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=== Log initialised at 2019-12-26 23:17:32.694961 ===
2019-12-26 23:17:32.694793 pytorch_prototype.py 0011: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695013 level_14_master_main 0011: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695090 level_13_updates_mai 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695154 level_12_losses_main 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695211 level_11_optimizers_ 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695264 level_10_schedulers_ 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695317 level_9_transforms_m 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695369 level_8_layers_main. 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695421 level_7_aux_main.py 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695472 level_6_params_main. 0009: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695529 level_5_state_base.p 0009: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695585 level_4_evaluation_b 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695638 level_3_training_bas 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695689 level_2_model_base.p 0009: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695744 level_1_dataset_base 0010: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695797 base_class.py 0013: __init__ -> ENTRY: verbose = 1
2019-12-26 23:17:32.695852 base_system_state.py 0005: get_base_system_dict -> ENTRY
2019-12-26 23:17:32.695962 base_system_state.py 0005: get_base_system_dict <- EXIT : {'verbose': 1, 'cwd': False, 'master_systems_dir': False, 'master_systems_dir_relative': False, 'project_name': False, 'project_dir': False, 'project_dir_relative': False, 'experiment_name': False, 'experiment_dir': False, 'experiment_dir_relative': False, 'origin': False, 'master_comparison_dir': False, 'master_comparison_dir_relative': False, 'library': False, 'output_dir': False, 'output_dir_relative': False, 'model_dir': False, 'model_dir_relative': False, 'log_dir': False, 'log_dir_relative': False, 'fname': False, 'fname_relative': False, 'dataset': {'dataset_type': False, 'train_path': False, 'val_path': False, 'csv_train': False, 'csv_val': False, 'test_path': False, 'csv_test': False, 'params': {'input_size': False, 'batch_size': False, 'train_shuffle': False, 'num_workers': False, 'weighted_sample': False, 'num_classes': False, 'classes': False, 'num_train_images': False, 'num_val_images': False, 'num_test_images': False, 'delimiter': ',', 'test_delimiter': ',', 'dataset_test_type': False, 'train_val_split': 0.9}, 'transforms': {'train': [], 'val': [], 'test': []}, 'status': False}, 'model': {'status': False, 'custom_network': [], 'final_layer': False, 'params': {'model_name': False, 'model_path': False, 'use_gpu': False, 'use_pretrained': False, 'freeze_base_network': False, 'num_layers': False, 'num_params_to_update': False, 'num_freeze': False, 'gpu_memory_fraction': 0.6}}, 'hyper-parameters': {'status': False, 'learning_rate': False, 'num_epochs': False, 'optimizer': {'name': False, 'params': {}}, 'learning_rate_scheduler': {'name': False, 'params': {}}, 'loss': {'name': False, 'params': {}}}, 'training': {'settings': {'display_progress_realtime': False, 'display_progress': False, 'save_intermediate_models': False, 'save_training_logs': False, 'intermediate_model_prefix': False}, 'outputs': {'max_gpu_memory_usage': 0, 'best_val_acc': 0, 'best_val_acc_epoch_num': 0, 'epochs_completed': 0}, 'status': False}, 'testing': {'status': False, 'num_images': False, 'num_correct_predictions': False, 'percentage_accuracy': False, 'class_accuracy': False}, 'states': {'eval_infer': False, 'resume_train': False, 'copy_from': False, 'pseudo_copy_from': False}, 'local': {'projects_list': [], 'num_projects': False, 'experiments_list': [], 'num_experiments': False, 'project_experiment_list': [], 'transforms_train': [], 'transforms_val': [], 'transforms_test': [], 'normalize': False, 'mean_subtract': False, 'applied_train_tensor': False, 'applied_test_tensor': False, 'data_transforms': {}, 'image_datasets': {}, 'data_loaders': {}, 'data_generators': {}, 'model': False, 'ctx': False, 'params_to_update': [], 'device': False, 'learning_rate_scheduler': False, 'optimizer': False, 'criterion': False}}
2019-12-26 23:17:32.696613 common.py 0083: create_dir -> ENTRY: dir_path = workspace/
2019-12-26 23:17:32.696680 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.696729 common.py 0083: create_dir -> ENTRY: dir_path = workspace/comparison/
2019-12-26 23:17:32.696782 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.696847 base_class.py 0013: __init__ <- EXIT
