| 196 | |
| 197 | |
| 198 | class Downsample2D(nn.Module): |
| 199 | def __init__(self, channels, use_conv=False, out_channels=None, padding=1): |
| 200 | super().__init__() |
| 201 | self.channels = channels |
| 202 | self.out_channels = out_channels or channels |
| 203 | self.use_conv = use_conv |
| 204 | self.padding = padding |
| 205 | stride = 2 |
| 206 | |
| 207 | if use_conv: |
| 208 | self.conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) |
| 209 | else: |
| 210 | assert self.channels == self.out_channels |
| 211 | self.conv = nn.AvgPool2d(kernel_size=stride, stride=stride) |
| 212 | |
| 213 | def forward(self, x): |
| 214 | assert x.shape[-1] == self.channels |
| 215 | if self.use_conv and self.padding == 0: |
| 216 | pad = (0, 0, 0, 1, 0, 1) |
| 217 | x = F.pad(x, pad, mode="constant", value=0) |
| 218 | |
| 219 | assert x.shape[-1] == self.channels |
| 220 | # x = self.conv(x) |
| 221 | x = base_conv2d(x, self.conv, channel_last=True) |
| 222 | return x |
| 223 | |
| 224 | |
| 225 | |