| 74 | |
| 75 | |
| 76 | class Downsample(nn.Module): |
| 77 | def __init__(self, in_channels, with_conv): |
| 78 | super().__init__() |
| 79 | self.with_conv = with_conv |
| 80 | if self.with_conv: |
| 81 | # Do time downsampling here |
| 82 | # no asymmetric padding in torch conv, must do it ourselves |
| 83 | self.conv = torch.nn.Conv2d( |
| 84 | in_channels, in_channels, kernel_size=3, stride=2, padding=0 |
| 85 | ) |
| 86 | |
| 87 | def forward(self, x): |
| 88 | if self.with_conv: |
| 89 | pad = (0, 1, 0, 1) |
| 90 | x = torch.nn.functional.pad(x, pad, mode="constant", value=0) |
| 91 | x = self.conv(x) |
| 92 | else: |
| 93 | x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) |
| 94 | return x |
| 95 | |
| 96 | |
| 97 | class DownsampleTimeStride4(nn.Module): |