2019-12-26 23:17:32.696888 level_1_dataset_base 0010: __init__ <- EXIT
2019-12-26 23:17:32.696924 level_2_model_base.p 0009: __init__ <- EXIT
2019-12-26 23:17:32.696959 level_3_training_bas 0010: __init__ <- EXIT
2019-12-26 23:17:32.696994 level_4_evaluation_b 0010: __init__ <- EXIT
2019-12-26 23:17:32.697028 level_5_state_base.p 0009: __init__ <- EXIT
2019-12-26 23:17:32.697063 level_6_params_main. 0009: __init__ <- EXIT
2019-12-26 23:17:32.697097 level_7_aux_main.py 0010: __init__ <- EXIT
2019-12-26 23:17:32.697132 level_8_layers_main. 0010: __init__ <- EXIT
2019-12-26 23:17:32.697166 level_9_transforms_m 0010: __init__ <- EXIT
2019-12-26 23:17:32.697200 level_10_schedulers_ 0010: __init__ <- EXIT
2019-12-26 23:17:32.697234 level_11_optimizers_ 0010: __init__ <- EXIT
2019-12-26 23:17:32.697268 level_12_losses_main 0010: __init__ <- EXIT
2019-12-26 23:17:32.697302 level_13_updates_mai 0010: __init__ <- EXIT
2019-12-26 23:17:32.697336 level_14_master_main 0011: __init__ <- EXIT
2019-12-26 23:17:32.697387 base_class.py 0137: custom_print -> ENTRY: msg = Pytorch Version: 1.2.0
2019-12-26 23:17:32.697517 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.697807 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:17:32.697926 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.698074 pytorch_prototype.py 0011: __init__ <- EXIT
2019-12-26 23:17:32.698280 pytorch_prototype.py 0022: Prototype -> ENTRY: project_name = exp-1, experiment_name = proj-1, eval_infer = False, resume_train = False, copy_from = False, pseudo_copy_from = False, summary = False
2019-12-26 23:17:32.698398 base_class.py 0037: set_system_project -> ENTRY: project_name = exp-1
2019-12-26 23:17:32.698472 common.py 0083: create_dir -> ENTRY: dir_path = /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/
2019-12-26 23:17:32.698542 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.698595 base_class.py 0049: set_system_select_project -> ENTRY: project_name = exp-1
2019-12-26 23:17:32.698662 base_class.py 0049: set_system_select_project <- EXIT
2019-12-26 23:17:32.698705 base_class.py 0037: set_system_project <- EXIT
2019-12-26 23:17:32.698771 base_class.py 0067: set_system_experiment -> ENTRY: experiment_name = proj-1, eval_infer = False, copy_from = False, pseudo_copy_from = False, resume_train = False, summary = False
2019-12-26 23:17:32.698867 common.py 0083: create_dir -> ENTRY: dir_path = /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/proj-1/
2019-12-26 23:17:32.698933 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.698983 base_class.py 0097: set_system_select_experiment -> ENTRY: experiment_name = proj-1
2019-12-26 23:17:32.699032 base_class.py 0097: set_system_select_experiment <- EXIT
2019-12-26 23:17:32.699078 base_class.py 0114: set_system_delete_create_dir -> ENTRY
2019-12-26 23:17:32.699119 common.py 0089: delete_dir -> ENTRY: dir_path = workspace/exp-1/proj-1/output/
2019-12-26 23:17:32.715816 common.py 0089: delete_dir <- EXIT
2019-12-26 23:17:32.715915 common.py 0083: create_dir -> ENTRY: dir_path = workspace/exp-1/proj-1/output/
2019-12-26 23:17:32.716025 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.716081 common.py 0083: create_dir -> ENTRY: dir_path = workspace/exp-1/proj-1/output/models/
2019-12-26 23:17:32.716165 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.716215 common.py 0083: create_dir -> ENTRY: dir_path = workspace/exp-1/proj-1/output/logs/
2019-12-26 23:17:32.716292 common.py 0083: create_dir <- EXIT
2019-12-26 23:17:32.716334 base_class.py 0114: set_system_delete_create_dir <- EXIT
2019-12-26 23:17:32.716379 common.py 0061: save -> ENTRY: ---
2019-12-26 23:17:32.716425 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:17:32.716478 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:17:32.716525 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:17:32.716910 common.py 0017: write_json <- EXIT
2019-12-26 23:17:32.716971 common.py 0061: save <- EXIT
2019-12-26 23:17:32.717014 base_class.py 0067: set_system_experiment <- EXIT
2019-12-26 23:17:32.717065 base_class.py 0137: custom_print -> ENTRY: msg = Experiment Details
2019-12-26 23:17:32.717170 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.717229 base_class.py 0137: custom_print -> ENTRY: msg = Project: exp-1
2019-12-26 23:17:32.717299 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.717353 base_class.py 0137: custom_print -> ENTRY: msg = Experiment: proj-1
2019-12-26 23:17:32.717421 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.717474 base_class.py 0137: custom_print -> ENTRY: msg = Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/proj-1/
