import torch torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True import torch.nn.functional as F import torch.distributed as dist from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from torchvision import transforms from tqdm import tqdm import os from PIL import Image import numpy as np import argparse import itertools from skimage.metrics import peak_signal_noise_ratio as psnr_loss from skimage.metrics import structural_similarity as ssim_loss from dataset.augmentation import center_crop_arr from dataset.build import build_dataset from tokenizer.tokenizer_image.vq_model import VQ_models def create_npz_from_sample_folder(sample_dir, num=50000): """ Builds a single .npz file from a folder of .png samples. """ samples = [] for i in tqdm(range(num), desc="Building .npz file from samples"): sample_pil = Image.open(f"{sample_dir}/{i:06d}.png") sample_np = np.asarray(sample_pil).astype(np.uint8) samples.append(sample_np) samples = np.stack(samples) assert samples.shape == (num, samples.shape[1], samples.shape[2], 3) npz_path = f"{sample_dir}.npz" np.savez(npz_path, arr_0=samples) print(f"Saved .npz file to {npz_path} [shape={samples.shape}].") return npz_path def main(args): # Setup PyTorch: assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU. sample.py supports CPU-only usage" torch.set_grad_enabled(False) # Setup DDP: dist.init_process_group("nccl") rank = dist.get_rank() device = rank % torch.cuda.device_count() seed = args.global_seed * dist.get_world_size() + rank torch.manual_seed(seed) torch.cuda.set_device(device) print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.") # create and load model vq_model = VQ_models[args.vq_model]( codebook_size=args.codebook_size, codebook_embed_dim=args.codebook_embed_dim) vq_model.to(device) vq_model.eval() checkpoint = torch.load(args.vq_ckpt, map_location="cpu") if "ema" in checkpoint: # ema model_weight = checkpoint["ema"] elif "model" in checkpoint: # ddp model_weight = checkpoint["model"] elif "state_dict" in checkpoint: model_weight = checkpoint["state_dict"] else: raise Exception("please check model weight") vq_model.load_state_dict(model_weight) del checkpoint # Create folder to save samples: folder_name = (f"{args.vq_model}-{args.dataset}-size-{args.image_size}-size-{args.image_size_eval}" f"-codebook-size-{args.codebook_size}-dim-{args.codebook_embed_dim}-seed-{args.global_seed}") sample_folder_dir = f"{args.sample_dir}/{folder_name}" if rank == 0: os.makedirs(sample_folder_dir, exist_ok=True) print(f"Saving .png samples at {sample_folder_dir}") dist.barrier() # Setup data: transform = transforms.Compose([ transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, args.image_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) ]) if args.dataset == 'imagenet': dataset = build_dataset(args, transform=transform) num_fid_samples = 50000 elif args.dataset == 'coco': dataset = build_dataset(args, transform=transform) num_fid_samples = 5000 else: raise Exception("please check dataset") sampler = DistributedSampler( dataset, num_replicas=dist.get_world_size(), rank=rank, shuffle=False, seed=args.global_seed ) loader = DataLoader( dataset, batch_size=args.per_proc_batch_size, shuffle=False, sampler=sampler, num_workers=args.num_workers, pin_memory=True, drop_last=False ) # Figure out how many samples we need to generate on each GPU and how many iterations we need to run: n = args.per_proc_batch_size global_batch_size = n * dist.get_world_size() psnr_val_rgb = [] ssim_val_rgb = [] loader = tqdm(loader) if rank == 0 else loader total = 0 for x, _ in loader: if args.image_size_eval != args.image_size: rgb_gts = F.interpolate(x, size=(args.image_size_eval, args.image_size_eval), mode='bicubic') else: rgb_gts = x rgb_gts = (rgb_gts.permute(0, 2, 3, 1).to("cpu").numpy() + 1.0) / 2.0 # rgb_gt value is between [0, 1] x = x.to(device, non_blocking=True) with torch.no_grad(): latent, _, [_, _, indices] = vq_model.encode(x) samples = vq_model.decode_code(indices, latent.shape) # output value is between [-1, 1] if args.image_size_eval != args.image_size: samples = F.interpolate(samples, size=(args.image_size_eval, args.image_size_eval), mode='bicubic') samples = torch.clamp(127.5 * samples + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy() # Save samples to disk as individual .png files for i, (sample, rgb_gt) in enumerate(zip(samples, rgb_gts)): index = i * dist.get_world_size() + rank + total Image.fromarray(sample).save(f"{sample_folder_dir}/{index:06d}.png") # metric rgb_restored = sample.astype(np.float32) / 255. # rgb_restored value is between [0, 1] psnr = psnr_loss(rgb_restored, rgb_gt) ssim = ssim_loss(rgb_restored, rgb_gt, multichannel=True, data_range=2.0, channel_axis=-1) psnr_val_rgb.append(psnr) ssim_val_rgb.append(ssim) total += global_batch_size # ------------------------------------ # Summary # ------------------------------------ # Make sure all processes have finished saving their samples dist.barrier() world_size = dist.get_world_size() gather_psnr_val = [None for _ in range(world_size)] gather_ssim_val = [None for _ in range(world_size)] dist.all_gather_object(gather_psnr_val, psnr_val_rgb) dist.all_gather_object(gather_ssim_val, ssim_val_rgb) if rank == 0: gather_psnr_val = list(itertools.chain(*gather_psnr_val)) gather_ssim_val = list(itertools.chain(*gather_ssim_val)) psnr_val_rgb = sum(gather_psnr_val) / len(gather_psnr_val) ssim_val_rgb = sum(gather_ssim_val) / len(gather_ssim_val) print("PSNR: %f, SSIM: %f " % (psnr_val_rgb, ssim_val_rgb)) result_file = f"{sample_folder_dir}_results.txt" print("writing results to {}".format(result_file)) with open(result_file, 'w') as f: print("PSNR: %f, SSIM: %f " % (psnr_val_rgb, ssim_val_rgb), file=f) create_npz_from_sample_folder(sample_folder_dir, num_fid_samples) print("Done.") dist.barrier() dist.destroy_process_group() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--data-path", type=str, required=True) parser.add_argument("--dataset", type=str, choices=['imagenet', 'coco'], default='imagenet') parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16") parser.add_argument("--vq-ckpt", type=str, default=None, help="ckpt path for vq model") parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization") parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization") parser.add_argument("--image-size", type=int, choices=[256, 384, 512], default=256) parser.add_argument("--image-size-eval", type=int, choices=[256, 384, 512], default=256) parser.add_argument("--sample-dir", type=str, default="reconstructions") parser.add_argument("--per-proc-batch-size", type=int, default=32) parser.add_argument("--global-seed", type=int, default=0) parser.add_argument("--num-workers", type=int, default=4) args = parser.parse_args() main(args)