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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='./experiments/pretrained_models/vqgan')
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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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sum_latent = np.zeros((codebook_size)).astype('float64')
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size_latent = 16
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latent = {}
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latent['orig'] = {}
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latent['hflip'] = {}
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for i in ['orig', 'hflip']:
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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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img = cv2.imread(img_path)
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if i == 'hflip':
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cv2.flip(img, 1, img)
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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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x, feat_dict = vqgan.encoder(img, True)
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x, _, log = vqgan.quantize(x)
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torch.cuda.empty_cache()
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min_encoding_indices = log['min_encoding_indices']
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min_encoding_indices = min_encoding_indices.view(size_latent,size_latent)
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latent[i][img_name[:-4]] = min_encoding_indices.cpu().numpy()
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print(img_name, latent[i][img_name[:-4]].shape)
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latent_save_path = os.path.join(save_root, f'latent_gt_code{codebook_size}.pth')
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torch.save(latent, latent_save_path)
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print(f'\nLatent GT code are saved in {save_root}')
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