python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_2/data_log/job/2025/May/16052025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/May/16052025/coverage/htmlcov new path : /data_2/data_log/job/2025/May/16052025/coverage/ command : coverage3 run /home/admin/workarea/git/Velours/python/tests/python_tests.py --short_python3 `cat ~/.fotonower_pass/bdd.py.pass` cat: /home/admin/.fotonower_pass/bdd.py.pass: Aucun fichier ou dossier de ce type 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 python version used : 3 #&_# 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 : 7037 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 : 0.1573801040649414 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 Fri May 16 17:20:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 7037 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 2025-05-16 17:20:32.831348: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-16 17:20:32.859246: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-16 17:20:32.861415: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa798000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:20:32.861470: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-16 17:20:32.865572: I tensorflow/stream_executor/platform/default/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 : [] 2025-05-16 17:20:44.057112: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:20:44.272754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:20:45.717886: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.718522: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.719443: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.720992: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.722579: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.724364: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.725943: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.726018: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.727779: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.727826: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.750534: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.750578: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.751519: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.751543: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.765479: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.765509: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.766406: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.766429: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.862727: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.862771: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.863674: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.863696: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.876052: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.876082: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.876987: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.877010: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:20:45.973061: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.973712: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.979498: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:45.980248: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.087218: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.088266: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.099043: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.099632: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.127245: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.127836: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.140682: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.141268: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.186059: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.186679: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.200047: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.200695: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.214024: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.214656: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.216363: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.216950: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.222654: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.223281: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.224866: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.225459: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.290360: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.291070: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.291654: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.292233: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.296429: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.297080: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.312989: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.313857: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.314483: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.315110: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.331830: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.332660: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.333396: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.334129: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.339613: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.340338: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.347826: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.348712: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.362218: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.363095: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.367658: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.368282: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.368865: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.369446: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.393962: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.394855: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.395786: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.396642: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.397525: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.398359: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.399215: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.400123: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.409718: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.410616: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.417549: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.418475: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.452740: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.452824: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-05-16 17:20:46.453437: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.454033: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.462032: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.462792: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.463561: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.464281: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.472705: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.473322: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.494344: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.495155: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.495845: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.496420: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.500648: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.501256: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.501836: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.502412: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.504568: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.514059: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.514677: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.525406: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.526044: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.526697: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.527381: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.528171: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-16 17:20:46.529018: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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 801360 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1914 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 : 2717 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 : 0.0006978511810302734 nb_pixel_total : 15551 time to create 1 rle with old method : 0.036444902420043945 length of segment : 256 time for calcul the mask position with numpy : 0.003238677978515625 nb_pixel_total : 145330 time to create 1 rle with old method : 0.3413112163543701 length of segment : 371 time for calcul the mask position with numpy : 0.0002923011779785156 nb_pixel_total : 14255 time to create 1 rle with old method : 0.03200244903564453 length of segment : 151 time for calcul the mask position with numpy : 0.00013899803161621094 nb_pixel_total : 5613 time to create 1 rle with old method : 0.0132598876953125 length of segment : 48 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 1825 time to create 1 rle with old method : 0.004492044448852539 length of segment : 39 time spent for convertir_results : 1.4178719520568848 time spend for datou_step_exec : 21.811203479766846 time spend to save output : 7.05718994140625e-05 total time spend for step 1 : 21.81127405166626 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 3331 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 : 0.012722253799438477 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, 0.9954848, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 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, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (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)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.99237967, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), 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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)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1747408829_801138_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 2461 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 : 0.12917423248291016 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 Fri May 16 17:20:53 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 Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3000 l 3632 free memory gpu now : 2461 wait 20 seconds l 3637 free memory gpu now : 2461 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-16 17:21:16.636450: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-16 17:21:16.667096: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-16 17:21:16.669644: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa7a4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:21:16.669715: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-16 17:21:16.676499: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-16 17:21:16.823345: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4125f30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:21:16.823401: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-16 17:21:16.824442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:21:16.825127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:16.829202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:16.831733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:21:16.833027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:21:16.836991: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:21:16.838616: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:21:16.846756: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:16.848192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:21:16.848322: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:16.849118: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-16 17:21:16.849141: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-16 17:21:16.849156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-16 17:21:16.850293: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 882 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-05-16 17:21:16.999599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:21:16.999738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:16.999763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:16.999784: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:21:16.999806: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:21:16.999826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:21:16.999846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:21:16.999867: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:17.000759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:21:17.001862: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:21:17.001923: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:17.001945: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:17.001966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:21:17.001986: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:21:17.002011: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:21:17.002031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:21:17.002052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:17.002997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:21:17.003042: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-16 17:21:17.003054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-16 17:21:17.003064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-16 17:21:17.004006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 882 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 : [] 2025-05-16 17:21:27.851620: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:28.027411: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:29.543472: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.543536: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.550059: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.550095: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.556051: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.556080: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.587281: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.587341: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.637339: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-16 17:21:29.637395: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. local folder : /data/models_weight/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 806691 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3099 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 : 4664 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 : 0.0006003379821777344 nb_pixel_total : 16902 time to create 1 rle with old method : 0.04976248741149902 length of segment : 107 time for calcul the mask position with numpy : 0.020900726318359375 nb_pixel_total : 480747 time to create 1 rle with new method : 0.04452681541442871 length of segment : 632 time for calcul the mask position with numpy : 0.0005052089691162109 nb_pixel_total : 36642 time to create 1 rle with old method : 0.08856534957885742 length of segment : 133 time for calcul the mask position with numpy : 0.0001385211944580078 nb_pixel_total : 4793 time to create 1 rle with old method : 0.011520624160766602 length of segment : 51 time spent for convertir_results : 0.4904313087463379 time spend for datou_step_exec : 40.63029217720032 time spend to save output : 4.100799560546875e-05 total time spend for step 1 : 40.63033318519592 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 427 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 : 0.012396574020385742 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, 0.9988366, [(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, 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['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/1747408853_801138_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 : 0.17779183387756348 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 Fri May 16 17:21:35 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 : 7409 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-16 17:21:38.627690: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-16 17:21:38.655075: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-16 17:21:38.657488: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa7a4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:21:38.657565: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-16 17:21:38.672166: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-16 17:21:38.925102: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5989ff0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:21:38.925158: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-16 17:21:38.926718: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:21:38.927156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:38.930456: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:38.933111: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:21:38.933513: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:21:38.936129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:21:38.937519: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:21:38.942748: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:38.944318: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:21:38.944393: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:38.945170: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-16 17:21:38.945185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-16 17:21:38.945193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-16 17:21:38.946607: 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. 2025-05-16 17:21:39.058089: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:21:39.058175: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:39.058198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:39.058219: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:21:39.058241: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:21:39.058261: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:21:39.058282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:21:39.058303: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:39.059894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:21:39.061261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:21:39.061327: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:21:39.061350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:39.061371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:21:39.061392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:21:39.061412: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:21:39.061433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:21:39.061454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:21:39.063070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:21:39.063107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-16 17:21:39.063117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-16 17:21:39.063127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-16 17:21:39.064657: 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 : [] 2025-05-16 17:21:50.040144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:21:50.224571: 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 808154 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 : 0.23355555534362793 nb_pixel_total : 3693207 time to create 1 rle with new method : 0.49405860900878906 length of segment : 2042 time spent for convertir_results : 1.9784159660339355 time spend for datou_step_exec : 23.275980949401855 time spend to save output : 2.8133392333984375e-05 total time spend for step 1 : 23.27600908279419 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 722 chid ids of type : 445 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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 : 0.025391578674316406 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, 7, 2268, 118, 2241, 0.9850343, [(675, 120, 112), (520, 121, 481), (1050, 121, 381), (502, 122, 948), (486, 123, 982), (470, 124, 1015), (455, 125, 1046), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1223), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1267), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1346), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1375), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1390), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 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['1001,2150,936,2144,759,2089,694,2075,569,2027,215,1963,128,1971,97,1924,54,1825,53,1753,39,1677,39,1454,29,1239,27,757,21,696,27,543,39,458,93,308,126,270,210,206,291,179,363,135,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2021,306,2076,379,2148,535,2170,646,2162,845,2128,914,2112,994,2081,1068,2046,1106,1967,1255,1930,1370,1879,1444,1844,1679,1782,1863,1719,1973,1662,2015,1581,2015,1496,2039,1420,2046,1347,2068,1177,2101,1093,2142'])], 'temp/1747408895_801138_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690560 proportion of common points : 0.9995301025513942 #&_# TEST FAILED #&_# : tests/mask_test #&_# #&_# 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 : 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 : sam 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 : 0.19728422164916992 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 : False number of steps : 1 step1:sam Fri May 16 17:22:04 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 Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0019366741180419922 nb_pixel_total : 3753 time to create 1 rle with old method : 0.008677005767822266 time for calcul the mask position with numpy : 0.001500844955444336 nb_pixel_total : 8639 time to create 1 rle with old method : 0.019837617874145508 time for calcul the mask position with numpy : 0.0015478134155273438 nb_pixel_total : 6626 time to create 1 rle with old method : 0.014732837677001953 time for calcul the mask position with numpy : 0.0014412403106689453 nb_pixel_total : 5615 time to create 1 rle with old method : 0.014023303985595703 time for calcul the mask position with numpy : 0.0018985271453857422 nb_pixel_total : 10690 time to create 1 rle with old method : 0.03557538986206055 time for calcul the mask position with numpy : 0.0020952224731445312 nb_pixel_total : 84091 time to create 1 rle with old method : 0.20924019813537598 time for calcul the mask position with numpy : 0.0018436908721923828 nb_pixel_total : 9881 time to create 1 rle with old method : 0.028287410736083984 time for calcul the mask position with numpy : 0.0018973350524902344 nb_pixel_total : 29480 time to create 1 rle with old method : 0.07653212547302246 time for calcul the mask position with numpy : 0.0017592906951904297 nb_pixel_total : 2940 time to create 1 rle with old method : 0.008058547973632812 time for calcul the mask position with numpy : 0.002036571502685547 nb_pixel_total : 13909 time to create 1 rle with old method : 0.036330461502075195 time for calcul the mask position with numpy : 0.0018093585968017578 nb_pixel_total : 7498 time to create 1 rle with old method : 0.02045893669128418 time for calcul the mask position with numpy : 0.0018677711486816406 nb_pixel_total : 2451 time to create 1 rle with old method : 0.007070064544677734 time for calcul the mask position with numpy : 0.0019423961639404297 nb_pixel_total : 4278 time to create 1 rle with old method : 0.012139320373535156 time for calcul the mask position with numpy : 0.0018527507781982422 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0034182071685791016 time for calcul the mask position with numpy : 0.0017085075378417969 nb_pixel_total : 3951 time to create 1 rle with old method : 0.01044464111328125 time for calcul the mask position with numpy : 0.0019240379333496094 nb_pixel_total : 13036 time to create 1 rle with old method : 0.03544759750366211 time for calcul the mask position with numpy : 0.0016570091247558594 nb_pixel_total : 2076 time to create 1 rle with old method : 0.005505561828613281 time for calcul the mask position with numpy : 0.0015957355499267578 nb_pixel_total : 4184 time to create 1 rle with old method : 0.011675834655761719 time for calcul the mask position with numpy : 0.0015692710876464844 nb_pixel_total : 2342 time to create 1 rle with old method : 0.005896091461181641 time for calcul the mask position with numpy : 0.0015799999237060547 nb_pixel_total : 341 time to create 1 rle with old method : 0.0009491443634033203 time for calcul the mask position with numpy : 0.0016520023345947266 nb_pixel_total : 18393 time to create 1 rle with old method : 0.050897836685180664 time for calcul the mask position with numpy : 0.0017604827880859375 nb_pixel_total : 16265 time to create 1 rle with old method : 0.04276871681213379 time for calcul the mask position with numpy : 0.0019567012786865234 nb_pixel_total : 4272 time to create 1 rle with old method : 0.012783288955688477 time for calcul the mask position with numpy : 0.0017230510711669922 nb_pixel_total : 5481 time to create 1 rle with old method : 0.014959335327148438 time for calcul the mask position with numpy : 0.0019295215606689453 nb_pixel_total : 38772 time to create 1 rle with old method : 0.1071786880493164 time for calcul the mask position with numpy : 0.0018303394317626953 nb_pixel_total : 3532 time to create 1 rle with old method : 0.010593414306640625 time for calcul the mask position with numpy : 0.0017473697662353516 nb_pixel_total : 8619 time to create 1 rle with old method : 0.02542710304260254 time for calcul the mask position with numpy : 0.0022749900817871094 nb_pixel_total : 2495 time to create 1 rle with old method : 0.007752180099487305 time for calcul the mask position with numpy : 0.0017669200897216797 nb_pixel_total : 3322 time to create 1 rle with old method : 0.009824991226196289 time for calcul the mask position with numpy : 0.001959562301635742 nb_pixel_total : 2727 time to create 1 rle with old method : 0.008527755737304688 time for calcul the mask position with numpy : 0.0019268989562988281 nb_pixel_total : 13015 time to create 1 rle with old method : 0.041716814041137695 time for calcul the mask position with numpy : 0.002153158187866211 nb_pixel_total : 11904 time to create 1 rle with old method : 0.037012577056884766 time for calcul the mask position with numpy : 0.0020051002502441406 nb_pixel_total : 2781 time to create 1 rle with old method : 0.008172988891601562 time for calcul the mask position with numpy : 0.0017547607421875 nb_pixel_total : 5414 time to create 1 rle with old method : 0.016976118087768555 time for calcul the mask position with numpy : 0.0021517276763916016 nb_pixel_total : 10624 time to create 1 rle with old method : 0.03372597694396973 time for calcul the mask position with numpy : 0.002357959747314453 nb_pixel_total : 1025 time to create 1 rle with old method : 0.003689289093017578 time for calcul the mask position with numpy : 0.001935720443725586 nb_pixel_total : 1251 time to create 1 rle with old method : 0.003810405731201172 time for calcul the mask position with numpy : 0.001733541488647461 nb_pixel_total : 1655 time to create 1 rle with old method : 0.004805803298950195 time for calcul the mask position with numpy : 0.0018985271453857422 nb_pixel_total : 830 time to create 1 rle with old method : 0.0022735595703125 time for calcul the mask position with numpy : 0.0020694732666015625 nb_pixel_total : 4124 time to create 1 rle with old method : 0.012943029403686523 time for calcul the mask position with numpy : 0.0018122196197509766 nb_pixel_total : 3934 time to create 1 rle with old method : 0.012653827667236328 time for calcul the mask position with numpy : 0.002081155776977539 nb_pixel_total : 2373 time to create 1 rle with old method : 0.007269859313964844 time for calcul the mask position with numpy : 0.0020782947540283203 nb_pixel_total : 14337 time to create 1 rle with old method : 0.03616905212402344 time for calcul the mask position with numpy : 0.0018067359924316406 nb_pixel_total : 861 time to create 1 rle with old method : 0.0033354759216308594 time for calcul the mask position with numpy : 0.001665353775024414 nb_pixel_total : 877 time to create 1 rle with old method : 0.0034317970275878906 time for calcul the mask position with numpy : 0.0018007755279541016 nb_pixel_total : 594 time to create 1 rle with old method : 0.00213623046875 time for calcul the mask position with numpy : 0.0017828941345214844 nb_pixel_total : 2406 time to create 1 rle with old method : 0.008767366409301758 time for calcul the mask position with numpy : 0.002274751663208008 nb_pixel_total : 39220 time to create 1 rle with old method : 0.11655235290527344 time for calcul the mask position with numpy : 0.0015993118286132812 nb_pixel_total : 2324 time to create 1 rle with old method : 0.005789041519165039 time for calcul the mask position with numpy : 0.0016262531280517578 nb_pixel_total : 885 time to create 1 rle with old method : 0.002341032028198242 time for calcul the mask position with numpy : 0.0015611648559570312 nb_pixel_total : 577 time to create 1 rle with old method : 0.0015201568603515625 time for calcul the mask position with numpy : 0.0015408992767333984 nb_pixel_total : 1714 time to create 1 rle with old method : 0.004595041275024414 time for calcul the mask position with numpy : 0.0016040802001953125 nb_pixel_total : 1602 time to create 1 rle with old method : 0.004250764846801758 time for calcul the mask position with numpy : 0.001695394515991211 nb_pixel_total : 692 time to create 1 rle with old method : 0.0019855499267578125 time for calcul the mask position with numpy : 0.0019261837005615234 nb_pixel_total : 337 time to create 1 rle with old method : 0.0010843276977539062 time for calcul the mask position with numpy : 0.0015990734100341797 nb_pixel_total : 1056 time to create 1 rle with old method : 0.003043651580810547 time for calcul the mask position with numpy : 0.0016074180603027344 nb_pixel_total : 2770 time to create 1 rle with old method : 0.007322549819946289 time for calcul the mask position with numpy : 0.0015828609466552734 nb_pixel_total : 1073 time to create 1 rle with old method : 0.002798795700073242 time for calcul the mask position with numpy : 0.0016071796417236328 nb_pixel_total : 1208 time to create 1 rle with old method : 0.0031845569610595703 time for calcul the mask position with numpy : 0.0015869140625 nb_pixel_total : 585 time to create 1 rle with old method : 0.0016176700592041016 time for calcul the mask position with numpy : 0.001493215560913086 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007982254028320312 time for calcul the mask position with numpy : 0.0015716552734375 nb_pixel_total : 1610 time to create 1 rle with old method : 0.004301548004150391 time for calcul the mask position with numpy : 0.0018150806427001953 nb_pixel_total : 27642 time to create 1 rle with old method : 0.06812644004821777 time for calcul the mask position with numpy : 0.0017206668853759766 nb_pixel_total : 9562 time to create 1 rle with old method : 0.023990631103515625 time for calcul the mask position with numpy : 0.0015306472778320312 nb_pixel_total : 1802 time to create 1 rle with old method : 0.004975080490112305 time for calcul the mask position with numpy : 0.001528024673461914 nb_pixel_total : 8647 time to create 1 rle with old method : 0.02157282829284668 time for calcul the mask position with numpy : 0.0015947818756103516 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0035665035247802734 time for calcul the mask position with numpy : 0.0016443729400634766 nb_pixel_total : 1500 time to create 1 rle with old method : 0.003615140914916992 time for calcul the mask position with numpy : 0.0016312599182128906 nb_pixel_total : 16688 time to create 1 rle with old method : 0.03928661346435547 time for calcul the mask position with numpy : 0.0016145706176757812 nb_pixel_total : 9079 time to create 1 rle with old method : 0.021041154861450195 time for calcul the mask position with numpy : 0.0017919540405273438 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0043659210205078125 time for calcul the mask position with numpy : 0.001789093017578125 nb_pixel_total : 274 time to create 1 rle with old method : 0.0007197856903076172 time for calcul the mask position with numpy : 0.0014438629150390625 nb_pixel_total : 3168 time to create 1 rle with old method : 0.007419109344482422 time for calcul the mask position with numpy : 0.0014495849609375 nb_pixel_total : 713 time to create 1 rle with old method : 0.0017769336700439453 time for calcul the mask position with numpy : 0.0014324188232421875 nb_pixel_total : 1334 time to create 1 rle with old method : 0.0032651424407958984 time for calcul the mask position with numpy : 0.001459360122680664 nb_pixel_total : 970 time to create 1 rle with old method : 0.002286672592163086 time for calcul the mask position with numpy : 0.001430511474609375 nb_pixel_total : 222 time to create 1 rle with old method : 0.0005970001220703125 time for calcul the mask position with numpy : 0.0014247894287109375 nb_pixel_total : 616 time to create 1 rle with old method : 0.0015017986297607422 time for calcul the mask position with numpy : 0.0014383792877197266 nb_pixel_total : 248 time to create 1 rle with old method : 0.0006427764892578125 time for calcul the mask position with numpy : 0.0014264583587646484 nb_pixel_total : 966 time to create 1 rle with old method : 0.0023517608642578125 time for calcul the mask position with numpy : 0.0014276504516601562 nb_pixel_total : 594 time to create 1 rle with old method : 0.001504659652709961 time for calcul the mask position with numpy : 0.0016605854034423828 nb_pixel_total : 735 time to create 1 rle with old method : 0.0019059181213378906 time for calcul the mask position with numpy : 0.0014731884002685547 nb_pixel_total : 7502 time to create 1 rle with old method : 0.017396926879882812 time for calcul the mask position with numpy : 0.0015101432800292969 nb_pixel_total : 1654 time to create 1 rle with old method : 0.003934144973754883 time for calcul the mask position with numpy : 0.0014564990997314453 nb_pixel_total : 5012 time to create 1 rle with old method : 0.01190948486328125 time for calcul the mask position with numpy : 0.0014812946319580078 nb_pixel_total : 2341 time to create 1 rle with old method : 0.005645036697387695 time for calcul the mask position with numpy : 0.00144195556640625 nb_pixel_total : 298 time to create 1 rle with old method : 0.0008420944213867188 time for calcul the mask position with numpy : 0.0014362335205078125 nb_pixel_total : 917 time to create 1 rle with old method : 0.0024595260620117188 time for calcul the mask position with numpy : 0.0014448165893554688 nb_pixel_total : 2199 time to create 1 rle with old method : 0.0054471492767333984 time for calcul the mask position with numpy : 0.001451730728149414 nb_pixel_total : 1384 time to create 1 rle with old method : 0.0034761428833007812 time for calcul the mask position with numpy : 0.001466512680053711 nb_pixel_total : 1124 time to create 1 rle with old method : 0.002711772918701172 time for calcul the mask position with numpy : 0.0014469623565673828 nb_pixel_total : 947 time to create 1 rle with old method : 0.0023653507232666016 time for calcul the mask position with numpy : 0.0014338493347167969 nb_pixel_total : 884 time to create 1 rle with old method : 0.0022287368774414062 time for calcul the mask position with numpy : 0.0014438629150390625 nb_pixel_total : 476 time to create 1 rle with old method : 0.0012066364288330078 time for calcul the mask position with numpy : 0.0014691352844238281 nb_pixel_total : 1614 time to create 1 rle with old method : 0.003999471664428711 time for calcul the mask position with numpy : 0.0014576911926269531 nb_pixel_total : 890 time to create 1 rle with old method : 0.0022077560424804688 batch 1 Loaded 96 chid ids of type : 4677 Number RLEs to save : 8777 TO DO : save crop sub photo not yet done ! Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [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 time used for this insertion : 0.013698339462280273 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.233200073242188 time spend to save output : 0.013942241668701172 total time spend for step 1 : 11.247142314910889 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1747408924_801138_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 96 ############################### TEST frcnn ################################ 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 : frcnn 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 : 0.13618874549865723 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:frcnn Fri May 16 17:22:15 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 Faster rcnn ! 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/1747408935_801138_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 0.073s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.535719394683838 time spend to save output : 0.0001723766326904297 total time spend for step 1 : 2.5358917713165283 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False Inside saveFrcnn : final : True verbose : False threshold to save the result : 0.1 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 time used for this insertion : 0.01590728759765625 [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 time used for this insertion : 0.012808561325073242 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, 0.06384367, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052224293, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271038, None)], 'temp/1747408935_801138_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL 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 ! 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 ! List Step Type Loaded in datou : thcl, argmax 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 : 0.11732077598571777 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 : 2 step1:thcl Fri May 16 17:22:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.010996580123901367 time to convert the images to numpy array : 0.0012297630310058594 total time to convert the images to numpy array : 0.012729406356811523 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 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 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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 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 : 6717 max_wait_temp : 1 max_wait : 0 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)) 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/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 : 6717 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.009810686111450195 time used to do the prediction : 0.09252810478210449 save descriptor for thcl : 355 time to traite the descriptors : 0.07967495918273926 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.8160672187805176 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 1.52587890625e-05 save missing photos in datou_result : time spend for datou_step_exec : 5.844296216964722 time spend to save output : 1.5744800567626953 total time spend for step 1 : 7.418776273727417 step2:argmax Fri May 16 17:22:25 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 Beginning of datou_step Argmax ! calculate argmax for thcl : 355 Inside saveOutput : final : True verbose : False photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.01771326, 332, '355'), 'temp/1747408938_801138_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.008879423141479492 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.014959335327148438 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.015490293502807617 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 time used for this insertion : 6.198883056640625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00027108192443847656 time spend to save output : 0.039713144302368164 total time spend for step 2 : 0.03998422622680664 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', 0.01771326, 332, '355'), 'temp/1747408938_801138_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## 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 ! 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 ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.19086718559265137 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 : 2 step1:tfhub_classification2 Fri May 16 17:22:26 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 Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-05-16 17:22:29.711037: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-16 17:22:29.711747: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:22:29.711831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:22:29.711884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:22:29.713688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:22:29.713761: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:22:29.715842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:22:29.717084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:22:29.722059: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:22:29.723500: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:22:29.723982: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-16 17:22:29.755122: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-16 17:22:29.757001: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa504000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:22:29.757031: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-16 17:22:29.760492: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7cb5b20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-16 17:22:29.760521: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-16 17:22:29.761590: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-16 17:22:29.761710: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:22:29.761742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-16 17:22:29.761831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-16 17:22:29.761872: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-16 17:22:29.761922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-16 17:22:29.761975: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-16 17:22:29.762027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-16 17:22:29.763311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-16 17:22:29.763372: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-16 17:22:29.763421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-16 17:22:29.763433: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-16 17:22:29.763443: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-16 17:22:29.764749: 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 : 6717 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 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 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 : 11.658730506896973 time used to load_weights : 0.14891862869262695 0it [00:00, ?it/s] 3it [00:00, 686.35it/s]2025-05-16 17:22:44.125493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1747408945_801138_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1747408945_801138_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1747408945_801138_1171252764_29d5179a892cc50aadc9d67245534b59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 2.976930856704712 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 time to traite the descriptors : 0.031938791275024414 storage_type for insertDescriptorsMulti : 3 To insert : 1171252487 To insert : 1171252784 To insert : 1171252764 time to insert the descriptors : 0.882523775100708 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252487, 1171252784, 1171252764] Looping around the photos to save general results len do output : 3 /1171252487Didn't retrieve data . /1171252784Didn'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, '1171252487', None, None, None, None, None, None) ('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, '1171252764', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012378692626953125 save_final save missing photos in datou_result : time spend for datou_step_exec : 21.82439160346985 time spend to save output : 0.012675285339355469 total time spend for step 1 : 21.837066888809204 step2:argmax Fri May 16 17:22:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 Inside saveOutput : final : True verbose : False photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.9262545, 4674, '3609'), 'temp/1747408945_801138_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.967754, 4674, '3609'), 'temp/1747408945_801138_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.985362, 4674, '3609'), 'temp/1747408945_801138_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.008861064910888672 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.014529705047607422 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.014921188354492188 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 time used for this insertion : 6.67572021484375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00015473365783691406 time spend to save output : 0.043286800384521484 total time spend for step 2 : 0.0434415340423584 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252487': [(1171252487, 'jrm', 0.9262545, 4674, '3609'), 'temp/1747408945_801138_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252784': [(1171252784, 'jrm', 0.967754, 4674, '3609'), 'temp/1747408945_801138_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.985362, 4674, '3609'), 'temp/1747408945_801138_1171252764_29d5179a892cc50aadc9d67245534b59.