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