2019-12-26 23:17:32.717553 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.717725 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:17:32.718031 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:17:32.718159 pytorch_prototype.py 0022: Prototype <- EXIT
2019-12-26 23:18:12.741579 level_6_params_main. 0022: Dataset_Params -> ENTRY: dataset_path = ['cat_dog/train', 'cat_dog/val'], path_to_csv = False, delimiter = ,, split = 0.9, input_size = 224, batch_size = 4, shuffle_data = True, num_processors = 3
2019-12-26 23:18:12.742043 params.py 0006: set_input_size -> ENTRY: ---
2019-12-26 23:18:12.742190 params.py 0006: set_input_size <- EXIT : ---
2019-12-26 23:18:12.742321 params.py 0012: set_batch_size -> ENTRY: ---
2019-12-26 23:18:12.742432 params.py 0012: set_batch_size <- EXIT : ---
2019-12-26 23:18:12.742513 params.py 0018: set_data_shuffle -> ENTRY: ---
2019-12-26 23:18:12.742576 params.py 0018: set_data_shuffle <- EXIT : ---
2019-12-26 23:18:12.742787 params.py 0027: set_num_processors -> ENTRY: ---
2019-12-26 23:18:12.742855 params.py 0027: set_num_processors <- EXIT : ---
2019-12-26 23:18:12.742929 paths.py 0007: set_dataset_train_path -> ENTRY: ---
2019-12-26 23:18:12.742994 paths.py 0007: set_dataset_train_path <- EXIT : ---
2019-12-26 23:18:12.743079 base_class.py 0137: custom_print -> ENTRY: msg = Dataset Details
2019-12-26 23:18:12.743296 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.743762 base_class.py 0137: custom_print -> ENTRY: msg = Train path: cat_dog/train
2019-12-26 23:18:12.744155 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.744277 base_class.py 0137: custom_print -> ENTRY: msg = Val path: cat_dog/val
2019-12-26 23:18:12.744545 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.744659 base_class.py 0137: custom_print -> ENTRY: msg = CSV train path: None
2019-12-26 23:18:12.744781 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.745139 base_class.py 0137: custom_print -> ENTRY: msg = CSV val path: None
2019-12-26 23:18:12.745245 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.745436 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:12.745531 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.745715 base_class.py 0137: custom_print -> ENTRY: msg = Dataset Params
2019-12-26 23:18:12.745811 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.745992 base_class.py 0137: custom_print -> ENTRY: msg = Input Size: 224
2019-12-26 23:18:12.746098 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.746279 base_class.py 0137: custom_print -> ENTRY: msg = Batch Size: 4
2019-12-26 23:18:12.746377 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.746556 base_class.py 0137: custom_print -> ENTRY: msg = Data Shuffle: True
2019-12-26 23:18:12.746655 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.746833 base_class.py 0137: custom_print -> ENTRY: msg = Processors: 3
2019-12-26 23:18:12.746927 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.747104 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:12.747194 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.747246 level_6_params_main. 0022: Dataset_Params <- EXIT
2019-12-26 23:18:12.747954 level_9_transforms_m 0068: apply_random_horizontal_flip -> ENTRY: ---
2019-12-26 23:18:12.748063 transforms.py 0111: transform_random_horizontal_flip -> ENTRY: ---
2019-12-26 23:18:12.748147 transforms.py 0111: transform_random_horizontal_flip <- EXIT : ---
2019-12-26 23:18:12.748206 level_9_transforms_m 0068: apply_random_horizontal_flip <- EXIT
2019-12-26 23:18:12.748398 level_9_transforms_m 0145: apply_normalize -> ENTRY: ---
2019-12-26 23:18:12.748515 transforms.py 0282: transform_normalize -> ENTRY: ---
2019-12-26 23:18:12.748620 transforms.py 0282: transform_normalize <- EXIT : ---
2019-12-26 23:18:12.748679 level_9_transforms_m 0145: apply_normalize <- EXIT
2019-12-26 23:18:12.748806 level_14_master_main 0017: Dataset -> ENTRY
2019-12-26 23:18:12.748883 level_1_dataset_base 0104: set_dataset_final -> ENTRY: test = False
2019-12-26 23:18:12.748948 return_transform.py 0006: set_transform_trainval -> ENTRY: ---
2019-12-26 23:18:12.749005 return_transform.py 0006: set_transform_trainval <- EXIT : ---
2019-12-26 23:18:12.749069 level_1_dataset_base 0017: set_dataset_dataloader -> ENTRY: test = False
2019-12-26 23:18:12.750497 level_1_dataset_base 0017: set_dataset_dataloader <- EXIT
2019-12-26 23:18:12.750583 level_1_dataset_base 0104: set_dataset_final <- EXIT
2019-12-26 23:18:12.750645 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:12.750701 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:12.750752 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:12.750802 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:12.751520 common.py 0017: write_json <- EXIT
