mask position with numpy : 0.001420736312866211 nb_pixel_total : 5498 time to create 1 rle with old method : 0.006533145904541016 time for calcul the mask position with numpy : 0.0014030933380126953 nb_pixel_total : 1507 time to create 1 rle with old method : 0.0018529891967773438 time for calcul the mask position with numpy : 0.0016617774963378906 nb_pixel_total : 38953 time to create 1 rle with old method : 0.045633554458618164 time for calcul the mask position with numpy : 0.0013377666473388672 nb_pixel_total : 1648 time to create 1 rle with old method : 0.0020003318786621094 time for calcul the mask position with numpy : 0.0014202594757080078 nb_pixel_total : 981 time to create 1 rle with old method : 0.0013120174407958984 time for calcul the mask position with numpy : 0.0016396045684814453 nb_pixel_total : 8645 time to create 1 rle with old method : 0.009964704513549805 time for calcul the mask position with numpy : 0.0018017292022705078 nb_pixel_total : 12094 time to create 1 rle with old method : 0.01383662223815918 time for calcul the mask position with numpy : 0.0016744136810302734 nb_pixel_total : 2379 time to create 1 rle with old method : 0.0028989315032958984 time for calcul the mask position with numpy : 0.0013434886932373047 nb_pixel_total : 3530 time to create 1 rle with old method : 0.0040400028228759766 time for calcul the mask position with numpy : 0.0013232231140136719 nb_pixel_total : 5342 time to create 1 rle with old method : 0.006261110305786133 time for calcul the mask position with numpy : 0.001558542251586914 nb_pixel_total : 27548 time to create 1 rle with old method : 0.03096604347229004 time for calcul the mask position with numpy : 0.0016303062438964844 nb_pixel_total : 2727 time to create 1 rle with old method : 0.0031995773315429688 time for calcul the mask position with numpy : 0.0014224052429199219 nb_pixel_total : 842 time to create 1 rle with old method : 0.001112222671508789 time for calcul the mask position with numpy : 0.0014276504516601562 nb_pixel_total : 3309 time to create 1 rle with old method : 0.00406336784362793 time for calcul the mask position with numpy : 0.0014472007751464844 nb_pixel_total : 2448 time to create 1 rle with old method : 0.002724170684814453 time for calcul the mask position with numpy : 0.001416921615600586 nb_pixel_total : 970 time to create 1 rle with old method : 0.0011811256408691406 time for calcul the mask position with numpy : 0.0013904571533203125 nb_pixel_total : 2778 time to create 1 rle with old method : 0.0035037994384765625 time for calcul the mask position with numpy : 0.0014412403106689453 nb_pixel_total : 1658 time to create 1 rle with old method : 0.002091646194458008 time for calcul the mask position with numpy : 0.0014121532440185547 nb_pixel_total : 8757 time to create 1 rle with old method : 0.010267496109008789 time for calcul the mask position with numpy : 0.0014805793762207031 nb_pixel_total : 10621 time to create 1 rle with old method : 0.012318611145019531 time for calcul the mask position with numpy : 0.0014257431030273438 nb_pixel_total : 338 time to create 1 rle with old method : 0.0004787445068359375 time for calcul the mask position with numpy : 0.00139617919921875 nb_pixel_total : 1026 time to create 1 rle with old method : 0.0012781620025634766 time for calcul the mask position with numpy : 0.0014181137084960938 nb_pixel_total : 1244 time to create 1 rle with old method : 0.0015087127685546875 time for calcul the mask position with numpy : 0.0014033317565917969 nb_pixel_total : 3852 time to create 1 rle with old method : 0.004625082015991211 time for calcul the mask position with numpy : 0.0014407634735107422 nb_pixel_total : 4168 time to create 1 rle with old method : 0.0048830509185791016 time for calcul the mask position with numpy : 0.0014252662658691406 nb_pixel_total : 2324 time to create 1 rle with old method : 0.0028581619262695312 time for calcul the mask position with numpy : 0.001325845718383789 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007200241088867188 time for calcul the mask position with numpy : 0.0013933181762695312 nb_pixel_total : 2200 time to create 1 rle with old method : 0.0026531219482421875 time for calcul the mask position with numpy : 0.001443624496459961 nb_pixel_total : 3096 time to create 1 rle with old method : 0.0036721229553222656 time for calcul the mask position with numpy : 0.001384735107421875 nb_pixel_total : 2409 time to create 1 rle with old method : 0.0029668807983398438 time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 577 time to create 1 rle with old method : 0.0006954669952392578 time for calcul the mask position with numpy : 0.0013835430145263672 nb_pixel_total : 2770 time to create 1 rle with old method : 0.0034935474395751953 time for calcul the mask position with numpy : 0.0014181137084960938 nb_pixel_total : 877 time to create 1 rle with old method : 0.0012180805206298828 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 303 time to create 1 rle with old method : 0.00047898292541503906 time for calcul the mask position with numpy : 0.0014050006866455078 nb_pixel_total : 1206 time to create 1 rle with old method : 0.001522064208984375 time for calcul the mask position with numpy : 0.001415252685546875 nb_pixel_total : 693 time to create 1 rle with old method : 0.0009207725524902344 time for calcul the mask position with numpy : 0.0013856887817382812 nb_pixel_total : 585 time to create 1 rle with old method : 0.0008320808410644531 time for calcul the mask position with numpy : 0.0013971328735351562 nb_pixel_total : 1740 time to create 1 rle with old method : 0.0020182132720947266 time for calcul the mask position with numpy : 0.0014221668243408203 nb_pixel_total : 915 time to create 1 rle with old method : 0.0010979175567626953 time for calcul the mask position with numpy : 0.005964040756225586 nb_pixel_total : 16682 time to create 1 rle with old method : 0.02121281623840332 time for calcul the mask position with numpy : 0.0016498565673828125 nb_pixel_total : 9510 time to create 1 rle with old method : 0.01151132583618164 time for calcul the mask position with numpy : 0.0014438629150390625 nb_pixel_total : 713 time to create 1 rle with old method : 0.0009970664978027344 time for calcul the mask position with numpy : 0.0014209747314453125 nb_pixel_total : 249 time to create 1 rle with old method : 0.0003743171691894531 time for calcul the mask position with numpy : 0.0014231204986572266 nb_pixel_total : 222 time to create 1 rle with old method : 0.0003459453582763672 time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 267 time to create 1 rle with old method : 0.0003829002380371094 time for calcul the mask position with numpy : 0.0014357566833496094 nb_pixel_total : 1334 time to create 1 rle with old method : 0.001817941665649414 time for calcul the mask position with numpy : 0.0014607906341552734 nb_pixel_total : 9057 time to create 1 rle with old method : 0.011382579803466797 time for calcul the mask position with numpy : 0.0016224384307861328 nb_pixel_total : 39068 time to create 1 rle with old method : 0.04684567451477051 time for calcul the mask position with numpy : 0.0017132759094238281 nb_pixel_total : 3170 time to create 1 rle with old method : 0.003973960876464844 time for calcul the mask position with numpy : 0.0013992786407470703 nb_pixel_total : 1502 time to create 1 rle with old method : 0.0018796920776367188 time for calcul the mask position with numpy : 0.0012941360473632812 nb_pixel_total : 1438 time to create 1 rle with old method : 0.0018990039825439453 time for calcul the mask position with numpy : 0.0013468265533447266 nb_pixel_total : 616 time to create 1 rle with old method : 0.0007579326629638672 time for calcul the mask position with numpy : 0.0012888908386230469 nb_pixel_total : 1131 time to create 1 rle with old method : 0.0014410018920898438 time for calcul the mask position with numpy : 0.0014314651489257812 nb_pixel_total : 18566 time to create 1 rle with old method : 0.022858381271362305 time for calcul the mask position with numpy : 0.0016601085662841797 nb_pixel_total : 8427 time to create 1 rle with old method : 0.010624885559082031 time for calcul the mask position with numpy : 0.0014929771423339844 nb_pixel_total : 735 time to create 1 rle with old method : 0.0011525154113769531 time for calcul the mask position with numpy : 0.0015134811401367188 nb_pixel_total : 7536 time to create 1 rle with old method : 0.009122848510742188 time for calcul the mask position with numpy : 0.0016028881072998047 nb_pixel_total : 1636 time to create 1 rle with old method : 0.002221822738647461 time for calcul the mask position with numpy : 0.0015101432800292969 nb_pixel_total : 5047 time to create 1 rle with old method : 0.006719350814819336 time for calcul the mask position with numpy : 0.0016961097717285156 nb_pixel_total : 1442 time to create 1 rle with old method : 0.002113819122314453 time for calcul the mask position with numpy : 0.0016808509826660156 nb_pixel_total : 595 time to create 1 rle with old method : 0.0009441375732421875 time for calcul the mask position with numpy : 0.00168609619140625 nb_pixel_total : 917 time to create 1 rle with old method : 0.001577615737915039 time for calcul the mask position with numpy : 0.0015780925750732422 nb_pixel_total : 890 time to create 1 rle with old method : 0.0013363361358642578 time for calcul the mask position with numpy : 0.0015649795532226562 nb_pixel_total : 2199 time to create 1 rle with old method : 0.003366708755493164 time for calcul the mask position with numpy : 0.0015344619750976562 nb_pixel_total : 950 time to create 1 rle with old method : 0.0015339851379394531 time for calcul the mask position with numpy : 0.0015804767608642578 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0019316673278808594 time for calcul the mask position with numpy : 0.001683950424194336 nb_pixel_total : 883 time to create 1 rle with old method : 0.0015213489532470703 time for calcul the mask position with numpy : 0.0017168521881103516 nb_pixel_total : 3385 time to create 1 rle with old method : 0.00484466552734375 time for calcul the mask position with numpy : 0.001710653305053711 nb_pixel_total : 1614 time to create 1 rle with old method : 0.002215147018432617 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 97 chid ids of type : 4677 Number RLEs to save : 9107 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3709509017', '465', '201', '3') ... last line : ('3709509113', '280', '238', '18') 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 : 0.017478466033935547 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.542696714401245 time spend to save output : 0.01776289939880371 total time spend for step 1 : 9.560459613800049 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1741876607_3346697_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 97 ############################### 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 : 0.13345003128051758 #### 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 Mar 13 15:36:57 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/1741876617_3346697_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1741876617_3346697_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/1741876617_3346697_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 0.079s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.0638403 (374.12643, 293.91928, 430.80942, 317.80807) proba : 0.05222364 (382.1775, 297.18842, 552.3555, 344.6579) proba : 0.012273403 (345.3583, 272.43048, 468.85745, 320.72382) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 2.629624366760254 time spend to save output : 0.00017905235290527344 total time spend for step 1 : 2.629803419113159 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, 0.0638403, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222364, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012273403, 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 : 0.014011144638061523 [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 : 0.011906623840332031 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, 0.0638403, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222364, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012273403, None)], 'temp/1741876617_3346697_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 : 0.09190702438354492 #### 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 Mar 13 15:37:00 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/1741876620_3346697_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1741876620_3346697_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 : 0.005880594253540039 time to convert the images to numpy array : 0.001508474349975586 total time to convert the images to numpy array : 0.007794380187988281 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': 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'svm_portfolios_learning': 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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 : 0.014216423034667969 time used to do the prediction : 0.08824849128723145 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 : 0.06268000602722168 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 : 0.9633190631866455 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', 0.0018818064, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011634157, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008159604, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011773166, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.002585188, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.004171324, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034804747, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.0073647615, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021883717, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.0005797611, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.0045915116, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031590192, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022511985, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.0053201444, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0011001164, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054015247, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.003919007, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014787155, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019782244, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012442284, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015050836, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021701131, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.002708817, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.0047027944, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019422566, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009586651, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007700253, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019468744, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013727534, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016204662, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013925597, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010041429, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010793942, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.00084468554, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012520885, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025847116, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012430712, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.002865049, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012951064, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010335276, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018770929, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.001134526, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015759895, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980353, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028745206, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023355451, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012111204, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011448001, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022341842, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017465058, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.0046994747, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021924467, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022632312, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018171, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.0009057781, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020400702, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.002533338, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.0044895033, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034243723, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031355924, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010256949, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015352872, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.00059150124, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040303874, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011587086, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012156186, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014969137, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011322077, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010540471, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.001198779, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.004185225, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023635272, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016325793, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015571731, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013957756, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021980675, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.00882932, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014614402, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008825842, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024711767, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.001009261, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015964231, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015332071, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.001491595, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014587407, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.005645982, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.0011166066, 