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## 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 ! 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 ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.2293689250946045 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 : 2 step1:tfhub_classification2 Fri May 16 17:22:48 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 Beginning of datou_step TFHub with tf2 ! 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 havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 149 wait 20 seconds l 3637 free memory gpu now : 149 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 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 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 : 10.93679928779602 time used to load_weights : 0.16006255149841309 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.1680419445037842 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 time to traite the descriptors : 0.0425868034362793 storage_type for insertDescriptorsMulti : 3 To insert : 1171291875 To insert : 1171275372 To insert : 1171275314 time to insert the descriptors : 0.8162426948547363 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171291875, 1171275372, 1171275314] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275372Didn't retrieve data . /1171275314Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.016066789627075195 save_final save missing photos in datou_result : time spend for datou_step_exec : 42.64039993286133 time spend to save output : 0.016456127166748047 total time spend for step 1 : 42.656856060028076 step2:argmax Fri May 16 17:23:30 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 Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.9706439, 4723, '3655'), 'temp/1747408968_801138_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96744484, 4723, '3655'), 'temp/1747408968_801138_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96516705, 4723, '3655'), 'temp/1747408968_801138_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.008695363998413086 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.01608729362487793 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.015716552734375 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 time used for this insertion : 5.245208740234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0001823902130126953 time spend to save output : 0.045877933502197266 total time spend for step 2 : 0.04606032371520996 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', 0.9706439, 4723, '3655'), 'temp/1747408968_801138_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96744484, 4723, '3655'), 'temp/1747408968_801138_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96516705, 4723, '3655'), 'temp/1747408968_801138_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 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 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only 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 : rotate 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 : 0.15339040756225586 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:rotate Fri May 16 17:23:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! Needs to change image size ! Needs to change image size ! 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/1747409012_801138 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.1113874912261963 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.3629746437072754 time spend to save output : 6.389617919921875e-05 total time spend for step 1 : 1.3630385398864746 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 3 /1359015090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015092Didn'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 time used for this insertion : 0.01768040657043457 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1359015090: ['917849322', 'temp/1747409011_801138_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1359015091: ['917849322', 'temp/1747409011_801138_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1359015092: ['917849322', 'temp/1747409011_801138_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel 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) 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 ! List Step Type Loaded in datou : thcl, argmax, rotate 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 : 0.17353034019470215 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 : 3 step1:thcl Fri May 16 17:23:33 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 Thcl ! we are using the classfication for only one thcl 500 time to import caffe and check if the image exist : 0.000194549560546875 time to convert the images to numpy array : 1.2402188777923584 total time to convert the images to numpy array : 1.2407763004302979 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 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 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 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)) 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/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 : 3.809404134750366 time used to do the prediction : 0.1377408504486084 save descriptor for thcl : 500 time to traite the descriptors : 0.07112646102905273 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.8760919570922852 time spend for datou_step_exec : 11.31176233291626 time spend to save output : 5.53131103515625e-05 total time spend for step 1 : 11.311817646026611 step2:argmax Fri May 16 17:23:44 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 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step Argmax ! calculate argmax for thcl : 500 time spend for datou_step_exec : 0.00018930435180664062 time spend to save output : 3.600120544433594e-05 total time spend for step 2 : 0.00022530555725097656 step3:rotate Fri May 16 17:23:44 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 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure 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 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! 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/1747409025_801138 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.4045779705047607 Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 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 : [] time spend for datou_step_exec : 1.5075879096984863 time spend to save output : 3.457069396972656e-05 total time spend for step 3 : 1.507622480392456 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 1 /1359015265Didn'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 time used for this insertion : 0.012316703796386719 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1359015265: ['917849322', 'temp/1747409013_801138_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ # 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 : 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) 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 ! List Step Type Loaded in datou : crop, tile, rotate 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 : 0.12844204902648926 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 : 3 step1:crop Fri May 16 17:23:46 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 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 Loaded 4 chid ids of type : 407 +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 WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 4 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 : 23074326 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1747409028_801138 we have uploaded 4 photos in the portfolio 23074326 time of upload the photos Elapsed time : 3.936673879623413 Now we prepare data that will be used for ellipse search ! About to compute ellipse and record with type : 520 score : 5120 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2311 | arg_min : -30.0 min_score : 1968 | arg_min : 17.8125 min_score : 1614 | arg_min : 31.875 min_score : 1105 | arg_min : 28.5 min_score : 1105 arg_min : 1.9500000000000002 min_score : 1105 arg_min : 25.0 min_score : 1088 arg_min : 24.9375 min_score : 979 arg_min : 31.875 min_score : 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 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! score : 5362 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2281 | arg_min : -10.0 min_score : 2127 | arg_min : 25.0 min_score : 2127 | arg_min : 30.9375 min_score : 714 | arg_min : 25.0 min_score : 714 arg_min : 1.9500000000000002 min_score : 714 arg_min : -5.0 min_score : 668 arg_min : 23.4375 min_score : 655 arg_min : 29.53125 min_score : 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 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! score : 4603 strategy_opt : 5| | arg_min : 1.85 min_score : 2981 | arg_min : -50.0 min_score : 1356 | arg_min : 30.28125 min_score : 1079 | arg_min : 23.625 min_score : 995 | arg_min : 27.0 min_score : 995 arg_min : 1.6500000000000001 min_score : 961 arg_min : -70.0 min_score : 852 arg_min : 28.6875 min_score : 847 arg_min : 23.625 min_score : 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 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! score : 7970 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 1576 | arg_min : 40.0 min_score : 632 | arg_min : 20.15625 min_score : 561 | arg_min : 26.0 min_score : 561 | arg_min : 26.0 min_score : 561 arg_min : 1.8 min_score : 520 arg_min : 40.0 min_score : 520 arg_min : 18.8125 min_score : 494 arg_min : 26.0 min_score : 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 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1747409026_801138_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 : 23074329 Result OK ! uploaded one batch 0 Elapsed time : 20.265115976333618 time spend for datou_step_exec : 27.868565559387207 time spend to save output : 4.5299530029296875e-05 total time spend for step 1 : 27.868610858917236 step2:tile Fri May 16 17:24:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We 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 verbose : False 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/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 4 chid ids of type : 520 ++++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 23074334 with name tile_taggage_varroa feed_id_new_photos : 23074334 filename : temp/1747409026_801138_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)] new_crops_tiles : 1 crop_transformed : 4 batch 1 Loaded 1 chid ids of type : 17 treat the image : temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.008610010147094727 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/1747409061_801138 we have uploaded 1 photos in the portfolio 23074334 Importing ! upload mediasElapsed time : 0.5609793663024902 , 0Saving 4 CHIs. batch 1 Loaded 4 chid ids of type : 521 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.6378099918365479 time spend for datou_step_exec : 7.405222654342651 time spend to save output : 2.765655517578125e-05 total time spend for step 2 : 7.405250310897827 step3:rotate Fri May 16 17:24:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_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 Loaded 4 chid ids of type : 521 ++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 23074338 incorrect image 1359015700 cannot identify image file 'temp/1747409026_801138_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg' About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! Len new_chis : 0 Len list_new_chi_with_photo_id : 0 of type : 529 time spend for datou_step_exec : 1.8897905349731445 time spend to save output : 2.574920654296875e-05 total time spend for step 3 : 1.8898162841796875 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1359015700'] Looping around the photos to save general results len do output : 0 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, '1359015700', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.012627124786376953 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {} number of sub image is incorrect than we expected , got : 0 ; expected : 24 list chi : [] ERROR in DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE WRONG SIZE OF DATA : flat_list_chi : 0 vs flat_list_chi_non_reg : 28 ERROR data_augmentation_ellipse_varroa_tile_rotate FAILED ############################### TEST flip ################################ t 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 : flip 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 : 0.17988300323486328 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:flip Fri May 16 17:24:23 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_flip ! We are in a linear step without datou_depend ! batch 1 Loaded 6 chid ids of type : 741 +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points + map_chi_objs of length : 1 photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 Horizontal flip of photo 911785586 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/1747409064_801138 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 1.1082420349121094 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 1.216367483139038 time spend to save output : 6.008148193359375e-05 total time spend for step 1 : 1.2164275646209717 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] Looping around the photos to save general results len do output : 2 /1359015743 /1359015745 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 time used for this insertion : 0.019454240798950195 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1359015743': ['911785586', 'temp/1747409063_801138_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1359015745': ['911785586', 'temp/1747409063_801138_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 : 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 : crop 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 : 0.1578817367553711 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:crop Fri May 16 17:24:25 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 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 Loaded 8 chid ids of type : 755 ++++++++WARNING : margin is only used for type bib ! we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles map_result returned by crop_photo_return_map_crop : length : 8 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 : 23074340 Result OK ! uploaded one batch 0 Elapsed time : 20.697758436203003 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 20.772303342819214 time spend to save output : 2.193450927734375e-05 total time spend for step 1 : 20.77232527732849 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] Looping around the photos to save general results len do output : 8 /1359015790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1359015899Didn'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 time used for this insertion : 0.01296091079711914 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1359015790': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1359015812': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1359015825': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1359015841': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1359015855': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1359015869': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1359015885': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1359015899': ['950103132', 'temp/1747409065_801138_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 ############################### TEST angular_coeff ################################ t 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 : angular_coeff 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 : 0.11964249610900879 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:angular_coeff Fri May 16 17:24:46 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 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 Loaded 19 chid ids of type : 846 treating photo 932296368 time spend for datou_step_exec : 0.06006360054016113 time spend to save output : 4.4345855712890625e-05 total time spend for step 1 : 0.06010794639587402 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 : 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 : detection_filter_by_crop 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 : 0.20618295669555664 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:detection_filter_by_crop Fri May 16 17:24:46 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 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 Loaded 3 chid ids of type : 445 +++batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++batch 1 Loaded 3 chid ids of type : 445 +++ treating photo 946711423 time spend for datou_step_exec : 0.12329721450805664 time spend to save output : 8.916854858398438e-05 total time spend for step 1 : 0.12338638305664062 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, 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'312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, ['474,289,453,289,452,288,439,288,437,286,431,286,427,284,423,284,422,283,422,275,427,275,428,273,430,272,435,272,436,271,438,271,442,269,447,269,450,267,454,267,460,264,464,264,467,262,483,261,484,260,488,260,489,259,494,259,495,258,502,258,503,257,505,257,509,255,512,255,516,252,520,252,521,251,526,250,530,248,534,248,535,247,546,247,547,248,549,248,549,250,550,251,550,266,551,267,551,275,550,276,550,278,549,279,549,281,537,282,535,284,528,284,527,285,504,285,503,286,495,286,492,288,488,287,487,288,475,288']), (946711423, 503548896, 631, 301, 540, 339, 403, 0.740756, 3140491551, ['442,401,371,401,371,397,366,390,365,386,356,386,353,384,348,383,319,383,319,378,314,370,310,370,305,368,304,357,305,353,330,353,339,356,378,356,379,357,474,357,475,356,488,356,493,353,501,354,507,352,517,352,522,351,527,346,530,347,533,351,530,355,527,356,515,356,505,362,503,365,497,368,494,372,489,374,492,376,488,378,490,380,495,380,487,382,485,385,476,387,469,392,461,393,456,395,451,399,447,399', '519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t 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 : detection_filter_by_classif list_input_json : [] origin we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.004682779312133789 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Fri May 16 17:24:46 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 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 batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ treating photo 946711423 batch 1 Loaded 0 chid ids of type : 0 batch 1 Loaded 23 chid ids of type : 816 Number RLEs to save : 1600 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.34009814262390137 time spend to save output : 0.00011563301086425781 total time spend for step 1 : 0.3402137756347656 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 : 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 : blur_detection 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 : 0.13618803024291992 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:blur_detection Fri May 16 17:24:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1747409087_801138_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.27732133865356445 time spend to save output : 6.29425048828125e-05 total time spend for step 1 : 0.27738428115844727 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 : 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 ! 