Convert the model into training mode will keeping the normalization layer freezed.
(self, mode=True)
| 631 | return y_list |
| 632 | |
| 633 | def train(self, mode=True): |
| 634 | """Convert the model into training mode will keeping the normalization |
| 635 | layer freezed.""" |
| 636 | super(HRNet, self).train(mode) |
| 637 | self._freeze_stages() |
| 638 | if mode and self.norm_eval: |
| 639 | for m in self.modules(): |
| 640 | # trick: eval have effect on BatchNorm only |
| 641 | if isinstance(m, _BatchNorm): |
| 642 | m.eval() |
nothing calls this directly
no test coverage detected