332, '355'), 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('916235064', 'viano_1027_gao__port_506211', 0.0026940198, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.0008320695, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037387044, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.00079203624, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030529061, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037233124, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017739895, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015305344, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004087416, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011864142, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026954985, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011408117, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012529538, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015146016, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.003165841, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017348058, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016717425, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018257836, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.0021465817, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014154266, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020547928, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022963479, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013944698, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.0119662015, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010548512, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020711147, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017867454, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.005723829, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027671084, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017144902, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.004825718, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018691106, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.001257952, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012627396, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.00092460297, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007464916, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.00072899077, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010386852, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.00069125846, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021417057, 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 : 5.7220458984375e-06 save missing photos in datou_result : time spend for datou_step_exec : 6.5930016040802 time spend to save output : 3.0195248126983643 total time spend for step 1 : 9.612526416778564 step2:argmax Thu Mar 13 15:37:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876620_3346697_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1741876620_3346697_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', 0.017707383, 332, '355'), 'temp/1741876620_3346697_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 : 0.018320083618164062 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 : 0.01613640785217285 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', '0.017707383', None)] time used for this insertion : 0.013807296752929688 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 : 3.337860107421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00046133995056152344 time spend to save output : 0.04939150810241699 total time spend for step 2 : 0.049852848052978516 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', 0.017707383, 332, '355'), 'temp/1741876620_3346697_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 1171252784 download finish for photo 1171252487 download finish for photo 1171252764 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 : 0.18963980674743652 #### 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 Mar 13 15:37:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764} map_photo_id_path_extension : {1171252784: {'path': 'temp/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.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 2025-03-13 15:37:13.207342: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-13 15:37:13.208033: 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 2025-03-13 15:37:13.208104: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:37:13.208150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:37:13.209877: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:37:13.209942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:37:13.212493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:37:13.213457: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:37:13.218138: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:37:13.219271: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:37:13.219617: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-13 15:37:13.251259: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-13 15:37:13.253081: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f233c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:37:13.253125: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-13 15:37:13.256504: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x317253a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-13 15:37:13.256534: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-13 15:37:13.257583: 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 2025-03-13 15:37:13.257698: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:37:13.257729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-13 15:37:13.257817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-13 15:37:13.257857: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-13 15:37:13.257905: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-13 15:37:13.257957: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-13 15:37:13.258009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-13 15:37:13.259672: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-13 15:37:13.259749: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-13 15:37:13.259798: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-13 15:37:13.259814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-13 15:37:13.259826: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-13 15:37:13.261530: 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 : 8.516865730285645 time used to load_weights : 0.14693641662597656 0it [00:00, ?it/s] 3it [00:00, 657.07it/s]2025-03-13 15:37:24.447404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.4233546257019043 ['temp/image000000000_1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'temp/image000000001_1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'temp/image000000002_1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.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, 6, 0, 0, 1, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000001'| 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'| time to traite the descriptors : 0.03468465805053711 Testing : ['1171252784', '1171252487', '1171252764'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171252784,1171252487,1171252764) 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 : 1171252784 To insert : 1171252487 To insert : 1171252764 time to insert the descriptors : 2.198035955429077 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 [1171252784, 1171252487, 1171252764] map_info['map_portfolio_photo'] : {} final : False mtd_id 4567 list_pids : [1171252784, 1171252487, 1171252764] Looping around the photos to save general results len do output : 3 /1171252784Didn't retrieve data . /1171252487Didn't retrieve data . /1171252764Didn'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, '1171252784', None, None, None, None, None, None) ('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) 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, '1171252784', 'None', None, None, None, None, None), ('4567', None, '1171252487', 'None', None, None, None, None, None), ('4567', None, '1171252764', 'None', None, None, None, None, None)] time used for this insertion : 0.012032032012939453 save_final save missing photos in datou_result : time spend for datou_step_exec : 20.06417441368103 time spend to save output : 0.012312650680541992 total time spend for step 1 : 20.076487064361572 step2:argmax Thu Mar 13 15:37:30 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/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764} map_photo_id_path_extension : {1171252784: {'path': 'temp/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.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 : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677384, 4674, '3609'), 'temp/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.92628103, 4674, '3609'), 'temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.98536384, 4674, '3609'), 'temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.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 : ('1171252784', '495916461', '4674') ... last line : ('1171252764', '495916461', '4674') time used for this insertion : 1.4391119480133057 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 : 0.02165818214416504 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, '1171252784', 'jrm', None, None, '495916461', '0.9677384', None), ('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.92628103', None), ('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.98536384', None)] time used for this insertion : 0.0258328914642334 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 : 1.0728836059570312e-05 save missing photos in datou_result : time spend for datou_step_exec : 0.0001239776611328125 time spend to save output : 1.4909751415252686 total time spend for step 2 : 1.4910991191864014 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252784': [(1171252784, 'jrm', 0.9677384, 4674, '3609'), 'temp/1741876630_3346697_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252487': [(1171252487, 'jrm', 0.92628103, 4674, '3609'), 'temp/1741876630_3346697_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252764': [(1171252764, 'jrm', 0.98536384, 4674, '3609'), 'temp/1741876630_3346697_1171252764_29d5179a892cc50aadc9d67245534b59.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 1171291875 download finish for photo 1171275314 download finish for photo 1171275372 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 : 0.27117395401000977 #### 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 Mar 13 15:37:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.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 : 2944 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 : 8.336835384368896 time used to load_weights : 0.1333754062652588 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.2967631816864014 ['temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.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, 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'| Got the blobs of the net to insert : [0, 0, 0, 0, 14, 0, 1, 4, 0, 0] code_as_byte_string:b'000000000e'| time to traite the descriptors : 0.04206514358520508 Testing : ['1171291875', '1171275314', '1171275372'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171291875,1171275314,1171275372) 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 : 1171291875 To insert : 1171275314 To insert : 1171275372 time to insert the descriptors : 1.4812395572662354 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 [1171291875, 1171275314, 1171275372] map_info['map_portfolio_photo'] : {} final : False mtd_id 4621 list_pids : [1171291875, 1171275314, 1171275372] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275314Didn't retrieve data . /1171275372Didn'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, '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) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', 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, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275314', 'None', None, None, None, None, None), ('4621', None, '1171275372', 'None', None, None, None, None, None)] time used for this insertion : 0.015537023544311523 save_final save missing photos in datou_result : time spend for datou_step_exec : 21.36707592010498 time spend to save output : 0.01595592498779297 total time spend for step 1 : 21.383031845092773 step2:argmax Thu Mar 13 15:37:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.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 : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.9706973, 4723, '3655'), 'temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96514505, 4723, '3655'), 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96740943, 4723, '3655'), 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.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 : ('1171291875', '2107748999', '4723') ... last line : ('1171275372', '2107748999', '4723') time used for this insertion : 0.013351202011108398 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 : 0.014870405197143555 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, '1171291875', 'tapis_vide', None, None, '2107748999', '0.9706973', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.96514505', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96740943', None)] time used for this insertion : 0.011906623840332031 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 : 4.291534423828125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.000152587890625 time spend to save output : 0.04489755630493164 total time spend for step 2 : 0.04505014419555664 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171291875': [(1171291875, 'tapis_vide', 0.9706973, 4723, '3655'), 'temp/1741876651_3346697_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96514505, 4723, '3655'), 'temp/1741876651_3346697_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96740943, 4723, '3655'), 'temp/1741876651_3346697_1171275372_76d81364ff7df843bff095f45c07ba35.