| 158 | |
| 159 | class AttnBlock(nn.Module): |
| 160 | def __init__(self, in_channels): |
| 161 | super().__init__() |
| 162 | self.in_channels = in_channels |
| 163 | |
| 164 | self.norm = Normalize(in_channels) |
| 165 | self.q = torch.nn.Conv2d(in_channels, |
| 166 | in_channels, |
| 167 | kernel_size=1, |
| 168 | stride=1, |
| 169 | padding=0) |
| 170 | self.k = torch.nn.Conv2d(in_channels, |
| 171 | in_channels, |
| 172 | kernel_size=1, |
| 173 | stride=1, |
| 174 | padding=0) |
| 175 | self.v = torch.nn.Conv2d(in_channels, |
| 176 | in_channels, |
| 177 | kernel_size=1, |
| 178 | stride=1, |
| 179 | padding=0) |
| 180 | self.proj_out = torch.nn.Conv2d(in_channels, |
| 181 | in_channels, |
| 182 | kernel_size=1, |
| 183 | stride=1, |
| 184 | padding=0) |
| 185 | |
| 186 | def forward(self, x): |
| 187 | h_ = x |