(self, in_channels, with_conv)
| 66 | |
| 67 | class Downsample(nn.Module): |
| 68 | def __init__(self, in_channels, with_conv): |
| 69 | super().__init__() |
| 70 | self.with_conv = with_conv |
| 71 | if self.with_conv: |
| 72 | # no asymmetric padding in torch conv, must do it ourselves |
| 73 | self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) |
| 74 | |
| 75 | def forward(self, x): |
| 76 | if self.with_conv: |