(self, opt)
| 376 | class EvaluatorModelWrapper(object): |
| 377 | |
| 378 | def __init__(self, opt): |
| 379 | |
| 380 | if opt.dataset_name == 't2m': |
| 381 | opt.dim_pose = 263 |
| 382 | elif opt.dataset_name == 'kit': |
| 383 | opt.dim_pose = 251 |
| 384 | else: |
| 385 | raise KeyError('Dataset not Recognized!!!') |
| 386 | |
| 387 | opt.dim_word = 300 |
| 388 | opt.max_motion_length = 196 |
| 389 | opt.dim_pos_ohot = len(POS_enumerator) |
| 390 | opt.dim_motion_hidden = 1024 |
| 391 | opt.max_text_len = 20 |
| 392 | opt.dim_text_hidden = 512 |
| 393 | opt.dim_coemb_hidden = 512 |
| 394 | |
| 395 | self.text_encoder, self.motion_encoder, self.movement_encoder = build_models(opt) |
| 396 | self.opt = opt |
| 397 | self.device = opt.device |
| 398 | |
| 399 | self.text_encoder.to(opt.device) |
| 400 | self.motion_encoder.to(opt.device) |
| 401 | self.movement_encoder.to(opt.device) |
| 402 | |
| 403 | self.text_encoder.eval() |
| 404 | self.motion_encoder.eval() |
| 405 | self.movement_encoder.eval() |
| 406 | |
| 407 | # Please note that the results does not following the order of inputs |
| 408 | def get_co_embeddings(self, word_embs, pos_ohot, cap_lens, motions, m_lens): |
nothing calls this directly
no test coverage detected