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import argparse |
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import glob |
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import numpy as np |
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import os |
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import cv2 |
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import torch |
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from torchvision.transforms.functional import normalize |
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from basicsr.utils import imwrite, img2tensor, tensor2img |
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from basicsr.utils.registry import ARCH_REGISTRY |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-i', '--test_path', type=str, default='datasets/ffhq/ffhq_512') |
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parser.add_argument('-o', '--save_root', type=str, default='./results/vqgan_rec') |
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parser.add_argument('--codebook_size', type=int, default=1024) |
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parser.add_argument('--ckpt_path', type=str, default='./experiments/pretrained_models/vqgan/net_g.pth') |
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args = parser.parse_args() |
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if args.save_root.endswith('/'): |
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args.save_root = args.save_root[:-1] |
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dir_name = os.path.abspath(args.save_root) |
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os.makedirs(dir_name, exist_ok=True) |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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test_path = args.test_path |
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save_root = args.save_root |
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ckpt_path = args.ckpt_path |
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codebook_size = args.codebook_size |
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vqgan = ARCH_REGISTRY.get('VQAutoEncoder')(512, 64, [1, 2, 2, 4, 4, 8], 'nearest', |
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codebook_size=codebook_size).to(device) |
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checkpoint = torch.load(ckpt_path)['params_ema'] |
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vqgan.load_state_dict(checkpoint) |
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vqgan.eval() |
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for img_path in sorted(glob.glob(os.path.join(test_path, '*.[jp][pn]g'))): |
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img_name = os.path.basename(img_path) |
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print(img_name) |
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img = cv2.imread(img_path) |
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img = img2tensor(img / 255., bgr2rgb=True, float32=True) |
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normalize(img, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) |
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img = img.unsqueeze(0).to(device) |
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with torch.no_grad(): |
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output = vqgan(img)[0] |
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output = tensor2img(output, min_max=[-1,1]) |
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img = tensor2img(img, min_max=[-1,1]) |
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restored_img = np.concatenate([img, output], axis=1) |
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restored_img = output |
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del output |
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torch.cuda.empty_cache() |
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path = os.path.splitext(os.path.join(save_root, img_name))[0] |
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save_path = f'{path}.png' |
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imwrite(restored_img, save_path) |
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print(f'\nAll results are saved in {save_root}') |
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