| 162 | |
| 163 | |
| 164 | class AttnBlock(nn.Module): |
| 165 | def __init__(self, in_channels, zq_ch=None, add_conv=False): |
| 166 | super().__init__() |
| 167 | self.in_channels = in_channels |
| 168 | |
| 169 | self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv) |
| 170 | self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| 171 | self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| 172 | self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| 173 | self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| 174 | |
| 175 | def forward(self, x, zq): |
| 176 | h_ = x |
| 177 | h_ = self.norm(h_, zq) |
| 178 | q = self.q(h_) |
| 179 | k = self.k(h_) |
| 180 | v = self.v(h_) |
| 181 | |
| 182 | # compute attention |
| 183 | b, c, h, w = q.shape |
| 184 | q = q.reshape(b, c, h * w) |
| 185 | q = q.permute(0, 2, 1) # b,hw,c |
| 186 | k = k.reshape(b, c, h * w) # b,c,hw |
| 187 | w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] |
| 188 | w_ = w_ * (int(c) ** (-0.5)) |
| 189 | w_ = torch.nn.functional.softmax(w_, dim=2) |
| 190 | |
| 191 | # attend to values |
| 192 | v = v.reshape(b, c, h * w) |
| 193 | w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) |
| 194 | h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] |
| 195 | h_ = h_.reshape(b, c, h, w) |
| 196 | |
| 197 | h_ = self.proj_out(h_) |
| 198 | |
| 199 | return x + h_ |
| 200 | |
| 201 | |
| 202 | class MOVQDecoder(nn.Module): |