(opti, config)
| 24 | np.random.seed(np.random.get_state()[1][0] + worker_id) |
| 25 | |
| 26 | def build_lambda_sche(opti, config): |
| 27 | if config.get('decay_step') is not None: |
| 28 | lr_lbmd = lambda e: max(config.lr_decay ** (e / config.decay_step), config.lowest_decay) |
| 29 | scheduler = torch.optim.lr_scheduler.LambdaLR(opti, lr_lbmd) |
| 30 | else: |
| 31 | raise NotImplementedError() |
| 32 | return scheduler |
| 33 | |
| 34 | def build_lambda_bnsche(model, config): |
| 35 | if config.get('decay_step') is not None: |