Residual convolution module.
| 153 | |
| 154 | |
| 155 | class ResidualConvUnit(nn.Module): |
| 156 | """Residual convolution module. |
| 157 | """ |
| 158 | |
| 159 | def __init__(self, features): |
| 160 | """Init. |
| 161 | |
| 162 | Args: |
| 163 | features (int): number of features |
| 164 | """ |
| 165 | super().__init__() |
| 166 | |
| 167 | self.conv1 = nn.Conv2d( |
| 168 | features, features, kernel_size=3, stride=1, padding=1, bias=True |
| 169 | ) |
| 170 | |
| 171 | self.conv2 = nn.Conv2d( |
| 172 | features, features, kernel_size=3, stride=1, padding=1, bias=True |
| 173 | ) |
| 174 | |
| 175 | self.relu = nn.ReLU(inplace=True) |
| 176 | |
| 177 | def forward(self, x): |
| 178 | """Forward pass. |
| 179 | |
| 180 | Args: |
| 181 | x (tensor): input |
| 182 | |
| 183 | Returns: |
| 184 | tensor: output |
| 185 | """ |
| 186 | out = self.relu(x) |
| 187 | out = self.conv1(out) |
| 188 | out = self.relu(out) |
| 189 | out = self.conv2(out) |
| 190 | |
| 191 | return out + x |
| 192 | |
| 193 | |
| 194 | class FeatureFusionBlock(nn.Module): |