forked from mrq/DL-Art-School
Fix datasets
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codes/data/README.md
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codes/data/README.md
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@ -84,6 +84,7 @@ class BaseUnsupervisedImageDataset(data.Dataset):
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h, w = self.target_hq_size, self.target_hq_size
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else:
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hqs_adjusted, hq_refs_adjusted, hq_masks_adjusted, hq_centers_adjusted = imgs_hq, refs_hq, masks_hq, centers_hq
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hq_masks_adjusted = [m.squeeze(-1) for m in hq_masks_adjusted] # This is done implicitly above..
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hq_multiple = self.multiple * self.scale # Multiple must apply to LQ image.
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if h % hq_multiple != 0 or w % hq_multiple != 0:
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hqs_conformed, hq_refs_conformed, hq_masks_conformed, hq_centers_conformed = [], [], [], []
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@ -150,6 +150,9 @@ class TiledDataset(data.Dataset):
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thres_sz = self.opt['thres_sz']
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h, w, c = img.shape
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if crop_sz > h:
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return []
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h_space = np.arange(0, h - crop_sz + 1, step)
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if h - (h_space[-1] + crop_sz) > thres_sz:
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h_space = np.append(h_space, h - crop_sz)
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@ -291,7 +291,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_teco_vix_using_rrdb_features.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_adrianna_srflow8x.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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