tokenizer/consistencydecoder/reconstruction_cd_ddp.py

import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import torch.distributed as dist
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision.datasets import ImageFolder
from torchvision import transforms
from tqdm import tqdm
import os
import itertools
from PIL import Image
import numpy as np
import argparse
import random

from skimage.metrics import peak_signal_noise_ratio as psnr_loss
from skimage.metrics import structural_similarity as ssim_loss
from diffusers.models import ConsistencyDecoderVAE


class SingleFolderDataset(Dataset):
    def __init__(self, directory, transform=None):
        super().__init__()
        self.directory = directory
        self.transform = transform
        self.image_paths = [os.path.join(directory, file_name) for file_name in os.listdir(directory)
                            if os.path.isfile(os.path.join(directory, file_name))]

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        image_path = self.image_paths[idx]
        image = Image.open(image_path).convert('RGB')
        if self.transform:
            image = self.transform(image)
        return image, torch.tensor(0)


def create_npz_from_sample_folder(sample_dir, num=50_000):
    """
    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)

    random.shuffle(samples) # This is very important for IS(Inception Score) !!!
    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 center_crop_arr(pil_image, image_size):
    """
    Center cropping implementation from ADM.
    https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
    """
    while min(*pil_image.size) >= 2 * image_size:
        pil_image = pil_image.resize(
            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
        )

    scale = image_size / min(*pil_image.size)
    pil_image = pil_image.resize(
        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
    )

    arr = np.array(pil_image)
    crop_y = (arr.shape[0] - image_size) // 2
    crop_x = (arr.shape[1] - image_size) // 2
    return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])


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 env
    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
    vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16).to("cuda:{}".format(device))

    # Create folder to save samples:
    folder_name = f"openai-consistencydecoder-{args.dataset}-size-{args.image_size}-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 = ImageFolder(args.data_path, transform=transform)
        num_fid_samples = 50000
    elif args.dataset == 'coco':
        dataset = SingleFolderDataset(args.data_path, 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:
        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.half().to("cuda:{}".format(device))
        with torch.no_grad():
            # Map input images to latent space + normalize latents:
            latent = vae.encode(x).latent_dist.sample().mul_(0.18215)
            # reconstruct:
            samples = vae.decode(latent / 0.18215).sample # output value is between [-1, 1]
        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("--image-size", type=int, choices=[256, 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)