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import os.path as osp
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import glob
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import cv2
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import numpy as np
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import torch
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import RRDBNet_arch as arch
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model_path = 'models/RRDB_ESRGAN_x4.pth'
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device = torch.device('cpu')
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test_img_folder = 'LR/*'
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model = arch.RRDBNet(3, 3, 64, 23, gc=32)
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model.load_state_dict(torch.load(model_path), strict=True)
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model.eval()
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model = model.to(device)
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print('Model path {:s}. \nTesting...'.format(model_path))
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idx = 0
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for path in glob.glob(test_img_folder):
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idx += 1
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base = osp.splitext(osp.basename(path))[0]
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print(idx, base)
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img = cv2.imread(path, cv2.IMREAD_COLOR)
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img = img * 1.0 / 255
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img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
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img_LR = img.unsqueeze(0)
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img_LR = img_LR.to(device)
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with torch.no_grad():
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output = model(img_LR).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()
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cv2.imwrite('results/{:s}_rlt.png'.format(base), output)
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