Residual convolution module.
| 244 | |
| 245 | |
| 246 | class ResidualConvUnit(nn.Module): |
| 247 | """Residual convolution module. |
| 248 | """ |
| 249 | |
| 250 | def __init__(self, features): |
| 251 | """Init. |
| 252 | |
| 253 | Args: |
| 254 | features (int): number of features |
| 255 | """ |
| 256 | super().__init__() |
| 257 | |
| 258 | self.conv1 = nn.Conv2d( |
| 259 | features, features, kernel_size=3, stride=1, padding=1, bias=True |
| 260 | ) |
| 261 | |
| 262 | self.conv2 = nn.Conv2d( |
| 263 | features, features, kernel_size=3, stride=1, padding=1, bias=True |
| 264 | ) |
| 265 | |
| 266 | self.relu = nn.ReLU(inplace=True) |
| 267 | |
| 268 | def forward(self, x): |
| 269 | """Forward pass. |
| 270 | |
| 271 | Args: |
| 272 | x (tensor): input |
| 273 | |
| 274 | Returns: |
| 275 | tensor: output |
| 276 | """ |
| 277 | out = self.relu(x) |
| 278 | out = self.conv1(out) |
| 279 | out = self.relu(out) |
| 280 | out = self.conv2(out) |
| 281 | |
| 282 | return out + x |
| 283 | |
| 284 | |
| 285 | class FeatureFusionBlock(nn.Module): |