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39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
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# Copyright (c) Alibaba, Inc. and its affiliates.
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from functools import partial
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import mmcv
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import numpy as np
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from six.moves import map, zip
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def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
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num_imgs = tensor.size(0)
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mean = np.array(mean, dtype=np.float32)
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std = np.array(std, dtype=np.float32)
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imgs = []
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for img_id in range(num_imgs):
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img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
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img = mmcv.imdenormalize(
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img, mean, std, to_bgr=to_rgb).astype(np.uint8)
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imgs.append(np.ascontiguousarray(img))
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return imgs
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def multi_apply(func, *args, **kwargs):
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pfunc = partial(func, **kwargs) if kwargs else func
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map_results = map(pfunc, *args)
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return tuple(map(list, zip(*map_results)))
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def unmap(data, count, inds, fill=0):
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""" Unmap a subset of item (data) back to the original set of items (of
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size count) """
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if data.dim() == 1:
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ret = data.new_full((count, ), fill)
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ret[inds] = data
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else:
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new_size = (count, ) + data.size()[1:]
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ret = data.new_full(new_size, fill)
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ret[inds, :] = data
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return ret
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