()
| 21 | |
| 22 | |
| 23 | def main(): |
| 24 | args = create_argparser().parse_args() |
| 25 | |
| 26 | dist_util.setup_dist() |
| 27 | logger.configure() |
| 28 | |
| 29 | logger.log("creating model...") |
| 30 | model, diffusion = sr_create_model_and_diffusion( |
| 31 | **args_to_dict(args, sr_model_and_diffusion_defaults().keys()) |
| 32 | ) |
| 33 | model.load_state_dict( |
| 34 | dist_util.load_state_dict(args.model_path, map_location="cpu") |
| 35 | ) |
| 36 | model.to(dist_util.dev()) |
| 37 | if args.use_fp16: |
| 38 | model.convert_to_fp16() |
| 39 | model.eval() |
| 40 | |
| 41 | logger.log("loading data...") |
| 42 | data = load_data_for_worker(args.base_samples, args.batch_size, args.class_cond) |
| 43 | |
| 44 | logger.log("creating samples...") |
| 45 | all_images = [] |
| 46 | while len(all_images) * args.batch_size < args.num_samples: |
| 47 | model_kwargs = next(data) |
| 48 | model_kwargs = {k: v.to(dist_util.dev()) for k, v in model_kwargs.items()} |
| 49 | sample = diffusion.p_sample_loop( |
| 50 | model, |
| 51 | (args.batch_size, 3, args.large_size, args.large_size), |
| 52 | clip_denoised=args.clip_denoised, |
| 53 | model_kwargs=model_kwargs, |
| 54 | ) |
| 55 | sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8) |
| 56 | sample = sample.permute(0, 2, 3, 1) |
| 57 | sample = sample.contiguous() |
| 58 | |
| 59 | all_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())] |
| 60 | dist.all_gather(all_samples, sample) # gather not supported with NCCL |
| 61 | for sample in all_samples: |
| 62 | all_images.append(sample.cpu().numpy()) |
| 63 | logger.log(f"created {len(all_images) * args.batch_size} samples") |
| 64 | |
| 65 | arr = np.concatenate(all_images, axis=0) |
| 66 | arr = arr[: args.num_samples] |
| 67 | if dist.get_rank() == 0: |
| 68 | shape_str = "x".join([str(x) for x in arr.shape]) |
| 69 | out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz") |
| 70 | logger.log(f"saving to {out_path}") |
| 71 | np.savez(out_path, arr) |
| 72 | |
| 73 | dist.barrier() |
| 74 | logger.log("sampling complete") |
| 75 | |
| 76 | |
| 77 | def load_data_for_worker(base_samples, batch_size, class_cond): |
no test coverage detected