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 ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFBFBFFFBFFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 1.884448766708374 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 : 2 step1:thcl Fri May 16 17:24:49 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 Thcl ! we are using the classfication for only one thcl 1528 time to import caffe and check if the image exist : 0.0007503032684326172 time to convert the images to numpy array : 0.006122112274169922 time to import caffe and check if the image exist : 0.0006690025329589844 time to convert the images to numpy array : 0.06474518775939941 time to import caffe and check if the image exist : 0.0005321502685546875 time to convert the images to numpy array : 0.07079291343688965 time to import caffe and check if the image exist : 0.017065763473510742 time to convert the images to numpy array : 0.05778098106384277 time to import caffe and check if the image exist : 0.014896392822265625 time to convert the images to numpy array : 0.06297111511230469 time to import caffe and check if the image exist : 0.011893510818481445 time to convert the images to numpy array : 0.07084441184997559 time to import caffe and check if the image exist : 0.015428304672241211 time to convert the images to numpy array : 0.0665121078491211 time to import caffe and check if the image exist : 0.014564752578735352 time to convert the images to numpy array : 0.06711077690124512 time to import caffe and check if the image exist : 0.034996986389160156 time to convert the images to numpy array : 0.047618865966796875 time to import caffe and check if the image exist : 0.042377471923828125 time to convert the images to numpy array : 0.039545297622680664 total time to convert the images to numpy array : 0.09385848045349121 list photo_ids error: [] list photo_ids correct : [987515238, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515239, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515247, 987515248, 987515249, 987515250, 987515175, 987515176, 987515177, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515185, 987515186, 987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237] 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 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 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 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)) 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/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 : 0.0663454532623291 time used to do the prediction : 0.2724883556365967 save descriptor for thcl : 1528 time to traite the descriptors : 4.082318067550659 storage_type for insertDescriptorsMulti : 1 To insert : 987515238 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515239 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 time to insert the descriptors : 13.41935658454895 time spend for datou_step_exec : 21.780303478240967 time spend to save output : 0.00013875961303710938 total time spend for step 1 : 21.780442237854004 step2:argmax Fri May 16 17:25:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0012204647064208984 time spend to save output : 1.3113021850585938e-05 total time spend for step 2 : 0.0012335777282714844 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515238': [('987515238', 'Carton', 0.9995795, 1927, '1528'), 'temp/1747409087_801138_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.873759, 1927, '1528'), 'temp/1747409087_801138_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.9917139, 1927, '1528'), 'temp/1747409087_801138_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9679578, 1927, '1528'), 'temp/1747409087_801138_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9733794, 1927, '1528'), 'temp/1747409087_801138_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.9869387, 1927, '1528'), 'temp/1747409087_801138_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98693776, 1927, '1528'), 'temp/1747409087_801138_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939015, 1927, '1528'), 'temp/1747409087_801138_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97745436, 1927, '1528'), 'temp/1747409087_801138_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5287485, 1927, '1528'), 'temp/1747409087_801138_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993692, 1927, '1528'), 'temp/1747409087_801138_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.99637717, 1927, '1528'), 'temp/1747409087_801138_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99746203, 1927, '1528'), 'temp/1747409087_801138_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.99207777, 1927, '1528'), 'temp/1747409087_801138_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1747409087_801138_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515178': [('987515178', 'Carton', 0.8575808, 1927, '1528'), 'temp/1747409087_801138_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9270254, 1927, '1528'), 'temp/1747409087_801138_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.99001324, 1927, '1528'), 'temp/1747409087_801138_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977804, 1927, '1528'), 'temp/1747409087_801138_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242616, 1927, '1528'), 'temp/1747409087_801138_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1747409087_801138_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973065, 1927, '1528'), 'temp/1747409087_801138_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515247': [('987515247', 'Carton', 0.99966896, 1927, '1528'), 'temp/1747409087_801138_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.98136854, 1927, '1528'), 'temp/1747409087_801138_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813098, 1927, '1528'), 'temp/1747409087_801138_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.98082733, 1927, '1528'), 'temp/1747409087_801138_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.99981385, 1927, '1528'), 'temp/1747409087_801138_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998135, 1927, '1528'), 'temp/1747409087_801138_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9772495, 1927, '1528'), 'temp/1747409087_801138_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515240': [('987515240', 'Carton', 0.9995204, 1927, '1528'), 'temp/1747409087_801138_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9824499, 1927, '1528'), 'temp/1747409087_801138_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9359404, 1927, '1528'), 'temp/1747409087_801138_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8744783, 1927, '1528'), 'temp/1747409087_801138_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81743747, 1927, '1528'), 'temp/1747409087_801138_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8658914, 1927, '1528'), 'temp/1747409087_801138_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992312, 1927, '1528'), 'temp/1747409087_801138_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7977854, 1927, '1528'), 'temp/1747409087_801138_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9846746, 1927, '1528'), 'temp/1747409087_801138_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98118293, 1927, '1528'), 'temp/1747409087_801138_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515188': [('987515188', 'Carton', 0.99565685, 1927, '1528'), 'temp/1747409087_801138_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.99778795, 1927, '1528'), 'temp/1747409087_801138_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763703, 1927, '1528'), 'temp/1747409087_801138_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999113, 1927, '1528'), 'temp/1747409087_801138_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993956, 1927, '1528'), 'temp/1747409087_801138_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846572, 1927, '1528'), 'temp/1747409087_801138_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98467594, 1927, '1528'), 'temp/1747409087_801138_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.9660446, 1927, '1528'), 'temp/1747409087_801138_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9859781, 1927, '1528'), 'temp/1747409087_801138_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99545044, 1927, '1528'), 'temp/1747409087_801138_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911143, 1927, '1528'), 'temp/1747409087_801138_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.995089, 1927, '1528'), 'temp/1747409087_801138_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908539, 1927, '1528'), 'temp/1747409087_801138_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.9084675, 1927, '1528'), 'temp/1747409087_801138_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869977, 1927, '1528'), 'temp/1747409087_801138_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.90041643, 1927, '1528'), 'temp/1747409087_801138_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5220121, 1927, '1528'), 'temp/1747409087_801138_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940705, 1927, '1528'), 'temp/1747409087_801138_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994211, 1927, '1528'), 'temp/1747409087_801138_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992435, 1927, '1528'), 'temp/1747409087_801138_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98342806, 1927, '1528'), 'temp/1747409087_801138_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9449098, 1927, '1528'), 'temp/1747409087_801138_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8919158, 1927, '1528'), 'temp/1747409087_801138_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5365976, 1927, '1528'), 'temp/1747409087_801138_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.77002335, 1927, '1528'), 'temp/1747409087_801138_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg']} 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 : detect_points 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 : 0.13100099563598633 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:detect_points Fri May 16 17:25:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step predict points ! Inside try reload ! 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/1747409111_801138_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 : 0.041295528411865234 time to do a prediction : 0.3319680690765381 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) time spend for datou_step_exec : 1.6397662162780762 time spend to save output : 4.649162292480469e-05 total time spend for step 1 : 1.639812707901001 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, 6.274903773667573e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.493546515103251e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.065193000471254e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.438095970726863e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9244735085521825e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.772288982872851e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001225323067046702), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9466080377460457e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.362034123848389e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2100437035987852e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.379213614427499e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4769037761652726e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1247823749727104e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.0001569449668750167), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.00044476945186033845), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.549080717377365e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3290435845192405e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6189902680707746e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.519927647881559e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6149714383573155e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.252297680475749e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.642733882879838e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032743567135185003), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003053006948903203), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8581864424049854e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.944638127810322e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7082287488155998e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.8013604858424515e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3010627046460286e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.7010705050779507e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.542570877674734e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.777473340160213e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.105691682023462e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.644767166908423e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9604910903581185e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4359214901560335e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.8643952292623e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2871787475887686e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.299607881985139e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1687419575755484e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.8257699088717345e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.5486208414804423e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.962813596241176e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.7561418391996995e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.326902853790671e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.179982614819892e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.1535350495250896e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.922893665730953e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2119948223698884e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.578663977852557e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.3526801416883245e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.00015443330630660057), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.0002588070055935532), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.00018810349865816534), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021336395002435893), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016433073324151337), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.5519741433963645e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.734410943754483e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.93025581818074e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012190371489850804), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019640076789073646), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018780652317218482), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.00012343382695689797), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.0002720299526117742), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.5827910715415783e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.102005277673015e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 7.001269750617212e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008715185103937984), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.0026481891982257366), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.0033706019166857004), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.031320683658123016), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.05582199990749359), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012370257172733545), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020897394279018044), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.00036820583045482635), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.0068500107154250145), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015879813581705093), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009388452395796776), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009777115657925606), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.022143781185150146), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.02186235971748829), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.1931353509426117), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09658389538526535), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12367630749940872), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5322320461273193), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4608687162399292), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.771844208240509), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8664957880973816), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8503315448760986), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9842862486839294), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9848434329032898), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919453263282776), (987515173, 1982, 'Carton', 240, 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'Carton', 176, -1, 336, -1, 0.9911750555038452), (987515173, 1982, 'Carton', 208, -1, 336, -1, 0.9869382381439209), (987515173, 1982, 'Carton', 240, -1, 336, -1, 0.908354640007019), (987515173, 1982, 'Carton', 272, -1, 336, -1, 0.9452760219573975), (987515173, 1982, 'Carton', 304, -1, 336, -1, 0.9365354776382446), (987515173, 1982, 'Carton', 336, -1, 336, -1, 0.9808305501937866), (987515173, 1982, 'Kraft', 112, -1, 112, -1, 1.9600427947352728e-09), (987515173, 1982, 'Kraft', 144, -1, 112, -1, 1.7233226401458523e-08), (987515173, 1982, 'Kraft', 176, -1, 112, -1, 9.652620747147012e-07), (987515173, 1982, 'Kraft', 208, -1, 112, -1, 3.1329145713243634e-05), (987515173, 1982, 'Kraft', 240, -1, 112, -1, 4.443995203473605e-05), (987515173, 1982, 'Kraft', 272, -1, 112, -1, 0.00020611807121895254), (987515173, 1982, 'Kraft', 304, -1, 112, -1, 0.0010795426787808537), (987515173, 1982, 'Kraft', 336, -1, 112, -1, 0.0008287880336865783), (987515173, 1982, 'Kraft', 112, -1, 144, -1, 2.6189019990852103e-05), (987515173, 1982, 'Kraft', 144, -1, 144, -1, 6.98218582328991e-06), (987515173, 1982, 'Kraft', 176, -1, 144, -1, 3.6286169233790133e-06), (987515173, 1982, 'Kraft', 208, -1, 144, -1, 3.56663076672703e-05), (987515173, 1982, 'Kraft', 240, -1, 144, -1, 6.69383880449459e-05), (987515173, 1982, 'Kraft', 272, -1, 144, -1, 8.629138028481975e-05), (987515173, 1982, 'Kraft', 304, -1, 144, -1, 0.00012233381858095527), (987515173, 1982, 'Kraft', 336, -1, 144, -1, 0.00011262611951678991), (987515173, 1982, 'Kraft', 112, -1, 176, -1, 0.0004982984974049032), (987515173, 1982, 'Kraft', 144, -1, 176, -1, 0.00012324689305387437), (987515173, 1982, 'Kraft', 176, -1, 176, -1, 9.089996456168592e-05), (987515173, 1982, 'Kraft', 208, -1, 176, -1, 5.17409025633242e-05), (987515173, 1982, 'Kraft', 240, -1, 176, -1, 0.0001155634963652119), (987515173, 1982, 'Kraft', 272, -1, 176, -1, 0.00043244942207820714), (987515173, 1982, 'Kraft', 304, -1, 176, -1, 0.0009215309401042759), (987515173, 1982, 'Kraft', 336, -1, 176, -1, 0.0014269235543906689), (987515173, 1982, 'Kraft', 112, -1, 208, -1, 6.875136023154482e-05), (987515173, 1982, 'Kraft', 144, -1, 208, -1, 1.8485739929019473e-05), (987515173, 1982, 'Kraft', 176, -1, 208, -1, 2.598129867692478e-05), (987515173, 1982, 'Kraft', 208, -1, 208, -1, 3.542553895385936e-05), (987515173, 1982, 'Kraft', 240, -1, 208, -1, 3.675187690532766e-05), (987515173, 1982, 'Kraft', 272, -1, 208, -1, 8.65395413711667e-05), (987515173, 1982, 'Kraft', 304, -1, 208, -1, 0.00012343072739895433), (987515173, 1982, 'Kraft', 336, -1, 208, -1, 0.0003906369092874229), (987515173, 1982, 'Kraft', 112, -1, 240, -1, 0.0003069973608944565), (987515173, 1982, 'Kraft', 144, -1, 240, -1, 4.164619531366043e-05), (987515173, 1982, 'Kraft', 176, -1, 240, -1, 1.2254342436790466e-05), (987515173, 1982, 'Kraft', 208, -1, 240, -1, 7.319390533666592e-06), (987515173, 1982, 'Kraft', 240, -1, 240, -1, 2.2957819965085946e-05), (987515173, 1982, 'Kraft', 272, 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0.0010816646972671151), (987515173, 1982, 'Kraft', 240, -1, 304, -1, 0.001785692642442882), (987515173, 1982, 'Kraft', 272, -1, 304, -1, 0.004692047834396362), (987515173, 1982, 'Kraft', 304, -1, 304, -1, 0.004680394195020199), (987515173, 1982, 'Kraft', 336, -1, 304, -1, 0.012516648508608341), (987515173, 1982, 'Kraft', 112, -1, 336, -1, 0.0021805078722536564), (987515173, 1982, 'Kraft', 144, -1, 336, -1, 0.005710909608751535), (987515173, 1982, 'Kraft', 176, -1, 336, -1, 0.0008289668476209044), (987515173, 1982, 'Kraft', 208, -1, 336, -1, 0.0012682747328653932), (987515173, 1982, 'Kraft', 240, -1, 336, -1, 0.007807531394064426), (987515173, 1982, 'Kraft', 272, -1, 336, -1, 0.012541648000478745), (987515173, 1982, 'Kraft', 304, -1, 336, -1, 0.017939437180757523), (987515173, 1982, 'Kraft', 336, -1, 336, -1, 0.007751245517283678), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 112, -1, 1.495490398184529e-10), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 112, -1, 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-1, 8.837343921186402e-05), (987515173, 1982, 'autre_refus', 144, -1, 208, -1, 0.00018576587899588048), (987515173, 1982, 'autre_refus', 176, -1, 208, -1, 0.0003197148907929659), (987515173, 1982, 'autre_refus', 208, -1, 208, -1, 0.00035766419023275375), (987515173, 1982, 'autre_refus', 240, -1, 208, -1, 0.00019636577053461224), (987515173, 1982, 'autre_refus', 272, -1, 208, -1, 0.0002867773873731494), (987515173, 1982, 'autre_refus', 304, -1, 208, -1, 0.00020215213589835912), (987515173, 1982, 'autre_refus', 336, -1, 208, -1, 0.00024372282496187836), (987515173, 1982, 'autre_refus', 112, -1, 240, -1, 0.00023348937975242734), (987515173, 1982, 'autre_refus', 144, -1, 240, -1, 