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 : 0.1997969150543213 #### 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 Mar 13 15:38:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1741876687_3346697_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/1741876687_3346697_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/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1741876687_3346697_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/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1741876687_3346697_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/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741876687_3346697_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/1741876688_3346697 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.051086187362671 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1343748559, 'temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1343748560, 'temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1343748561} 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 : 1.2815473079681396 time spend to save output : 7.271766662597656e-05 total time spend for step 1 : 1.2816200256347656 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 /1343748559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748561Didn'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, '1343748559', 'None', None, None, None, None, None), ('230', None, '1343748560', 'None', None, None, None, None, None), ('230', None, '1343748561', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.013374090194702148 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1343748559: ['917849322', 'temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1343748560: ['917849322', 'temp/1741876687_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1343748561: ['917849322', 'temp/1741876687_3346697_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 : 0.15567827224731445 #### 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 Mar 13 15:38:09 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/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1741876689_3346697_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 : 0.00020766258239746094 time to convert the images to numpy array : 1.1460611820220947 total time to convert the images to numpy array : 1.1465990543365479 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 : 2.364959955215454 time used to do the prediction : 0.2152700424194336 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 : 0.05088996887207031 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 : 0.743598461151123 After datou_step_exec type output : time spend for datou_step_exec : 9.776201725006104 time spend to save output : 7.414817810058594e-05 total time spend for step 1 : 9.776275873184204 step2:argmax Thu Mar 13 15:38:19 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', 0.9976488, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005038699, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036611478, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014812145, 507, '500')]]} input_args_next_step : {'917849322': ()} output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976488, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005038699, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036611478, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014812145, 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', 0.9976488, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005038699, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036611478, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014812145, 507, '500')],) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1741876689_3346697_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 : 0.0001728534698486328 time spend to save output : 3.790855407714844e-05 total time spend for step 2 : 0.00021076202392578125 step3:rotate Thu Mar 13 15:38:19 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', 0.9976488, 507, '500'), 'temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ()} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976488, 507, '500'), 'temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 1 complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976488, 507, '500'), 'temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ('temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976488, 507, '500'), 'temp/1741876689_3346697_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/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.9976488, 507, '500')) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1741876689_3346697_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/1741876689_3346697_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/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741876689_3346697_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/1741876699_3346697 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.792288064956665 map_filename_photo_id : 1 map_filename_photo_id : {'temp/1741876689_3346697_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 1343748597} 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 : 0.8983516693115234 time spend to save output : 4.76837158203125e-05 total time spend for step 3 : 0.8983993530273438 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 /1343748597Didn'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, '1343748597', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.016344070434570312 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1343748597: ['917849322', 'temp/1741876689_3346697_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 (3709398044,3709398045,3709398046,3709398047) # 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 : 0.0989539623260498 #### 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 Mar 13 15:38:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786} map_photo_id_path_extension : {937852786: {'path': 'temp/1741876700_3346697_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/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg new_file_path_bib_crop : temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg new_file_path_bib_crop : temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg new_file_path_bib_crop : temp/1741876700_3346697_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/1741876700_3346697_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/1741876700_3346697_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/1741876700_3346697_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/1741876700_3346697_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 : 21378184 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741876703_3346697 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (21378184, 1343748617, 0, NOW()),(21378184, 1343748618, 0, NOW()),(21378184, 1343748619, 0, NOW()),(21378184, 1343748620, 0, NOW()) 4 we have uploaded 4 photos in the portfolio 21378184 time of upload the photos Elapsed time : 3.5366482734680176 {'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1343748617, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1343748618, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1343748619, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1343748620} list_errors : [] map_result_insert : {'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1343748617, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1343748618, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1343748619, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1343748620} 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/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg sub_photo_id found to be used 1343748617 chi_id found to be used 8165076 path of cropped varroa found to be used to match on an ellipse temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg sub_photo_id found to be used 1343748618 chi_id found to be used 8165077 path of cropped varroa found to be used to match on an ellipse temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg sub_photo_id found to be used 1343748619 chi_id found to be used 8165078 path of cropped varroa found to be used to match on an ellipse temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg sub_photo_id found to be used 1343748620 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '1343748617', 31), (8165076, '1343748618', 31), (8165077, '1343748619', 31), (8165078, '1343748620', 31)] map of cropped photos with some data : {'1343748617': ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', (426, 467, 312, 347)], '1343748618': ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', (411, 445, 443, 480)], '1343748619': ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', (103, 138, 358, 396)], '1343748620': ['937852786', 'temp/1741876700_3346697_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/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1741876700_3346697_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 : 21378185 Result OK ! uploaded one batch 0 Elapsed time : 19.370169401168823 After datou_step_exec type output : time spend for datou_step_exec : 24.35020089149475 time spend to save output : 1.811981201171875e-05 total time spend for step 1 : 24.350219011306763 step2:tile Thu Mar 13 15:38:44 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/1741876700_3346697_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/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1343748617, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1343748618, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1343748619, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1343748620} map_photo_id_path_extension : {937852786: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1343748617: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1343748618: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1343748619: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1343748620: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}} map_subphoto_mainphoto : {1343748617: 937852786, 1343748618: 937852786, 1343748619: 937852786, 1343748620: 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/1741876700_3346697_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 (3709509954,3709509955,3709509956,3709509957) ++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3709509954,3709509955,3709509956,3709509957) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3709509954,3709509955,3709509956,3709509957) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21378218 with name tile_taggage_varroa feed_id_new_photos : 21378218 filename : temp/1741876700_3346697_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/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.010197639465332031 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/1741876731_3346697 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (21378218, 1343748709, 0, NOW()) 1 we have uploaded 1 photos in the portfolio 21378218 Importing ! upload mediasElapsed time : 0.8117203712463379 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1343748709, 0)] Saving 4 CHIs. list_chi_tile : [": {'photo_id': 1343748709, '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': 1343748709, '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': 1343748709, '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': 1343748709, '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 : 0.8885712623596191 map_pid_results : {'1343748709': ['temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 7.5969932079315186 time spend to save output : 7.700920104980469e-05 total time spend for step 2 : 7.597070217132568 step3:rotate Thu Mar 13 15:38:52 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 : {'1343748709': ['temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} input_args_next_step : {'1343748709': ()} output_args : {'1343748709': ['temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} args : 1343748709 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/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1343748617, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1343748618, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1343748619, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1343748620, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1343748709} map_photo_id_path_extension : {937852786: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1343748617: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1343748618: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1343748619: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1343748620: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1343748709: {'path': 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}} map_subphoto_mainphoto : {1343748617: 937852786, 1343748618: 937852786, 1343748619: 937852786, 1343748620: 937852786, 1343748709: 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 ( 1343748709) 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 (3709509961,3709509962,3709509960,3709509959) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3709509959. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3709509960. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3709509961. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3709509962. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3709509961,3709509962,3709509960,3709509959) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3709509961,3709509962,3709509960,3709509959) map_chi : {1343748709: [, , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 21378219 photo_id in download_rotate_and_save : 1343748709 list_chi_loc : 4 Use all angle ! Rotation of photo 1343748709 of 0 degree temp/1741876700_3346697_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 : 0.0004894733428955078 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0020704269409179688 .time for calcul the mask position with numpy : 0.00040149688720703125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014007091522216797 . 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 1343748709 of 15 degree temp/1741876700_3346697_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 : 0.00034809112548828125 nb_pixel_total : 694 time to create 1 rle with old method : 0.0008263587951660156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003943443298339844 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0017728805541992188 . 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 1343748709 of 30 degree temp/1741876700_3346697_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 : 0.0003497600555419922 nb_pixel_total : 221 time to create 1 rle with old method : 0.00028443336486816406 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003235340118408203 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014100074768066406 . 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 1343748709 of 45 degree temp/1741876700_3346697_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 : 0.00033664703369140625 nb_pixel_total : 143 time to create 1 rle with old method : 0.000194549560546875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003554821014404297 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0014066696166992188 . 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 1343748709 of 60 degree temp/1741876700_3346697_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 : 0.0003490447998046875 nb_pixel_total : 414 time to create 1 rle with old method : 0.0005154609680175781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036334991455078125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014522075653076172 . 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 1343748709 of 75 degree temp/1741876700_3346697_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 : 0.0003914833068847656 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0014262199401855469 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000415802001953125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0017981529235839844 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003421306610107422 nb_pixel_total : 264 time to create 1 rle with old method : 0.0003509521484375 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 1343748709 of 90 degree temp/1741876700_3346697_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 : 0.00038170814514160156 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0017828941345214844 .time for calcul the mask position with numpy : 0.00036215782165527344 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0013933181762695312 . 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 1343748709 of 105 degree temp/1741876700_3346697_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 : 0.000362396240234375 nb_pixel_total : 694 time to create 1 rle with old method : 0.0008556842803955078 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004374980926513672 nb_pixel_total : 1162 time to create 1 rle with old method : 0.001760244369506836 . 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 1343748709 of 120 degree temp/1741876700_3346697_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 : 0.0003485679626464844 nb_pixel_total : 221 time to create 1 rle with old method : 0.0002987384796142578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0013957023620605469 . 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 1343748709 of 135 degree temp/1741876700_3346697_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 : 0.00037384033203125 nb_pixel_total : 143 time to create 1 rle with old method : 0.0003497600555419922 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040268898010253906 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0014946460723876953 . 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 1343748709 of 150 degree temp/1741876700_3346697_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 : 0.00041484832763671875 nb_pixel_total : 414 time to create 1 rle with old method : 0.0006134510040283203 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003826618194580078 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014557838439941406 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00037217140197753906 nb_pixel_total : 1 time to create 1 rle with old method : 3.504753112792969e-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 1343748709 of 165 degree temp/1741876700_3346697_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 : 0.00039386749267578125 nb_pixel_total : 1204 time to create 1 rle with old method : 0.001554250717163086 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003712177276611328 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0015096664428710938 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003383159637451172 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004410743713378906 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 1343748709 of 180 degree temp/1741876700_3346697_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 : 0.00039649009704589844 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0017790794372558594 .time for calcul the mask position with numpy : 0.0003829002380371094 nb_pixel_total : 1157 time to create 1 rle with old method : 0.001455068588256836 . 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 1343748709 of 195 degree temp/1741876700_3346697_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 : 0.0003933906555175781 nb_pixel_total : 727 time to create 1 rle with old method : 0.0010056495666503906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035834312438964844 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0015149116516113281 . 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 1343748709 of 210 degree temp/1741876700_3346697_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 : 0.00037980079650878906 nb_pixel_total : 250 time to create 1 rle with old method : 0.00040650367736816406 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037860870361328125 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0015099048614501953 . 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 1343748709 of 225 degree temp/1741876700_3346697_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 : 0.0003714561462402344 nb_pixel_total : 169 time to create 1 rle with old method : 0.00032401084899902344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035452842712402344 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0015592575073242188 . 