noise repair, fingerprint check
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Si11ium
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4a6b32d1dd
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d88a6bcf71
@ -31,13 +31,25 @@ main_arg_parser.add_argument("--data_mixup", type=strtobool, default=False, help
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main_arg_parser.add_argument("--data_stretch", type=strtobool, default=True, help="")
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# Transformation Parameters
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main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0.4, help="") # 0.4
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main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="") # 0.3
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main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0.4, help="") # 0.4
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main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0.2, help="") # 0.2
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main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0, help="") # 0.4
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main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0, help="") # 0.3
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main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0, help="") # 0.4
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main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0, help="") # 0.2
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main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0.3, help="") # 0.3
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main_arg_parser.add_argument("--data_speed_factor", type=float, default=0.7, help="") # 0.7
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# Model Parameters
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main_arg_parser.add_argument("--model_type", type=str, default="RCC", help="")
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main_arg_parser.add_argument("--model_secondary_type", type=str, default="RCC", help="")
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main_arg_parser.add_argument("--model_weight_init", type=str, default="xavier_normal_", help="")
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main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
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main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="")
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main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
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main_arg_parser.add_argument("--model_lat_dim", type=int, default=128, help="")
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main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_norm", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_dropout", type=float, default=0.2, help="")
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# Training Parameters
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main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
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main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
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@ -49,18 +61,6 @@ main_arg_parser.add_argument("--train_batch_size", type=int, default=300, help="
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main_arg_parser.add_argument("--train_lr", type=float, default=1e-4, help="")
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main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
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# Model Parameters
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main_arg_parser.add_argument("--model_type", type=str, default="BCMC", help="")
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main_arg_parser.add_argument("--model_secondary_type", type=str, default="BCMC", help="")
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main_arg_parser.add_argument("--model_weight_init", type=str, default="xavier_normal_", help="")
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main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
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main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="")
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main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
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main_arg_parser.add_argument("--model_lat_dim", type=int, default=128, help="")
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main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_norm", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_dropout", type=float, default=0.2, help="")
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# Project Parameters
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main_arg_parser.add_argument("--project_name", type=str, default=_ROOT.name, help="")
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main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
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@ -7,6 +7,7 @@ import variables as V
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from torch.utils.data import DataLoader, Dataset
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from torchvision.transforms import Compose, RandomApply
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from ml_lib.audio_toolset.audio_augmentation import Speed
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from ml_lib.audio_toolset.audio_io import AudioToMel, NormalizeLocal, MelToImage
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# Dataset and Dataloaders
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@ -17,6 +18,7 @@ from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipu
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from ml_lib.utils.logging import Logger
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from ml_lib.utils.model_io import SavedLightningModels
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from ml_lib.utils.transforms import ToTensor
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from ml_lib.visualization.tools import Plotter
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from util.config import MConfig
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# Datasets
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@ -25,23 +27,21 @@ from datasets.binar_masks import BinaryMasksDataset
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def prepare_dataloader(config_obj):
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mel_transforms = Compose([
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# Audio to Mel Transformations
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Speed(0, 0),
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AudioToMel(sr=config_obj.data.sr, n_mels=config_obj.data.n_mels, n_fft=config_obj.data.n_fft,
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hop_length=config_obj.data.hop_length), MelToImage()])
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hop_length=config_obj.data.hop_length),
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MelToImage()])
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transforms = Compose([NormalizeLocal(), ToTensor()])
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aug_transforms = Compose([
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RandomApply([
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NoiseInjection(config_obj.data.noise_ratio),
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LoudnessManipulator(config_obj.data.loudness_ratio),
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ShiftTime(config_obj.data.shift_ratio),
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MaskAug(config_obj.data.mask_ratio),
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], p=0.6),
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# Utility
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NoiseInjection(0.4),
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LoudnessManipulator(0.4),
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ShiftTime(0.3),
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MaskAug(0.2),
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NormalizeLocal(), ToTensor()
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])
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dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='train',
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mel_transforms=mel_transforms, transforms=transforms
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mel_transforms=mel_transforms, transforms=aug_transforms
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)
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# noinspection PyTypeChecker
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return DataLoader(dataset, batch_size=None, num_workers=0, shuffle=False)
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@ -69,6 +69,13 @@ if __name__ == '__main__':
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config = MConfig()
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config.read_file((outpath / model_type / parameters / version / config_filename).open('r'))
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test_dataloader = prepare_dataloader(config)
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p = Plotter(outpath)
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from matplotlib import pyplot as plt
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d = test_dataloader.dataset[100][0].squeeze()
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plt.imshow(d)
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p.save_current_figure('100')
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loaded_model = restore_logger_and_model(config)
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loaded_model.eval()
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@ -12,6 +12,8 @@ config_file_name = 'config.ini'
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if __name__ == '__main__':
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for model_path in outpath.iterdir():
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if not model_path.is_dir():
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continue
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out_file = (model_path / metric_file_name)
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for paramter_configuration in model_path.iterdir():
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uar_scores = defaultdict(list)
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@ -46,5 +48,8 @@ if __name__ == '__main__':
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writer = csv.DictWriter(f, delimiter=',', lineterminator='\n', fieldnames=headers)
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if not file_existed:
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writer.writeheader() # file doesn't exist yet, write a header
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try:
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for row_idx in range(len(uar_scores['mean'])):
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writer.writerow({key: uar_scores[key][row_idx] for key in headers})
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except IndexError:
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print('could not read: ')
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