2019-12-26 23:18:12.751593 common.py 0061: save <- EXIT
2019-12-26 23:18:12.751660 base_class.py 0137: custom_print -> ENTRY: msg = Pre-Composed Train Transforms
2019-12-26 23:18:12.751782 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.752020 base_class.py 0137: custom_print -> ENTRY: msg = [{'RandomHorizontalFlip': {'p': 0.5}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]
2019-12-26 23:18:12.752144 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.752307 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:12.752386 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.752582 base_class.py 0137: custom_print -> ENTRY: msg = Pre-Composed Val Transforms
2019-12-26 23:18:12.752672 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.752831 base_class.py 0137: custom_print -> ENTRY: msg = [{'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]
2019-12-26 23:18:12.752948 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.753102 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:12.753180 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.753330 base_class.py 0137: custom_print -> ENTRY: msg = Dataset Numbers
2019-12-26 23:18:12.753411 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.753561 base_class.py 0137: custom_print -> ENTRY: msg = Num train images: 200
2019-12-26 23:18:12.753643 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.753790 base_class.py 0137: custom_print -> ENTRY: msg = Num val images: 50
2019-12-26 23:18:12.753872 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.754020 base_class.py 0137: custom_print -> ENTRY: msg = Num classes: 2
2019-12-26 23:18:12.754104 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.754191 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:12.754337 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:12.754469 level_14_master_main 0017: Dataset <- EXIT
2019-12-26 23:18:16.576830 level_6_params_main. 0074: Model_Params -> ENTRY: model_name = resnet18, freeze_base_network = True, use_gpu = True, use_pretrained = True, model_path = False
2019-12-26 23:18:16.579018 params.py 0006: set_model_name -> ENTRY: ---
2019-12-26 23:18:16.579535 params.py 0006: set_model_name <- EXIT : ---
2019-12-26 23:18:16.579895 params.py 0030: set_pretrained -> ENTRY: ---
2019-12-26 23:18:16.580154 params.py 0030: set_pretrained <- EXIT : ---
2019-12-26 23:18:16.580510 params.py 0017: set_device -> ENTRY: ---
2019-12-26 23:18:16.609740 params.py 0017: set_device <- EXIT : ---
2019-12-26 23:18:16.609988 params.py 0037: set_freeze_base_network -> ENTRY: ---
2019-12-26 23:18:16.610049 params.py 0037: set_freeze_base_network <- EXIT : ---
2019-12-26 23:18:16.610129 base_class.py 0137: custom_print -> ENTRY: msg = Model Params
2019-12-26 23:18:16.610348 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.610417 base_class.py 0137: custom_print -> ENTRY: msg = Model name: resnet18
2019-12-26 23:18:16.610964 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.611198 base_class.py 0137: custom_print -> ENTRY: msg = Use Gpu: True
2019-12-26 23:18:16.611324 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.611524 base_class.py 0137: custom_print -> ENTRY: msg = Use pretrained: True
2019-12-26 23:18:16.611652 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.611811 base_class.py 0137: custom_print -> ENTRY: msg = Freeze base network: True
2019-12-26 23:18:16.611906 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.612054 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:16.612130 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.612270 level_6_params_main. 0074: Model_Params <- EXIT
2019-12-26 23:18:16.612607 level_14_master_main 0138: Model -> ENTRY
2019-12-26 23:18:16.612686 level_2_model_base.p 0016: set_model_final -> ENTRY: path = False
2019-12-26 23:18:16.612751 base_class.py 0137: custom_print -> ENTRY: msg = Model Details
2019-12-26 23:18:16.612838 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.613001 base_class.py 0137: custom_print -> ENTRY: msg = Loading pretrained model
2019-12-26 23:18:16.613081 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:16.613224 return_model.py 0043: setup_model -> ENTRY: ---
2019-12-26 23:18:16.613297 models.py 0024: get_base_model -> ENTRY: model_name = resnet18, use_pretrained = True, num_classes = 2, freeze_base_network = True
2019-12-26 23:18:16.911508 common.py 0007: set_parameter_requires_grad -> ENTRY: ---
2019-12-26 23:18:16.911983 common.py 0007: set_parameter_requires_grad <- EXIT : ---
2019-12-26 23:18:16.912091 models.py 0024: get_base_model <- EXIT : ---
2019-12-26 23:18:16.912434 return_model.py 0043: setup_model <- EXIT : ---
2019-12-26 23:18:16.912551 common.py 0050: model_to_device -> ENTRY: ---