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 1343748709 of 240 degree temp/1741876700_3346697_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 : 0.00037598609924316406 nb_pixel_total : 450 time to create 1 rle with old method : 0.0006501674652099609 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003542900085449219 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014600753784179688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003256797790527344 nb_pixel_total : 1 time to create 1 rle with old method : 2.0265579223632812e-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 1343748709 of 255 degree temp/1741876700_3346697_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 : 0.0003762245178222656 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0015354156494140625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003459453582763672 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0014712810516357422 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00032806396484375 nb_pixel_total : 234 time to create 1 rle with old method : 0.00040602684020996094 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 1343748709 of 270 degree temp/1741876700_3346697_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 : 0.0004029273986816406 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0016231536865234375 .time for calcul the mask position with numpy : 0.00035691261291503906 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014994144439697266 . 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 1343748709 of 285 degree temp/1741876700_3346697_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 : 0.0004010200500488281 nb_pixel_total : 727 time to create 1 rle with old method : 0.0009784698486328125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003552436828613281 nb_pixel_total : 1162 time to create 1 rle with old method : 0.001542806625366211 . 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 1343748709 of 300 degree temp/1741876700_3346697_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 : 0.0003960132598876953 nb_pixel_total : 250 time to create 1 rle with old method : 0.0004048347473144531 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003781318664550781 nb_pixel_total : 1155 time to create 1 rle with old method : 0.001493692398071289 . 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 1343748709 of 315 degree temp/1741876700_3346697_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 : 0.0003917217254638672 nb_pixel_total : 169 time to create 1 rle with old method : 0.0003018379211425781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039196014404296875 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0014770030975341797 . 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 1343748709 of 330 degree temp/1741876700_3346697_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 : 0.0003895759582519531 nb_pixel_total : 450 time to create 1 rle with old method : 0.0006392002105712891 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003695487976074219 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014808177947998047 . 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 1343748709 of 345 degree temp/1741876700_3346697_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 : 0.0004143714904785156 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0015735626220703125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003559589385986328 nb_pixel_total : 1157 time to create 1 rle with old method : 0.018793582916259766 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003457069396972656 nb_pixel_total : 234 time to create 1 rle with old method : 0.00043582916259765625 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 : 21378219 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741876734_3346697 we have uploaded 24 photos in the portfolio 21378219 time of upload the photos Elapsed time : 5.548304080963135 map_filename_photo_id : 24 map_filename_photo_id : {'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 1343748717, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 1343748718, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 1343748719, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 1343748720, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 1343748721, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 1343748722, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 1343748723, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 1343748724, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 1343748725, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 1343748726, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 1343748727, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 1343748728, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 1343748729, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 1343748730, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 1343748731, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 1343748732, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 1343748733, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 1343748734, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 1343748735, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 1343748736, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 1343748737, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 1343748738, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 1343748739, 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 1343748740} 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 : 8.920093059539795 time spend to save output : 8.368492126464844e-05 total time spend for step 3 : 8.92017674446106 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, '1343748709'] map_info['map_portfolio_photo'] : {} final : True mtd_id 243 list_pids : [937852786, 937852786, '1343748709'] Looping around the photos to save general results len do output : 24 /1343748717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748740Didn'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, '1343748709', 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, '1343748717', 'None', None, None, None, None, None), ('243', None, '1343748718', 'None', None, None, None, None, None), ('243', None, '1343748719', 'None', None, None, None, None, None), ('243', None, '1343748720', 'None', None, None, None, None, None), ('243', None, '1343748721', 'None', None, None, None, None, None), ('243', None, '1343748722', 'None', None, None, None, None, None), ('243', None, '1343748723', 'None', None, None, None, None, None), ('243', None, '1343748724', 'None', None, None, None, None, None), ('243', None, '1343748725', 'None', None, None, None, None, None), ('243', None, '1343748726', 'None', None, None, None, None, None), ('243', None, '1343748727', 'None', None, None, None, None, None), ('243', None, '1343748728', 'None', None, None, None, None, None), ('243', None, '1343748729', 'None', None, None, None, None, None), ('243', None, '1343748730', 'None', None, None, None, None, None), ('243', None, '1343748731', 'None', None, None, None, None, None), ('243', None, '1343748732', 'None', None, None, None, None, None), ('243', None, '1343748733', 'None', None, None, None, None, None), ('243', None, '1343748734', 'None', None, None, None, None, None), ('243', None, '1343748735', 'None', None, None, None, None, None), ('243', None, '1343748736', 'None', None, None, None, None, None), ('243', None, '1343748737', 'None', None, None, None, None, None), ('243', None, '1343748738', 'None', None, None, None, None, None), ('243', None, '1343748739', 'None', None, None, None, None, None), ('243', None, '1343748740', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1343748709', None, None, None, None, None, None)] time used for this insertion : 0.023641347885131836 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1343748717: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1343748718: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1343748719: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1343748720: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1343748721: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1343748722: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1343748723: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1343748724: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1343748725: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1343748726: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1343748727: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1343748728: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1343748729: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1343748730: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1343748731: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1343748732: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1343748733: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1343748734: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1343748735: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1343748736: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1343748737: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1343748738: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1343748739: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1343748740: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} ret_da : {1343748717: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1343748718: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1343748719: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1343748720: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1343748721: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1343748722: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1343748723: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1343748724: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1343748725: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1343748726: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1343748727: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1343748728: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1343748729: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1343748730: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1343748731: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1343748732: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1343748733: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1343748734: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1343748735: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1343748736: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1343748737: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1343748738: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1343748739: ['937852786', 'temp/1741876700_3346697_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1343748740: ['937852786', 'temp/1741876700_3346697_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 : 0.12977361679077148 #### 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 Mar 13 15:39:04 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/1741876744_3346697_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586} map_photo_id_path_extension : {911785586: {'path': 'temp/1741876744_3346697_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/1741876744_3346697_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg Horizontal flip of photo 911785586 version de PIL : 9.5.0 horizontally flipped image is saved in temp/1741876744_3346697_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/1741876744_3346697 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8524315357208252 map_filename_photo_id : 2 map_filename_photo_id : {'temp/1741876744_3346697_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 1343748742, 'temp/1741876744_3346697_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 1343748743} 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 : 0.9646289348602295 time spend to save output : 7.748603820800781e-05 total time spend for step 1 : 0.9647064208984375 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 /1343748742 /1343748743 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 : 0.010696172714233398 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1343748742': ['911785586', 'temp/1741876744_3346697_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1343748743': ['911785586', 'temp/1741876744_3346697_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 : 0.28803038597106934 #### 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 Mar 13 15:39:05 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/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132} map_photo_id_path_extension : {950103132: {'path': 'temp/1741876745_3346697_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/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1741876745_3346697_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 : 21378220 in upload media Upload medias : ['temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg after data_to_send, before sending request after request b'{"photo_ids":["1343748752","1343748760","1343748748","1343748759","1343748750","1343748747","1343748756","1343748755"],"photo_ids_order":["1343748747","1343748748","1343748750","1343748752","1343748755","1343748756","1343748759","1343748760"],"photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/13/f7eb22311dc2cb31e228cee8c495cfa8.jpg","text":"TemporaryFile(/tmp/multipartBody6858565053947705511asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_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/2025/3/13/9fbe5aafe800bc252106da2906bda045.jpg","text":"TemporaryFile(/tmp/multipartBody4668148909917460446asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_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/2025/3/13/4edfd9c3ef1011f05bf114b673759646.jpg","text":"TemporaryFile(/tmp/multipartBody6275127363472762884asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/13/7ebe28526ea9e2c2046944aa4fc7cd41.jpg","text":"TemporaryFile(/tmp/multipartBody8333824539914917073asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_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/2025/3/13/22158875d017d7d15a7cd20882327c67.jpg","text":"TemporaryFile(/tmp/multipartBody4810997694205395916asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_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/2025/3/13/20c3d9f08913b75d08ac350310ebbff7.jpg","text":"TemporaryFile(/tmp/multipartBody2585865845233498037asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_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/2025/3/13/747dd5264ed13317ccfa4bd58945dfb4.jpg","text":"TemporaryFile(/tmp/multipartBody5950793021994424087asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_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/2025/3/13/baff337b753af08bd54a5a89a2ed4e89.jpg","text":"TemporaryFile(/tmp/multipartBody91349973551847063asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1741876748439,"filename":"1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg","height":0,"width":0}],"map_files_photo_id":{"file2":"1343748750","file6":"1343748759","file1":"1343748748","file7":"1343748760","file0":"1343748747","file4":"1343748755","file5":"1343748756","file3":"1343748752"},"map_files_photo_id_array":[{"photo_id":"1343748756","filename":"file5"},{"photo_id":"1343748750","filename":"file2"},{"photo_id":"1343748755","filename":"file4"},{"photo_id":"1343748760","filename":"file7"},{"photo_id":"1343748748","filename":"file1"},{"photo_id":"1343748747","filename":"file0"},{"photo_id":"1343748752","filename":"file3"},{"photo_id":"1343748759","filename":"file6"}],"portfolio_id":21378220,"hashtag_by_photo_ids":[{"1343748752":["hashtag1","hashtag2"]},{"1343748760":["hashtag1","hashtag2"]},{"1343748748":["hashtag1","hashtag2"]},{"1343748759":["hashtag1","hashtag2"]},{"1343748750":["hashtag1","hashtag2"]},{"1343748747":["hashtag1","hashtag2"]},{"1343748756":["hashtag1","hashtag2"]},{"1343748755":["hashtag1","hashtag2"]}],"comms":"Portfolio 21378220 used, photo_id : ArrayBuffer(1343748752, 1343748760, 1343748748, 1343748759, 1343748750, 1343748747, 1343748756, 1343748755)","result":[],"list_datou_current":[]}' Result OK ! uploaded one batch 0 Elapsed time : 19.64479947090149 map_result_insert : {'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1343748750, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1343748759, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1343748748, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1343748760, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1343748747, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1343748755, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1343748756, 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1343748752} 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/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg sub_photo_id found to be used 1343748747 chi_id found to be used 1947670932 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg sub_photo_id found to be used 1343748748 chi_id found to be used 1947670933 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg sub_photo_id found to be used 1343748750 chi_id found to be used 1947670934 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg sub_photo_id found to be used 1343748752 chi_id found to be used 1947670935 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg sub_photo_id found to be used 1343748755 chi_id found to be used 1947670936 