mirror of
https://github.com/hpcaitech/ColossalAI.git
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bcf0181ecd
* Distrifusion Support source * comp comm overlap optimization * sd3 benchmark * pixart distrifusion bug fix * sd3 bug fix and benchmark * generation bug fix * naming fix * add docstring, fix counter and shape error * add reference * readme and requirement
81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
# Code from https://github.com/mit-han-lab/distrifuser/blob/main/scripts/compute_metrics.py
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import argparse
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import os
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import numpy as np
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import torch
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from cleanfid import fid
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from PIL import Image
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from torch.utils.data import DataLoader, Dataset
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from torchmetrics.image import LearnedPerceptualImagePatchSimilarity, PeakSignalNoiseRatio
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from torchvision.transforms import Resize
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from tqdm import tqdm
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def read_image(path: str):
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"""
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input: path
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output: tensor (C, H, W)
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"""
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img = np.asarray(Image.open(path))
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if len(img.shape) == 2:
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img = np.repeat(img[:, :, None], 3, axis=2)
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img = torch.from_numpy(img).permute(2, 0, 1)
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return img
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class MultiImageDataset(Dataset):
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def __init__(self, root0, root1, is_gt=False):
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super().__init__()
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self.root0 = root0
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self.root1 = root1
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file_names0 = os.listdir(root0)
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file_names1 = os.listdir(root1)
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self.image_names0 = sorted([name for name in file_names0 if name.endswith(".png") or name.endswith(".jpg")])
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self.image_names1 = sorted([name for name in file_names1 if name.endswith(".png") or name.endswith(".jpg")])
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self.is_gt = is_gt
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assert len(self.image_names0) == len(self.image_names1)
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def __len__(self):
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return len(self.image_names0)
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def __getitem__(self, idx):
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img0 = read_image(os.path.join(self.root0, self.image_names0[idx]))
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if self.is_gt:
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# resize to 1024 x 1024
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img0 = Resize((1024, 1024))(img0)
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img1 = read_image(os.path.join(self.root1, self.image_names1[idx]))
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batch_list = [img0, img1]
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return batch_list
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--batch_size", type=int, default=64)
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parser.add_argument("--num_workers", type=int, default=8)
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parser.add_argument("--is_gt", action="store_true")
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parser.add_argument("--input_root0", type=str, required=True)
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parser.add_argument("--input_root1", type=str, required=True)
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args = parser.parse_args()
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psnr = PeakSignalNoiseRatio(data_range=(0, 1), reduction="elementwise_mean", dim=(1, 2, 3)).to("cuda")
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lpips = LearnedPerceptualImagePatchSimilarity(normalize=True).to("cuda")
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dataset = MultiImageDataset(args.input_root0, args.input_root1, is_gt=args.is_gt)
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dataloader = DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers)
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progress_bar = tqdm(dataloader)
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with torch.inference_mode():
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for i, batch in enumerate(progress_bar):
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batch = [img.to("cuda") / 255 for img in batch]
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batch_size = batch[0].shape[0]
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psnr.update(batch[0], batch[1])
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lpips.update(batch[0], batch[1])
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fid_score = fid.compute_fid(args.input_root0, args.input_root1)
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print("PSNR:", psnr.compute().item())
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print("LPIPS:", lpips.compute().item())
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print("FID:", fid_score)
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