| |
| 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) |
|
|