2019-12-26 23:18:21.298304 common.py 0050: model_to_device <- EXIT : ---
2019-12-26 23:18:21.298456 base_class.py 0137: custom_print -> ENTRY: msg = Model Loaded on device
2019-12-26 23:18:21.298599 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:21.298816 common.py 0081: get_num_layers -> ENTRY: ---
2019-12-26 23:18:21.299139 common.py 0081: get_num_layers <- EXIT : ---
2019-12-26 23:18:21.299379 base_class.py 0137: custom_print -> ENTRY: msg = Model name: resnet18
2019-12-26 23:18:21.299480 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:21.299641 base_class.py 0137: custom_print -> ENTRY: msg = Num layers in model: 31
2019-12-26 23:18:21.299730 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:21.299868 base_class.py 0137: custom_print -> ENTRY: msg = Num trainable layers: 1
2019-12-26 23:18:21.299949 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:21.300081 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:21.300153 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:21.300273 level_2_model_base.p 0016: set_model_final <- EXIT
2019-12-26 23:18:21.300329 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:21.300391 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:21.300440 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:21.300501 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:21.301030 common.py 0017: write_json <- EXIT
2019-12-26 23:18:21.301094 common.py 0061: save <- EXIT
2019-12-26 23:18:21.301165 level_14_master_main 0138: Model <- EXIT
2019-12-26 23:18:28.026301 level_11_optimizers_ 0206: optimizer_sgd -> ENTRY: learning_rate = 0.001, momentum = 0, dampening = 0, weight_decay = 0, nesterov = False
2019-12-26 23:18:28.026476 optimizers.py 0143: sgd -> ENTRY: ---
2019-12-26 23:18:28.026543 optimizers.py 0143: sgd <- EXIT : ---
2019-12-26 23:18:28.026603 base_class.py 0137: custom_print -> ENTRY: msg = Optimizer
2019-12-26 23:18:28.026694 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.026753 base_class.py 0137: custom_print -> ENTRY: msg = Name: sgd
2019-12-26 23:18:28.026821 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.026874 base_class.py 0137: custom_print -> ENTRY: msg = Learning rate: 0.001
2019-12-26 23:18:28.026942 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.027000 base_class.py 0137: custom_print -> ENTRY: msg = Params: {'lr': 0.001, 'dampening': 0, 'nesterov': False, 'weight_decay': 0, 'momentum': 0}
2019-12-26 23:18:28.027088 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.027147 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:28.027207 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.027245 level_11_optimizers_ 0206: optimizer_sgd <- EXIT
2019-12-26 23:18:28.027364 level_10_schedulers_ 0016: lr_fixed -> ENTRY
2019-12-26 23:18:28.027416 schedulers.py 0006: scheduler_fixed -> ENTRY: ---
2019-12-26 23:18:28.027458 schedulers.py 0006: scheduler_fixed <- EXIT : ---
2019-12-26 23:18:28.027507 base_class.py 0137: custom_print -> ENTRY: msg = Learning rate scheduler
2019-12-26 23:18:28.027578 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.027630 base_class.py 0137: custom_print -> ENTRY: msg = Name: fixed
2019-12-26 23:18:28.027692 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.027742 base_class.py 0137: custom_print -> ENTRY: msg = Params: {}
2019-12-26 23:18:28.030291 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.030432 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:28.030552 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.030643 level_10_schedulers_ 0016: lr_fixed <- EXIT
2019-12-26 23:18:28.030814 level_12_losses_main 0018: loss_softmax_crossentropy -> ENTRY: weight = None, size_average = None, ignore_index = -100, reduction = mean
2019-12-26 23:18:28.033511 losses.py 0008: softmax_crossentropy -> ENTRY: ---
2019-12-26 23:18:28.033618 losses.py 0008: softmax_crossentropy <- EXIT : ---
2019-12-26 23:18:28.033689 base_class.py 0137: custom_print -> ENTRY: msg = Loss
2019-12-26 23:18:28.033795 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.033855 base_class.py 0137: custom_print -> ENTRY: msg = Name: softmaxcrossentropy
2019-12-26 23:18:28.033927 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.033986 base_class.py 0137: custom_print -> ENTRY: msg = Params: {'weight': None, 'size_average': None, 'ignore_index': -100, 'reduce': None, 'reduction': 'mean'}
2019-12-26 23:18:28.034328 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.034486 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:28.034566 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:28.034708 level_12_losses_main 0018: loss_softmax_crossentropy <- EXIT
