Fix dataset for a val set that includes lq
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@ -232,9 +232,14 @@ class FullImageDataset(data.Dataset):
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if self.paths_LQ:
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LQ_path = self.get_lq_path(index)
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img_lq_full = util.read_img(None, LQ_path, None)
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img_lq_full = util.augment([img_lq_full], self.opt['use_flip'], self.opt['use_rot'])[0]
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img_lq_full = self.get_square_image(img_lq_full)
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img_LQ, lq_fullsize_ref, lq_mask, lq_center = self.pull_tile(img_lq_full, lq=True)
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if self.opt['phase'] == 'train':
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img_lq_full = util.augment([img_lq_full], self.opt['use_flip'], self.opt['use_rot'])[0]
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img_lq_full = self.get_square_image(img_lq_full)
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img_LQ, lq_fullsize_ref, lq_mask, lq_center = self.pull_tile(img_lq_full, lq=True)
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else:
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img_LQ, lq_fullsize_ref = img_lq_full, img_lq_full
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lq_mask = np.ones(img_lq_full.shape[:2], dtype=lq_fullsize_ref.dtype)
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lq_center = torch.tensor([img_lq_full.shape[0] // 2, img_lq_full.shape[1] // 2], dtype=torch.long)
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else: # down-sampling on-the-fly
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# randomly scale during training
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if self.opt['phase'] == 'train':
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