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import warnings |
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import torch.nn as nn |
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import torch.nn.functional as F |
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def resize(input, |
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size=None, |
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scale_factor=None, |
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mode='nearest', |
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align_corners=None, |
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warning=False): |
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if warning: |
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if size is not None and align_corners: |
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input_h, input_w = tuple(int(x) for x in input.shape[2:]) |
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output_h, output_w = tuple(int(x) for x in size) |
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if output_h > input_h or output_w > output_h: |
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if ((output_h > 1 and output_w > 1 and input_h > 1 |
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and input_w > 1) and (output_h - 1) % (input_h - 1) |
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and (output_w - 1) % (input_w - 1)): |
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warnings.warn( |
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f'When align_corners={align_corners}, ' |
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'the output would more aligned if ' |
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f'input size {(input_h, input_w)} is `x+1` and ' |
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f'out size {(output_h, output_w)} is `nx+1`') |
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return F.interpolate(input, size, scale_factor, mode, align_corners) |
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class Upsample(nn.Module): |
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def __init__(self, |
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size=None, |
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scale_factor=None, |
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mode='nearest', |
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align_corners=None): |
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super(Upsample, self).__init__() |
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self.size = size |
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if isinstance(scale_factor, tuple): |
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self.scale_factor = tuple(float(factor) for factor in scale_factor) |
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else: |
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self.scale_factor = float(scale_factor) if scale_factor else None |
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self.mode = mode |
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self.align_corners = align_corners |
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def forward(self, x): |
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if not self.size: |
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size = [int(t * self.scale_factor) for t in x.shape[-2:]] |
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else: |
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size = self.size |
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return resize(x, size, None, self.mode, self.align_corners) |
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