Update learning rate. Args: current_iter (int): Current iteration. warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up. Default： -1.
(self, current_iter, warmup_iter=-1)
| 183 | return init_lr_groups_l |
| 184 | |
| 185 | def update_learning_rate(self, current_iter, warmup_iter=-1): |
| 186 | """Update learning rate. |
| 187 | |
| 188 | Args: |
| 189 | current_iter (int): Current iteration. |
| 190 | warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up. |
| 191 | Default： -1. |
| 192 | """ |
| 193 | if current_iter > 1: |
| 194 | for scheduler in self.schedulers: |
| 195 | scheduler.step() |
| 196 | # set up warm-up learning rate |
| 197 | if current_iter < warmup_iter: |
| 198 | # get initial lr for each group |
| 199 | init_lr_g_l = self._get_init_lr() |
| 200 | # modify warming-up learning rates |
| 201 | # currently only support linearly warm up |
| 202 | warm_up_lr_l = [] |
| 203 | for init_lr_g in init_lr_g_l: |
| 204 | warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g]) |
| 205 | # set learning rate |
| 206 | self._set_lr(warm_up_lr_l) |
| 207 | |
| 208 | def get_current_learning_rate(self): |
| 209 | return [param_group['lr'] for param_group in self.optimizers[0].param_groups] |
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