29 lines
1.1 KiB
Python
29 lines
1.1 KiB
Python
import torch.nn.functional as F
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class InputPadder:
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"""Pads images such that dimensions are divisible by 8"""
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# TODO: Ideally, this should be part of the eval transforms preset, instead
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# of being part of the validation code. It's not obvious what a good
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# solution would be, because we need to unpad the predicted flows according
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# to the input images' size, and in some datasets (Kitti) images can have
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# variable sizes.
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def __init__(self, dims, mode="sintel"):
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self.ht, self.wd = dims[-2:]
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pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
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pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8
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if mode == "sintel":
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self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]
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else:
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self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, 0, pad_ht]
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def pad(self, *inputs):
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return [F.pad(x, self._pad, mode="replicate") for x in inputs]
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def unpad(self, x):
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ht, wd = x.shape[-2:]
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c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
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return x[..., c[0] : c[1], c[2] : c[3]]
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