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| import torch | |
| import numpy as np | |
| from lipreading.preprocess import * | |
| from lipreading.dataset import MyDataset, pad_packed_collate | |
| def get_preprocessing_pipelines(modality='video'): | |
| # -- preprocess for the video stream | |
| preprocessing = {} | |
| # -- LRW config | |
| if modality == 'video': | |
| crop_size = (88, 88) | |
| (mean, std) = (0.421, 0.165) | |
| # train : | |
| preprocessing['train'] = Compose([ # 여러 개의 preprocess를 사용할 때 Compose()를 사용한다. preprocess.py에 설정되어 있음 | |
| Normalize(0.0,255.0), | |
| RandomCrop(crop_size), | |
| HorizontalFlip(0.5), | |
| Normalize(mean, std) ]) | |
| preprocessing['val'] = Compose([ | |
| Normalize( 0.0,255.0 ), | |
| CenterCrop(crop_size), | |
| Normalize(mean, std) ]) | |
| preprocessing['test'] = preprocessing['val'] # test와 val이 같다 | |
| elif modality == 'raw_audio': | |
| preprocessing['train'] = Compose([ | |
| AddNoise( noise=np.load('./data/babbleNoise_resample_16K.npy')), # train에만 노이즈를 추가해 준다. | |
| NormalizeUtterance()]) | |
| preprocessing['val'] = NormalizeUtterance() # z-score 정규화를 수행 | |
| preprocessing['test'] = NormalizeUtterance() | |
| return preprocessing | |
| def get_data_loaders(args): | |
| preprocessing = get_preprocessing_pipelines( args.modality) | |
| # create dataset object for each partition | |
| dsets = {partition: MyDataset( | |
| modality=args.modality, | |
| data_partition=partition, | |
| data_dir=args.data_dir, | |
| label_fp=args.label_path, | |
| annonation_direc=args.annonation_direc, | |
| preprocessing_func=preprocessing[partition], | |
| data_suffix='.npz' | |
| ) for partition in ['train', 'val', 'test']} | |
| dset_loaders = {x: torch.utils.data.DataLoader( | |
| dsets[x], | |
| batch_size=args.batch_size, | |
| shuffle=True, | |
| collate_fn=pad_packed_collate, | |
| pin_memory=True, | |
| num_workers=args.workers, | |
| worker_init_fn=np.random.seed(1)) for x in ['train', 'val', 'test']} | |
| return dset_loaders | |