Convert the model into training mode while keep normalization layer freezed.
(self, mode=True)
| 617 | return tuple(outs) |
| 618 | |
| 619 | def train(self, mode=True): |
| 620 | """Convert the model into training mode while keep normalization layer |
| 621 | freezed.""" |
| 622 | super(ResNet, self).train(mode) |
| 623 | self._freeze_stages() |
| 624 | if mode and self.norm_eval: |
| 625 | for m in self.modules(): |
| 626 | # trick: eval have effect on BatchNorm only |
| 627 | if isinstance(m, _BatchNorm): |
| 628 | m.eval() |
| 629 | |
| 630 | |
| 631 | class ResNetV1d(ResNet): |
no test coverage detected