| 187 | |
| 188 | class AttnBlock(nn.Module): |
| 189 | def __init__(self, in_channels): |
| 190 | super().__init__() |
| 191 | self.in_channels = in_channels |
| 192 | |
| 193 | self.norm = Normalize(in_channels) |
| 194 | self.q = torch.nn.Conv2d(in_channels, |
| 195 | in_channels, |
| 196 | kernel_size=1, |
| 197 | stride=1, |
| 198 | padding=0) |
| 199 | self.k = torch.nn.Conv2d(in_channels, |
| 200 | in_channels, |
| 201 | kernel_size=1, |
| 202 | stride=1, |
| 203 | padding=0) |
| 204 | self.v = torch.nn.Conv2d(in_channels, |
| 205 | in_channels, |
| 206 | kernel_size=1, |
| 207 | stride=1, |
| 208 | padding=0) |
| 209 | self.proj_out = torch.nn.Conv2d(in_channels, |
| 210 | in_channels, |
| 211 | kernel_size=1, |
| 212 | stride=1, |
| 213 | padding=0) |
| 214 | |
| 215 | |
| 216 | def forward(self, x): |