(self, name)
| 56 | test_mode=test_mode) |
| 57 | |
| 58 | def load_anno(self, name): |
| 59 | results = {} |
| 60 | if self.siamese_mode: |
| 61 | motion_path = os.path.join(self.motion_dir, name + '.npz') |
| 62 | motion_data = np.load(motion_path) |
| 63 | results['motion1'] = motion_data['motion1'] |
| 64 | results['motion2'] = motion_data['motion2'] |
| 65 | assert results['motion1'].shape == results['motion2'].shape |
| 66 | else: |
| 67 | motion_path = os.path.join(self.motion_dir, name + '.npy') |
| 68 | motion_data = np.load(motion_path) |
| 69 | results['motion'] = motion_data |
| 70 | text_path = os.path.join(self.text_dir, name + '.txt') |
| 71 | text_data = [] |
| 72 | for line in open(text_path, 'r'): |
| 73 | text_data.append(line.strip()) |
| 74 | results['text'] = text_data |
| 75 | if self.token_dir is not None: |
| 76 | token_path = os.path.join(self.token_dir, name + '.txt') |
| 77 | token_data = [] |
| 78 | for line in open(token_path, 'r'): |
| 79 | token_data.append(line.strip()) |
| 80 | results['token'] = token_data |
| 81 | if self.clip_feat_dir is not None: |
| 82 | clip_feat_path = os.path.join(self.clip_feat_dir, name + '.npy') |
| 83 | clip_feat = torch.from_numpy(np.load(clip_feat_path)) |
| 84 | results['clip_feat'] = clip_feat |
| 85 | return results |
| 86 | |
| 87 | def prepare_data(self, idx: int): |
| 88 | """"Prepare raw data for the f'{idx'}-th data.""" |
nothing calls this directly
no outgoing calls
no test coverage detected