74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
from pathlib import Path
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import torch
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from tqdm import tqdm
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import variables as V
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from torch.utils.data import DataLoader, Dataset
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from torchvision.transforms import Compose
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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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# =============================================================================
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# Transforms
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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 util.config import MConfig
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# Datasets
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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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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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transforms = Compose([NormalizeLocal(), ToTensor()])
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dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='test',
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mel_transforms=mel_transforms, transforms=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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def restore_logger_and_model(config_obj):
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logger = Logger(config_obj)
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model = SavedLightningModels.load_checkpoint(models_root_path=logger.log_dir, n=-2)
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model = model.restore()
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if torch.cuda.is_available():
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model.cuda()
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else:
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model.cpu()
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return model
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if __name__ == '__main__':
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outpath = Path('output')
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model_type = 'BandwiseConvMultiheadClassifier'
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parameters = 'BCMC_9c70168a5711c269b33701f1650adfb9/'
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version = 'version_1'
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config_filename = 'config.ini'
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inference_out = 'manual_test_out.csv'
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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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loaded_model = restore_logger_and_model(config)
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loaded_model.eval()
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with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile:
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outfile.write(f'file_name,prediction\n')
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for batch in tqdm(test_dataloader, total=len(test_dataloader)):
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batch_x, file_name = batch
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y = loaded_model(batch_x.unsqueeze(0).to(device='cuda' if torch.cuda.is_available() else 'cpu')).main_out
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prediction = (y.squeeze() >= 0.5).int().item()
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prediction = 'clear' if prediction == V.CLEAR else 'mask'
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outfile.write(f'{file_name},{prediction}\n')
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print('Done')
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