74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import argparse
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import cv2
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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 torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from torch.nn import functional as F
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth')
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parser.add_argument('--scale', type=int, default=4)
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parser.add_argument('--suffix', type=str, default='_out')
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parser.add_argument('--input', type=str, default='inputs', help='input image or folder')
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args = parser.parse_args()
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# set up model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=args.scale)
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loadnet = torch.load(args.model_path)
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if 'params_ema' in loadnet:
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keyname = 'params_ema'
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else:
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keyname = 'params'
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model.load_state_dict(loadnet[keyname], strict=True)
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model.eval()
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model = model.to(device)
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os.makedirs('results/', exist_ok=True)
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for idx, path in enumerate(sorted(glob.glob(os.path.join(args.input, '*')))):
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imgname = os.path.splitext(os.path.basename(path))[0]
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print('Testing', idx, imgname)
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# read image
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img = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
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img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
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img = img.unsqueeze(0).to(device)
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if args.scale == 2:
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mod_scale = 2
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elif args.scale == 1:
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mod_scale = 4
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else:
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mod_scale = None
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if mod_scale is not None:
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h_pad, w_pad = 0, 0
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_, _, h, w = img.size()
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if (h % mod_scale != 0):
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h_pad = (mod_scale - h % mod_scale)
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if (w % mod_scale != 0):
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w_pad = (mod_scale - w % mod_scale)
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img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect')
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try:
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# inference
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with torch.no_grad():
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output = model(img)
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# remove extra pad
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if mod_scale is not None:
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_, _, h, w = output.size()
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output = output[:, :, 0:h - h_pad, 0:w - w_pad]
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# save image
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output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
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output = (output * 255.0).round().astype(np.uint8)
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cv2.imwrite(f'results/{imgname}_{args.suffix}.png', output)
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except Exception as error:
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print('Error', error)
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if __name__ == '__main__':
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main()
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