(self)
| 342 | f"in {(t1 - t0) * 1000:.2f} ms") |
| 343 | |
| 344 | def load_dar(self): |
| 345 | ckpt = Path(self.ckpt) |
| 346 | dar_cfg_path = ckpt.parent / '.hydra' / 'config.yaml' |
| 347 | # dar_cfg_path = ckpt.parent / 'cfg.yaml' |
| 348 | self.dar_cfg = OmegaConf.load(dar_cfg_path) |
| 349 | |
| 350 | cfg = self.dar_cfg |
| 351 | cfg.device = str(self._device) |
| 352 | cfg.ckpt.dar = str(ckpt) |
| 353 | cfg.train.manager.device = str(self._device) |
| 354 | cfg.train.manager.platform._target_ = 'robotmdar.train.train_platforms.NoPlatform' |
| 355 | cfg.data.datadir = self.config.dar.datadir |
| 356 | cfg.skeleton.asset.assetRoot = self.config.dar.skeleton_assetRoot |
| 357 | cfg.data.val.split = 'none' |
| 358 | cfg.data.val.batch_size = 1 |
| 359 | cfg.use_full_sample = self.use_full_sample |
| 360 | cfg.guidance_scale = self.guidance_scale |
| 361 | |
| 362 | seed.set(cfg.seed) |
| 363 | self.clip_model = load_and_freeze_clip("ViT-B/32", device=self._device) |
| 364 | val_data: Dataset = instantiate(cfg.data.val) |
| 365 | vae: VAE = instantiate(cfg.vae) |
| 366 | denoiser: Denoiser = instantiate(cfg.denoiser) |
| 367 | |
| 368 | schedule_sampler: SSampler = instantiate( |
| 369 | cfg.diffusion.schedule_sampler) |
| 370 | diffusion: Diffusion = schedule_sampler.diffusion |
| 371 | |
| 372 | vae.eval() |
| 373 | denoiser.eval() |
| 374 | |
| 375 | # Load checkpoints |
| 376 | manager: DARManager = instantiate(cfg.train.manager) |
| 377 | manager.hold_model(vae, denoiser, None, val_data) |
| 378 | |
| 379 | # vae_trt = vae |
| 380 | # denoiser_trt = denoiser |
| 381 | try: |
| 382 | vae_trt = torch.compile(vae, backend='tensorrt') |
| 383 | denoiser_trt = torch.compile(denoiser, backend='tensorrt') |
| 384 | except KeyError as e: |
| 385 | error_key = e.args[0] |
| 386 | if error_key == 'torch_dynamo_backends': |
| 387 | # Now we are in jetson env, which does not support torch.compile |
| 388 | vae_trt, denoiser_trt = jetson_compatible_torch_compile( |
| 389 | vae, denoiser, cfg) |
| 390 | else: |
| 391 | raise e |
| 392 | |
| 393 | # vae_trt = torch.compile(vae, backend='inductor') |
| 394 | # denoiser_trt = torch.compile(denoiser, backend='inductor') |
| 395 | |
| 396 | cfg_denoiser = ClassifierFreeWrapper(denoiser_trt) |
| 397 | |
| 398 | self.dataset = val_data |
| 399 | self.dataiter = iter(val_data) |
| 400 | self._motion_dt = 1 / val_data.fps |
| 401 | self.future_len = cfg.data.future_len |
no test coverage detected