(opt, batch_size, trainer, ground_truth_dataset, mm_num_samples, mm_num_repeats)
| 332 | |
| 333 | |
| 334 | def get_motion_loader(opt, batch_size, trainer, ground_truth_dataset, mm_num_samples, mm_num_repeats): |
| 335 | |
| 336 | # Currently the configurations of two datasets are almost the same |
| 337 | if opt.dataset_name == 't2m' or opt.dataset_name == 'kit': |
| 338 | w_vectorizer = WordVectorizer('./data/glove', 'our_vab') |
| 339 | else: |
| 340 | raise KeyError('Dataset not recognized!!') |
| 341 | print('Generating %s ...' % opt.name) |
| 342 | |
| 343 | dataset = EvaluationDataset(opt, trainer, ground_truth_dataset, w_vectorizer, mm_num_samples, mm_num_repeats) |
| 344 | mm_dataset = MMGeneratedDataset(opt, dataset, w_vectorizer) |
| 345 | |
| 346 | motion_loader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn, drop_last=True, num_workers=4) |
| 347 | mm_motion_loader = DataLoader(mm_dataset, batch_size=1, num_workers=1) |
| 348 | |
| 349 | print('Generated Dataset Loading Completed!!!') |
| 350 | |
| 351 | return motion_loader, mm_motion_loader |
| 352 | |
| 353 | |
| 354 | def build_models(opt): |
no test coverage detected