Improve multiframe dataset memory usage
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@ -30,9 +30,6 @@ class BaseUnsupervisedImageDataset(data.Dataset):
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cache_path = os.path.join(path, 'cache.pth')
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if os.path.exists(cache_path):
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chunks = torch.load(cache_path)
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# Update the options.
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for c in chunks:
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c.reload(opt)
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else:
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chunks = [ChunkWithReference(opt, d) for d in sorted(os.scandir(path), key=lambda e: e.name) if d.is_dir()]
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# Prune out chunks that have no images
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@ -6,29 +6,15 @@ import numpy as np
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# Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
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class ChunkWithReference:
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def __init__(self, opt, path):
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self.reload(opt)
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self.path = path.path
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self.tiles, _ = util.get_image_paths('img', self.path)
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self.centers = None
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def reload(self, opt):
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self.opt = opt
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self.ref = None # This is loaded on the fly.
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self.cache_ref = opt['cache_ref'] if 'cache_ref' in opt.keys() else False
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def __getitem__(self, item):
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# Load centers on the fly and always cache.
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if self.centers is None:
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self.centers = torch.load(osp.join(self.path, "centers.pt"))
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if self.cache_ref:
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if self.ref is None:
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self.ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
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ref = self.ref
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else:
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ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
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centers = torch.load(osp.join(self.path, "centers.pt"))
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ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
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tile = util.read_img(None, self.tiles[item], rgb=True)
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tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
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center, tile_width = self.centers[tile_id]
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center, tile_width = centers[tile_id]
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mask = np.full(tile.shape[:2] + (1,), fill_value=.1, dtype=tile.dtype)
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mask[center[0] - tile_width // 2:center[0] + tile_width // 2, center[1] - tile_width // 2:center[1] + tile_width // 2] = 1
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@ -56,8 +56,7 @@ class MultiFrameDataset(BaseUnsupervisedImageDataset):
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lq_ref = torch.cat([lq_ref, lq_mask], dim=1)
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return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref,
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'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long),
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'LQ_path': path, 'GT_path': path}
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'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long)}
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if __name__ == '__main__':
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