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg sub_photo_id found to be used 1343748756 chi_id found to be used 1947670937 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg sub_photo_id found to be used 1343748759 chi_id found to be used 1947670938 path of cropped varroa found to be used to match on an ellipse temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg sub_photo_id found to be used 1343748760 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1343748747', 31), (1947670932, '1343748748', 31), (1947670933, '1343748750', 31), (1947670934, '1343748752', 31), (1947670935, '1343748755', 31), (1947670936, '1343748756', 31), (1947670937, '1343748759', 31), (1947670938, '1343748760', 31)] map of cropped photos with some data : {'1343748747': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1343748748': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1343748750': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1343748752': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1343748755': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1343748756': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1343748759': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1343748760': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} After datou_step_exec type output : time spend for datou_step_exec : 20.934334754943848 time spend to save output : 7.724761962890625e-05 total time spend for step 1 : 20.934412002563477 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 /1343748747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1343748760Didn'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, '1343748747', 'None', None, None, None, None, None), ('686', None, '1343748748', 'None', None, None, None, None, None), ('686', None, '1343748750', 'None', None, None, None, None, None), ('686', None, '1343748752', 'None', None, None, None, None, None), ('686', None, '1343748755', 'None', None, None, None, None, None), ('686', None, '1343748756', 'None', None, None, None, None, None), ('686', None, '1343748759', 'None', None, None, None, None, None), ('686', None, '1343748760', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)] time used for this insertion : 0.01961231231689453 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1343748747': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1343748748': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1343748750': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1343748752': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1343748755': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1343748756': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1343748759': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1343748760': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} ret_da : {'1343748747': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1343748748': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1343748750': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1343748752': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1343748755': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1343748756': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1343748759': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1343748760': ['950103132', 'temp/1741876745_3346697_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 Found filename_to_hash : temp/1741876745_3346697_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 : 0.12959885597229004 #### 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 Mar 13 15:39:26 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/1741876766_3346697_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368} map_photo_id_path_extension : {932296368: {'path': 'temp/1741876766_3346697_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 : 0.08686423301696777 time spend to save output : 0.0008985996246337891 total time spend for step 1 : 0.08776283264160156 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 : 0.22599053382873535 #### 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 Mar 13 15:39:27 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876766_3346697_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423} map_photo_id_path_extension : {946711423: {'path': 'temp/1741876766_3346697_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 : 0.11852550506591797 time spend to save output : 4.982948303222656e-05 total time spend for step 1 : 0.1185753345489502 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, 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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 (3709399303,3709399302,3709399301,3709399310,3709399309,3709399308,3709399307,3709399306,3709399315,3709399318,3709399304,3709399305,3709399314,3709399313,3709399319,3709399320,3709399321,3709399323,3709399311,3709399317,3709399316,3709399322,3709399312) 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 : 0.004152536392211914 #### 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 Mar 13 15:39:27 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 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 : ('3709510851', '117', '95', '16') ... last line : ('3709510873', '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 : 0.527890682220459 time spend to save output : 0.00011444091796875 total time spend for step 1 : 0.5280051231384277 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 : 0.10475444793701172 #### 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 Mar 13 15:39:28 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 : {'temp/1741876767_3346697_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675} map_photo_id_path_extension : {930729675: {'path': 'temp/1741876767_3346697_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blur_detection methode: ratio et variance treat image : temp/1741876767_3346697_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 : 0.1964278221130371 time spend to save output : 4.1484832763671875e-05 total time spend for step 1 : 0.19646930694580078 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 987515224 begin to download photo : 987515226 download finish for photo 987515188 begin to download photo : 987515189 download finish for photo 987515239 begin to download photo : 987515240 download finish for photo 987515207 begin to download photo : 987515208 download finish for photo 987515226 begin to download photo : 987515227 download finish for photo 987515175 begin to download photo : 987515176 download finish for photo 987515240 begin to download photo : 987515241 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 987515241 begin to download photo : 987515242 download finish for photo 987515190 begin to download photo : 987515192 download finish for photo 987515227 begin to download photo : 987515228 download finish for photo 987515209 begin to download photo : 987515211 download finish for photo 987515242 begin to download photo : 987515243 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 987515243 begin to download photo : 987515244 download finish for photo 987515211 begin to download photo : 987515212 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 987515244 begin to download photo : 987515245 download finish for photo 987515212 begin to download photo : 987515213 download finish for photo 987515195 begin to download photo : 987515196 download finish for photo 987515179 begin to download photo : 987515180 download finish for photo 987515231 begin to download photo : 987515232 download finish for photo 987515213 begin to download photo : 987515215 download finish for photo 987515245 begin to download photo : 987515246 download finish for photo 987515196 begin to download photo : 987515198 download finish for photo 987515180 begin to download photo : 987515181 download finish for photo 987515215 begin to download photo : 987515216 download finish for photo 987515232 begin to download photo : 987515233 download finish for photo 987515246 begin to download photo : 987515247 download finish for photo 987515198 begin to download photo : 987515200 download finish for photo 987515181 download finish for photo 987515216 begin to download photo : 987515182 begin to download photo : 987515217 download finish for photo 987515233 begin to download photo : 987515234 download finish for photo 987515200 begin to download photo : 987515201 download finish for photo 987515247 begin to download photo : 987515248 download finish for photo 987515234 begin to download photo : 987515235 download finish for photo 987515217 begin to download photo : 987515219 download finish for photo 987515201 begin to download photo : 987515202 download finish for photo 987515235 begin to download photo : 987515236 download finish for photo 987515219 begin to download photo : 987515220 download finish for photo 987515182 begin to download photo : 987515183 download finish for photo 987515248 begin to download photo : 987515249 download finish for photo 987515202 begin to download photo : 987515204 download finish for photo 987515236 begin to download photo : 987515237 download finish for photo 987515249 begin to download photo : 987515250 download finish for photo 987515204 begin to download photo : 987515205 download finish for photo 987515220 begin to download photo : 987515222 download finish for photo 987515183 begin to download photo : 987515184 download finish for photo 987515237 begin to download photo : 987515238 download finish for photo 987515205 download finish for photo 987515250 download finish for photo 987515222 download finish for photo 987515184 begin to download photo : 987515223 begin to download photo : 987515185 download finish for photo 987515238 download finish for photo 987515223 download finish for photo 987515185 begin to download photo : 987515186 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 : 1.6816151142120361 #### 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 Mar 13 15:39:29 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/1741876768_3346697_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1741876768_3346697_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1741876768_3346697_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1741876768_3346697_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1741876768_3346697_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1741876768_3346697_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1741876768_3346697_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1741876768_3346697_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1741876768_3346697_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1741876768_3346697_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1741876768_3346697_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1741876768_3346697_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1741876768_3346697_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1741876768_3346697_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1741876768_3346697_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1741876768_3346697_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1741876768_3346697_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1741876768_3346697_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1741876768_3346697_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1741876768_3346697_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1741876768_3346697_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1741876768_3346697_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1741876768_3346697_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1741876768_3346697_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1741876768_3346697_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1741876768_3346697_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1741876768_3346697_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1741876768_3346697_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1741876768_3346697_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1741876768_3346697_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1741876768_3346697_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1741876768_3346697_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1741876768_3346697_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1741876768_3346697_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1741876768_3346697_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1741876768_3346697_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1741876768_3346697_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1741876768_3346697_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1741876768_3346697_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1741876768_3346697_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1741876768_3346697_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1741876768_3346697_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1741876768_3346697_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1741876768_3346697_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1741876768_3346697_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1741876768_3346697_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1741876768_3346697_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1741876768_3346697_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1741876768_3346697_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1741876768_3346697_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1741876768_3346697_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1741876768_3346697_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1741876768_3346697_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1741876768_3346697_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1741876768_3346697_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1741876768_3346697_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1741876768_3346697_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1741876768_3346697_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1741876768_3346697_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1741876768_3346697_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1741876768_3346697_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1741876768_3346697_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1741876768_3346697_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1741876768_3346697_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187} map_photo_id_path_extension : {987515188: {'path': 'temp/1741876768_3346697_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1741876768_3346697_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1741876768_3346697_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1741876768_3346697_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1741876768_3346697_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1741876768_3346697_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1741876768_3346697_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1741876768_3346697_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1741876768_3346697_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1741876768_3346697_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1741876768_3346697_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1741876768_3346697_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1741876768_3346697_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1741876768_3346697_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1741876768_3346697_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1741876768_3346697_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1741876768_3346697_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1741876768_3346697_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1741876768_3346697_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1741876768_3346697_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1741876768_3346697_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1741876768_3346697_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1741876768_3346697_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1741876768_3346697_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1741876768_3346697_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1741876768_3346697_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1741876768_3346697_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1741876768_3346697_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1741876768_3346697_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1741876768_3346697_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1741876768_3346697_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1741876768_3346697_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1741876768_3346697_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1741876768_3346697_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1741876768_3346697_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1741876768_3346697_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1741876768_3346697_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1741876768_3346697_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1741876768_3346697_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1741876768_3346697_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1741876768_3346697_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1741876768_3346697_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1741876768_3346697_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1741876768_3346697_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1741876768_3346697_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1741876768_3346697_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1741876768_3346697_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1741876768_3346697_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1741876768_3346697_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1741876768_3346697_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1741876768_3346697_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515175: {'path': 'temp/1741876768_3346697_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1741876768_3346697_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1741876768_3346697_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1741876768_3346697_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1741876768_3346697_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1741876768_3346697_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1741876768_3346697_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1741876768_3346697_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1741876768_3346697_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1741876768_3346697_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1741876768_3346697_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1741876768_3346697_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1741876768_3346697_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 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 ! 