Forward pass. Args: x (tensor): input Returns: tensor: output
(self, x)
| 261 | self.skip_add = nn.quantized.FloatFunctional() |
| 262 | |
| 263 | def forward(self, x): |
| 264 | """Forward pass. |
| 265 | |
| 266 | Args: |
| 267 | x (tensor): input |
| 268 | |
| 269 | Returns: |
| 270 | tensor: output |
| 271 | """ |
| 272 | |
| 273 | out = self.activation(x) |
| 274 | out = self.conv1(out) |
| 275 | if self.bn==True: |
| 276 | out = self.bn1(out) |
| 277 | |
| 278 | out = self.activation(out) |
| 279 | out = self.conv2(out) |
| 280 | if self.bn==True: |
| 281 | out = self.bn2(out) |
| 282 | |
| 283 | if self.groups > 1: |
| 284 | out = self.conv_merge(out) |
| 285 | |
| 286 | return self.skip_add.add(out, x) |
| 287 | |
| 288 | # return out + x |
| 289 | |
| 290 | |
| 291 | class FeatureFusionBlock_custom(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected