| 51 | |
| 52 | |
| 53 | class Upsample(nn.Module): |
| 54 | def __init__(self, in_channels, with_conv): |
| 55 | super().__init__() |
| 56 | self.with_conv = with_conv |
| 57 | if self.with_conv: |
| 58 | self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) |
| 59 | |
| 60 | def forward(self, x): |
| 61 | x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") |
| 62 | if self.with_conv: |
| 63 | x = self.conv(x) |
| 64 | return x |
| 65 | |
| 66 | |
| 67 | class Downsample(nn.Module): |