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 ! time to import caffe and check if the image exist : 0.0010597705841064453 time to convert the images to numpy array : 0.012362480163574219 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 ! 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 : 0.004736900329589844 time to convert the images to numpy array : 0.036837100982666016 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 : 0.0073468685150146484 time to convert the images to numpy array : 0.035188913345336914 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 : 0.008224248886108398 time to convert the images to numpy array : 0.0357816219329834 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 : 0.007582426071166992 time to convert the images to numpy array : 0.038741350173950195 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 : 0.008292436599731445 time to convert the images to numpy array : 0.03902268409729004 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 : 0.013172388076782227 time to convert the images to numpy array : 0.03357648849487305 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 : 0.008749723434448242 time to convert the images to numpy array : 0.03821754455566406 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 : 0.0052568912506103516 time to convert the images to numpy array : 0.04379415512084961 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 : 0.008715152740478516 time to convert the images to numpy array : 0.03948473930358887 total time to convert the images to numpy array : 0.051229238510131836 list photo_ids error: [] list photo_ids correct : [987515187, 987515222, 987515223, 987515175, 987515176, 987515177, 987515178, 987515179, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515247, 987515248, 987515249, 987515250, 987515224, 987515226, 987515227, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515239, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515207, 987515208, 987515209, 987515211, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186] 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 : 2944 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 : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['res5b', 'prob']) time used to do the prepocess of the images : 0.05509352684020996 time used to do the prediction : 0.2650308609008789 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 : [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'| 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 : [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 : [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, 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, 2, 3, 6, 8, 12, 9, 9, 3, 5] code_as_byte_string:b'0502030608'| 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 : [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 : [1, 1, 0, 0, 0, 0, 0, 3, 5, 1] code_as_byte_string:b'0101000000'| 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 : [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, 1, 0, 0, 1, 3] code_as_byte_string:b'0002000101'| 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, 0, 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 : [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 : [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'| time to traite the descriptors : 3.273683786392212 Testing : ['987515187', '987515222', '987515223', '987515175', '987515176', '987515177', '987515178', '987515179', '987515212', '987515213', '987515215', '987515216', '987515217', '987515219', '987515220', '987515247', '987515248', '987515249', '987515250', '987515224', '987515226', '987515227', '987515188', '987515189', '987515190', '987515192', '987515193', '987515195', '987515196', '987515198', '987515200', '987515201', '987515202', '987515204', '987515205', '987515239', '987515228', '987515230', '987515231', '987515232', '987515233', '987515234', '987515235', '987515236', '987515237', '987515238', '987515207', '987515208', '987515209', '987515211', '987515240', '987515241', '987515242', '987515243', '987515244', '987515245', '987515246', '987515180', '987515181', '987515182', '987515183', '987515184', '987515185', '987515186'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (987515187,987515222,987515223,987515175,987515176,987515177,987515178,987515179,987515212,987515213,987515215,987515216,987515217,987515219,987515220,987515247,987515248,987515249,987515250,987515224,987515226,987515227,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515239,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515207,987515208,987515209,987515211,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515180,987515181,987515182,987515183,987515184,987515185,987515186) 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': 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'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 : 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 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515239 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 time to insert the descriptors : 17.25298523902893 After datou_step_exec type output : time spend for datou_step_exec : 24.7054386138916 time spend to save output : 0.00017499923706054688 total time spend for step 1 : 24.705613613128662 step2:argmax Thu Mar 13 15:39:54 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/1741876768_3346697_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1741876768_3346697_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1741876768_3346697_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1741876768_3346697_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1741876768_3346697_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1741876768_3346697_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1741876768_3346697_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1741876768_3346697_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 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'extension': 'jpg'}, 987515223: {'path': 'temp/1741876768_3346697_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515175: {'path': 'temp/1741876768_3346697_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1741876768_3346697_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1741876768_3346697_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1741876768_3346697_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1741876768_3346697_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1741876768_3346697_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1741876768_3346697_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1741876768_3346697_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1741876768_3346697_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1741876768_3346697_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1741876768_3346697_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1741876768_3346697_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1741876768_3346697_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': '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 : 0.0019023418426513672 time spend to save output : 0.0001671314239501953 total time spend for step 2 : 0.0020694732666015625 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', 0.98167247, 1927, '1528'), 'temp/1741876768_3346697_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515222': [('987515222', 'Carton', 0.9974679, 1927, '1528'), 'temp/1741876768_3346697_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920535, 1927, '1528'), 'temp/1741876768_3346697_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998136, 1927, '1528'), 'temp/1741876768_3346697_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1741876768_3346697_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.977132, 1927, '1528'), 'temp/1741876768_3346697_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8585939, 1927, '1528'), 'temp/1741876768_3346697_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9268937, 1927, '1528'), 'temp/1741876768_3346697_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515212': [('987515212', 'Carton', 0.9869559, 1927, '1528'), 'temp/1741876768_3346697_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98691714, 1927, '1528'), 'temp/1741876768_3346697_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99391586, 1927, '1528'), 'temp/1741876768_3346697_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774341, 1927, '1528'), 'temp/1741876768_3346697_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5288816, 1927, '1528'), 'temp/1741876768_3346697_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936885, 1927, '1528'), 'temp/1741876768_3346697_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.99637103, 1927, '1528'), 'temp/1741876768_3346697_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515247': [('987515247', 'Carton', 0.99966884, 1927, '1528'), 'temp/1741876768_3346697_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9813085, 1927, '1528'), 'temp/1741876768_3346697_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9812646, 1927, '1528'), 'temp/1741876768_3346697_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9804432, 1927, '1528'), 'temp/1741876768_3346697_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.9088164, 1927, '1528'), 'temp/1741876768_3346697_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.986971, 1927, '1528'), 'temp/1741876768_3346697_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9000447, 1927, '1528'), 'temp/1741876768_3346697_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515188': [('987515188', 'Carton', 0.99565965, 1927, '1528'), 'temp/1741876768_3346697_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977875, 1927, '1528'), 'temp/1741876768_3346697_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97648335, 1927, '1528'), 'temp/1741876768_3346697_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1741876768_3346697_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939585, 1927, '1528'), 'temp/1741876768_3346697_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846452, 1927, '1528'), 'temp/1741876768_3346697_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.984559, 1927, '1528'), 'temp/1741876768_3346697_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.966241, 1927, '1528'), 'temp/1741876768_3346697_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98593664, 1927, '1528'), 'temp/1741876768_3346697_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954529, 1927, '1528'), 'temp/1741876768_3346697_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911271, 1927, '1528'), 'temp/1741876768_3346697_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950781, 1927, '1528'), 'temp/1741876768_3346697_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99084723, 1927, '1528'), 'temp/1741876768_3346697_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.9997831, 1927, '1528'), 'temp/1741876768_3346697_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.52225244, 1927, '1528'), 'temp/1741876768_3346697_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940634, 1927, '1528'), 'temp/1741876768_3346697_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994215, 1927, '1528'), 'temp/1741876768_3346697_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924505, 1927, '1528'), 'temp/1741876768_3346697_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98336303, 1927, '1528'), 'temp/1741876768_3346697_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94477403, 1927, '1528'), 'temp/1741876768_3346697_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.891997, 1927, '1528'), 'temp/1741876768_3346697_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5369102, 1927, '1528'), 'temp/1741876768_3346697_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7694158, 1927, '1528'), 'temp/1741876768_3346697_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995735, 1927, '1528'), 'temp/1741876768_3346697_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.874091, 1927, '1528'), 'temp/1741876768_3346697_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.9917464, 1927, '1528'), 'temp/1741876768_3346697_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.967767, 1927, '1528'), 'temp/1741876768_3346697_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.973497, 1927, '1528'), 'temp/1741876768_3346697_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515240': [('987515240', 'Carton', 0.9995204, 1927, '1528'), 'temp/1741876768_3346697_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98212653, 1927, '1528'), 'temp/1741876768_3346697_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9358458, 1927, '1528'), 'temp/1741876768_3346697_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8742939, 1927, '1528'), 'temp/1741876768_3346697_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81770885, 1927, '1528'), 'temp/1741876768_3346697_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8662887, 1927, '1528'), 'temp/1741876768_3346697_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.999233, 1927, '1528'), 'temp/1741876768_3346697_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515180': [('987515180', 'Carton', 0.98998344, 1927, '1528'), 'temp/1741876768_3346697_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.99778086, 1927, '1528'), 'temp/1741876768_3346697_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242485, 1927, '1528'), 'temp/1741876768_3346697_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1741876768_3346697_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997321, 1927, '1528'), 'temp/1741876768_3346697_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.79774237, 1927, '1528'), 'temp/1741876768_3346697_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9846949, 1927, '1528'), 'temp/1741876768_3346697_987515186_797def426440b544aa80dbd63a19234a.