2019-12-26 23:18:31.393096 level_6_params_main. 0109: Training_Params -> ENTRY: num_epochs = 4, display_progress = True, display_progress_realtime = True, save_intermediate_models = True, intermediate_model_prefix = intermediate_model_, save_training_logs = True
2019-12-26 23:18:31.393280 params.py 0006: set_num_epochs -> ENTRY: ---
2019-12-26 23:18:31.393405 params.py 0006: set_num_epochs <- EXIT : ---
2019-12-26 23:18:31.393476 params.py 0014: set_display_progress_realtime -> ENTRY: ---
2019-12-26 23:18:31.393525 params.py 0014: set_display_progress_realtime <- EXIT : ---
2019-12-26 23:18:31.393574 params.py 0021: set_display_progress -> ENTRY: ---
2019-12-26 23:18:31.393615 params.py 0021: set_display_progress <- EXIT : ---
2019-12-26 23:18:31.393661 params.py 0028: set_save_intermediate_models -> ENTRY: ---
2019-12-26 23:18:31.393700 params.py 0028: set_save_intermediate_models <- EXIT : ---
2019-12-26 23:18:31.393785 params.py 0035: set_save_training_logs -> ENTRY: ---
2019-12-26 23:18:31.393830 params.py 0035: set_save_training_logs <- EXIT : ---
2019-12-26 23:18:31.393899 params.py 0042: set_intermediate_model_prefix -> ENTRY: ---
2019-12-26 23:18:31.393950 params.py 0042: set_intermediate_model_prefix <- EXIT : ---
2019-12-26 23:18:31.394007 base_class.py 0137: custom_print -> ENTRY: msg = Training params
2019-12-26 23:18:31.394102 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.394638 base_class.py 0137: custom_print -> ENTRY: msg = Num Epochs: 4
2019-12-26 23:18:31.394761 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.394823 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:31.394888 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.394937 base_class.py 0137: custom_print -> ENTRY: msg = Display params
2019-12-26 23:18:31.395282 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.395355 base_class.py 0137: custom_print -> ENTRY: msg = Display progress: True
2019-12-26 23:18:31.395443 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.395497 base_class.py 0137: custom_print -> ENTRY: msg = Display progress realtime: True
2019-12-26 23:18:31.395698 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.395860 base_class.py 0137: custom_print -> ENTRY: msg = Save Training logs: True
2019-12-26 23:18:31.395953 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.396098 base_class.py 0137: custom_print -> ENTRY: msg = Save Intermediate models: True
2019-12-26 23:18:31.396189 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.396333 base_class.py 0137: custom_print -> ENTRY: msg = Intermediate model prefix: intermediate_model_
2019-12-26 23:18:31.396425 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.396612 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:31.396731 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.396894 level_6_params_main. 0109: Training_Params <- EXIT
2019-12-26 23:18:31.397047 level_14_master_main 0151: Train -> ENTRY
2019-12-26 23:18:31.397114 level_3_training_bas 0087: set_training_final -> ENTRY
2019-12-26 23:18:31.397168 base_class.py 0137: custom_print -> ENTRY: msg = Training Start
2019-12-26 23:18:31.397258 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.397351 return_optimizer.py 0005: load_optimizer -> ENTRY: ---
2019-12-26 23:18:31.397502 return_optimizer.py 0005: load_optimizer <- EXIT : ---
2019-12-26 23:18:31.397706 return_scheduler.py 0006: load_scheduler -> ENTRY: ---
2019-12-26 23:18:31.397765 return_scheduler.py 0006: load_scheduler <- EXIT : ---
2019-12-26 23:18:31.397815 return_loss.py 0006: load_loss -> ENTRY: ---
2019-12-26 23:18:31.397934 return_loss.py 0006: load_loss <- EXIT : ---
2019-12-26 23:18:31.405829 base_class.py 0137: custom_print -> ENTRY: msg = Epoch 1/4
2019-12-26 23:18:31.406017 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:31.406267 base_class.py 0137: custom_print -> ENTRY: msg = ----------
2019-12-26 23:18:31.406360 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.678422 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:32.678740 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.678815 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:32.678887 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.678952 base_class.py 0137: custom_print -> ENTRY: msg = curr_lr - 0.001
2019-12-26 23:18:32.679024 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.679665 base_class.py 0137: custom_print -> ENTRY: msg = [Epoch 1] Train-acc: 0.570, Train-loss: 0.702 | Val-acc: 0.820000, Val-loss: 0.532, | time: 1.3 sec
