File size: 7,119 Bytes
d7b4cbb |
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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import logging
from bisect import bisect_right
from collections.abc import Callable
from typing import TYPE_CHECKING
from monai.config import IgniteInfo
from monai.utils import min_version, optional_import
if TYPE_CHECKING:
from ignite.engine import Engine, Events
else:
Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
class ParamSchedulerHandler:
"""
General purpose scheduler for parameters values. By default it can schedule in a linear, exponential, step or
multistep function. One can also pass Callables to have customized scheduling logic.
Args:
parameter_setter (Callable): Function that sets the required parameter
value_calculator (Union[str,Callable]): Either a string ('linear', 'exponential', 'step' or 'multistep')
or Callable for custom logic.
vc_kwargs (Dict): Dictionary that stores the required parameters for the value_calculator.
epoch_level (bool): Whether the step is based on epoch or iteration. Defaults to False.
name (Optional[str]): Identifier of logging.logger to use, if None, defaulting to ``engine.logger``.
event (Optional[str]): Event to which the handler attaches. Defaults to Events.ITERATION_COMPLETED.
"""
def __init__(
self,
parameter_setter: Callable,
value_calculator: str | Callable,
vc_kwargs: dict,
epoch_level: bool = False,
name: str | None = None,
event: str | None = None,
):
self.epoch_level = epoch_level
self.event = event if event is not None else Events.ITERATION_COMPLETED
self._calculators = {
"linear": self._linear,
"exponential": self._exponential,
"step": self._step,
"multistep": self._multistep,
}
self._parameter_setter = parameter_setter
self._vc_kwargs = vc_kwargs
self._value_calculator = self._get_value_calculator(value_calculator=value_calculator)
self.logger = logging.getLogger(name)
self._name = name
def _get_value_calculator(self, value_calculator):
if isinstance(value_calculator, str):
return self._calculators[value_calculator]
if callable(value_calculator):
return value_calculator
raise ValueError(
f"value_calculator must be either a string from {list(self._calculators.keys())} or a Callable."
)
def __call__(self, engine: Engine) -> None:
if self.epoch_level:
self._vc_kwargs["current_step"] = engine.state.epoch
else:
self._vc_kwargs["current_step"] = engine.state.iteration
new_value = self._value_calculator(**self._vc_kwargs)
self._parameter_setter(new_value)
def attach(self, engine: Engine) -> None:
"""
Args:
engine: Ignite Engine that is used for training.
"""
if self._name is None:
self.logger = engine.logger
engine.add_event_handler(self.event, self)
@staticmethod
def _linear(
initial_value: float, step_constant: int, step_max_value: int, max_value: float, current_step: int
) -> float:
"""
Keeps the parameter value to zero until step_zero steps passed and then linearly increases it to 1 until an
additional step_one steps passed. Continues the trend until it reaches max_value.
Args:
initial_value (float): Starting value of the parameter.
step_constant (int): Step index until parameter's value is kept constant.
step_max_value (int): Step index at which parameter's value becomes max_value.
max_value (float): Max parameter value.
current_step (int): Current step index.
Returns:
float: new parameter value
"""
if current_step <= step_constant:
delta = 0.0
elif current_step > step_max_value:
delta = max_value - initial_value
else:
delta = (max_value - initial_value) / (step_max_value - step_constant) * (current_step - step_constant)
return initial_value + delta
@staticmethod
def _exponential(initial_value: float, gamma: float, current_step: int) -> float:
"""
Decays the parameter value by gamma every step.
Based on the closed form of ExponentialLR from Pytorch:
https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ExponentialLR.html.
Args:
initial_value (float): Starting value of the parameter.
gamma (float): Multiplicative factor of parameter value decay.
current_step (int): Current step index.
Returns:
float: new parameter value
"""
return initial_value * gamma**current_step
@staticmethod
def _step(initial_value: float, gamma: float, step_size: int, current_step: int) -> float:
"""
Decays the parameter value by gamma every step_size.
Based on StepLR from Pytorch:
https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html.
Args:
initial_value (float): Starting value of the parameter.
gamma (float): Multiplicative factor of parameter value decay.
step_size (int): Period of parameter value decay.
current_step (int): Current step index.
Returns
float: new parameter value
"""
return initial_value * gamma ** (current_step // step_size)
@staticmethod
def _multistep(initial_value: float, gamma: float, milestones: list[int], current_step: int) -> float:
"""
Decays the parameter value by gamma once the number of steps reaches one of the milestones.
Based on MultiStepLR from Pytorch.
https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.MultiStepLR.html.
Args:
initial_value (float): Starting value of the parameter.
gamma (float): Multiplicative factor of parameter value decay.
milestones (List[int]): List of step indices. Must be increasing.
current_step (int): Current step index.
Returns:
float: new parameter value
"""
return initial_value * gamma ** bisect_right(milestones, current_step)
|