0.00010851183469640091), (987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.494006811408326e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.5190911401296034e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.31239197193645e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.0001408644748153165), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.941345731727779e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.16819301689975e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.0002683921775314957), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011157889821333811), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.00012473773676902056), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.116454485687427e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.920964834629558e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.2751780711114407e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.839499110355973e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.00014227435167413205), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.00011590302892727777), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.0002175104891648516), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.00042556514381431043), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.0004272244405001402), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.566621595993638e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.167213435517624e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1660118616418913e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8812448615790345e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.00024736483464948833), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.00046999737969599664), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.00033431506017223), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023676679120399058), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010704481246648356), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.567313827574253e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001310749794356525), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007315005059354007)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier 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) 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 ! List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] origin Catched exception ! Connect or reconnect ! We have 1 , 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 : 0.19759893417358398 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 : 7 step1:init_dechet Fri May 16 17:25:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [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 time used for this insertion : 0.015320777893066406 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.00020265579223632812 time spend to save output : 0.015636205673217773 total time spend for step 1 : 0.0158388614654541 step2:tile Fri May 16 17:25:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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 verbose : False 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/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 Loaded 0 chid ids of type : 0 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 23074416 with name tile_correct_upm feed_id_new_photos : 23074416 filename : temp/1747409113_801138_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)] new_crops_tiles : 1 crop_transformed : 0 batch 1 Loaded 1 chid ids of type : 1522 treat the image : temp/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010815858840942383 About to upload 1 photos upload in portfolio : 23074416 Result OK ! uploaded one batch 0 Elapsed time : 5.305088520050049 upload mediasElapsed time : 5.31599497795105 , 0Saving 0 CHIs. end of tileElapsed time : 5.32765007019043 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1359015987'] Looping around the photos to save general results len do output : 1 /1359015987Didn'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, '1359015987', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.011577844619750977 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.097790718078613 time spend to save output : 0.01180577278137207 total time spend for step 2 : 12.109596490859985 step3:detect_points Fri May 16 17:25:25 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 Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step predict points ! Inside try reload ! 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/1747409113_801138_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 : 0.07713580131530762 time to do a prediction : 15.621504545211792 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) Inside saveOutput : final : False verbose : False Inside savePoints : final : False verbose : False threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 final : False save missing photos in datou_result : time spend for datou_step_exec : 17.042237520217896 time spend to save output : 0.06398200988769531 total time spend for step 3 : 17.10621953010559 step4:count_percent_refus Fri May 16 17:25:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure debut step count percent refus (987321136, 0.9481481481481482) ('temp/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1359015987] 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 Inside saveOutput : final : False verbose : False begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.054840087890625 save missing photos in datou_result : time spend for datou_step_exec : 0.017772197723388672 time spend to save output : 0.05505704879760742 total time spend for step 4 : 0.0728292465209961 step5:brightness Fri May 16 17:25:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea.jpg Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.00917959213256836 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.008630990982055664 save missing photos in datou_result : time spend for datou_step_exec : 0.10332965850830078 time spend to save output : 0.022271394729614258 total time spend for step 5 : 0.12560105323791504 step6:blur_detection Fri May 16 17:25:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1747409113_801138_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.009990692138671875 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.01036691665649414 save missing photos in datou_result : time spend for datou_step_exec : 0.15173959732055664 time spend to save output : 0.024674177169799805 total time spend for step 6 : 0.17641377449035645 step7:send_mail_dechet Fri May 16 17:25:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 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 dans la step send mail dechet senders@fotonower.com retour de l'envoi du mail : None Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1359015987'] 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, '1359015987', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.012160539627075195 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.5548090934753418 time spend to save output : 0.01251530647277832 total time spend for step 7 : 0.5673243999481201 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 : 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 : image_temperature_detection 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 : 0.230865478515625 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:image_temperature_detection Fri May 16 17:25:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanche_jaune_detection treat image : temp/1747409143_801138_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.16768598556518555 time spend to save output : 5.888938903808594e-05 total time spend for step 1 : 0.16774487495422363 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 : 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 : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.017744064331054688 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Fri May 16 17:25:44 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 split portfolio by speed calcul order for each photo with time calcul time for a portfolio 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]]} 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"; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 time spend for datou_step_exec : 0.14276981353759766 time spend to save output : 9.965896606445312e-05 total time spend for step 1 : 0.1428694725036621 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(23074434, 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 : 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 ! 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 ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.31432127952575684 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 : 2 step1:frcnn Fri May 16 17:25:44 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 Beginning of datou step Faster rcnn ! Inside try reload ! 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 F0516 17:25:46.542096 801138 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1952: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1958: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2143: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2149: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None Name Stmts Miss Cover Missing -------------------------------------------------------------------------------------------------------------------------------------- /home/admin/.local/lib/python3.8/site-packages/PIL/BmpImagePlugin.py 218 181 17% 52, 56, 76-264, 276-284, 291-355, 366, 384, 388-449 /home/admin/.local/lib/python3.8/site-packages/PIL/ExifTags.py 340 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/GifImagePlugin.py 585 527 10% 55, 71-74, 77-80, 84-108, 112-120, 124-139, 142-155, 158-410, 413-430, 433-456, 459, 480-491, 506-543, 547-564, 568-574, 578-649, 653, 658-670, 674-680, 684-746, 756-793, 812-844, 849-854, 865-872, 882, 886-905, 914-967, 971-983, 1002-1015, 1036-1048 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpGradientFile.py 68 53 22% 32-43, 47, 51, 55, 59, 70-98, 105-137 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/home/admin/.local/lib/python3.8/site-packages/cffi/error.py 19 8 58% 8-15 /home/admin/.local/lib/python3.8/site-packages/cffi/lock.py 10 6 40% 4-7, 11-12 /home/admin/.local/lib/python3.8/site-packages/cffi/model.py 389 251 35% 16, 21, 30-45, 48, 51, 54, 57-63, 66, 69, 75, 79, 82, 92, 99, 166, 168, 170, 172, 175, 182-183, 186, 189, 196-197, 200, 208-220, 232, 236, 243-254, 258, 269, 273-274, 288-290, 303-306, 311, 314, 317-322, 332-333, 336-337, 340-341, 351-356, 359-362, 365-376, 382-394, 397-401, 404-464, 467, 470-471, 474-477, 495-499, 502-505, 508-509, 512-514, 520-557, 561-566, 569-572, 582-587, 590-610, 613, 616-617 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/api.py 195 181 7% 62-497, 515, 543-544 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/assets/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/cd.py 189 164 13% 24-50, 63-71, 80-91, 100-112, 120-129, 138-164, 175-244, 253-283, 291-311, 319-338, 350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__about__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__init__.py 7 1 86% 18 /home/admin/.local/lib/python3.8/site-packages/cryptography/utils.py 75 45 40% 29-30, 34-37, 41, 47-50, 59-61, 66-67, 70-74, 77, 80-84, 87, 97-104, 108-119, 126, 129 /home/admin/.local/lib/python3.8/site-packages/cv2/__init__.py 16 2 88% 18-19 /home/admin/.local/lib/python3.8/site-packages/cv2/data/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/__init__.py 13 4 69% 6-7, 17, 24 /home/admin/.local/lib/python3.8/site-packages/dateutil/_common.py 25 15 40% 14-17, 20-25, 28, 34, 37-41 /home/admin/.local/lib/python3.8/site-packages/dateutil/_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/__init__.py 33 4 88% 31-32, 47-48 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py 812 687 15% 63-75, 91-184, 187, 190-194, 197, 201, 206, 211, 216, 222-223, 226-231, 234, 238, 320, 323-327, 330-334, 337-340, 343-346, 349, 352, 355-358, 367-378, 382-391, 396-400, 404, 408, 412, 415-426, 429-454, 461-472, 475-565, 636-659, 707-873, 877-1004, 1007-1038, 1042-1054, 1057, 1070-1090, 1093-1097, 1103-1109, 1116-1127, 1135-1139, 1142-1152, 1160-1175, 1178-1215, 1218-1240, 1243-1248, 1257-1264, 1365-1368, 1383, 1386-1388, 1391-1579, 1586, 1598-1601, 1604-1605 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/isoparser.py 185 150 19% 26-37, 51-55, 134-146, 159-163, 176-179, 199, 207-210, 213-251, 254-295, 316-328, 331-381, 384-412 /home/admin/.local/lib/python3.8/site-packages/dateutil/relativedelta.py 241 206 15% 112-229, 232-262, 266, 270, 273-280, 294-308, 318-402, 405, 408, 411-413, 440, 458, 476, 496-501, 521-531, 548, 568, 571-576, 581-592, 597 /home/admin/.local/lib/python3.8/site-packages/dateutil/rrule.py 979 862 12% 26-27, 72, 87-89, 96-103, 106-111, 114-122, 125-147, 150-169, 172-180, 186-189, 195-210, 216-228, 249-269, 276-302, 434-698, 707-760, 765-774, 777-1030, 1062-1077, 1103-1109, 1119-1121, 1125-1251, 1254, 1257-1261, 1265-1276, 1279-1282, 1285-1292, 1295-1300, 1303, 1317-1323, 1326-1333, 1338, 1341, 1344, 1347, 1350-1354, 1360, 1366, 1374, 1381, 1384-1413, 1475, 1478, 1493, 1497-1504, 1507, 1513-1533, 1542-1561, 1566-1613, 1625-1725, 1732 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_common.py 161 124 23% 20-28, 55-129, 139-144, 169-177, 196-202, 205, 222-242, 258-264, 290, 293-300, 303-310, 314-317, 321-350, 366-372, 375-393, 396-405, 409, 414, 417 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_factories.py 49 21 57% 22, 34-52, 64-79 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/tz.py 803 644 20% 75, 78, 82, 98, 106, 109-112, 118, 121, 144-152, 155, 158, 162, 166, 180, 183-186, 191, 194, 206-216, 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71 53 25% 18, 40, 59-69, 79, 90-92, 107-116, 130-154, 168-191 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/point.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/publicKey.py 48 32 33% 11-12, 15-23, 26-32, 35, 39, 43-76, 80-97 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/signature.py 35 23 34% 10-12, 15-18, 21, 25-40, 44-45 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/base.py 8 2 75% 8, 12 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/binary.py 15 5 67% 15, 26, 36, 48-49 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/compatibility.py 24 13 46% 13, 19, 22-39 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/der.py 149 110 26% 27-28, 32-43, 47-53, 57, 61, 65, 69-74, 78-89, 93-111, 115-121, 125-131, 135-144, 148-152, 156-164, 168-180, 184-194, 198-207, 211-227, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 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81-82, 88, 91, 102, 129-149, 178-181, 200-210, 252-264, 285-293, 341-343, 364-371, 396-403, 415-425, 458-495, 509-518, 527, 531, 537, 555-563, 573, 583-587, 591, 595-620, 636, 643-647, 654-658, 665-666, 718-724 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 666 20% 156-202, 205, 208, 211, 215-221, 232, 251-300, 305, 310-341, 345-419, 431, 434, 443-471, 484-485, 494, 529-531, 535, 545-553, 558, 562, 574-582, 610-623, 635, 638, 649, 652, 676-690, 700-703, 707, 722-723, 727, 730-734, 748-751, 754, 757-760, 764, 767-783, 798-801, 815-817, 822, 825, 841-859, 868-897, 901, 906-925, 943, 957-967, 971-972, 994-997, 1000-1001, 1004, 1069-1144, 1170-1173, 1189-1215, 1221-1226, 1238-1244, 1264-1271, 1315-1320, 1323, 1326, 1330-1337, 1408-1412, 1415-1421, 1434-1448, 1451, 1454, 1457, 1460, 1473, 1478, 1541-1549, 1555-1556, 1560-1571, 1575-1580, 1585, 1591, 1598-1603, 1613-1618, 1622, 1626-1635, 1639, 1643-1652, 1656, 1659, 1663, 1680-1683, 1711-1718, 1723-1760, 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1118-1124, 1132, 1140-1165 /home/admin/.local/lib/python3.8/site-packages/matplotlib/figure.py 1041 867 17% 69-70, 83-84, 88, 92, 96-98, 102-103, 107, 110, 116-118, 152-154, 161-178, 187-214, 218-239, 262-282, 286, 304-308, 314, 357-394, 401-404, 411-414, 421-425, 429, 433, 441, 451, 457, 467, 477, 489-490, 516-527, 615-641, 744-770, 774-783, 904-919, 926-957, 969-992, 1009, 1128-1150, 1188-1200, 1277-1315, 1346-1357, 1400-1418, 1460-1478, 1501-1502, 1543-1545, 1587-1614, 1639-1641, 1645-1647, 1659-1660, 1680-1693, 1705-1729, 1732-1737, 1766-1808, 1812-1827, 1831-1837, 1949-2148, 2151-2154, 2219-2252, 2256, 2260, 2266, 2276-2277, 2280, 2292-2308, 2316, 2332, 2335, 2350, 2358-2373, 2399, 2402, 2505-2597, 2600-2601, 2610-2620, 2652-2684, 2689, 2698-2700, 2736-2744, 2758, 2763-2766, 2769, 2780-2787, 2793, 2814-2819, 2827, 2851-2856, 2889-2890, 2909-2923, 2933, 3007-3024, 3056-3066, 3087, 3091, 3095, 3099, 3109-3110, 3127, 3144, 3148-3153, 3161-3185, 3192-3194, 3200, 3203-3220, 3223-3247, 3253, 3366-3378, 3429-3474, 3484-3494, 3507-3509, 3539-3549, 3600-3629 /home/admin/.local/lib/python3.8/site-packages/matplotlib/font_manager.py 563 423 25% 135-136, 177, 190-191, 207-212, 217-244, 250-258, 269-291, 295-301, 305-307, 347-456, 474-524, 594-608, 622-631, 634-642, 645, 648, 658, 664, 670, 676, 685, 693, 699, 705, 715, 725-729, 739-742, 752-755, 768-781, 794-807, 820-837, 844, 853-857, 865, 885-892, 896, 907-920, 938, 958-962, 991-1024, 1037-1047, 1053, 1060, 1067, 1073, 1077-1079, 1094-1111, 1123-1128, 1136-1139, 1149-1157, 1171-1175, 1188-1199, 1257-1265, 1269, 1316-1359, 1365-1444, 1454-1458, 1463-1464, 1490, 1515-1523, 1539-1540, 1545-1548 /home/admin/.local/lib/python3.8/site-packages/matplotlib/gridspec.py 277 216 22% 48-56, 59-63, 79, 83, 97-99, 108-113, 121, 130-135, 143, 167-205, 213-226, 230-263, 273-316, 371-379, 400-410, 425-434, 443, 467-474, 501-505, 511-521, 529, 553-555, 558, 570-605, 612, 616, 619, 629-630, 635-636, 641-645, 648, 651, 654, 657, 663-673, 679-683, 691, 697, 739 /home/admin/.local/lib/python3.8/site-packages/matplotlib/hatch.py 143 103 28% 16-17, 20-28, 33-34, 37-45, 50-55, 58-64, 69-75, 78-84, 91-97, 102-121, 126-129, 136-137, 144-145, 153-154, 162-168, 183-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 661 13% 83-110, 123-157, 171-213, 221-227, 259-274, 277-281, 285, 289-292, 302-306, 318, 325-326, 358-587, 607, 615, 620-646, 650-677, 681-683, 695-731, 743, 754, 771-776, 788-792, 796-797, 811-814, 818, 830-831, 835, 846-850, 854, 920-922, 936-938, 942-949, 954, 977-1002, 1006-1014, 1025-1041, 1058-1059, 1063, 1067-1133, 1148-1165, 1168, 1177-1180, 1183-1185, 1188, 1191, 1194-1196, 1199-1201, 1245-1248, 1252-1281, 1284, 1304-1338, 1341, 1345-1354, 1379-1389, 1393-1394, 1399-1410, 1416-1417, 1440-1451, 1454-1462, 1466-1476, 1480-1486, 1530-1564, 1619-1689, 1708-1724, 1734-1754, 1796-1818 /home/admin/.local/lib/python3.8/site-packages/matplotlib/layout_engine.py 69 39 43% 63-64, 70, 78-80, 88-90, 96, 103, 122-124, 130, 158-162, 181-189, 207-209, 249-259, 269-274, 303-305 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend.py 470 385 18% 69-74, 77-80, 83-90, 93-94, 343, 416-657, 666-671, 677-680, 684, 687, 694-706, 711-737, 764, 769, 774, 778-779, 797-806, 816-906, 921-941, 945, 949, 953, 957, 963, 977-979, 983, 1001-1012, 1016, 1020-1022, 1026, 1030, 1040-1041, 1047-1050, 1072-1093, 1110, 1121-1158, 1161-1164, 1189-1198, 1202, 1209-1238, 1243-1250, 1298-1348 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend_handler.py 343 255 26% 41-43, 79-82, 85, 89-93, 98-102, 125-139, 164, 189-192, 195-206, 231-236, 249-273, 290-312, 343-350, 355-359, 369, 375-384, 389-396, 404-407, 410-415, 420-428, 440-443, 447-464, 468-473, 477, 487-502, 510, 521, 538-545, 551-629, 659-664, 670-712, 719-720, 748-773, 782-807, 813-817 /home/admin/.local/lib/python3.8/site-packages/matplotlib/lines.py 679 562 17% 36-60, 64-69, 78-106, 118-201, 262-271, 310-414, 440-484, 492, 506-508, 518, 537-538, 596-597, 605, 616-618, 622-624, 627-635, 645-651, 654, 657-699, 708-714, 718-720, 724-726, 732-878, 882, 890, 898, 906, 914, 922, 930, 938-948, 956, 959-965, 973, 981, 989, 997, 1006-1010, 1019-1023, 1027-1029, 1035-1037, 1047-1049, 1059-1061, 1089-1096, 1116-1119, 1130-1134, 1167-1179, 1192-1193, 1196-1207, 1217, 1227, 1237, 1248-1252, 1263-1266, 1276-1287, 1297-1308, 1329-1332, 1336-1355, 1368-1371, 1384-1387, 1395, 1403, 1416-1419, 1432-1435, 1443, 1451, 1462, 1472-1481, 1484-1521, 1525-1526, 1566-1575, 1590, 1594-1599 /home/admin/.local/lib/python3.8/site-packages/matplotlib/markers.py 427 328 23% 253-272, 278-291, 294, 297, 300, 312-316, 319, 322, 325, 340-367, 376, 383-386, 395, 402-405, 408, 412-413, 424-429, 445-458, 474-480, 483, 486-488, 491, 494, 497-515, 523-541, 544, 547-556, 559, 562-573, 584-611, 614, 617, 620, 623, 626-641, 644-655, 658-659, 662-682, 685-704, 707-728, 731-754, 757-775, 780-783, 786-787, 792-795, 798-801, 806-809, 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250, 254, 261, 265, 272, 279, 287-289, 308-319, 328-345, 348, 351, 394-417, 441-464, 477-483, 495, 537-546, 587-590, 599-601, 619-642, 651, 662, 671-682, 704-725, 735-738, 749-762, 772-788, 796, 807-810, 834-878, 889-922, 942-1001, 1019, 1029-1030, 1043-1045, 1077-1083 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/__init__.py 28 8 71% 75, 95, 104-110 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/geo.py 273 183 33% 24, 27-28, 33-34, 41-57, 61-106, 111-114, 119-120, 123, 126, 129-130, 133, 136, 139, 142, 145-146, 152, 159-162, 170-172, 179-181, 187-188, 195, 205, 213, 216, 219, 222, 236-237, 240, 244-245, 256-267, 271, 278, 282, 285-288, 291, 302-309, 313, 319-323, 327, 330-333, 336, 347-377, 381, 387-393, 397, 400-403, 406, 421-423, 427-442, 446, 454-456, 460-473, 477, 483-488, 492-493, 496, 502 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/polar.py 719 577 20% 50-54, 63, 68-77, 81-131, 135, 165-169, 175-184, 207-210, 219-231, 235, 246-253, 259, 262, 265, 268, 271, 274, 277, 290-291, 294-295, 298-302, 305-306, 324-332, 337-344, 347-352, 355-396, 413-416, 420-422, 425-433, 437-445, 458-459, 462, 466-471, 478-479, 483-487, 490-494, 514-518, 523-544, 559-561, 566-615, 618-695, 710-711, 714-716, 720-722, 725-726, 735, 744, 761-765, 771-800, 815-821, 825-844, 848-849, 857-951, 955-956, 959, 962, 965-970, 973-982, 985-991, 994-1037, 1040, 1043-1053, 1057, 1061, 1065, 1069, 1087-1098, 1104-1106, 1112, 1129-1138, 1150-1159, 1171, 1181, 1190, 1200, 1209, 1219, 1227, 1230, 1244-1256, 1266, 1277, 1280-1281, 1285, 1288, 1340-1349, 1402-1415, 1419-1441, 1453, 1463, 1473, 1476-1486, 1496, 1499-1523 /home/admin/.local/lib/python3.8/site-packages/matplotlib/pyplot.py 860 526 39% 119-120, 135-157, 163-167, 175, 180-182, 186-193, 204-209, 231-356, 360-375, 383-384, 397, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 803-869, 882-890, 902-906, 911, 916, 921-923, 940, 945, 