2019-12-26 23:18:32.679830 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.679994 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:32.680074 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.680222 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:32.680291 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:32.680342 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:32.680392 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:32.680990 common.py 0017: write_json <- EXIT
2019-12-26 23:18:32.681056 common.py 0061: save <- EXIT
2019-12-26 23:18:32.681120 base_class.py 0137: custom_print -> ENTRY: msg = Epoch 2/4
2019-12-26 23:18:32.681212 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:32.681267 base_class.py 0137: custom_print -> ENTRY: msg = ----------
2019-12-26 23:18:32.681333 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.117012 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:34.117905 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.118132 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:34.118370 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.118576 base_class.py 0137: custom_print -> ENTRY: msg = curr_lr - 0.001
2019-12-26 23:18:34.118827 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.120754 base_class.py 0137: custom_print -> ENTRY: msg = [Epoch 2] Train-acc: 0.825, Train-loss: 0.471 | Val-acc: 0.880000, Val-loss: 0.396, | time: 1.4 sec
2019-12-26 23:18:34.121238 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.121459 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:34.121697 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.121887 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:34.122094 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:34.122256 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:34.122421 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:34.124764 common.py 0017: write_json <- EXIT
2019-12-26 23:18:34.124980 common.py 0061: save <- EXIT
2019-12-26 23:18:34.125183 base_class.py 0137: custom_print -> ENTRY: msg = Epoch 3/4
2019-12-26 23:18:34.125457 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:34.125638 base_class.py 0137: custom_print -> ENTRY: msg = ----------
2019-12-26 23:18:34.126156 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.397830 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:35.399961 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.400250 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:35.400584 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.400814 base_class.py 0137: custom_print -> ENTRY: msg = curr_lr - 0.001
2019-12-26 23:18:35.401468 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.402395 base_class.py 0137: custom_print -> ENTRY: msg = [Epoch 3] Train-acc: 0.830, Train-loss: 0.425 | Val-acc: 0.880000, Val-loss: 0.329, | time: 1.3 sec
2019-12-26 23:18:35.402899 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.403125 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:35.403344 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.403534 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:35.404395 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:35.404719 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:35.404928 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:35.406831 common.py 0017: write_json <- EXIT
2019-12-26 23:18:35.407034 common.py 0061: save <- EXIT
2019-12-26 23:18:35.407239 base_class.py 0137: custom_print -> ENTRY: msg = Epoch 4/4
2019-12-26 23:18:35.407522 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:35.407702 base_class.py 0137: custom_print -> ENTRY: msg = ----------
2019-12-26 23:18:35.408550 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.933706 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:36.934125 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.934235 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:36.934328 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.934399 base_class.py 0137: custom_print -> ENTRY: msg = curr_lr - 0.001
2019-12-26 23:18:36.934472 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.935112 base_class.py 0137: custom_print -> ENTRY: msg = [Epoch 4] Train-acc: 0.850, Train-loss: 0.386 | Val-acc: 0.960000, Val-loss: 0.265, | time: 1.5 sec
2019-12-26 23:18:36.935271 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.935341 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:36.935570 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.935747 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:36.935827 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:36.935891 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:36.935947 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:36.936543 common.py 0017: write_json <- EXIT
2019-12-26 23:18:36.936609 common.py 0061: save <- EXIT
2019-12-26 23:18:36.936677 base_class.py 0137: custom_print -> ENTRY: msg = Training completed in: 0m 4s
