10822158814

Committed 12 Sep 2024 12:59AM UTC coverage: 83.288% (+0.02%) from 83.266%

Build # 10822158814

Build Type

Pull #509

github

Committed by

web-flow

Commit Message

Merge 4885f624e into fdd3d322a

Pull Request Pull Request #509: Apply line-length=120 to black format

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437 of 618 new or added lines in 157 files covered. (70.71%)

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80.0

/pypots/optim/lr_scheduler/lambda_lrs.py

"""
Lambda learning rate scheduler.
"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

from typing import Callable, Union

from .base import LRScheduler, logger


class LambdaLR(LRScheduler):
    """Sets the learning rate of each parameter group to the initial lr times a given function.
    When last_epoch=-1, sets initial lr as lr.

    Parameters
    ----------
    lr_lambda: Callable or list,
        A function which computes a multiplicative factor given an integer parameter epoch, or a list of such
        functions, one for each group in optimizer.param_groups.

    last_epoch: int,
        The index of last epoch. Default: -1.

    verbose: bool,
        If ``True``, prints a message to stdout for each update. Default: ``False``.

    Notes
    -----
    This class works the same with ``torch.optim.lr_scheduler.LambdaLR``.
    The only difference that is also why we implement them is that you don't have to pass according optimizers
    into them immediately while initializing them.

    Example
    -------
    >>> lambda1 = lambda epoch: epoch // 30
    >>> scheduler = LambdaLR(lr_lambda=lambda1)
    >>> adam = pypots.optim.Adam(lr=1e-3, lr_scheduler=scheduler)

    """

    def __init__(
        self,
        lr_lambda: Union[Callable, list],
        last_epoch: int = -1,
        verbose: bool = False,
    ):
        super().__init__(last_epoch, verbose)
        self.lr_lambda = lr_lambda
        self.lr_lambdas = None

    def init_scheduler(self, optimizer):
        if not isinstance(self.lr_lambda, list) and not isinstance(self.lr_lambda, tuple):
            self.lr_lambdas = [self.lr_lambda] * len(optimizer.param_groups)
        else:
            if len(self.lr_lambda) != len(optimizer.param_groups):
                raise ValueError(
                    "Expected {} lr_lambdas, but got {}".format(len(optimizer.param_groups), len(self.lr_lambda))
                )
            self.lr_lambdas = list(self.lr_lambda)

        super().init_scheduler(optimizer)

    def get_lr(self):
        if not self._get_lr_called_within_step:
            logger.warning("⚠️ To get the last learning rate computed by the scheduler, please use `get_last_lr()`.")

        return [base_lr * lmbda(self.last_epoch) for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)]

1	"""	6✔
2	Lambda learning rate scheduler.
3	"""
4
5	# Created by Wenjie Du <wenjay.du@gmail.com>
6	# License: BSD-3-Clause
7
8	from typing import Callable, Union	6✔
9
10	from .base import LRScheduler, logger	6✔
11
12
13	class LambdaLR(LRScheduler):	6✔
14	"""Sets the learning rate of each parameter group to the initial lr times a given function.	6✔
15	When last_epoch=-1, sets initial lr as lr.
16
17	Parameters
18	----------
19	lr_lambda: Callable or list,
20	A function which computes a multiplicative factor given an integer parameter epoch, or a list of such
21	functions, one for each group in optimizer.param_groups.
22
23	last_epoch: int,
24	The index of last epoch. Default: -1.
25
26	verbose: bool,
27	If ``True``, prints a message to stdout for each update. Default: ``False``.
28
29	Notes
30	-----
31	This class works the same with ``torch.optim.lr_scheduler.LambdaLR``.
32	The only difference that is also why we implement them is that you don't have to pass according optimizers
33	into them immediately while initializing them.
34
35	Example
36	-------
37	>>> lambda1 = lambda epoch: epoch // 30
38	>>> scheduler = LambdaLR(lr_lambda=lambda1)
39	>>> adam = pypots.optim.Adam(lr=1e-3, lr_scheduler=scheduler)
40
41	"""
42
43	def __init__(	6✔
44	self,
45	lr_lambda: Union[Callable, list],
46	last_epoch: int = -1,
47	verbose: bool = False,
48	):
49	super().__init__(last_epoch, verbose)	6✔
50	self.lr_lambda = lr_lambda	6✔
51	self.lr_lambdas = None	6✔
52
53	def init_scheduler(self, optimizer):	6✔
54	if not isinstance(self.lr_lambda, list) and not isinstance(self.lr_lambda, tuple):	6✔
55	self.lr_lambdas = [self.lr_lambda] * len(optimizer.param_groups)	6✔
56	else:
57	if len(self.lr_lambda) != len(optimizer.param_groups):	×
58	raise ValueError(	×
59	"Expected {} lr_lambdas, but got {}".format(len(optimizer.param_groups), len(self.lr_lambda))
60	)
61	self.lr_lambdas = list(self.lr_lambda)	×
62
63	super().init_scheduler(optimizer)	6✔
64
65	def get_lr(self):	6✔
66	if not self._get_lr_called_within_step:	6✔
NEW 67	logger.warning("⚠️ To get the last learning rate computed by the scheduler, please use `get_last_lr()`.")	×
68
69	return [base_lr * lmbda(self.last_epoch) for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)]	6✔

WenjieDu / PyPOTS / 10822158814

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