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 : 0.1106569766998291 #### 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 Mar 13 15:39:54 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/1741876794_3346697_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173} map_photo_id_path_extension : {987515173: {'path': 'temp/1741876794_3346697_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/1741876794_3346697_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 : 0.04422736167907715 time to do a prediction : 0.36168646812438965 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 : 1.6121478080749512 time spend to save output : 3.647804260253906e-05 total time spend for step 1 : 1.6121842861175537 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, 6.287207299920938e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.437726756565617e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0604192191010497e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.456277622466587e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9245335352024995e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.7174904718995094e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001226268068421632), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9407516194623895e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.371588969651839e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2065814064831102e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3835199297318468e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4805073078605346e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1282223567832261e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015792236081324518), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.00044413990690372884), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.538108573295176e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.327363747805066e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6222836620727321e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5199915398843586e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6141910919031943e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.203773864399409e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.609006181359291e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032637055846862495), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003049898659810424), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.854743459261954e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.946257028379478e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7011050406144932e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.8050104699796066e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3420736397383735e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6951822544797324e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.5433585000864696e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.857565262587741e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.139705874375068e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6431505400760216e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9631415852927603e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.436905904483865e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.866509804443922e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2845644960179925e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.337963092548307e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1681633370462805e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.831920366792474e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.543427171985968e-06), (987515173, 1982, 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272, -1, 304, -1, 3.165351154166274e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1643293873930816e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.9232698832638562e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.00024751710589043796), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.00047796135186217725), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003346908197272569), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023675945703871548), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.0001096327614504844), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.555197902955115e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013124471297487617), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007334651309065521)]} result thcl : {'987515187': [('987515187', 'Carton', 0.98167247, 1927, '1528'), 'temp/1741876768_3346697_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515222': [('987515222', 'Carton', 0.9974679, 1927, '1528'), 'temp/1741876768_3346697_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920535, 1927, '1528'), 'temp/1741876768_3346697_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998136, 1927, '1528'), 'temp/1741876768_3346697_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1741876768_3346697_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.977132, 1927, '1528'), 'temp/1741876768_3346697_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8585939, 1927, '1528'), 'temp/1741876768_3346697_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9268937, 1927, '1528'), 'temp/1741876768_3346697_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515212': [('987515212', 'Carton', 0.9869559, 1927, '1528'), 'temp/1741876768_3346697_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98691714, 1927, '1528'), 'temp/1741876768_3346697_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99391586, 1927, '1528'), 'temp/1741876768_3346697_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774341, 1927, '1528'), 'temp/1741876768_3346697_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5288816, 1927, '1528'), 'temp/1741876768_3346697_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936885, 1927, '1528'), 'temp/1741876768_3346697_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.99637103, 1927, '1528'), 'temp/1741876768_3346697_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515247': [('987515247', 'Carton', 0.99966884, 1927, '1528'), 'temp/1741876768_3346697_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9813085, 1927, '1528'), 'temp/1741876768_3346697_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9812646, 1927, '1528'), 'temp/1741876768_3346697_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9804432, 1927, '1528'), 'temp/1741876768_3346697_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.9088164, 1927, '1528'), 'temp/1741876768_3346697_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.986971, 1927, '1528'), 'temp/1741876768_3346697_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9000447, 1927, '1528'), 'temp/1741876768_3346697_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515188': [('987515188', 'Carton', 0.99565965, 1927, '1528'), 'temp/1741876768_3346697_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977875, 1927, '1528'), 'temp/1741876768_3346697_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97648335, 1927, '1528'), 'temp/1741876768_3346697_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1741876768_3346697_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939585, 1927, '1528'), 'temp/1741876768_3346697_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846452, 1927, '1528'), 'temp/1741876768_3346697_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.984559, 1927, '1528'), 'temp/1741876768_3346697_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.966241, 1927, '1528'), 'temp/1741876768_3346697_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98593664, 1927, '1528'), 'temp/1741876768_3346697_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.9954529, 1927, '1528'), 'temp/1741876768_3346697_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911271, 1927, '1528'), 'temp/1741876768_3346697_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950781, 1927, '1528'), 'temp/1741876768_3346697_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99084723, 1927, '1528'), 'temp/1741876768_3346697_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.9997831, 1927, '1528'), 'temp/1741876768_3346697_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.52225244, 1927, '1528'), 'temp/1741876768_3346697_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940634, 1927, '1528'), 'temp/1741876768_3346697_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994215, 1927, '1528'), 'temp/1741876768_3346697_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924505, 1927, '1528'), 'temp/1741876768_3346697_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98336303, 1927, '1528'), 'temp/1741876768_3346697_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94477403, 1927, '1528'), 'temp/1741876768_3346697_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.891997, 1927, '1528'), 'temp/1741876768_3346697_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5369102, 1927, '1528'), 'temp/1741876768_3346697_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7694158, 1927, '1528'), 'temp/1741876768_3346697_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995735, 1927, '1528'), 'temp/1741876768_3346697_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.874091, 1927, '1528'), 'temp/1741876768_3346697_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.9917464, 1927, '1528'), 'temp/1741876768_3346697_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.967767, 1927, '1528'), 'temp/1741876768_3346697_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.973497, 1927, '1528'), 'temp/1741876768_3346697_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515240': [('987515240', 'Carton', 0.9995204, 1927, '1528'), 'temp/1741876768_3346697_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98212653, 1927, '1528'), 'temp/1741876768_3346697_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9358458, 1927, '1528'), 'temp/1741876768_3346697_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8742939, 1927, '1528'), 'temp/1741876768_3346697_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81770885, 1927, '1528'), 'temp/1741876768_3346697_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8662887, 1927, '1528'), 'temp/1741876768_3346697_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.999233, 1927, '1528'), 'temp/1741876768_3346697_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515180': [('987515180', 'Carton', 0.98998344, 1927, '1528'), 'temp/1741876768_3346697_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.99778086, 1927, '1528'), 'temp/1741876768_3346697_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242485, 1927, '1528'), 'temp/1741876768_3346697_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1741876768_3346697_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997321, 1927, '1528'), 'temp/1741876768_3346697_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.79774237, 1927, '1528'), 'temp/1741876768_3346697_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9846949, 1927, '1528'), 'temp/1741876768_3346697_987515186_797def426440b544aa80dbd63a19234a.jpg']} result detect_point : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.287207299920938e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.437726756565617e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0604192191010497e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.456277622466587e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9245335352024995e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.7174904718995094e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001226268068421632), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9407516194623895e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.371588969651839e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2065814064831102e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3835199297318468e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4805073078605346e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1282223567832261e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015792236081324518), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.00044413990690372884), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.538108573295176e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.327363747805066e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6222836620727321e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5199915398843586e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6141910919031943e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.203773864399409e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.609006181359291e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032637055846862495), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003049898659810424), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.854743459261954e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.946257028379478e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7011050406144932e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.8050104699796066e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3420736397383735e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6951822544797324e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.5433585000864696e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.857565262587741e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.139705874375068e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6431505400760216e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9631415852927603e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.436905904483865e-06), (987515173, 1982, 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-1, 272, -1, 6.842060247436166e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.00014206586638465524), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.0001152063996414654), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021720206132158637), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.0004260139830876142), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.0004280619614291936), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.558332097483799e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.165351154166274e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1643293873930816e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.9232698832638562e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.00024751710589043796), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.00047796135186217725), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003346908197272569), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023675945703871548), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.0001096327614504844), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.555197902955115e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013124471297487617), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007334651309065521)]} ############################### 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 : 0.2133018970489502 #### 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 Mar 13 15:39:56 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} debut step init detect dechets input : temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1741876796_3346697_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 : 0.02013373374938965 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0001232624053955078 time spend to save output : 0.020323514938354492 total time spend for step 1 : 0.02044677734375 step2:tile Thu Mar 13 15:39:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'987321136': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1741876796_3346697_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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_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/1741876796_3346697_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 : 21378233 with name tile_correct_upm feed_id_new_photos : 21378233 filename : temp/1741876796_3346697_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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010367155075073242 About to upload 1 photos upload in portfolio : 21378233 Result OK ! uploaded one batch 0 Elapsed time : 5.082577228546143 upload mediasElapsed time : 5.093007326126099 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1608847328, 1343748793, 0)] Saving 0 CHIs. list_chi_tile : [] end of tileElapsed time : 5.184774160385132 map_pid_results : {'1343748793': ['temp/1741876796_3346697_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, '1343748793'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136, 987321136, '1343748793'] Looping around the photos to save general results len do output : 1 /1343748793Didn'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, '1343748793', 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, '1343748793', 'None', None, None, None, None, None), ('1848', '1902940', '987321136', None, None, None, None, None, None)] time used for this insertion : 0.09222865104675293 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.825533866882324 time spend to save output : 0.09244489669799805 total time spend for step 2 : 11.917978763580322 step3:detect_points Thu Mar 13 15:40:08 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 : {'1343748793': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'1343748793': ()} output_args : {'1343748793': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1343748793 depend.output_id : 0 complete output_args for input 1 : {'987321136': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'1343748793': ('temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',), '987321136': ()} output_args : {'987321136': ['temp/1741876796_3346697_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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1343748793} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1343748793: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1343748793: 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/1741876796_3346697_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 : 0.03718233108520508 time to do a prediction : 14.22029972076416 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 : {1343748793: [(1343748793, 1945, 'Autre_Environement', 185, -1, 118, -1, 1.8337410438107327e-05), (1343748793, 1945, 'Autre_Environement', 320, -1, 118, -1, 0.0001291327498620376), (1343748793, 1945, 'Autre_Environement', 388, -1, 151, -1, 1.3445685908664018e-05), (1343748793, 1945, 'Autre_Environement', 286, -1, 185, -1, 0.00013466527161654085), (1343748793, 1945, 'Autre_Environement', 118, -1, 219, -1, 9.86390750767896e-06), (1343748793, 1945, 'Autre_Environement', 219, -1, 219, -1, 0.00038494516047649086), (1343748793, 1945, 'Autre_Environement', 354, -1, 219, -1, 0.0003431888180784881), (1343748793, 1945, 'Autre_Environement', 421, -1, 253, -1, 1.494663450785083e-07), (1343748793, 1945, 'Autre_Environement', 185, -1, 286, -1, 1.814520658172114e-07), (1343748793, 1945, 'Autre_Environement', 320, -1, 286, -1, 4.115241154067917e-06), (1343748793, 1945, 'Autre_Environement', 118, -1, 320, -1, 2.5274063397695556e-10), (1343748793, 1945, 'Autre_Environement', 253, -1, 320, -1, 1.0447603199237321e-10), (1343748793, 1945, 'Autre_Environement', 388, -1, 320, -1, 4.5192118136583304e-07), (1343748793, 1945, 'Carton', 151, -1, 118, -1, 0.9897055625915527), (1343748793, 1945, 'Carton', 286, -1, 118, -1, 0.8210961222648621), (1343748793, 1945, 'Carton', 421, -1, 118, -1, 0.4851985573768616), (1343748793, 1945, 'Carton', 219, -1, 151, -1, 0.9841372966766357), (1343748793, 1945, 'Carton', 118, -1, 185, -1, 0.9978604912757874), (1343748793, 1945, 'Carton', 185, -1, 219, -1, 0.7996194958686829), (1343748793, 1945, 'Carton', 354, -1, 219, -1, 0.13047637045383453), (1343748793, 1945, 'Carton', 286, -1, 253, -1, 