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Improve multiframe dataset memory usage

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This commit is contained in:
James Betker 2020-10-09 08:40:00 -06:00
parent afe6af88af
commit b3d0baaf17
3 changed files with 4 additions and 22 deletions
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3
codes/data/base_unsupervised_image_dataset.py
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@ -30,9 +30,6 @@ class BaseUnsupervisedImageDataset(data.Dataset):
cache_path = os.path.join(path, 'cache.pth') cache_path = os.path.join(path, 'cache.pth')
if os.path.exists(cache_path): if os.path.exists(cache_path):
chunks = torch.load(cache_path) chunks = torch.load(cache_path)
# Update the options.
for c in chunks:
c.reload(opt)
else: else:
chunks = [ChunkWithReference(opt, d) for d in sorted(os.scandir(path), key=lambda e: e.name) if d.is_dir()] chunks = [ChunkWithReference(opt, d) for d in sorted(os.scandir(path), key=lambda e: e.name) if d.is_dir()]
# Prune out chunks that have no images # Prune out chunks that have no images

18
codes/data/chunk_with_reference.py
View File

@ -6,29 +6,15 @@ import numpy as np
# Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates. # Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
class ChunkWithReference: class ChunkWithReference:
def __init__(self, opt, path): def __init__(self, opt, path):
self.reload(opt)
self.path = path.path self.path = path.path
self.tiles, _ = util.get_image_paths('img', self.path) self.tiles, _ = util.get_image_paths('img', self.path)
self.centers = None
def reload(self, opt):
self.opt = opt
self.ref = None # This is loaded on the fly.
self.cache_ref = opt['cache_ref'] if 'cache_ref' in opt.keys() else False
def __getitem__(self, item): def __getitem__(self, item):
# Load centers on the fly and always cache. centers = torch.load(osp.join(self.path, "centers.pt"))
if self.centers is None:
self.centers = torch.load(osp.join(self.path, "centers.pt"))
if self.cache_ref:
if self.ref is None:
self.ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
ref = self.ref
else:
ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True) ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
tile = util.read_img(None, self.tiles[item], rgb=True) tile = util.read_img(None, self.tiles[item], rgb=True)
tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0]) tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
center, tile_width = self.centers[tile_id] center, tile_width = centers[tile_id]
mask = np.full(tile.shape[:2] + (1,), fill_value=.1, dtype=tile.dtype) mask = np.full(tile.shape[:2] + (1,), fill_value=.1, dtype=tile.dtype)
mask[center[0] - tile_width // 2:center[0] + tile_width // 2, center[1] - tile_width // 2:center[1] + tile_width // 2] = 1 mask[center[0] - tile_width // 2:center[0] + tile_width // 2, center[1] - tile_width // 2:center[1] + tile_width // 2] = 1

3
codes/data/multi_frame_dataset.py
View File

@ -56,8 +56,7 @@ class MultiFrameDataset(BaseUnsupervisedImageDataset):
lq_ref = torch.cat([lq_ref, lq_mask], dim=1) lq_ref = torch.cat([lq_ref, lq_mask], dim=1)
return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref, return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref,
'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long), 'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long)}
'LQ_path': path, 'GT_path': path}
if __name__ == '__main__': if __name__ == '__main__':