Returns: scheduler (dict):{ 'scheduler': lr_scheduler, 'interval': 'step', # or 'epoch' }
(config, optimizer)
| 38 | |
| 39 | |
| 40 | def build_scheduler(config, optimizer): |
| 41 | """ |
| 42 | Returns: |
| 43 | scheduler (dict):{ |
| 44 | 'scheduler': lr_scheduler, |
| 45 | 'interval': 'step', # or 'epoch' |
| 46 | } |
| 47 | """ |
| 48 | # scheduler = {'interval': config.TRAINER.SCHEDULER_INTERVAL} |
| 49 | name = config.scheduler |
| 50 | lr = config.canonical_lr |
| 51 | |
| 52 | if name == 'OneCycleLR': |
| 53 | # scheduler = OneCycleLR(optimizer, ) |
| 54 | if hasattr(config, 'twins_lr_factor'): |
| 55 | factor = config.twins_lr_factor |
| 56 | scheduler = OneCycleLR(optimizer, [lr, lr*factor], config.num_steps+100, |
| 57 | pct_start=0.05, cycle_momentum=False, anneal_strategy=config.anneal_strategy) |
| 58 | else: |
| 59 | scheduler = OneCycleLR(optimizer, lr, config.num_steps+100, |
| 60 | pct_start=0.05, cycle_momentum=False, anneal_strategy=config.anneal_strategy) |
| 61 | # elif name == 'MultiStepLR': |
| 62 | # scheduler.update( |
| 63 | # {'scheduler': MultiStepLR(optimizer, config.TRAINER.MSLR_MILESTONES, gamma=config.TRAINER.MSLR_GAMMA)}) |
| 64 | #elif name == 'CosineAnnealing': |
| 65 | # scheduler = CosineAnnealingLR(optimizer, config.num_steps+100) |
| 66 | # scheduler.update( |
| 67 | # {'scheduler': CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}) |
| 68 | # elif name == 'ExponentialLR': |
| 69 | # scheduler.update( |
| 70 | # {'scheduler': ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}) |
| 71 | else: |
| 72 | raise NotImplementedError() |
| 73 | |
| 74 | return scheduler |