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3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 155 43% 69, 76, 86, 105-113, 120, 145-150, 153, 156, 180-182, 186, 190-198, 205-209, 213, 218-236, 239, 246-247, 250, 253, 256, 281-282, 288-291, 297, 301-304, 333-335, 339, 343, 350-361, 364-371, 374, 382-388, 391-398, 401, 440-441, 449-453, 457, 465-469, 472, 475, 483-484, 487, 490, 551-557, 562, 565-574, 581-584, 588-593, 596, 599, 606-607, 611, 614, 617, 646-648, 652, 657-665, 677-679, 708-709, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 261 17% 33, 54-86, 90-99, 103-109, 113-114, 126-131, 136-140, 153-197, 200, 203-206, 216-219, 223-225, 230-282, 287-290, 313-325, 329-330, 334-386, 408-419, 423, 429-442, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 539, 543, 546, 549, 552-555, 559-574, 578, 582, 585, 588 /home/admin/.local/lib/python3.8/site-packages/matplotlib/stackplot.py 42 37 12% 71-127 /home/admin/.local/lib/python3.8/site-packages/matplotlib/streamplot.py 370 328 11% 91-241, 247-248, 274-284, 288, 291, 294, 297, 300-301, 304-305, 308-311, 314, 321-362, 366, 372, 386-396, 399, 403-404, 408-409, 417-426, 443-502, 535-602, 607-624, 633-667, 678-707 /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/core.py 92 45 51% 22, 127-180, 220-224, 242, 256, 262-266 /home/admin/.local/lib/python3.8/site-packages/matplotlib/table.py 335 272 19% 94-103, 108-110, 113-114, 118, 122-123, 127, 131-138, 142-149, 153-164, 170, 176-177, 188-189, 202, 206-218, 222-228, 298-321, 342-345, 351-361, 365, 384, 388-389, 392, 401-415, 423-427, 431-444, 448, 452-457, 464-488, 500-508, 512-516, 520-521, 525-539, 543-545, 568-570, 574-577, 584-635, 650, 737-830 /home/admin/.local/lib/python3.8/site-packages/matplotlib/texmanager.py 151 103 32% 48-49, 105-106, 110-115, 120-130, 134-171, 178-187, 194-195, 200, 205-207, 246-249, 253-275, 284-305, 314-329, 334-344, 357-361, 366-373 /home/admin/.local/lib/python3.8/site-packages/matplotlib/text.py 812 676 17% 41-49, 67-90, 97, 105, 130, 165-183, 201-219, 223-233, 236-239, 246-268, 274-275, 278-281, 292-313, 317-321, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 996 19% 165-167, 170, 173, 176, 179, 182, 186, 193, 196-197, 213, 217-218, 225, 233, 236, 245, 255, 261, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 428-439, 452, 481-486, 498, 509-512, 520, 531, 544-564, 572-578, 588, 620-622, 626-650, 654-666, 672-694, 698-703, 706-740, 748-776, 780-809, 874-883, 893, 902, 914, 925, 933-984, 987-993, 997-1016, 1019-1020, 1024, 1028-1046, 1054-1062, 1072, 1076-1110, 1120-1125, 1167-1172, 1184, 1193, 1205, 1218, 1221-1260, 1263-1286, 1289-1292, 1295-1313, 1318-1322, 1388-1392, 1395, 1398-1401, 1406, 1409-1412, 1417-1421, 1438-1473, 1503-1506, 1510-1512, 1536-1556, 1559, 1570-1579, 1583, 1616, 1623, 1631, 1644-1649, 1665, 1673, 1685-1686, 1690-1693, 1697-1698, 1701, 1717-1719, 1723-1724, 1727, 1739-1747, 1756, 1767, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 1864-1865, 1869-1870, 1873-1879, 1886-1896, 1900-1907, 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/home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/art3d.py 488 390 20% 28-31, 36-39, 62-73, 97-98, 102, 116-119, 129-130, 144-146, 150-159, 164, 179-180, 208-209, 224-229, 248-254, 265, 269-273, 289-290, 296-300, 306-314, 320-329, 337-344, 354-355, 361-362, 368-377, 382-384, 404-405, 421-422, 426, 429-434, 455-456, 472-473, 476-481, 486-489, 494-496, 501-506, 529-531, 534, 546-547, 551-552, 570-580, 583-592, 595-602, 605, 611-613, 636-640, 643-645, 649-650, 668-695, 698-700, 703-705, 708, 720-721, 724-754, 758-768, 771-778, 781, 787-789, 809-816, 871-896, 914-916, 920-928, 944-947, 953-955, 960-966, 970-971, 977-1040, 1044-1045, 1049-1050, 1054-1065, 1070-1073, 1078-1081, 1097-1101, 1111-1118, 1127-1132, 1141-1146, 1173-1189, 1198-1227 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py 1305 1135 13% 122-180, 183-184, 187-188, 195, 200-207, 211-213, 217, 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534-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/proj3d.py 101 81 20% 20-29, 39-48, 58-68, 95-107, 125-130, 148-150, 154-162, 167-173, 177-181, 185-192, 199-206, 210, 217-218, 230-231, 235, 239-240, 244-249 /home/admin/.local/lib/python3.8/site-packages/numpy/__config__.py 30 16 47% 12-16, 27-28, 69-78 /home/admin/.local/lib/python3.8/site-packages/numpy/__init__.py 142 52 63% 124, 128-132, 279-313, 317-325, 351-358, 368-374, 378-391, 410-417 /home/admin/.local/lib/python3.8/site-packages/numpy/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/_globals.py 19 2 89% 26, 85 /home/admin/.local/lib/python3.8/site-packages/numpy/_pytesttester.py 51 43 16% 38-44, 128-201 /home/admin/.local/lib/python3.8/site-packages/numpy/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/_inspect.py 67 17 75% 75, 86, 107, 122-123, 126-129, 136, 182-191 /home/admin/.local/lib/python3.8/site-packages/numpy/compat/py3k.py 59 25 58% 39-41, 44-46, 49-51, 54, 57, 60, 65, 68-71, 74-77, 85, 103, 106, 109, 134-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/__init__.py 85 16 81% 23-48, 62-68, 125-126, 135, 142, 148-151 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs.py 261 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs_scalars.py 48 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_asarray.py 34 26 24% 19, 94-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype.py 157 137 13% 27-28, 35-42, 46-49, 60, 65, 95-100, 104-156, 163-175, 180-186, 191-230, 245-253, 257-279, 286-296, 300-301, 308-318, 324-342 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype_ctypes.py 54 36 33% 33, 37-68, 84-93, 106, 108, 110, 112, 116 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_exceptions.py 98 57 42% 11-14, 35, 42-44, 47, 58-59, 62, 75-78, 85-86, 90-91, 103-104, 108-109, 128-138, 145-146, 150-153, 160-190, 193-194 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_internal.py 430 327 24% 16-17, 24, 27-51, 57-76, 89-133, 141, 158-203, 207, 209, 211, 213, 215, 220, 222-223, 227, 230-233, 242, 246, 251-265, 282-284, 291-293, 300-302, 320, 332, 343, 352, 361-363, 370-372, 379-381, 388-392, 400-416, 431-434, 457-466, 490-495, 561-562, 565-567, 570-573, 576-585, 589, 592, 596-598, 601-742, 746-757, 761-779, 782-785, 789-791, 794, 798-803, 810-811, 830, 834, 852, 871, 877-878, 907, 909 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py 155 118 24% 40, 44, 48, 52, 58, 62-64, 68-86, 93-99, 102-104, 108-123, 126-159, 163-193, 197-258, 262-272, 275, 282-287, 290 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_string_helpers.py 15 5 67% 68-69, 97-100 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_type_aliases.py 122 13 89% 47-53, 108, 224-230 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_ufunc_config.py 87 23 74% 192-203, 217, 302-310, 356, 432, 437 /home/admin/.local/lib/python3.8/site-packages/numpy/core/arrayprint.py 550 435 21% 29-30, 66-98, 252-265, 295, 325-330, 340-350, 355-359, 362, 365, 372-414, 420-452, 472, 490-511, 520, 673-698, 702-712, 719-740, 751-858, 861-865, 872-894, 898-978, 981-1001, 1081-1087, 1169-1178, 1186-1191, 1194, 1201, 1204, 1212-1224, 1230-1237, 1242-1253, 1257, 1260-1263, 1270-1284, 1287-1289, 1292, 1300, 1305, 1308-1310, 1322, 1330-1336, 1339-1346, 1355, 1360, 1362, 1390-1398, 1411-1423, 1430-1470, 1475, 1523, 1530, 1540, 1551, 1556, 1595, 1658-1664 /home/admin/.local/lib/python3.8/site-packages/numpy/core/defchararray.py 438 243 45% 57-61, 68, 79-84, 92-94, 98, 124, 150, 177, 203, 229, 255, 259, 283, 307-311, 315, 339-344, 349, 376, 415-416, 420, 451-456, 461, 507, 511, 556, 592, 597, 640, 645, 680, 716, 745, 772, 798, 824, 851, 878, 904, 931, 935, 960, 966, 996-1001, 1036-1037, 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334-337 /home/admin/.local/lib/python3.8/site-packages/numpy/core/multiarray.py 104 23 78% 145, 244-245, 338, 492-495, 610, 661, 822, 880, 957, 1018, 1068, 1148, 1206, 1290, 1365, 1406, 1460, 1554, 1622, 1690 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numeric.py 495 346 30% 73, 138-142, 146, 202, 215, 280-282, 286, 337-345, 354, 416-418, 422, 485-496, 564, 568, 614-618, 622, 663, 667, 741, 745, 837-844, 848, 934-936, 940, 1072-1133, 1137, 1211-1238, 1242, 1317-1332, 1379-1391, 1395, 1447-1466, 1471, 1475, 1592-1673, 1764-1779, 1783, 1842-1846, 1855, 2011-2051, 2093-2108, 2116-2123, 2127, 2164, 2176, 2249-2250, 2254, 2337-2378, 2382, 2439-2453, 2457, 2496-2505 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numerictypes.py 150 71 53% 173-181, 219-227, 270-281, 355, 420, 436, 500-506, 566-572, 576-588, 651-672 /home/admin/.local/lib/python3.8/site-packages/numpy/core/overrides.py 58 9 84% 102, 107, 175-181 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/home/admin/.local/lib/python3.8/site-packages/numpy/fft/_pocketfft.py 164 120 27% 50-75, 79-88, 93-102, 111-114, 119, 211-216, 312-317, 405-410, 509-514, 607-612, 674-679, 683-698, 702-708, 712, 815, 918, 1014, 1107, 1200-1205, 1257, 1362-1367, 1424 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/helper.py 46 33 28% 16, 64-73, 111-120, 160-169, 216-221 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/__init__.py 39 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 139 21% 59-66, 104-128, 146-147, 150-151, 192-193, 248-254, 258-261, 267-268, 274-278, 288-291, 295-300, 306-315, 325-342, 357-373, 399-415, 421-429, 463-485, 521-533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 300 15% 30-35, 42-46, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 166 16% 34, 82-122, 127-130, 135, 270-317, 325-359, 364, 430-463, 467, 500-510, 514, 584-631, 635, 732-733, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 234 12% 192-194, 212-216, 230-235, 238-245, 270-281, 304-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 552-567, 576-625, 663-696, 731-789, 842-890, 902-918 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 979 15% 56, 114-143, 147, 231-241, 277, 377-419, 486-490, 494-497, 588-618, 622-623, 665-719, 723, 804, 810-811, 989-1157, 1161, 1249-1294, 1298, 1406-1439, 1443, 1485-1496, 1500, 1569-1596, 1600, 1627-1637, 1641, 1679-1694, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 2120, 2122, 2131, 2140-2163, 2168-2232, 2236-2253, 2257-2316, 2321, 2448-2543, 2548, 2679-2701, 2796-2801, 2905-2910, 3009-3014, 3109-3114, 3182-3190, 3194, 3198, 3202, 3256-3262, 3388-3392, 3396, 3475-3477, 3481, 3508-3510, 3539-3565, 3570, 3655-3660, 3666-3716, 3721, 3863-3867, 3873, 3976-3979, 3986-3992, 3997-4004, 4009-4015, 4020-4122, 4126, 4215-4240, 4244, 4353-4375, 4379, 4447-4560, 4564, 4656-4755, 4759, 4811-4817, 4821, 4915-4932 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/histograms.py 287 254 11% 29, 49-50, 72-73, 96-97, 118-119, 146-161, 182-196, 224-226, 263-270, 285-301, 309-331, 342-357, 382-451, 460, 467, 668-670, 675, 791-929, 934-940, 1014-1129 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/index_tricks.py 258 185 28% 32, 93-107, 149-207, 325-420, 423, 593, 607, 610, 657-661, 665, 678-681, 695-696, 758-761, 775, 891-909, 977-978, 982, 1006-1013 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/mixins.py 59 12 80% 10-13, 19-21, 29-31, 39, 54 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py 279 219 22% 61-66, 96-110, 135-139, 164-180, 209-221, 225, 313-336, 340, 428-451, 455, 494-500, 504, 544-550, 554, 647-648, 652, 717-718, 722, 787-788, 792, 854-855, 859, 937-957, 965-974, 984-1000, 1010-1020, 1025, 1113-1124, 1129, 1245-1249, 1255, 1358-1362, 1371-1381, 1391-1405, 1413-1418, 1424, 1518-1567, 1572, 1670-1676 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/npyio.py 854 780 9% 34-37, 83, 86-89, 97, 108-112, 189-202, 205, 208, 215-221, 224, 228, 231, 242-260, 269-273, 277-281, 396-450, 455, 519-529, 534-535, 618, 622-623, 689, 695-726, 732-758, 768, 903-1188, 1199, 1326-1447, 1510-1541, 1557, 1751-2284, 2311-2317, 2339-2345, 2369-2377, 2403-2415 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/polynomial.py 438 345 21% 41, 138-165, 169, 229-261, 265, 342-366, 370, 432-446, 450, 620-689, 693, 764-772, 776, 830-844, 884-898, 956-961, 965, 1020-1038, 1042-1065, 1180, 1185-1186, 1191, 1197, 1202, 1208, 1211, 1219-1243, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 196 25% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 660, 716-723, 727-732, 736, 766-792, 796, 866-874, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1168, 1238-1260 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 70 23% 21-22, 26-35, 97-114, 119, 301-335, 340-359, 363, 411, 420-428, 470-471, 475, 536-544 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 114 36% 34-40, 44, 95-98, 151-154, 158, 211, 216, 220, 231, 289-303, 346-363, 367, 412-424, 433, 469-472, 498-501, 505, 582-597, 602-615, 741-752, 821-823, 904-906, 911, 938-940, 1023-1025, 1057-1059 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/type_check.py 138 90 35% 70-77, 81, 112-114, 118, 157-160, 164, 200-203, 207, 240-244, 300, 336-341, 390, 395-397, 401, 498-521, 526, 574-583, 588-590, 618, 696, 713, 753-769 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/ufunclike.py 57 30 47% 24-36, 50-53, 65, 70, 117-124, 188-196, 260-268 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/utils.py 454 415 9% 38-46, 50-51, 67-69, 76-127, 134-141, 188-194, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1003-1004, 1028-1043, 1056-1070 /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py 678 574 15% 88, 91, 94, 97, 100, 103-105, 108-110, 113, 126, 129, 133, 137-157, 165-174, 177-185, 188-190, 194-196, 200-203, 206-208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 473, 538-546, 550, 618-666, 756-764, 770, 890-982, 1059-1077, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1479, 1617-1674, 1678, 1761-1797, 1801, 1898-1906, 1912, 1995-2011, 2092-2101, 2153-2160, 2166, 2266-2328, 2354-2356, 2360, 2514-2611, 2617-2618, 2707-2739, 2750-2760, 2780-2801, 2806-2812 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ma/core.py 2405 1774 26% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 439-469, 532-534, 543-547, 575-579, 627-636, 646-663, 708-714, 768-776, 779, 804, 812-813, 831-832, 848-853, 868-869, 884-885, 895, 928-969, 1011-1050, 1057-1080, 1087-1105, 1112-1116, 1155-1192, 1284-1291, 1295-1297, 1305, 1466-1469, 1534-1537, 1544-1547, 1621-1636, 1682-1686, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2361-2373, 2400, 2407, 2414, 2421, 2424, 2439-2447, 2489-2495, 2525-2550, 2581-2590, 2647-2653, 2656, 2659-2670, 2674-2676, 2696-2703, 2834, 2840, 2845-2847, 2855-2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2943, 2956, 3012, 3030-3042, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3224-3337, 3347-3402, 3411-3419, 3427-3431, 3438-3500, 3512, 3516, 3532-3535, 3539, 3554-3555, 3570-3571, 3576, 3591-3594, 3599, 3629-3630, 3635, 3652, 3660, 3664-3665, 3697-3706, 3710-3725, 3776-3809, 3833-3836, 3898-3908, 3915-3939, 3942, 3949-4026, 4031-4040, 4053-4104, 4117, 4130, 4137-4139, 4148, 4155-4157, 4164, 4168-4170, 4179, 4186-4188, 4195-4197, 4204, 4211-4213, 4220, 4227-4229, 4236, 4243-4253, 4260-4269, 4276-4285, 4292-4303, 4310-4321, 4328-4339, 4346-4360, 4367-4373, 4380-4385, 4407-4409, 4434-4436, 4497-4538, 4585-4591, 4652-4658, 4673-4676, 4739-4761, 4785-4787, 4815, 4841-4855, 4871-4885, 4983, 4990-4997, 5037, 5078-5099, 5133-5140, 5160-5181, 5206-5213, 5241-5260, 5293-5300, 5317-5363, 5380-5388, 5401-5413, 5479-5493, 5535-5538, 5572-5575, 5648-5658, 5692-5724, 5788-5792, 5826-5859, 5936-5947, 5950-5953, 5956-5959, 5964-5985, 6024-6046, 6057-6061, 6101, 6116, 6163-6175, 6184-6186, 6200-6203, 6209, 6214-6221, 6229-6231, 6241-6257, 6262, 6269-6284, 6287-6291, 6294-6302, 6308-6316, 6319, 6343, 6356-6366, 6422, 6462-6469, 6472, 6475, 6478, 6481-6485, 6491-6500, 6505, 6510, 6524, 6527, 6530, 6536-6542, 6559, 6611-6616, 6640-6647, 6651-6679, 6683-6695, 6698-6705, 6710-6717, 6723-6729, 6766-6777, 6812-6813, 6833-6865, 6872-6882, 6898-6908, 6923, 6967-6983, 6998-7001, 7016-7022, 7037-7043, 7059-7062, 7083-7101, 7136-7139, 7154-7158, 7216-7222, 7230, 7237, 7243, 7308-7340, 7388-7415, 7442-7448, 7527-7540, 7605-7625, 7636-7642, 7650-7660, 7670-7682, 7710, 7738, 7783-7796, 7871-7904, 7951-7952, 8000-8002, 8011, 8017, 8082, 8116-8127, 8186 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/extras.py 560 468 16% 48, 101-102, 152-154, 207-209, 259, 262, 272-280, 290-293, 306-318, 333-341, 364-369, 376-451, 459-477, 587-631, 700-714, 719-799, 823-842, 894-896, 911-914, 928-931, 975-981, 1024-1030, 1049-1063, 1078-1087, 1113-1119, 1133-1146, 1168-1188, 1209-1210, 1225, 1248-1253, 1267-1301, 1358-1374, 1425-1461, 1486-1487, 1491-1494, 1569-1576, 1621-1626, 1674-1684, 1744-1760, 1769-1789, 1825-1828, 1864-1867, 1880-1884, 1894-1921 /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py 238 178 25% 15-33, 69, 116-165, 168-187, 190-213, 216-221, 224, 227-228, 231, 234-235, 238, 244-251, 257-260, 284, 319, 372, 411, 445, 479, 513, 546, 569, 609, 644, 683, 718, 757, 790, 830-835, 865, 894, 933, 966, 998-1001, 1011-1032, 1089-1111 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/__init__.py 18 7 61% 171-180 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/_polybase.py 419 296 29% 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 193-198, 216, 234, 252, 280-288, 291-304, 307-311, 314-324, 327-329, 337-367, 375-380, 389-394, 398-403, 409, 413-462, 469-473, 476, 481-483, 486, 489, 494, 497, 500-505, 508-513, 516-521, 527-532, 535-538, 541-544, 547-556, 559-561, 564-568, 571-575, 578-582, 586, 591, 594-597, 600-603, 606-614, 617-622, 625, 640, 653, 678, 700-701, 723-730, 761-767, 796, 823-829, 849-851, 865-866, 894-898, 971-986, 1014-1027, 1054-1060, 1091-1099, 1137-1141 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/chebyshev.py 357 294 18% 152-155, 177-180, 207, 243-274, 302-306, 333-340, 389-394, 441-455, 508-511, 566, 608, 652, 686-698, 742-747, 797-814, 855-872, 935-964, 1052-1091, 1153-1175, 1224, 1277, 1328, 1384, 1422-1437, 1490, 1544, 1670, 1700-1715, 1766-1776, 1827-1843, 1881-1888, 1915-1916, 1946-1953, 1979-1986, 2065-2069 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite.py 267 214 20% 134-139, 180-197, 251-254, 310, 350, 390, 432-443, 485-509, 557, 594, 652-677, 763-799, 871-895, 944, 997, 1048, 1104, 1151-1165, 1218, 1272, 1403, 1433-1448, 1502-1512, 1544-1555, 1594-1622, 1649-1650 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite_e.py 264 211 20% 135-140, 181-197, 250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/version.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/_structures.py 36 16 56% 8, 11, 14, 17, 20, 23, 26, 29, 37, 40, 43, 46, 49, 52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/packaging/version.py 163 67 59% 69, 76, 81-84, 87-90, 93-96, 99-102, 105-108, 198, 228, 236-261, 272-273, 289-290, 305-306, 317, 328, 339-342, 355, 371-380, 397, 408, 419, 428, 439, 450, 461, 470, 472, 474, 476, 482-484, 497, 527, 533, 547, 560 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/home/admin/.local/lib/python3.8/site-packages/pygit2/credentials.py 56 18 68% 52, 56, 60, 63, 74-75, 79, 83, 86, 113-116, 120, 124, 127, 132, 138 /home/admin/.local/lib/python3.8/site-packages/pygit2/errors.py 26 20 23% 34-65, 70 /home/admin/.local/lib/python3.8/site-packages/pygit2/ffi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/pygit2/index.py 226 171 24% 44-50, 54-59, 63, 66, 69, 72-77, 80-94, 97, 110-111, 115-116, 119-120, 129-144, 157-167, 172-173, 178-180, 188-190, 202-212, 232-249, 272-295, 322-331, 338-343, 348, 353, 356, 359-360, 365-369, 377-384, 388-396, 402, 405-417, 420-421, 424, 430-434, 437, 440, 443-457, 460 /home/admin/.local/lib/python3.8/site-packages/pygit2/packbuilder.py 40 23 42% 37-43, 47, 50, 53, 57-59, 62-64, 67-69, 72, 75-77, 81 /home/admin/.local/lib/python3.8/site-packages/pygit2/refspec.py 37 17 54% 37-38, 43, 48, 53, 58, 63, 69, 74, 77-84, 90, 96 /home/admin/.local/lib/python3.8/site-packages/pygit2/remote.py 155 112 28% 41-60, 69-71, 74, 80, 86, 92, 97-101, 106-107, 122-130, 138-164, 169-171, 177, 181-182, 188-192, 198-202, 220-224, 241-249, 252-265, 268-275, 284-296, 306-319, 326-327, 332-333, 338-339, 345-346, 352-353 /home/admin/.local/lib/python3.8/site-packages/pygit2/repository.py 552 422 24% 77, 85, 105-117, 121, 139-163, 169-174, 184-198, 204-205, 208-211, 214, 217, 223-224, 237-241, 250-254, 281-291, 308-316, 324-346, 353-354, 361-362, 369-373, 410-429, 444-453, 459-479, 543-568, 575, 615-636, 644-648, 690-698, 704-729, 743-762, 818-838, 894-919, 983-1039, 1074-1091, 1095-1104, 1128-1129, 1141, 1148-1149, 1187-1224, 1249-1262, 1288-1303, 1310-1316, 1326-1327, 1347-1363, 1372-1375, 1383-1393, 1396-1399, 1402-1404, 1407, 1410, 1413-1416, 1423-1424, 1427, 1439-1442, 1445-1446, 1449, 1452, 1455, 1459, 1462, 1491, 1493, 1497, 1501-1506 /home/admin/.local/lib/python3.8/site-packages/pygit2/settings.py 74 25 66% 40, 43, 65-71, 81, 86, 90, 98, 102, 110, 120, 128, 138, 148, 153, 158, 163, 168, 173, 178 /home/admin/.local/lib/python3.8/site-packages/pygit2/submodule.py 37 19 49% 35-40, 43, 47-51, 56-57, 62-63, 68-69, 74-75, 80-81 /home/admin/.local/lib/python3.8/site-packages/pygit2/utils.py 60 46 23% 33-36, 40-49, 53-62, 66-70, 85-102, 105, 108, 119-121, 124, 127-132 /home/admin/.local/lib/python3.8/site-packages/python_http_client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/python_http_client/client.py 106 76 28% 11-15, 29-31, 38, 45, 52, 59-62, 92-99, 109, 118-136, 145, 154-155, 174-184, 196, 207-290, 293, 296 /home/admin/.local/lib/python3.8/site-packages/python_http_client/exceptions.py 46 16 65% 8-17, 20, 30, 93-97 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 117-119, 144-148, 151, 156, 183-194, 198-204, 251-259, 320-397, 422-428, 461-467, 499-505, 508-517, 524, 542-580 /home/admin/.local/lib/python3.8/site-packages/requests/__init__.py 68 27 60% 49-50, 54-55, 64, 80-86, 91-100, 108-109, 123-124, 127-136 /home/admin/.local/lib/python3.8/site-packages/requests/__version__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/_internal_utils.py 21 10 52% 30-35, 45-50 /home/admin/.local/lib/python3.8/site-packages/requests/adapters.py 194 147 24% 61, 74, 93, 97, 142-155, 158, 163-169, 188-192, 211-235, 249-290, 304-329, 340-358, 366-368, 384-397, 411, 426-432, 453-538 /home/admin/.local/lib/python3.8/site-packages/requests/api.py 19 10 47% 58-59, 73, 85, 99-100, 115, 130, 145, 157 /home/admin/.local/lib/python3.8/site-packages/requests/auth.py 173 141 18% 35-66, 73, 80-81, 84, 92, 95-96, 103-104, 111-114, 118-124, 131-234, 238-239, 250-284, 288-304, 307, 315 /home/admin/.local/lib/python3.8/site-packages/requests/certs.py 4 1 75% 17 /home/admin/.local/lib/python3.8/site-packages/requests/compat.py 30 5 83% 12-13, 36-37, 42 /home/admin/.local/lib/python3.8/site-packages/requests/cookies.py 239 176 26% 19-20, 36-38, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 88, 92, 96, 100, 115, 118, 121, 131-137, 146-148, 156-167, 201-204, 212-223, 231-232, 240, 248-249, 257, 265-266, 275, 279-283, 287-291, 299-304, 313-319, 322-325, 334, 341, 347, 350-356, 360-364, 378-384, 398-413, 417-420, 424-426, 430-433, 437, 441-452, 461-489, 495-504, 530-539, 549-561 /home/admin/.local/lib/python3.8/site-packages/requests/exceptions.py 37 8 78% 19-24, 41-42 /home/admin/.local/lib/python3.8/site-packages/requests/hooks.py 14 11 21% 16, 24-33 /home/admin/.local/lib/python3.8/site-packages/requests/models.py 455 368 19% 89-104, 115-134, 146-203, 210-216, 223-227, 273-291, 294, 298-311, 337-350, 367-378, 381, 384-392, 396-398, 402-408, 417-482, 487-493, 502-571, 575-587, 593-609, 622-629, 636-638, 660-704, 707, 710, 715-718, 721-726, 729, 739, 749, 753, 764-768, 775, 780, 788, 793, 812-851, 863-885, 891-904, 920-942, 952-975, 981-992, 997-1021, 1029-1034 /home/admin/.local/lib/python3.8/site-packages/requests/packages.py 17 4 76% 5-10 /home/admin/.local/lib/python3.8/site-packages/requests/sessions.py 268 219 18% 56, 67-88, 97-103, 115-125, 129-157, 173-281, 288-301, 315-332, 338-354, 396-451, 454, 457, 469-500, 563-591, 601-602, 612-613, 623-624, 637, 649, 661, 671, 680-749, 758-780, 788-794, 798-799, 806-810, 813-814, 817-818, 833 /home/admin/.local/lib/python3.8/site-packages/requests/status_codes.