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0122a25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | """Optimizer configuration."""
from __future__ import annotations
from ml_collections import ConfigDict
from mapdet3d.config.typing import (
LrSchedulerConfig,
OptimizerConfig,
ParamGroupCfg,
)
def get_lr_scheduler_cfg(
scheduler: ConfigDict,
begin: int = 0,
end: int = -1,
epoch_based: bool = True,
convert_epochs_to_steps: bool = False,
convert_attributes: list[str] | None = None,
) -> LrSchedulerConfig:
"""Default learning rate scheduler configuration.
This creates a config object that can be initialized as a LearningRate
scheduler for training.
Args:
scheduler (ConfigDict): Learning rate scheduler configuration.
begin (int, optional): Begin epoch. Defaults to 0.
end (int, optional): End epoch. Defaults to None. Defaults to -1.
epoch_based (bool, optional): Whether the learning rate scheduler is
epoch based or step based. Defaults to True.
convert_epochs_to_steps (bool): Whether to convert the begin and end
for a step based scheduler to steps automatically based on length
of train dataloader. Enables users to set the iteration breakpoints
as epochs. Defaults to False.
convert_attributes (list[str] | None): List of attributes in the
scheduler that should be converted to steps. Defaults to None.
Returns:
LrSchedulerConfig: Config dict that can be instantiated as LearningRate
scheduler.
"""
lr_scheduler = LrSchedulerConfig()
lr_scheduler.scheduler = scheduler
lr_scheduler.begin = begin
lr_scheduler.end = end
lr_scheduler.epoch_based = epoch_based
lr_scheduler.convert_epochs_to_steps = convert_epochs_to_steps
lr_scheduler.convert_attributes = convert_attributes
return lr_scheduler
def get_optimizer_cfg(
optimizer: ConfigDict,
lr_schedulers: list[LrSchedulerConfig] | None = None,
param_groups: list[ParamGroupCfg] | None = None,
) -> OptimizerConfig:
"""Default optimizer configuration.
This creates a config object that can be initialized as an Optimizer for
training.
Args:
optimizer (ConfigDict): Optimizer configuration.
lr_schedulers (list[LrSchedulerConfig] | None, optional): Learning rate
schedulers configuration. Defaults to None.
param_groups (list[ParamGroupCfg] | None, optional): Parameter groups
configuration. Defaults to None.
Returns:
OptimizerConfig: Config dict that can be instantiated as Optimizer.
"""
optim = OptimizerConfig()
optim.optimizer = optimizer
optim.lr_schedulers = lr_schedulers
optim.param_groups = param_groups
return optim
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