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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
from mmengine.optim.scheduler import PolyLR
from mmseg.registry import PARAM_SCHEDULERS
@PARAM_SCHEDULERS.register_module()
class PolyLRRatio(PolyLR):
"""Implements polynomial learning rate decay with ratio.
This scheduler adjusts the learning rate of each parameter group
following a polynomial decay equation. The decay can occur in
conjunction with external parameter adjustments made outside this
scheduler.
Args:
optimizer (Optimizer or OptimWrapper): Wrapped optimizer.
eta_min (float): Minimum learning rate at the end of scheduling.
Defaults to 0.
eta_min_ratio (float, optional): The ratio of the minimum parameter
value to the base parameter value. Either `eta_min` or
`eta_min_ratio` should be specified. Defaults to None.
power (float): The power of the polynomial. Defaults to 1.0.
begin (int): Step at which to start updating the parameters.
Defaults to 0.
end (int): Step at which to stop updating the parameters.
Defaults to INF.
last_step (int): The index of last step. Used for resume without
state dict. Defaults to -1.
by_epoch (bool): Whether the scheduled parameters are updated by
epochs. Defaults to True.
verbose (bool): Whether to print the value for each update.
Defaults to False.
"""
def __init__(self, eta_min_ratio: Optional[int] = None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.eta_min_ratio = eta_min_ratio
def _get_value(self):
"""Compute value using chainable form of the scheduler."""
if self.last_step == 0:
return [
group[self.param_name] for group in self.optimizer.param_groups
]
param_groups_value = []
for base_value, param_group in zip(self.base_values,
self.optimizer.param_groups):
eta_min = self.eta_min if self.eta_min_ratio is None else \
base_value * self.eta_min_ratio
step_ratio = (1 - 1 /
(self.total_iters - self.last_step + 1))**self.power
step_value = (param_group[self.param_name] -
eta_min) * step_ratio + eta_min
param_groups_value.append(step_value)
return param_groups_value
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