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/structures.py 39 19 51% 41-44, 49, 52, 55, 58, 61, 65, 68-73, 77, 80, 91, 96, 99 /home/admin/.local/lib/python3.8/site-packages/requests/utils.py 485 411 15% 76-121, 127-130, 134-196, 202-253, 258-260, 268-297, 303-310, 331-337, 357-366, 393-398, 424-433, 445-459, 469-474, 485, 493-506, 521-535, 545-560, 566-577, 582-587, 602-626, 641-656, 667-678, 689-693, 703-704, 711-715, 724-739, 750-761, 772-821, 830-833, 842-859, 873-886, 902, 920-946, 962-984, 993-1013, 1022-1029, 1038-1040, 1044-1056, 1068-1076, 1083-1094 /home/admin/.local/lib/python3.8/site-packages/sendgrid/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/base_interface.py 22 15 32% 23-30, 38-46, 49, 59-62 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/endpoints/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/__init__.py 14 6 57% 19, 30, 46-50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/eventwebhook_header.py 5 1 80% 10 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/__init__.py 63 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/amp_html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/asm.py 33 20 39% 16-23, 31, 40-43, 52, 62-65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/attachment.py 75 47 37% 43-62, 70, 79-82, 90, 99-102, 110, 119-122, 137, 162-165, 176, 191-194, 203-218 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/batch_id.py 15 7 53% 14-17, 25, 34, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings.py 27 16 41% 16-23, 31, 40, 48, 57, 66-72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings_email.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_bounce_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_list_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_spam_management.py 16 9 44% 15-18, 26, 35, 44-47 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_unsubscribe_management.py 16 9 44% 17-20, 28, 37, 46-49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/category.py 13 6 54% 10-13, 21, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/cc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/click_tracking.py 27 16 41% 12-19, 27, 36, 45, 56, 65-71 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content.py 30 18 40% 19-27, 36, 48, 56, 65-66, 75-81 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content_id.py 13 6 54% 13-16, 27, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/custom_arg.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/disposition.py 13 6 54% 21-24, 39, 63, 72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/dynamic_template_data.py 24 12 50% 16-22, 30, 39, 47, 57, 64, 73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/email.py 79 43 46% 41-60, 68, 77-80, 92, 108, 120, 137, 145, 154, 162, 171, 179, 189, 198-213, 222-228 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/exceptions.py 22 10 55% 24-31, 39, 48, 56, 65 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_content.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_name.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_type.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_html.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_settings.py 38 23 39% 14-25, 33, 42, 50, 59, 67, 76, 85-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_text.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/from_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ganalytics.py 63 35 44% 26-38, 48-49, 57, 66, 75, 86, 94, 103, 111, 120, 128, 137, 145, 154, 163-176 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/group_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/groups_to_display.py 15 8 47% 13-16, 25, 37-39, 48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/header.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ip_pool_name.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail.py 470 334 29% 50-80, 88, 100-102, 112-114, 122-124, 133, 150-190, 198, 208, 213, 229-241, 258-276, 280, 296-308, 324-330, 335, 356-368, 388-394, 406, 415-433, 441, 445, 454-458, 466-490, 494, 503-507, 515-535, 543, 547, 557-561, 569-592, 601, 612-629, 633, 642-653, 662, 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/home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/plain_text_content.py 25 14 44% 15-19, 27, 35, 44-45, 54-60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/reply_to.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/sandbox_mode.py 16 9 44% 12-15, 23, 32, 41-44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/section.py 25 14 44% 12-18, 26, 35, 43, 52, 61-64 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/send_at.py 24 12 50% 22-28, 36, 45, 53, 63, 70, 79 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_check.py 44 27 39% 18-27, 35, 44, 54, 68-71, 80, 91-94, 103-112 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_threshold.py 13 6 54% 15-18, 29, 44, 53 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_url.py 13 6 54% 12-15, 24, 35, 44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subject.py 23 11 52% 13-18, 26, 35, 43, 53, 60, 69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_html.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_substitution_tag.py 13 6 54% 18-21, 32, 48, 58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_text.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_tracking.py 49 30 39% 21-33, 41, 50, 59, 71, 80, 92, 103, 120, 129-142 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/substitution.py 34 19 44% 17-26, 34, 43, 51, 60, 68, 78, 87-90 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/template_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/to_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/tracking_settings.py 49 30 39% 30-45, 53, 63, 71, 81, 89, 98, 106, 115, 124-134 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_campaign.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_content.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_medium.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_source.py 13 6 54% 11-14, 23, 34, 43 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_term.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/validators.py 27 21 22% 18-28, 42-55, 66-69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/stats.py 166 108 35% 12-22, 29, 38-53, 61, 70, 78, 87, 95, 104, 112, 121, 129, 138, 146, 155, 163, 172, 187-194, 202-220, 228, 236-238, 253-260, 268-286, 294, 302-304, 317-319, 327, 336, 344, 357-359, 367, 376, 384 /home/admin/.local/lib/python3.8/site-packages/sendgrid/sendgrid.py 7 3 57% 55-58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/twilio_email.py 9 4 56% 63-73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelConfig.py 63 45 29% 15-18, 23-26, 30, 35-48, 54-68, 73-77, 81-85, 88-95, 98, 101 /home/admin/workarea/git/Velours/python/mtr/database_queries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/admin_queries.py 457 392 14% 32-39, 44-50, 56-66, 71-87, 92, 96-99, 102-116, 120-135, 138-143, 146-148, 156-165, 168-177, 180-187, 192-206, 211-227, 232-250, 254-271, 275-291, 294-295, 298-299, 302-308, 317-323, 326-331, 334-337, 340-349, 353-357, 360-367, 370-376, 379-389, 392-399, 402-404, 407-414, 417-430, 433-444, 447-469, 473-485, 488-495, 498-504, 507-510, 514-518, 522-540, 543-548, 551-556, 559-564, 568-577, 580-588, 591-600, 603-612, 615-621, 625-648 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_admin_tools.py 87 53 39% 27-28, 30-34, 45, 61, 64, 76-92, 97-105, 110-137, 142, 147-163, 166-171 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_queries.py 291 256 12% 22-42, 45-49, 52-71, 74-82, 85-91, 94-98, 101-106, 109-117, 124-134, 139-148, 152-159, 162-172, 176-197, 200-220, 223-233, 236-248, 253-261, 267-283, 301-363, 368-397, 402-429, 436-450, 455-485, 489-511, 514-528 /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/objet_thcl.py 146 114 22% 32-50, 56-65, 70-77, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129-132, 138-139, 143-147, 152-171, 177-196, 200-202, 205-212, 226-232, 237-269 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_queries.py 1475 1125 24% 44, 57, 69, 87, 95-97, 100-104, 117-135, 149-153, 166-170, 186-295, 303-308, 311-316, 319-326, 329-348, 351-359, 362-369, 378-402, 406-409, 420-461, 472-492, 503-523, 526-531, 534-541, 551-572, 576-597, 608, 620, 626-627, 630, 646, 651, 658-659, 662-663, 682, 689, 696, 712, 719, 722, 726, 743-783, 801, 808-811, 831, 851-923, 934-1044, 1083, 1090, 1123-1124, 1127-1129, 1135-1138, 1141-1146, 1150-1153, 1158, 1164, 1179, 1191-1195, 1204-1208, 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404-407, 412-435, 444-471, 474-477, 480-495, 499-556 /home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py 148 98 34% 12-13, 33-34, 36-37, 40-46, 49, 54-61, 70-80, 83-95, 103-114, 117-133, 137-140, 147-160, 163-167, 182 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 64 17% 22-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 158 111 30% 34, 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-155, 158-165, 168-183, 186-193, 196-207, 211-218, 221-226, 229-235 /home/admin/workarea/git/Velours/python/mtr/database_queries/mission_queries.py 520 478 8% 26-38, 42-250, 255-272, 275-314, 317-414, 418-430, 433-445, 448-460, 463-475, 479-491, 495-507, 510-522, 525-548, 551-552, 555-567, 570-582, 586-622, 625-644, 647-662, 665-671, 674-681, 697-741, 747-756, 773-799, 803-810, 815-822, 828-838, 841-843, 848-855, 859-873 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_insert_queries.py 105 84 20% 30-71, 74-81, 84-91, 94-103, 106-113, 118-138, 141-145, 149-163, 173-192, 203-218 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_retrieval_queries.py 558 474 15% 12, 50-75, 81-101, 107-123, 129-142, 148-161, 180-181, 188, 199-200, 212, 217, 221, 224-226, 229-231, 234-237, 248, 254, 261-266, 269, 271, 274, 277-278, 288-326, 332-348, 351-425, 428-475, 481-492, 495-544, 547-548, 555-605, 608-631, 634-668, 674-687, 694-721, 724-742, 749-802, 805-826, 832-849, 852-864, 868-922, 927-972, 975-986, 989-1010 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 453 11% 31-50, 56-72, 75-94, 97-114, 118-124, 127-136, 140-158, 163-180, 184-199, 202-212, 215-222, 225-235, 240-255, 258-263, 266-271, 274-284, 287-299, 302-311, 315-327, 332-341, 344-354, 357-365, 369-375, 378-383, 386-390, 393-397, 400-410, 415-468, 473-497, 513-519, 522-526, 535-541, 548-571, 576-584, 587-594, 598-608, 611-624, 627-631, 634-638, 642-662, 666-712, 717-748, 750-759, 764-801 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1755 1189 32% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 172-174, 188-189, 199, 206-209, 212-213, 227-229, 241-243, 246-247, 277, 302-306, 311, 328-332, 335, 344-406, 434, 462-463, 481-495, 509-516, 522-573, 580-624, 652-653, 660-663, 674-677, 691, 696, 700-702, 721, 729-730, 738-739, 773-776, 788-797, 807-810, 818, 821, 823-825, 829-853, 863-870, 874, 877, 886-942, 968-1161, 1165, 1171-1174, 1178-1180, 1185-1186, 1191-1192, 1196-1198, 1201-1281, 1326-1334, 1348, 1351-1353, 1365-1376, 1379-1397, 1401-1450, 1454-1500, 1505-1545, 1550-1556, 1561, 1564, 1567, 1570-1571, 1574-1577, 1580, 1583, 1586-1588, 1596, 1603-1609, 1612-1617, 1621, 1634-1653, 1656-1659, 1662-1672, 1675-1678, 1682-1735, 1739-1742, 1747-1785, 1790-1791, 1794-1806, 1809-1813, 1819-1822, 1827-1871, 1878-1894, 1903-1919, 1924-1941, 1945-1957, 1966-1969, 1975-1985, 1993-1996, 2002-2006, 2009-2012, 2015-2018, 2021-2024, 2027-2034, 2037-2041, 2045-2068, 2086-2110, 2113-2122, 2125-2151, 2154-2203, 2206-2241, 2258-2264, 2267-2277, 2280-2282, 2299, 2313, 2316-2328, 2331-2332, 2343-2345, 2355, 2365-2366, 2371, 2375-2381, 2391, 2422, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2440, 2442, 2444, 2446, 2448, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2555, 2559, 2562, 2564, 2566, 2568, 2570, 2572, 2574, 2577, 2580, 2582, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2600, 2603, 2605, 2607, 2609, 2611, 2616-2670, 2685, 2691, 2704-2706, 2721-2723, 2729, 2734, 2739, 2745-2747, 2749, 2754, 2761-2778, 2785-2792, 2802-2811, 2814, 2822-2836, 2840-2846, 2858-2876 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 208 56% 16-23, 35-37, 46, 51-62, 73-84, 101-122, 135-149, 178, 194, 200, 212, 215, 222-246, 252-290, 302-321, 335, 358-382, 385, 389, 392, 396, 399-402, 412, 417-418, 439-440, 456, 469-470, 495, 499-500, 506, 512-513, 522-523, 570, 577-580, 589, 616, 635-652, 660, 665, 675-679, 684-685, 687-691, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 204 197 3% 7-121, 125-162, 167-218, 221-294, 297-339 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 1218 5% 10-15, 18-183, 191-369, 374-436, 445-486, 489-553, 559-633, 638-744, 747-761, 764-779, 783-809, 813-864, 873-902, 907-936, 943-1043, 1047-1078, 1086-1087, 1095, 1098, 1102, 1118-1213, 1223-1253, 1257-1279, 1295-1332, 1336-1357, 1362-1387, 1393-1457, 1472-1500, 1503-1519, 1523-1534, 1538-1619, 1623-1638, 1658-1727, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1840-1875 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 135 14% 11-32, 35-36, 40-56, 62-70, 73-84, 88-102, 105-113, 117-122, 126-136, 139-143, 167-175, 178-194, 197-201, 204-205, 214-218, 233-257, 287-301, 304-307, 311 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_deprecated.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_end_or_aggreg.py 484 480 1% 7-29, 34-274, 288-767, 908-918 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_initialisation.py 372 364 2% 15-244, 249-267, 271-372, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1089 956 12% 29-62, 66-159, 165-343, 348-777, 782-905, 912-962, 970-1044, 1049-1119, 1137-1307, 2571, 2574-2576, 2579-2580, 2583-2590, 2600, 2607-2619, 2622, 2626-2630, 2637-2644, 2659-2676, 2693-2697, 2705-2707, 2747-2773, 2792-2805, 2808-2821, 2827-2829, 2836-2846, 2854, 2870, 2878-2903 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1403 1369 2% 34-89, 92-196, 200-500, 506, 510-696, 703-838, 843-885, 889-968, 975-1356, 1362-1459, 1463-1503, 1508-1551, 1555-1630, 1634-1715, 1719-1733, 1739-1893, 1896-1899, 1906-1987, 1990-2178 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 1981 1948 2% 35-308, 313-424, 427-556, 560-628, 632-779, 783-1028, 1032-1077, 1081-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1859-2027, 2031-2078, 2088-2370, 2374-2419, 2423-2482, 2486-2512, 2516-2620, 2627-2815, 2996-3194, 3453-3535, 3537-3599 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 540 3% 19-195, 200-268, 273-332, 336-379, 383-488, 493-623, 628-790, 795-841 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 188 3% 12-115, 119-171, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 285 4% 6-55, 62-134, 143-155, 162-209, 213-219, 224-231, 236-315, 319-324, 327-333, 337-398, 402-411, 423-467 /home/admin/workarea/git/Velours/python/mtr/datou/merge_rubbia.py 50 46 8% 12-36, 40-86 /home/admin/workarea/git/Velours/python/mtr/lib/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/fotonower_connect.py 322 307 5% 23-46, 52-92, 96-119, 123-184, 187-213, 218-335, 338-384, 389-412, 415-433, 436-461 /home/admin/workarea/git/Velours/python/mtr/mem_info.py 76 30 61% 33-34, 41, 49, 59-63, 72, 95-124 /home/admin/workarea/git/Velours/python/mtr/monitor_sys.py 131 88 33% 40, 44, 47-50, 52, 54, 59, 61, 65-68, 91-134, 137-150, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 55 44 20% 16, 20-44, 47-85 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/image_utils.py 328 255 22% 21-28, 37-52, 72-85, 88, 91-113, 118-122, 129, 138-162, 166, 174, 181-191, 194-236, 239, 242-253, 256, 259, 262, 265-298, 301-314, 343, 346-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/simple_image_editor.py 2091 1975 6% 24-25, 43-51, 60-81, 86-126, 131-134, 140-324, 329-332, 335-359, 365-387, 391-422, 429-446, 451-469, 475-485, 492-598, 605-613, 619-793, 798-815, 821-853, 859-907, 910-911, 916-936, 942-972, 979-1100, 1109-1145, 1151-1183, 1189-1227, 1232-1251, 1259-1567, 1575-1639, 1643-1654, 1660-1683, 1690-1756, 1762-1828, 1832-1907, 1913-1990, 1993-2006, 2014-2389, 2395-2421, 2431-2465, 2479-2741, 2752-2799, 2804-2840, 2846-2881, 2894, 2900, 2906, 2912, 2918, 2924, 2929-2966, 2973-3014, 3020-3044, 3052-3129, 3140-3156, 3164-3189, 3200-3304, 3336-3465, 3508-3540, 3562, 3579-3590, 3594-3600, 3603-3682, 3685-3688, 3691-3723, 3726-3752, 3757-3819, 3825-3877 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 131 13% 15-29, 32-48, 51-54, 57-79, 92-99, 103-111, 118-127, 130, 133-142, 145-156, 160-174, 180-184, 187-193, 197-210, 213, 216-217, 223-239 /home/admin/workarea/git/Velours/python/mtr/utils/general_util.py 57 32 44% 11-12, 20-27, 30, 33-57, 61-63, 69-70, 75, 86-90 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 53 9% 21-95 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 8 27% 12-20 /home/admin/workarea/git/Velours/python/tests/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/tests/cod_main_test.py 75 67 11% 8-59, 69-124, 128, 131, 134, 138 /home/admin/workarea/git/Velours/python/tests/datou_test.py 1923 1826 5% 24-72, 83-130, 140-232, 236-277, 280-366, 378-416, 428-472, 482-518, 523-588, 594-681, 692-752, 762-837, 851-889, 900-938, 949-1000, 1011-1073, 1084-1176, 1269-1339, 1350-1811, 1822-1879, 1889-1949, 1959-2067, 2081-2133, 2144-2215, 2230-2332, 2343-2401, 2411-2461, 2472-2558, 2563-2681, 2693-2748, 2759-2809, 2819-2869, 2881-2935, 2960, 2966-2968, 2981-2998, 3010-3056, 3066-3121, 3131-3186, 3196-3217, 3227-3270, 3280-3328, 3338-3371, 3381-3436, 3446-3492, 3503-3542, 3553-3592, 3674-3676, 3681, 3700-3701, 3706 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 61 72% 41, 94-95, 99, 102, 105, 107, 112, 122, 124, 128, 130, 132, 134, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 172, 188, 195-202, 208, 222-225, 245, 248-254, 259, 271-272, 275-278, 288-289, 370, 377 /usr/lib/python3/dist-packages/babel/__init__.py 3 0 100% /usr/lib/python3/dist-packages/babel/_compat.py 51 26 49% 34-56, 63-67, 77-80 /usr/lib/python3/dist-packages/babel/core.py 329 213 35% 27, 70-77, 102-105, 157-168, 192-193, 216-219, 261-331, 334-337, 343, 346, 349-355, 358, 363-365, 378-393, 418-421, 432-435, 446-449, 468, 482, 494, 506, 515, 531, 542, 554, 566, 580, 592, 604, 615-618, 626, 632, 641, 650, 659, 673, 687, 704, 720, 731, 740, 749, 759, 773, 787, 801, 814, 836, 851, 867, 884, 896, 907, 918, 932, 962-977, 1026-1040, 1083-1115, 1131-1133 /usr/lib/python3/dist-packages/babel/localedata.py 112 86 23% 34-39, 49-55, 66, 96-123, 138-156, 167, 170, 181-189, 198-201, 204, 207, 210-221, 224, 227, 230 /usr/lib/python3/dist-packages/babel/plural.py 280 181 35% 43-73, 112-119, 122-123, 137-139, 149-150, 158, 161, 164-166, 184-189, 211-229, 242-252, 272, 292, 306-315, 334-349, 353, 358-359, 363, 367, 371, 375, 413-421, 425-432, 435-438, 441-444, 447-461, 464-471, 474-478, 481-484, 487-495, 498, 520-521, 538, 550-553, 567-583, 598-603, 620, 623-629 /usr/lib/python3/dist-packages/certifi/__init__.py 2 0 100% /usr/lib/python3/dist-packages/certifi/core.py 5 0 100% /usr/lib/python3/dist-packages/chardet/__init__.py 11 7 36% 31-39 /usr/lib/python3/dist-packages/chardet/big5freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/big5prober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/chardistribution.py 117 83 29% 49-59, 65-68, 72-82, 88-98, 103, 110, 115-118, 125-129, 134-137, 144-148, 153-156, 163-167, 172-175, 182-189, 194-197, 204-214, 219-222, 229-233 /usr/lib/python3/dist-packages/chardet/charsetgroupprober.py 72 61 15% 34-37, 40-47, 51-55, 59-63, 66-83, 86-106 /usr/lib/python3/dist-packages/chardet/charsetprober.py 55 36 35% 40-42, 45, 49, 52, 56, 59, 63-64, 81-101, 115-145 /usr/lib/python3/dist-packages/chardet/codingstatemachine.py 28 18 36% 56-61, 64, 69-78, 81, 84, 88 /usr/lib/python3/dist-packages/chardet/compat.py 10 4 60% 26-29 /usr/lib/python3/dist-packages/chardet/cp949prober.py 16 6 62% 36-41, 45, 49 /usr/lib/python3/dist-packages/chardet/enums.py 35 1 97% 62 /usr/lib/python3/dist-packages/chardet/escprober.py 58 45 22% 43-56, 