| import math |
| from torch.optim.lr_scheduler import LambdaLR |
|
|
|
|
| def warmup_cosine(step, warmup_step, total_step, minimum_ratio=1e-5): |
| if step <= warmup_step and warmup_step > 0: |
| return step / warmup_step |
| return max( |
| 0.5 * (1 + math.cos((step - warmup_step) / (total_step - warmup_step) * math.pi)), |
| minimum_ratio |
| ) |
|
|
| def warmup_exp(step, warmup_step, total_step, **kwargs): |
| if step <= warmup_step and warmup_step > 0: |
| return step / warmup_step |
| return kwargs["gamma"] ** (step * 1. / (total_step - warmup_step)) |
|
|
|
|
| def get_scheduler(cfg, optimizer, total_steps): |
| warmup_steps = cfg.solver.sched.args.warmup_steps * cfg.num_gpu |
| minimum_ratio = cfg.solver.sched.args.get("minimum_ratio", 1e-5) |
| lambda_func = lambda step: globals()[cfg.solver.sched.name]( |
| step, warmup_steps, total_steps, minimum_ratio=minimum_ratio |
| ) |
| return LambdaLR(optimizer=optimizer, lr_lambda=lambda_func) |
|
|
