diff --git a/_paramters.py b/_paramters.py index 23de70c..0719c35 100644 --- a/_paramters.py +++ b/_paramters.py @@ -32,12 +32,24 @@ main_arg_parser.add_argument("--data_stretch", type=strtobool, default=True, hel # Transformation Parameters main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0, help="") # 0.4 -main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0, help="") # 0.3 +main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="") # 0.3 main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0, help="") # 0.4 main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0, help="") # 0.2 main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0, help="") # 0.3 main_arg_parser.add_argument("--data_speed_factor", type=float, default=0, help="") # 0.7 +# Model Parameters +main_arg_parser.add_argument("--model_type", type=str, default="RCC", help="") +main_arg_parser.add_argument("--model_secondary_type", type=str, default="RCC", help="") +main_arg_parser.add_argument("--model_weight_init", type=str, default="xavier_normal_", help="") +main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="") +main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="") +main_arg_parser.add_argument("--model_classes", type=int, default=2, help="") +main_arg_parser.add_argument("--model_lat_dim", type=int, default=128, help="") +main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="") +main_arg_parser.add_argument("--model_norm", type=strtobool, default=True, help="") +main_arg_parser.add_argument("--model_dropout", type=float, default=0.2, help="") + # Training Parameters main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="") main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="") @@ -49,18 +61,6 @@ main_arg_parser.add_argument("--train_batch_size", type=int, default=300, help=" main_arg_parser.add_argument("--train_lr", type=float, default=1e-4, help="") main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="") -# Model Parameters -main_arg_parser.add_argument("--model_type", type=str, default="BCMC", help="") -main_arg_parser.add_argument("--model_secondary_type", type=str, default="BCMC", help="") -main_arg_parser.add_argument("--model_weight_init", type=str, default="xavier_normal_", help="") -main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="") -main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="") -main_arg_parser.add_argument("--model_classes", type=int, default=2, help="") -main_arg_parser.add_argument("--model_lat_dim", type=int, default=128, help="") -main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="") -main_arg_parser.add_argument("--model_norm", type=strtobool, default=True, help="") -main_arg_parser.add_argument("--model_dropout", type=float, default=0.2, help="") - # Project Parameters main_arg_parser.add_argument("--project_name", type=str, default=_ROOT.name, help="") main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="") diff --git a/main_inference.py b/main_inference.py index 2480f14..960a718 100644 --- a/main_inference.py +++ b/main_inference.py @@ -7,6 +7,7 @@ import variables as V from torch.utils.data import DataLoader, Dataset from torchvision.transforms import Compose, RandomApply +from ml_lib.audio_toolset.audio_augmentation import Speed from ml_lib.audio_toolset.audio_io import AudioToMel, NormalizeLocal, MelToImage # Dataset and Dataloaders @@ -17,6 +18,7 @@ from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipu from ml_lib.utils.logging import Logger from ml_lib.utils.model_io import SavedLightningModels from ml_lib.utils.transforms import ToTensor +from ml_lib.visualization.tools import Plotter from util.config import MConfig # Datasets @@ -25,31 +27,27 @@ from datasets.binar_masks import BinaryMasksDataset def prepare_dataloader(config_obj): mel_transforms = Compose([ - # Audio to Mel Transformations AudioToMel(sr=config_obj.data.sr, n_mels=config_obj.data.n_mels, n_fft=config_obj.data.n_fft, - hop_length=config_obj.data.hop_length), MelToImage()]) + hop_length=config_obj.data.hop_length), + MelToImage()]) transforms = Compose([NormalizeLocal(), ToTensor()]) aug_transforms = Compose([ - RandomApply([ - NoiseInjection(config_obj.data.noise_ratio), - LoudnessManipulator(config_obj.data.loudness_ratio), - ShiftTime(config_obj.data.shift_ratio), - MaskAug(config_obj.data.mask_ratio), - ], p=0.6), - # Utility + NoiseInjection(0.4), + LoudnessManipulator(0.4), + ShiftTime(0.3), + MaskAug(0.2), NormalizeLocal(), ToTensor() ]) - dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='train', + dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='test', mel_transforms=mel_transforms, transforms=transforms ) # noinspection PyTypeChecker return DataLoader(dataset, batch_size=None, num_workers=0, shuffle=False) -def restore_logger_and_model(config_obj): - logger = Logger(config_obj) - model = SavedLightningModels.load_checkpoint(models_root_path=logger.log_dir, n=-2) +def restore_logger_and_model(log_dir): + model = SavedLightningModels.load_checkpoint(models_root_path=log_dir, n=-2) model = model.restore() if torch.cuda.is_available(): model.cuda() @@ -63,16 +61,18 @@ if __name__ == '__main__': model_type = 'CC' parameters = 'CC_213adb16e46592c5a405abfbd693835e/' version = 'version_41' + model_path = Path('/home/steffen/projects/inter_challenge_2020/output/CC/CC_fd2020a7ead9d5c80609a7364741f24b/version_40') config_filename = 'config.ini' inference_out = 'manual_test_out.csv' config = MConfig() - config.read_file((outpath / model_type / parameters / version / config_filename).open('r')) + config.read_file((Path(model_path) / config_filename).open('r')) test_dataloader = prepare_dataloader(config) - loaded_model = restore_logger_and_model(config) + + loaded_model = restore_logger_and_model(model_path) loaded_model.eval() - with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile: + with (model_path / inference_out).open(mode='w') as outfile: outfile.write(f'file_name,prediction\n') for batch in tqdm(test_dataloader, total=len(test_dataloader)): diff --git a/util/metric_reader.py b/util/metric_reader.py index 3cf1968..ae31c3e 100644 --- a/util/metric_reader.py +++ b/util/metric_reader.py @@ -12,8 +12,12 @@ config_file_name = 'config.ini' if __name__ == '__main__': for model_path in outpath.iterdir(): + if not model_path.is_dir(): + continue out_file = (model_path / metric_file_name) for paramter_configuration in model_path.iterdir(): + if not model_path.is_dir(): + continue uar_scores = defaultdict(list) for metric_file in paramter_configuration.rglob(metric_file_name): with metric_file.open('r') as f: @@ -35,7 +39,7 @@ if __name__ == '__main__': metric_dict[header].append(value) for score, func in zip(['mean', 'max', 'median', 'std'], [np.mean, np.max, np.median, np.std]): try: - uar_scores[score].append(func(np.asarray(metric_dict['uar_score'])).round(2)) + uar_scores[score].append(round(func(np.asarray(metric_dict['uar_score'])) * 100, 2)) except ValueError as e: print(e) pass @@ -46,5 +50,8 @@ if __name__ == '__main__': writer = csv.DictWriter(f, delimiter=',', lineterminator='\n', fieldnames=headers) if not file_existed: writer.writeheader() # file doesn't exist yet, write a header - for row_idx in range(len(uar_scores['mean'])): - writer.writerow({key: uar_scores[key][row_idx] for key in headers}) + try: + for row_idx in range(len(uar_scores['mean'])): + writer.writerow({key: uar_scores[key][row_idx] for key in headers}) + except IndexError: + print('could not read: ') \ No newline at end of file