Convert the model into training mode while keep normalization layer freezed.
(self, mode=True)
| 674 | return tuple(outs) |
| 675 | |
| 676 | def train(self, mode=True): |
| 677 | """Convert the model into training mode while keep normalization layer |
| 678 | freezed.""" |
| 679 | super(ResNet, self).train(mode) |
| 680 | self._freeze_stages() |
| 681 | if mode and self.norm_eval: |
| 682 | for m in self.modules(): |
| 683 | # trick: eval have effect on BatchNorm only |
| 684 | if isinstance(m, _BatchNorm): |
| 685 | m.eval() |
| 686 | |
| 687 | |
| 688 | @BACKBONES.register_module() |
nothing calls this directly
no test coverage detected