Convert the model into training mode while keep normalization layer freezed.
(self, mode=True)
| 364 | param.requires_grad = False |
| 365 | |
| 366 | def train(self, mode=True): |
| 367 | """Convert the model into training mode while keep normalization layer |
| 368 | freezed.""" |
| 369 | super(VoVNet, self).train(mode) |
| 370 | self._freeze_stages() |
| 371 | if mode and self.norm_eval: |
| 372 | for m in self.modules(): |
| 373 | # trick: eval have effect on BatchNorm only |
| 374 | if isinstance(m, _BatchNorm): |
| 375 | m.eval() |
no test coverage detected