2019-12-26 23:18:36.936777 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.936972 base_class.py 0137: custom_print -> ENTRY: msg = Best val Acc: 0.960000
2019-12-26 23:18:36.937068 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.937207 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:36.937282 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.937426 base_class.py 0137: custom_print -> ENTRY: msg = Training End
2019-12-26 23:18:36.937494 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.937697 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:36.937769 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.989846 base_class.py 0137: custom_print -> ENTRY: msg = Training Outputs
2019-12-26 23:18:36.990030 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.990104 base_class.py 0137: custom_print -> ENTRY: msg = Model Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/proj-1/output/models/
2019-12-26 23:18:36.990202 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.990259 base_class.py 0137: custom_print -> ENTRY: msg = Log Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/proj-1/output/logs/
2019-12-26 23:18:36.990344 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.990397 base_class.py 0137: custom_print -> ENTRY: msg = Final model: final
2019-12-26 23:18:36.990466 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.990655 base_class.py 0137: custom_print -> ENTRY: msg = Best model: best_model
2019-12-26 23:18:36.991014 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.991155 base_class.py 0137: custom_print -> ENTRY: msg = Log 1 - Validation accuracy history log: val_acc_history.npy
2019-12-26 23:18:36.991248 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.991381 base_class.py 0137: custom_print -> ENTRY: msg = Log 2 - Validation loss history log: val_loss_history.npy
2019-12-26 23:18:36.991471 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.991603 base_class.py 0137: custom_print -> ENTRY: msg = Log 3 - Training accuracy history log: train_acc_history.npy
2019-12-26 23:18:36.991691 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.991746 base_class.py 0137: custom_print -> ENTRY: msg = Log 4 - Training loss history log: train_loss_history.npy
2019-12-26 23:18:36.991822 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.991874 base_class.py 0137: custom_print -> ENTRY: msg = Log 5 - Training curve: train_loss_history.npy
2019-12-26 23:18:36.991955 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.992006 base_class.py 0137: custom_print -> ENTRY: msg = Log 6 - Validation curve: train_loss_history.npy
2019-12-26 23:18:36.992080 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:36.992131 base_class.py 0137: custom_print -> ENTRY: msg =
2019-12-26 23:18:36.992295 base_class.py 0137: custom_print <- EXIT
2019-12-26 23:18:37.256762 line.py 0007: create_train_test_plots_accuracy -> ENTRY: plots = [[tensor(0.5700, device='cuda:0', dtype=torch.float64), tensor(0.8250, device='cuda:0', dtype=torch.float64), tensor(0.8300, device='cuda:0', dtype=torch.float64), tensor(0.8500, device='cuda:0', dtype=torch.float64)], [tensor(0.8200, device='cuda:0', dtype=torch.float64), tensor(0.8800, device='cuda:0', dtype=torch.float64), tensor(0.8800, device='cuda:0', dtype=torch.float64), tensor(0.9600, device='cuda:0', dtype=torch.float64)]], labels = ['Epoch Num', 'Accuracy'], log_dir = /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/proj-1/output/logs/, show_img = False, save_img = True
2019-12-26 23:18:37.843191 line.py 0007: create_train_test_plots_accuracy <- EXIT
2019-12-26 23:18:37.843366 line.py 0020: create_train_test_plots_loss -> ENTRY: plots = [[0.7015185165405273, 0.471226949095726, 0.425327128469944, 0.3862212598323822], [0.532270929813385, 0.3959760844707489, 0.32916648328304293, 0.26463053584098817]], labels = ['Epoch Num', 'Loss'], log_dir = /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Resources/v0.1/workspace/exp-1/proj-1/output/logs/, show_img = False, save_img = True
2019-12-26 23:18:37.963512 line.py 0020: create_train_test_plots_loss <- EXIT
2019-12-26 23:18:37.965604 level_3_training_bas 0087: set_training_final <- EXIT
2019-12-26 23:18:37.965700 common.py 0061: save -> ENTRY: ---
2019-12-26 23:18:37.965765 base_system_state.py 0171: update_local_var -> ENTRY: ---
2019-12-26 23:18:37.965817 base_system_state.py 0171: update_local_var <- EXIT : ---
2019-12-26 23:18:37.966008 common.py 0017: write_json -> ENTRY: ---
2019-12-26 23:18:37.966643 common.py 0017: write_json <- EXIT
2019-12-26 23:18:37.966756 common.py 0061: save <- EXIT
2019-12-26 23:18:37.966814 level_14_master_main 0151: Train <- EXIT