| 259 | return x |
| 260 | |
| 261 | def init_params(self): |
| 262 | for m in self.modules(): |
| 263 | if isinstance(m, nn.Conv2d): |
| 264 | # init.kaiming_normal_(m.weight, mode='fan_out') |
| 265 | init.normal_(m.weight, std=0.01) |
| 266 | # init.xavier_normal_(m.weight) |
| 267 | if m.bias is not None: |
| 268 | init.constant_(m.bias, 0) |
| 269 | elif isinstance(m, nn.ConvTranspose2d): |
| 270 | # init.kaiming_normal_(m.weight, mode='fan_out') |
| 271 | init.normal_(m.weight, std=0.01) |
| 272 | # init.xavier_normal_(m.weight) |
| 273 | if m.bias is not None: |
| 274 | init.constant_(m.bias, 0) |
| 275 | elif isinstance(m, nn.BatchNorm2d): # NN.Batchnorm2d |
| 276 | init.constant_(m.weight, 1) |
| 277 | init.constant_(m.bias, 0) |
| 278 | elif isinstance(m, nn.Linear): |
| 279 | init.normal_(m.weight, std=0.01) |
| 280 | if m.bias is not None: |
| 281 | init.constant_(m.bias, 0) |
| 282 | |
| 283 | |
| 284 | |