| import os | |
| import glob | |
| import numpy as np | |
| import torch | |
| class LRW(): | |
| def __init__(self, folds, | |
| labels_file = './data/label_sorted.txt', | |
| root_path = '/beegfs/cy1355/lipread_datachunk_big/', | |
| transform = None): | |
| """ | |
| Args: | |
| labels_file (string): Path to the text file with labels | |
| root_path (string): Path to the file with the facial landmarks and audio features (MFCC) | |
| folds (string): train / val / test indicator | |
| transform (callable, optional): Optional transform to be applied | |
| on a sample | |
| """ | |
| self.folds = folds | |
| self.labels_file = labels_file | |
| self.root_path = root_path | |
| with open(self.labels_file) as myfile: | |
| self.data_dir = myfile.read().splitlines() | |
| self.video_file = os.path.join(self.root_path, 'video_' + self.folds+ '.npy') | |
| self.audio_file = os.path.join(self.root_path, 'audio_' + self.folds +'.npy') | |
| self.video = npy_loader(self.video_file) | |
| self.audio = npy_loader(self.audio_file) | |
| print('Loading {} part'.format(self.folds)) | |
| def __len__(self): | |
| return self.video.shape[0] | |
| def __getitem__(self, idx): | |
| vid = self.augmentation(self.video[idx, :, :, :]) | |
| aud = self.audio[idx, :, :] | |
| labels = 0 | |
| return (vid, aud), labels | |
| def augmentation(self, keypoints): | |
| keypoints_move = keypoints * 0.7 | |
| ones = torch.ones(keypoints.shape, dtype = torch.float) | |
| randint = torch.randint(1,73,(1,),dtype = torch.float) | |
| if self.folds == 'train': | |
| d = keypoints_move + ones * randint | |
| else: | |
| d = keypoints_move + ones * 38 | |
| return d | |
| def npy_loader(file): | |
| return torch.tensor(np.load(file)).float() | |