59-67, 71, 75, 78-81, 84-101 /usr/lib/python3/dist-packages/chardet/escsm.py 17 0 100% /usr/lib/python3/dist-packages/chardet/eucjpprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/euckrfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euckrprober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/euctwfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euctwprober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/gb2312freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/gb2312prober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/hebrewprober.py 77 48 38% 155-162, 165-171, 175-176, 179, 193, 223-253, 259-280, 284, 289-292 /usr/lib/python3/dist-packages/chardet/jisfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/jpcntx.py 81 61 25% 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191-214 /usr/lib/python3/dist-packages/keyring/__init__.py 3 0 100% /usr/lib/python3/dist-packages/keyring/backend.py 84 20 76% 85-87, 90-91, 99, 108, 119, 133-140, 151, 157, 165, 168, 196-198 /usr/lib/python3/dist-packages/keyring/backends/OS_X.py 46 25 46% 30, 33-43, 47-58, 62-69 /usr/lib/python3/dist-packages/keyring/backends/SecretService.py 62 40 35% 10, 13-15, 31-39, 46-59, 64-72, 77-84, 89-94 /usr/lib/python3/dist-packages/keyring/backends/Windows.py 95 59 38% 16-18, 22-24, 56, 60, 64-71, 74-84, 87-94, 97-103, 106-114, 117-126, 129-138, 151, 155, 159, 163-165 /usr/lib/python3/dist-packages/keyring/backends/_OS_X_API.py 151 136 10% 24-345 /usr/lib/python3/dist-packages/keyring/backends/__init__.py 0 0 100% /usr/lib/python3/dist-packages/keyring/backends/chainer.py 36 18 50% 47-50, 53-57, 60-64, 67-70 /usr/lib/python3/dist-packages/keyring/backends/fail.py 8 2 75% 19-25 /usr/lib/python3/dist-packages/keyring/backends/kwallet.py 95 55 42% 14-18, 35, 38-39, 46-48, 51-52, 55-75, 78-95, 100-107, 112-115, 121-126 /usr/lib/python3/dist-packages/keyring/core.py 80 42 48% 27, 34, 41-51, 57, 63, 69, 75, 79, 124-127, 135-138, 159-176, 181-185 /usr/lib/python3/dist-packages/keyring/credentials.py 37 14 62% 16, 20, 28-29, 33, 37, 46-47, 52-55, 59, 63 /usr/lib/python3/dist-packages/keyring/errors.py 26 1 96% 60 /usr/lib/python3/dist-packages/keyring/py27compat.py 35 6 83% 7-8, 17, 42, 49-50 /usr/lib/python3/dist-packages/keyring/py32compat.py 5 2 60% 3-4 /usr/lib/python3/dist-packages/keyring/py33compat.py 10 4 60% 28-31 /usr/lib/python3/dist-packages/keyring/util/__init__.py 13 2 85% 34-35 /usr/lib/python3/dist-packages/keyring/util/platform_.py 31 7 77% 8, 12, 16-18, 47-50 /usr/lib/python3/dist-packages/keyring/util/properties.py 14 6 57% 51-53, 56-58 /usr/lib/python3/dist-packages/keystoneauth1/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneauth1/_fair_semaphore.py 43 33 23% 35-42, 47-54, 58-60, 63-77, 87-88, 93-104 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/usr/lib/python3/dist-packages/keystoneauth1/exceptions/base.py 6 2 67% 23-24 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/catalog.py 8 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/connection.py 16 2 88% 50-51 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/discovery.py 16 1 94% 39 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/http.py 147 55 63% 72-83, 254-259, 394-460 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/oidc.py 14 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/response.py 7 2 71% 24-25 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/service_providers.py 7 3 57% 22-24 /usr/lib/python3/dist-packages/keystoneauth1/plugin.py 48 29 40% 33, 62, 95-100, 124-131, 149-154, 176-180, 193, 209, 224, 239, 254, 268, 287, 304, 315 /usr/lib/python3/dist-packages/keystoneauth1/session.py 539 443 18% 36-37, 65-70, 81-86, 91-106, 114, 118, 124-131, 138-139, 142-153, 162-195, 208-222, 238-240, 247-248, 251-254, 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70-79, 82, 106-116 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/password.py 29 16 45% 39-51, 78-89, 93-96 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/token.py 17 6 65% 30-31, 53, 57-65 /usr/lib/python3/dist-packages/keystoneclient/base.py 274 195 29% 38-49, 58-61, 66, 72-86, 103-104, 116-119, 122-124, 138-156, 167-168, 177-178, 192-195, 208-220, 232-237, 246-247, 251-265, 282-296, 304-323, 364-378, 382-383, 390, 396, 399-409, 418, 423-459, 463, 469-471, 479, 485-508, 528-531, 535-539, 544-548, 551-564, 568-576, 585-591, 595-600, 604, 607, 610, 613, 616 /usr/lib/python3/dist-packages/keystoneclient/baseclient.py 20 10 50% 19-24, 27-28, 31, 34, 37, 40, 43, 46 /usr/lib/python3/dist-packages/keystoneclient/exceptions.py 92 17 82% 75-78, 85-87, 113-115, 366-368, 375-377, 428-431, 438-439 /usr/lib/python3/dist-packages/keystoneclient/httpclient.py 331 245 26% 45-48, 80, 96, 127-143, 254-403, 406, 410-417, 420-423, 426, 429, 438, 442-443, 448-451, 455, 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/usr/lib/python3/dist-packages/keystoneclient/v2_0/certificates.py 9 5 44% 19, 28-29, 38-40 /usr/lib/python3/dist-packages/keystoneclient/v2_0/client.py 53 33 38% 150-176, 193-219 /usr/lib/python3/dist-packages/keystoneclient/v2_0/ec2.py 17 7 59% 22, 25, 36-38, 46, 54, 59 /usr/lib/python3/dist-packages/keystoneclient/v2_0/endpoints.py 13 5 62% 24, 34, 39-44, 48 /usr/lib/python3/dist-packages/keystoneclient/v2_0/extensions.py 8 2 75% 21, 31 /usr/lib/python3/dist-packages/keystoneclient/v2_0/roles.py 42 29 31% 25, 28, 37, 41-42, 46, 50, 53-59, 67-75, 83-91 /usr/lib/python3/dist-packages/keystoneclient/v2_0/services.py 15 6 60% 25, 35, 39, 43-46, 50 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tenants.py 76 54 29% 37, 40, 44-58, 61, 66, 71, 80-82, 85, 89-98, 110-130, 135-149, 153, 157, 161, 167 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tokens.py 58 36 38% 24, 28, 32, 36, 44-69, 72, 75, 85, 94-96, 108-115, 124-125 /usr/lib/python3/dist-packages/keystoneclient/v2_0/users.py 51 32 37% 27, 30, 33, 42-43, 46, 55-57, 61-63, 68-70, 75-78, 87-91, 97-102, 106, 113-126, 130 /usr/lib/python3/dist-packages/keystoneclient/v3/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/access_rules.py 29 14 52% 58-61, 73-76, 87-90, 104-107, 111, 116 /usr/lib/python3/dist-packages/keystoneclient/v3/application_credentials.py 49 32 35% 72-98, 121-124, 136-139, 150-153, 166-169, 173 /usr/lib/python3/dist-packages/keystoneclient/v3/auth.py 22 10 55% 42-48, 60-66 /usr/lib/python3/dist-packages/keystoneclient/v3/client.py 104 64 38% 218-267, 270, 277-286, 313-352 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_filter.py 82 62 24% 34-47, 50-64, 68-73, 77-82, 86-91, 95-99, 106-110, 117-122, 126-131, 135-140, 144-149, 156-160 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_policy.py 59 39 34% 28-38, 42, 47, 52, 56-66, 70, 75, 80, 85-97, 102, 108, 114, 125-134, 146-153 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/base.py 19 8 58% 30, 33-40 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/core.py 16 7 56% 24-31 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/domains.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/identity_providers.py 22 8 64% 36-38, 54, 69, 82, 96, 109 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/mappings.py 22 8 64% 36-38, 76, 89, 99, 136, 149 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/projects.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/protocols.py 31 16 48% 38-49, 52-54, 72, 90, 105, 123, 141 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/saml.py 16 9 44% 37-40, 56-59, 62-79 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/service_providers.py 22 8 64% 38-40, 52, 65, 75, 89, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/access_tokens.py 20 9 55% 23-24, 38-51 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/consumers.py 17 4 76% 38, 43, 47, 53 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/core.py 20 11 45% 22-31, 36-38, 62-65 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/request_tokens.py 33 20 39% 24-25, 30-36, 54-57, 60-73 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/utils.py 14 10 29% 28-38 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/simple_cert.py 11 6 45% 21-22, 31-33, 42-44 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/trusts.py 33 17 48% 59-74, 85, 90-92, 98, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/credentials.py 17 5 71% 62, 80, 93, 119, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/domain_configs.py 28 13 54% 37, 63-65, 78-79, 105-107, 121-122, 125, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/domains.py 19 7 63% 54, 70, 85-87, 105, 122 /usr/lib/python3/dist-packages/keystoneclient/v3/ec2.py 15 6 60% 30, 51, 68-69, 81, 97 /usr/lib/python3/dist-packages/keystoneclient/v3/endpoint_groups.py 20 6 70% 54, 72, 86, 99, 117, 135 /usr/lib/python3/dist-packages/keystoneclient/v3/endpoints.py 28 12 57% 50-53, 75-76, 94, 119-120, 149-150, 169 /usr/lib/python3/dist-packages/keystoneclient/v3/groups.py 27 15 44% 31-44, 68, 87-91, 106, 122, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/limits.py 21 9 57% 61-73, 93, 115, 133, 150 /usr/lib/python3/dist-packages/keystoneclient/v3/policies.py 24 12 50% 31-42, 64, 79, 92, 108, 124 /usr/lib/python3/dist-packages/keystoneclient/v3/projects.py 106 76 28% 41-55, 58, 61, 64, 67, 70, 73, 105-108, 136-157, 161-164, 168-171, 201-222, 225-227, 246, 264, 274, 283-285, 298-302, 311, 323-326, 337-345 /usr/lib/python3/dist-packages/keystoneclient/v3/regions.py 20 5 75% 61, 75, 87, 112, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/registered_limits.py 22 10 55% 61-72, 99, 120, 140, 157-158 /usr/lib/python3/dist-packages/keystoneclient/v3/role_assignments.py 69 48 30% 40-42, 45-47, 50-52, 55-57, 60-62, 95-124, 127, 131, 135, 139, 143, 147 /usr/lib/python3/dist-packages/keystoneclient/v3/roles.py 149 101 32% 61-92, 95-113, 116-121, 137-141, 156, 190-203, 217, 235, 275-284, 327-336, 377-386, 395, 401, 407, 413, 419, 430-433, 456-458, 481-482, 501-503, 521-523, 543-544, 559, 562, 566, 570 /usr/lib/python3/dist-packages/keystoneclient/v3/services.py 23 11 52% 57-58, 75, 89-90, 111-112, 130-134 /usr/lib/python3/dist-packages/keystoneclient/v3/tokens.py 37 28 24% 18-21, 28, 37-39, 54-58, 78-94, 116-121 /usr/lib/python3/dist-packages/keystoneclient/v3/users.py 57 34 40% 43-45, 82-92, 126-132, 148, 187-197, 213-226, 240-243, 259-262, 278-281, 295 /usr/lib/python3/dist-packages/netaddr/__init__.py 19 1 95% 16 /usr/lib/python3/dist-packages/netaddr/compat.py 60 37 38% 39, 50-53, 56-59, 62-113 /usr/lib/python3/dist-packages/netaddr/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/netaddr/contrib/subnet_splitter.py 17 11 35% 23, 27-38, 42, 46 /usr/lib/python3/dist-packages/netaddr/core.py 73 40 45% 61-74, 89, 112-113, 122-124, 136, 145-149, 158-161, 169-170, 184-196, 199-200, 203, 206 /usr/lib/python3/dist-packages/netaddr/eui/__init__.py 361 276 24% 24, 28, 32, 37-39, 44, 52, 72-101, 104-109, 112-117, 121, 125, 129-152, 157, 169, 173-174, 181, 202-216, 230-268, 271-276, 279-284, 288, 292, 296-308, 312, 316-318, 327, 357-390, 394, 401-413, 416, 419-450, 456, 459-468, 477-480, 485-488, 492, 500-501, 506, 515-525, 529-548, 552, 559-564, 571-576, 583-588, 595-600, 607-612, 619-624, 633, 638, 643, 652, 663-671, 685-687, 699-700, 710, 718-722, 726, 730 /usr/lib/python3/dist-packages/netaddr/ip/__init__.py 822 596 27% 33-38, 47, 54, 60, 69-72, 81-84, 93-96, 105-108, 117-120, 129-132, 136, 140-143, 151-154, 162-174, 181-184, 191-199, 206, 213, 222-223, 228, 262-266, 275, 278, 285-293, 305, 316-319, 323, 330-339, 348-371, 377-378, 384-385, 396-400, 411-415, 426-429, 442-445, 456-459, 468, 472, 480, 485-487, 492, 500, 508, 513, 521, 530, 535, 544-557, 570-586, 596-600, 609, 618, 627, 636, 645, 651, 657, 661, 676-678, 685, 693-697, 705-734, 743-752, 760, 768-776, 782, 787-788, 796-798, 804-816, 820-823, 827-828, 906-908, 911-913, 915-917, 919-921, 924, 934-935, 938, 946, 953-968, 972-977, 989, 994, 999-1002, 1010, 1018-1019, 1024-1025, 1030, 1035-1036, 1041, 1049, 1064-1072, 1085-1093, 1102-1123, 1129, 1135-1138, 1146-1161, 1174-1193, 1202-1205, 1214-1217, 1229-1240, 1255-1281, 1297-1318, 1322-1323, 1327, 1361, 1365, 1371-1375, 1378-1397, 1402, 1407, 1413, 1419-1420, 1427, 1431, 1435, 1446-1448, 1477-1531, 1549-1576, 1589-1591, 1606-1650, 1663-1684, 1701-1730, 1746-1767, 1782-1796, 1811-1823, 1838-1852 /usr/lib/python3/dist-packages/netaddr/ip/glob.py 137 117 15% 26-67, 79-97, 109-127, 141-201, 213, 225-231, 283-285, 289, 293-294, 297, 300-301, 308, 312 /usr/lib/python3/dist-packages/netaddr/ip/nmap.py 64 55 14% 22-45, 51-62, 69-87, 96-101, 115-117 /usr/lib/python3/dist-packages/netaddr/ip/rfc1924.py 28 18 36% 32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 232-234, 238-240, 244-246, 250-252, 256-258, 263-265, 269, 273 /usr/lib/python3/dist-packages/netaddr/strategy/ipv4.py 103 51 50% 16, 91-107, 121, 141-148, 158-161, 171, 182, 186, 196-199, 212-214, 218, 222, 226-228, 232, 236, 240, 261-278 /usr/lib/python3/dist-packages/netaddr/strategy/ipv6.py 106 47 56% 18, 24-25, 119-126, 141-142, 154-172, 182-187, 197-198, 221, 225-229, 233, 237, 241, 245-247, 251, 255, 259 /usr/lib/python3/dist-packages/oauthlib/__init__.py 10 2 80% 27, 34 /usr/lib/python3/dist-packages/oauthlib/common.py 212 144 32% 22-24, 27-28, 59, 64-70, 74-80, 84-89, 96-101, 108-113, 129-165, 176-194, 209, 221, 232-233, 237-251, 255-257, 266, 271-275, 280-285, 297-303, 308-328, 338-340, 343, 346-348, 351-352, 355, 358-359, 362-364, 385-430, 433-436, 439-447, 452, 456-458, 463-468 /usr/lib/python3/dist-packages/oauthlib/oauth1/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/__init__.py 125 91 27% 51, 86-101, 104-110, 122-151, 156-186, 199-223, 256-327 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py 8 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py 59 48 19% 44-54, 104-119, 131-217 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py 38 25 34% 50-57, 111-139, 154-163 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/base.py 87 75 14% 23-24, 32-66, 70-106, 110-112, 117-177, 182-216 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py 8 4 50% 11-14 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py 56 45 20% 42-49, 99-110, 122-211 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py 43 35 19% 70-165 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py 34 26 24% 35-84 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218-227, 233-246, 263-273 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 30 30% 19-22, 33-42, 57-60, 74-98 /usr/lib/python3/dist-packages/urllib3/packages/__init__.py 8 2 75% 10-11 /usr/lib/python3/dist-packages/urllib3/packages/ssl_match_hostname/__init__.py 11 6 45% 7, 10-16 /usr/lib/python3/dist-packages/urllib3/poolmanager.py 172 132 23% 89-114, 160-167, 170, 173-175, 187-202, 211, 224-234, 243-247, 257-271, 284-285, 297-307, 318-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 28 28% 42, 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 322 19% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 214-258, 268-271, 274-278, 283-287, 291, 294, 302, 308-354, 362-373, 377, 383-399, 406-410, 421-467, 490-541, 559-567, 578-599, 603, 606, 610, 614-621, 625-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 45 32% 17-26, 51-86, 90-94, 102-105, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 5 64% 7, 12, 15, 18, 21 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 25 50% 13, 63, 65, 71, 74, 77, 80, 85, 95-105, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 29 17% 15-35, 54-71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 102 32% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 323-326, 335-341, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 112 24% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 192-201, 208-217, 256-293, 327-383, 393-396, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 42 33% 96-99, 102, 120-153, 169, 183, 191-194, 204-208, 220-226, 245-258 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 152 26% 101-105, 112, 117-122, 127-129, 150-169, 172, 193-207, 214-241, 246-271, 275-299, 303-317, 322-327, 352-416, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 58 24% 8-9, 43-68, 72-87, 91-107, 111, 118-124, 133-139, 146, 153 /usr/local/lib/python3.8/dist-packages/MySQLdb/__init__.py 46 13 72% 21, 36-37, 44-46, 63, 66, 69, 72, 75-76, 79 /usr/local/lib/python3.8/dist-packages/MySQLdb/_exceptions.py 12 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/compat.py 12 5 58% 4-8 /usr/local/lib/python3.8/dist-packages/MySQLdb/connections.py 146 34 77% 42, 138, 140, 143, 150, 161, 197, 204, 245, 249, 260, 262, 265, 269, 282, 286-293, 304-308, 314-317, 324-328 /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/CLIENT.py 18 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FLAG.py 16 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/converters.py 35 13 63% 47-48, 52, 56, 63-68, 79, 82, 85 /usr/local/lib/python3.8/dist-packages/MySQLdb/cursors.py 261 96 63% 83-90, 93, 96-97, 107, 109, 114-120, 127, 135, 142-144, 160, 171, 187, 194-206, 227, 239-240, 259-260, 296-310, 328, 358-363, 368-372, 378, 391-400, 403-405, 417, 421-426, 431-434, 438-441, 444, 447-450 /usr/local/lib/python3.8/dist-packages/MySQLdb/release.py 3 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/times.py 76 49 36% 21, 25, 29, 34-37, 43-47, 53, 60-64, 75-76, 79-99, 102-113, 116-123, 127, 131 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1952: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1958: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2143: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2149: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /usr/local/lib/python3.8/dist-packages/pyparsing.py 3062 1772 42% 122-123, 127-128, 134-137, 141-145, 149-150, 191-194, 233-266, 274-278, 295-296, 307-308, 321, 329-336, 339-346, 349, 354-359, 361, 410-453, 487, 490, 498, 500, 563, 567, 576, 580, 588-591, 606-611, 616-634, 650, 653-656, 659, 662, 675-694, 738-754, 794-798, 813, 829-832, 838-839, 844-845, 848-850, 869-874, 877, 880, 883-891, 908, 930-944, 961-1016, 1019-1023, 1050-1063, 1086-1128, 1155, 1159, 1166-1173, 1176, 1179, 1188-1207, 1222-1223, 1235, 1240-1245, 1248, 1251, 1254, 1258, 1297-1302, 1319-1337, 1344-1345, 1370, 1392, 1396-1398, 1464, 1504-1516, 1559, 1562, 1612-1613, 1616-1626, 1630, 1636, 1641, 1653-1674, 1694-1712, 1717-1720, 1729-1730, 1735-1738, 1741-1746, 1750-1768, 1783-1786, 1792, 1800-1826, 1853-1856, 1896, 1939, 1945-1955, 1990-2031, 2053-2079, 2102-2111, 2129-2136, 2166, 2171-2173, 2181, 2186-2188, 2195-2201, 2207-2213, 2236, 2238, 2246, 2248-2253, 2258-2260, 2263, 2265, 2267, 2276-2279, 2284, 2290, 2297, 2300, 2302-2304, 2311-2317, 2323-2329, 2335-2341, 2347-2353, 2359-2365, 2376, 2398-2412, 2465-2466, 2482-2490, 2496-2500, 2539-2543, 2549, 2557, 2563, 2571-2585, 2589, 2591, 2593, 2597, 2603, 2606, 2622-2626, 2724-2788, 2795-2799, 2802-2815, 2818, 2821, 2846-2850, 2853, 2876-2879, 2891-2893, 2932-2950, 2953-2969, 2972-2974, 2980, 2994-2998, 3001-3003, 3016, 3052-3058, 3061-3084, 3146, 3159, 3164, 3181, 3187, 3191-3192, 3208, 3213-3218, 3223, 3248-3253, 3262-3267, 3304, 3313-3324, 3335, 3337, 3348-3349, 3353-3359, 3362-3368, 3392-3408, 3451-3512, 3515-3547, 3550-3558, 3587, 3593, 3610-3620, 3630, 3682, 3687-3688, 3691-3703, 3718-3719, 3722-3728, 3731-3736, 3766-3768, 3780-3788, 3799-3803, 3814, 3817-3820, 3832-3834, 3837-3841, 3852-3855, 3858-3863, 3873, 3876, 3878, 3883, 3886-3889, 3893-3895, 3907-3916, 3919-3926, 3963-3966, 3975-3977, 4007-4009, 4014-4024, 4036-4043, 4056-4057, 4059-4067, 4075-4077, 4080-4084, 4088, 4114-4118, 4121-4124, 4127-4184, 4188-4190, 4193-4199, 4202-4204, 4207-4216, 4241, 4260-4263, 4271, 4274-4276, 4280, 4288-4290, 4293-4302, 4363-4367, 4370-4372, 4375-4421, 4424-4430, 4433-4435, 4448, 4464, 4474-4483, 4492-4496, 4499-4504, 4540-4541, 4546-4549, 4581-4601, 4604-4624, 4658-4660, 4664, 4677, 4682, 4691, 4696, 4702, 4704, 4716-4726, 4757, 4787, 4797, 4800, 4852-4856, 4863, 4936, 4942-4986, 5016, 5019-5029, 5032, 5035-5036, 5039-5043, 5046-5053, 5056-5073, 5076-5081, 5084-5092, 5131-5135, 5138-5145, 5213-5234, 5263, 5270-5271, 5273-5277, 5279, 5306-5324, 5346, 5373-5384, 5387-5393, 5410-5423, 5440-5452, 5494-5547, 5586, 5617-5628, 5634, 5661-5662, 5708-5709, 5715-5718, 5733, 5748, 5787, 5792-5793, 5811-5812, 5818-5819, 5873, 5931-5943, 5981-5982, 6060-6115, 6192-6229, 6312-6355, 6365, 6622-6627, 6647-6652, 6680, 6705-6713, 6734-6740, 6745, 6750, 6755, 6760, 6886, 6889-6902, 6906-6921, 6924, 6927, 6940-6943, 6952-6955, 6964-6967, 6981-7028, 7034-7035, 7040-7105 /usr/local/lib/python3.8/dist-packages/wrapt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/wrapt/decorators.py 186 91 51% 11-23, 40-41, 55-56, 60, 64, 68, 72, 76, 86, 91, 95, 99-102, 105-106, 112, 117-120, 123, 138, 142, 146, 149-150, 154, 158, 162-163, 165, 205, 208-212, 253-279, 292-294, 322, 343-390, 411, 444-445, 450-451, 454, 464-514 /usr/local/lib/python3.8/dist-packages/wrapt/importer.py 102 75 26% 12, 37-45, 52-98, 103-109, 112-119, 128-135, 145-148, 153, 156-159, 164, 172-221, 227-230 /usr/local/lib/python3.8/dist-packages/wrapt/wrappers.py 472 304 36% 11, 32, 36, 40, 44, 51, 60, 78-87, 91, 95, 99, 103, 107, 111, 114, 117, 121, 124, 130, 134, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168-190, 196-199, 202-216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303-304, 307-308, 311-312, 315-316, 319-320, 323-324, 327-328, 331-332, 335-336, 339-340, 343-344, 347-348, 351-352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 431, 437, 442-453, 456-461, 471-477, 505-533, 542-566, 578-624, 704-719, 727-728, 733-771, 774, 777-780, 791-794, 797-798, 801, 804, 807-811, 819-828, 831, 834-836, 839-858, 870-880, 899-928, 936-947 -------------------------------------------------------------------------------------------------------------------------------------- TOTAL 130424 96709 26% ret : 34304 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 0 command : coverage3 report -i -m ret : 0 123.08user 46.59system 5:35.79elapsed 50%CPU (0avgtext+0avgdata 6385404maxresident)k 6487848inputs+43424outputs (7682major+6657923minor)pagefaults 0swaps