tokenizer_image/reconstruction_vq_ddp.py

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)