rahul003 commented on a change in pull request #11234: [MXNET-535] Add Warmup
Learning Rate Scheduler and fix bugs in LR Schedulers
URL: https://github.com/apache/incubator-mxnet/pull/11234#discussion_r196620100
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File path: python/mxnet/lr_scheduler.py
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@@ -153,18 +153,57 @@ class PolyScheduler(LRScheduler):
"""
- def __init__(self, max_update, base_lr=0.01, pwr=2):
- super(PolyScheduler, self).__init__(base_lr)
+ def __init__(self, max_update, pwr=2, **kwargs):
+ super(PolyScheduler, self).__init__(**kwargs)
assert isinstance(max_update, int)
if max_update < 1:
raise ValueError("maximum number of updates must be strictly
positive")
self.base_lr_orig = self.base_lr
self.max_update = max_update
self.power = pwr
- self.base_lr = self.base_lr_orig
def __call__(self, num_update):
if num_update <= self.max_update:
self.base_lr = self.base_lr_orig * pow(1.0 - float(num_update) /
float(self.max_update),
self.power)
return self.base_lr
+
+class WarmupScheduler(LRScheduler):
+ """Implement linear warmup starting from lr_begin to given scheduler's
base_lr.
+
+ Parameters
+ ----------
+ lr_begin: float
+ learning rate to start increasing from
+ warmup_steps: int
+ number of warmup steps
+ scheduler: LRScheduler
+ scheduler following the warmup
+ """
+ def __init__(self, lr_begin, warmup_steps, scheduler):
+ super(WarmupScheduler, self).__init__()
+ self.lr_begin = lr_begin
+ self.scheduler = scheduler
+ self.lr_final = self.scheduler.base_lr
+ if self.lr_begin > self.lr_final:
+ raise ValueError("Final lr has to be higher than beginning lr")
+ if warmup_steps <= 0:
+ raise ValueError("Warmup steps has to be positive")
+ self.warmup_steps = warmup_steps
+ self.lrs_updates = {}
Review comment:
We would have for each batch, number of calls to __call__ equal to the
number of learnable parameter arrays.
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