0.05070793256163597), (1343748793, 1945, 'Carton', 118, -1, 286, -1, 0.31989040970802307), (1343748793, 1945, 'Carton', 219, -1, 286, -1, 0.0034983144141733646), (1343748793, 1945, 'Carton', 421, -1, 286, -1, 0.01660206913948059), (1343748793, 1945, 'Carton', 354, -1, 320, -1, 0.0016258988762274384), (1343748793, 1945, 'Kraft', 185, -1, 118, -1, 0.004793279338628054), (1343748793, 1945, 'Kraft', 286, -1, 118, -1, 0.0023909551091492176), (1343748793, 1945, 'Kraft', 421, -1, 118, -1, 0.002102428814396262), (1343748793, 1945, 'Kraft', 354, -1, 151, -1, 0.05212335288524628), (1343748793, 1945, 'Kraft', 118, -1, 185, -1, 0.0017166697653010488), (1343748793, 1945, 'Kraft', 253, -1, 185, -1, 0.04599893465638161), (1343748793, 1945, 'Kraft', 185, -1, 219, -1, 0.021099306643009186), (1343748793, 1945, 'Kraft', 388, -1, 219, -1, 6.061792737455107e-05), (1343748793, 1945, 'Kraft', 286, -1, 253, -1, 0.0037522290367633104), (1343748793, 1945, 'Kraft', 118, -1, 286, -1, 0.010319208726286888), (1343748793, 1945, 'Kraft', 219, -1, 286, -1, 5.351284926291555e-05), (1343748793, 1945, 'Kraft', 421, -1, 286, -1, 9.467339623370208e-06), (1343748793, 1945, 'Kraft', 320, -1, 320, -1, 0.00017124204896390438), (1343748793, 1945, 'Lointain_Papier_Magazine', 185, -1, 118, -1, 2.352082447032444e-06), (1343748793, 1945, 'Lointain_Papier_Magazine', 320, -1, 118, -1, 1.991412864299491e-05), (1343748793, 1945, 'Lointain_Papier_Magazine', 253, -1, 151, -1, 1.1851832823595032e-05), (1343748793, 1945, 'Lointain_Papier_Magazine', 354, -1, 185, -1, 8.877575601218268e-05), (1343748793, 1945, 'Lointain_Papier_Magazine', 118, -1, 219, -1, 2.1015976017224602e-06), (1343748793, 1945, 'Lointain_Papier_Magazine', 219, -1, 219, -1, 7.86672972026281e-05), (1343748793, 1945, 'Lointain_Papier_Magazine', 421, -1, 219, -1, 5.019426794206083e-07), (1343748793, 1945, 'Lointain_Papier_Magazine', 320, -1, 253, -1, 8.220934978453442e-05), (1343748793, 1945, 'Lointain_Papier_Magazine', 185, -1, 286, -1, 3.6812457437918056e-07), (1343748793, 1945, 'Lointain_Papier_Magazine', 388, -1, 286, -1, 3.3782250739022857e-06), (1343748793, 1945, 'Lointain_Papier_Magazine', 118, -1, 320, -1, 3.5824958555252806e-09), (1343748793, 1945, 'Lointain_Papier_Magazine', 286, -1, 320, -1, 1.2866975396264024e-07), (1343748793, 1945, 'Metal', 185, -1, 118, -1, 7.473808364011347e-05), (1343748793, 1945, 'Metal', 286, -1, 118, -1, 3.432366065680981e-05), (1343748793, 1945, 'Metal', 118, -1, 151, -1, 1.1519473446242046e-06), (1343748793, 1945, 'Metal', 354, -1, 151, -1, 0.001217490527778864), (1343748793, 1945, 'Metal', 253, -1, 185, -1, 0.001836584648117423), (1343748793, 1945, 'Metal', 421, -1, 185, -1, 3.084278432652354e-05), (1343748793, 1945, 'Metal', 185, -1, 219, -1, 0.0011127882171422243), (1343748793, 1945, 'Metal', 118, -1, 253, -1, 2.7215228328714147e-05), (1343748793, 1945, 'Metal', 354, -1, 253, -1, 0.0005037166411057115), (1343748793, 1945, 'Metal', 219, -1, 286, -1, 1.657771281315945e-05), (1343748793, 1945, 'Metal', 421, -1, 286, -1, 2.2439740860136226e-05), (1343748793, 1945, 'Metal', 151, -1, 320, -1, 1.393576087860282e-10), (1343748793, 1945, 'Metal', 320, -1, 320, -1, 3.1560623028781265e-05), (1343748793, 1945, 'Papier_Magazine', 118, -1, 118, -1, 0.001665871823206544), (1343748793, 1945, 'Papier_Magazine', 253, -1, 118, -1, 0.28050497174263), (1343748793, 1945, 'Papier_Magazine', 185, -1, 151, -1, 0.003942570183426142), (1343748793, 1945, 'Papier_Magazine', 421, -1, 151, -1, 0.9828073978424072), (1343748793, 1945, 'Papier_Magazine', 354, -1, 185, -1, 0.7690255045890808), (1343748793, 1945, 'Papier_Magazine', 286, -1, 219, -1, 0.9288092851638794), (1343748793, 1945, 'Papier_Magazine', 118, -1, 253, -1, 0.04313919320702553), (1343748793, 1945, 'Papier_Magazine', 219, -1, 253, -1, 0.8978631496429443), (1343748793, 1945, 'Papier_Magazine', 388, -1, 253, -1, 0.9886879324913025), (1343748793, 1945, 'Papier_Magazine', 151, -1, 320, -1, 0.9999996423721313), (1343748793, 1945, 'Papier_Magazine', 253, -1, 320, -1, 0.9999960660934448), (1343748793, 1945, 'Papier_Magazine', 354, -1, 320, -1, 0.9942159056663513), (1343748793, 1945, 'Plastique', 118, -1, 118, -1, 1.236031312146224e-05), (1343748793, 1945, 'Plastique', 219, -1, 118, -1, 0.00030184732167981565), (1343748793, 1945, 'Plastique', 320, -1, 118, -1, 0.0002493929350748658), (1343748793, 1945, 'Plastique', 253, -1, 185, -1, 0.007031592074781656), (1343748793, 1945, 'Plastique', 354, -1, 185, -1, 0.032503753900527954), (1343748793, 1945, 'Plastique', 185, -1, 219, -1, 0.050375860184431076), (1343748793, 1945, 'Plastique', 421, -1, 219, -1, 0.00012226666149217635), (1343748793, 1945, 'Plastique', 118, -1, 253, -1, 0.003930176142603159), (1343748793, 1945, 'Plastique', 286, -1, 253, -1, 0.0025490771513432264), (1343748793, 1945, 'Plastique', 219, -1, 286, -1, 6.120907346485183e-05), (1343748793, 1945, 'Plastique', 354, -1, 286, -1, 0.00538312504068017), (1343748793, 1945, 'Plastique', 151, -1, 320, -1, 1.8926894773674263e-10), (1343748793, 1945, 'Plastique', 421, -1, 320, -1, 0.00020204381144139916), (1343748793, 1945, 'Sol_Environement', 185, -1, 118, -1, 9.372543900099117e-06), (1343748793, 1945, 'Sol_Environement', 320, -1, 118, -1, 2.7338848667568527e-05), (1343748793, 1945, 'Sol_Environement', 118, -1, 151, -1, 2.5881729470711434e-07), (1343748793, 1945, 'Sol_Environement', 253, -1, 185, -1, 0.00011454988998593763), (1343748793, 1945, 'Sol_Environement', 354, -1, 185, -1, 0.00020838991622440517), (1343748793, 1945, 'Sol_Environement', 185, -1, 219, -1, 9.349620813736692e-05), (1343748793, 1945, 'Sol_Environement', 421, -1, 219, -1, 1.1348908657282664e-07), (1343748793, 1945, 'Sol_Environement', 118, -1, 253, -1, 7.004572921687213e-07), (1343748793, 1945, 'Sol_Environement', 320, -1, 253, -1, 5.724640504922718e-05), (1343748793, 1945, 'Sol_Environement', 219, -1, 286, -1, 3.869550369017816e-08), (1343748793, 1945, 'Sol_Environement', 388, -1, 286, -1, 6.792529802623903e-06), (1343748793, 1945, 'Sol_Environement', 151, -1, 320, -1, 2.6225014184994705e-14), (1343748793, 1945, 'Sol_Environement', 286, -1, 320, -1, 1.5886031690115487e-07), (1343748793, 1945, 'Teint_Dans_La_Masse', 185, -1, 118, -1, 0.002272234996780753), (1343748793, 1945, 'Teint_Dans_La_Masse', 286, -1, 118, -1, 0.001813237671740353), (1343748793, 1945, 'Teint_Dans_La_Masse', 388, -1, 118, -1, 0.04538803920149803), (1343748793, 1945, 'Teint_Dans_La_Masse', 118, -1, 151, -1, 0.00010940170614048839), (1343748793, 1945, 'Teint_Dans_La_Masse', 253, -1, 185, -1, 0.0056123328395187855), (1343748793, 1945, 'Teint_Dans_La_Masse', 354, -1, 185, -1, 0.15166763961315155), (1343748793, 1945, 'Teint_Dans_La_Masse', 185, -1, 219, -1, 0.0013750138459727168), (1343748793, 1945, 'Teint_Dans_La_Masse', 118, -1, 253, -1, 6.252125604078174e-05), (1343748793, 1945, 'Teint_Dans_La_Masse', 286, -1, 253, -1, 0.0018639108166098595), (1343748793, 1945, 'Teint_Dans_La_Masse', 388, -1, 253, -1, 0.001438076258637011), (1343748793, 1945, 'Teint_Dans_La_Masse', 219, -1, 286, -1, 1.016586884361459e-05), (1343748793, 1945, 'Teint_Dans_La_Masse', 151, -1, 320, -1, 3.425693932967988e-07), (1343748793, 1945, 'Teint_Dans_La_Masse', 320, -1, 320, -1, 0.0020001486409455538), (1343748793, 1945, 'Teint_Dans_La_Masse', 421, -1, 320, -1, 8.311669807881117e-05), (1343748793, 1945, 'autre_refus', 185, -1, 118, -1, 0.028516046702861786), (1343748793, 1945, 'autre_refus', 354, -1, 118, -1, 0.0014720888575538993), (1343748793, 1945, 'autre_refus', 118, -1, 151, -1, 3.824138184427284e-05), (1343748793, 1945, 'autre_refus', 253, -1, 185, -1, 0.07516990602016449), (1343748793, 1945, 'autre_refus', 185, -1, 219, -1, 0.010234796442091465), (1343748793, 1945, 'autre_refus', 354, -1, 219, -1, 0.04460597038269043), (1343748793, 1945, 'autre_refus', 118, -1, 253, -1, 0.00015751634782645851), (1343748793, 1945, 'autre_refus', 286, -1, 253, -1, 0.01393966656178236), (1343748793, 1945, 'autre_refus', 219, -1, 286, -1, 4.3672833271557465e-05), (1343748793, 1945, 'autre_refus', 388, -1, 286, -1, 0.0030952864326536655), (1343748793, 1945, 'autre_refus', 151, -1, 320, -1, 1.5080686699420198e-10), (1343748793, 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) [('1343748793', '492774966', '1945', '151', '-1', '118', '-1', '0.9897055625915527'), ('1343748793', '492774966', '1945', '286', '-1', '118', '-1', '0.8210961222648621'), ('1343748793', '492774966', '1945', '421', '-1', '118', '-1', '0.4851985573768616'), ('1343748793', '492774966', '1945', '219', '-1', '151', '-1', '0.9841372966766357'), ('1343748793', '492774966', '1945', '118', '-1', '185', '-1', '0.9978604912757874'), ('1343748793', '492774966', '1945', '185', '-1', '219', '-1', '0.7996194958686829'), ('1343748793', '492774966', '1945', '354', '-1', '219', '-1', '0.13047637045383453'), ('1343748793', '492774966', '1945', '286', '-1', '253', '-1', '0.05070793256163597'), ('1343748793', '492774966', '1945', '118', '-1', '286', '-1', '0.31989040970802307'), ('1343748793', '493202403', '1945', '354', '-1', '151', '-1', '0.05212335288524628'), ('1343748793', '2107752386', '1945', '253', '-1', '118', '-1', '0.28050497174263'), ('1343748793', '2107752386', '1945', '421', '-1', '151', '-1', '0.9828073978424072'), ('1343748793', '2107752386', '1945', '354', '-1', '185', '-1', '0.7690255045890808'), ('1343748793', '2107752386', '1945', '286', '-1', '219', '-1', '0.9288092851638794'), ('1343748793', '2107752386', '1945', '219', '-1', '253', '-1', '0.8978631496429443'), ('1343748793', '2107752386', '1945', '388', '-1', '253', '-1', '0.9886879324913025'), ('1343748793', '2107752386', '1945', '151', '-1', '320', '-1', '0.9999996423721313'), ('1343748793', '2107752386', '1945', '253', '-1', '320', '-1', '0.9999960660934448'), ('1343748793', '2107752386', '1945', '354', '-1', '320', '-1', '0.9942159056663513'), ('1343748793', '492725882', '1945', '185', '-1', '219', '-1', '0.050375860184431076'), ('1343748793', '2107752385', '1945', '354', '-1', '185', '-1', '0.15166763961315155'), ('1343748793', '2107752406', '1945', '253', '-1', '185', '-1', '0.07516990602016449')] final : False save missing photos in datou_result : time spend for datou_step_exec : 15.45550799369812 time spend to save output : 0.06774377822875977 total time spend for step 3 : 15.52325177192688 step4:count_percent_refus Thu Mar 13 15:40:24 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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 1 complete output_args for input 1 : {'1343748793': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'987321136': (987321136,), '1343748793': ()} output_args : {'1343748793': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1343748793 depend.output_id : 0 complete output_args for input 2 : {'987321136': ['temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': (987321136,), '1343748793': ('temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} output_args : {'987321136': ['temp/1741876796_3346697_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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1343748793} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1343748793: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1343748793: 987321136} debut step count percent refus args : {'987321136': (987321136, 0.9481481481481482), '1343748793': ('temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} (987321136, 0.9481481481481482) ('temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) on trouve le portfolio_id = 1902940 list_photo : [987321136] list_photo_correc : [1343748793] 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}, [1343748793], {'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 : 0.06609654426574707 save missing photos in datou_result : time spend for datou_step_exec : 0.022691726684570312 time spend to save output : 0.06628537178039551 total time spend for step 4 : 0.08897709846496582 step5:brightness Thu Mar 13 15:40:24 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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1741876796_3346697_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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1343748793} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1343748793: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1343748793: 987321136} inside step calcul brightness treat image : temp/1741876796_3346697_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 : 0.0209503173828125 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 : 0.012902498245239258 save missing photos in datou_result : time spend for datou_step_exec : 0.06127142906188965 time spend to save output : 0.038431644439697266 total time spend for step 5 : 0.09970307350158691 step6:blur_detection Thu Mar 13 15:40:24 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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1741876796_3346697_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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1343748793} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1343748793: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1343748793: 987321136} inside step blur_detection score_blur_detection : {} methode: ratio et variance treat image : temp/1741876796_3346697_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 : 0.00892329216003418 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 : 0.23907089233398438 save missing photos in datou_result : time spend for datou_step_exec : 0.11114096641540527 time spend to save output : 0.2522251605987549 total time spend for step 6 : 0.36336612701416016 step7:send_mail_dechet Thu Mar 13 15:40:24 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}, [1343748793], {'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}, [1343748793], {'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}, [1343748793], {'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/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1343748793} map_photo_id_path_extension : {987321136: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1343748793: {'path': 'temp/1741876796_3346697_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1343748793: 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, '1343748793'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : True mtd_id 1848 list_pids : [987321136, 987321136, '1343748793'] 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, '1343748793', 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, '1343748793', None, None, None, None, None, None)] time used for this insertion : 0.016566753387451172 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.5254502296447754 time spend to save output : 0.01685643196105957 total time spend for step 7 : 0.542306661605835 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 : 0.29056859016418457 #### 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 Mar 13 15:40:25 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/1741876825_3346697_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg': 984484223} map_photo_id_path_extension : {984484223: {'path': 'temp/1741876825_3346697_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blanche_jaune_detection treat image : temp/1741876825_3346697_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 After datou_step_exec type output : time spend for datou_step_exec : 0.4886906147003174 time spend to save output : 8.320808410644531e-05 total time spend for step 1 : 0.48877382278442383 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 : 1.798100233078003 #### 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 Mar 13 15:40:27 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 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) 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 (21378234, 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 : 7.702158689498901 time spend to save output : 0.0001246929168701172 total time spend for step 1 : 7.7022833824157715 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(21378234, 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 950003812 begin to download photo : 950003696 download finish for photo 950003838 begin to download photo : 950003813 download finish for photo 950003696 download finish for photo 950003813 download finish for photo 950003695 begin to download photo : 926687666 download finish for photo 926687666 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 : 0.959080696105957 #### 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 Mar 13 15:40:36 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/1741876836_3346697_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1741876836_3346697_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696, 'temp/1741876836_3346697_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1741876836_3346697_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1741876836_3346697_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1741876836_3346697_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666} map_photo_id_path_extension : {950003812: {'path': 'temp/1741876836_3346697_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1741876836_3346697_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}, 950003838: {'path': 'temp/1741876836_3346697_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1741876836_3346697_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003695: {'path': 'temp/1741876836_3346697_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1741876836_3346697_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.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 F0313 15:40:38.637549 3346697 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 89.88user 49.15system 5:15.14elapsed 44%CPU (0avgtext+0avgdata 6290720maxresident)k 6573512inputs+43976outputs (7405major+6286751minor)pagefaults 0swaps