| 149 | |
| 150 | class AttnBlock(nn.Module): |
| 151 | def __init__(self, in_channels): |
| 152 | super().__init__() |
| 153 | self.in_channels = in_channels |
| 154 | |
| 155 | self.norm = Normalize(in_channels) |
| 156 | self.q = torch.nn.Conv2d(in_channels, |
| 157 | in_channels, |
| 158 | kernel_size=1, |
| 159 | stride=1, |
| 160 | padding=0) |
| 161 | self.k = torch.nn.Conv2d(in_channels, |
| 162 | in_channels, |
| 163 | kernel_size=1, |
| 164 | stride=1, |
| 165 | padding=0) |
| 166 | self.v = torch.nn.Conv2d(in_channels, |
| 167 | in_channels, |
| 168 | kernel_size=1, |
| 169 | stride=1, |
| 170 | padding=0) |
| 171 | self.proj_out = torch.nn.Conv2d(in_channels, |
| 172 | in_channels, |
| 173 | kernel_size=1, |
| 174 | stride=1, |
| 175 | padding=0) |
| 176 | |
| 177 | |
| 178 | def forward(self, x): |