(self, x)
| 225 | self.norm = nn.BatchNorm3d(self.out_filters, affine=True) |
| 226 | |
| 227 | def forward(self, x): |
| 228 | out = x.pop() |
| 229 | for up_block in self.up_blocks: |
| 230 | out = up_block(out) |
| 231 | skip = x.pop() |
| 232 | out = torch.cat([out, skip], dim=1) |
| 233 | out = self.conv(out) |
| 234 | out = self.norm(out) |
| 235 | out = F.relu(out) |
| 236 | return out |
| 237 | |
| 238 | |
| 239 | class Hourglass(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected