import itertools
import math
import numbers
import tempfile
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
from copy import copy
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union, cast
import torch
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
from ignite.engine import Engine
[docs]class ParamScheduler(metaclass=ABCMeta):
"""An abstract class for updating an optimizer's parameter value during
training.
Args:
optimizer: torch optimizer or any object with attribute ``param_groups``
as a sequence.
param_name: name of optimizer's parameter to update.
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
param_group_index: optimizer's parameters group to use
Note:
Parameter scheduler works independently of the internal state of the attached optimizer.
More precisely, whatever the state of the optimizer (newly created or used by another scheduler) the scheduler
sets defined absolute values.
"""
def __init__(
self,
optimizer: Optimizer,
param_name: str,
save_history: bool = False,
param_group_index: Optional[int] = None,
):
if not (
isinstance(optimizer, Optimizer)
or (hasattr(optimizer, "param_groups") and isinstance(optimizer.param_groups, Sequence))
):
raise TypeError(
"Argument optimizer should be torch.optim.Optimizer or has attribute 'param_groups' as list/tuple, "
f"but given {type(optimizer)}"
)
self.optimizer = optimizer
self.param_group_index = param_group_index
self.param_name = param_name
self.event_index = 0
self._save_history = save_history
self._state_attrs = ["event_index", "param_name", "save_history", "param_group_index"]
def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None:
value = self.get_param()
if isinstance(value, list):
if len(value) != len(self.optimizer_param_groups):
raise ValueError(
"size of value is different than optimizer_param_groups "
f"{len(value)} != {len(self.optimizer_param_groups)}"
)
for i, param_group in enumerate(self.optimizer_param_groups):
param_group[self.param_name] = value[i]
else:
for i, param_group in enumerate(self.optimizer_param_groups):
param_group[self.param_name] = value
if name is None:
name = self.param_name
if self.save_history and engine:
if not hasattr(engine.state, "param_history") or engine.state.param_history is None: # type: ignore
setattr(engine.state, "param_history", {})
engine.state.param_history.setdefault(name, []) # type: ignore[attr-defined]
values = [pg[self.param_name] for pg in self.optimizer_param_groups]
engine.state.param_history[name].append(values) # type: ignore[attr-defined]
self.event_index += 1
@property
def optimizer_param_groups(self) -> List[Dict[str, Any]]:
if self.param_group_index is None:
return self.optimizer.param_groups
return [self.optimizer.param_groups[self.param_group_index]]
@property
def save_history(self) -> bool:
return self._save_history
@save_history.setter
def save_history(self, value: bool) -> None:
self._save_history = value
[docs] def state_dict(self) -> Dict[str, Any]:
"""Returns a dictionary containing a whole state of ParamScheduler.
Returns:
dict:
a dictionary containing a whole state of ParamScheduler
"""
destination = OrderedDict()
for name in self._state_attrs:
if hasattr(self, name):
val = getattr(self, name)
if hasattr(val, "state_dict"):
val = val.state_dict()
destination[name] = copy(val)
return destination
[docs] def load_state_dict(self, state_dict: Mapping) -> None:
"""Copies parameters from :attr:`state_dict` into this ParamScheduler.
Args:
state_dict: a dict containing parameters.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}")
for name in self._state_attrs:
if name not in state_dict:
raise ValueError(
f"Required state attribute '{name}' is absent in provided state_dict '{state_dict.keys()}'"
)
val = state_dict[name]
obj = getattr(self, name)
if isinstance(val, Mapping) and hasattr(obj, "load_state_dict"):
obj.load_state_dict(val)
else:
setattr(self, name, val)
[docs] @abstractmethod
def get_param(self) -> Union[List[float], float]:
"""Method to get current optimizer's parameter values
Returns:
list of params, or scalar param
"""
pass
[docs] @classmethod
def simulate_values(cls, num_events: int, **scheduler_kwargs: Any) -> List[List[int]]:
"""Method to simulate scheduled values during `num_events` events.
Args:
num_events: number of events during the simulation.
scheduler_kwargs: parameter scheduler configuration kwargs.
Returns:
event_index, value
Examples:
.. code-block:: python
lr_values = np.array(LinearCyclicalScheduler.simulate_values(num_events=50, param_name='lr',
start_value=1e-1, end_value=1e-3,
cycle_size=10))
plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
"""
keys_to_remove = ["optimizer", "save_history"]
for key in keys_to_remove:
if key in scheduler_kwargs:
del scheduler_kwargs[key]
values = []
scheduler = cls(optimizer=_get_fake_optimizer(), save_history=False, **scheduler_kwargs)
for i in range(num_events):
scheduler(engine=None)
values.append([i, scheduler.optimizer_param_groups[0][scheduler.param_name]])
return values
[docs] @classmethod
def plot_values(cls, num_events: int, **scheduler_kwargs: Mapping) -> Any:
"""Method to plot simulated scheduled values during `num_events` events.
This class requires `matplotlib package <https://matplotlib.org/>`_ to be installed:
.. code-block:: bash
pip install matplotlib
Args:
num_events: number of events during the simulation.
scheduler_kwargs: parameter scheduler configuration kwargs.
Returns:
matplotlib.lines.Line2D
Examples:
.. code-block:: python
import matplotlib.pylab as plt
plt.figure(figsize=(10, 7))
LinearCyclicalScheduler.plot_values(num_events=50, param_name='lr',
start_value=1e-1, end_value=1e-3, cycle_size=10))
"""
try:
import matplotlib.pylab as plt
except ImportError:
raise RuntimeError(
"This method requires matplotlib to be installed. "
"Please install it with command: \n pip install matplotlib"
)
values = cls.simulate_values(num_events=num_events, **scheduler_kwargs)
label = scheduler_kwargs.get("param_name", "learning rate")
ax = plt.plot([e for e, _ in values], [v for _, v in values], label=label)
plt.legend()
plt.grid(which="both")
return ax
[docs]class CyclicalScheduler(ParamScheduler):
"""An abstract class for updating an optimizer's parameter value over a
cycle of some size.
Args:
optimizer: torch optimizer or any object with attribute ``param_groups``
as a sequence.
param_name: name of optimizer's parameter to update.
start_value: value at start of cycle.
end_value: value at the middle of the cycle.
cycle_size: length of cycle, value should be larger than 1.
cycle_mult: ratio by which to change the cycle_size.
at the end of each cycle (default=1.0).
start_value_mult: ratio by which to change the start value at the
end of each cycle (default=1.0).
end_value_mult: ratio by which to change the end value at the
end of each cycle (default=1.0).
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
param_group_index: optimizer's parameters group to use.
Note:
If the scheduler is bound to an 'ITERATION_*' event, 'cycle_size' should
usually be the number of batches in an epoch.
"""
def __init__(
self,
optimizer: Optimizer,
param_name: str,
start_value: float,
end_value: float,
cycle_size: int,
cycle_mult: float = 1.0,
start_value_mult: float = 1.0,
end_value_mult: float = 1.0,
save_history: bool = False,
param_group_index: Optional[int] = None,
):
super(CyclicalScheduler, self).__init__(
optimizer, param_name, save_history=save_history, param_group_index=param_group_index
)
self.start_value = start_value
self.end_value = end_value
self.cycle_size = int(cycle_size) # Ensure cycle_size is integer
self.cycle_mult = cycle_mult
self.cycle = 0
self.start_value_mult = start_value_mult
self.end_value_mult = end_value_mult
if self.cycle_size < 2:
raise ValueError(f"Argument cycle_size should be positive and larger than 1, but given {cycle_size}")
self._state_attrs += [
"start_value",
"end_value",
"cycle_size",
"cycle_mult",
"cycle",
"start_value_mult",
"end_value_mult",
]
def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None:
if self.event_index != 0 and self.event_index % self.cycle_size == 0:
self.event_index = 0
self.cycle_size = int(self.cycle_size * self.cycle_mult)
self.cycle += 1
self.start_value *= self.start_value_mult
self.end_value *= self.end_value_mult
return super(CyclicalScheduler, self).__call__(engine, name)
[docs]class LinearCyclicalScheduler(CyclicalScheduler):
"""Linearly adjusts param value to 'end_value' for a half-cycle, then linearly
adjusts it back to 'start_value' for a half-cycle.
Args:
optimizer: torch optimizer or any object with attribute ``param_groups``
as a sequence.
param_name: name of optimizer's parameter to update.
start_value: value at start of cycle.
end_value: value at the middle of the cycle.
cycle_size: length of cycle.
cycle_mult: ratio by which to change the cycle_size
at the end of each cycle (default=1).
start_value_mult: ratio by which to change the start value at the
end of each cycle (default=1.0).
end_value_mult: ratio by which to change the end value at the
end of each cycle (default=1.0).
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
param_group_index: optimizer's parameters group to use.
Note:
If the scheduler is bound to an 'ITERATION_*' event, 'cycle_size' should
usually be the number of batches in an epoch.
Examples:
.. code-block:: python
from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler
scheduler = LinearCyclicalScheduler(optimizer, 'lr', 1e-3, 1e-1, len(train_loader))
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
#
# Linearly increases the learning rate from 1e-3 to 1e-1 and back to 1e-3
# over the course of 1 epoch
#
"""
[docs] def get_param(self) -> float:
cycle_progress = self.event_index / self.cycle_size
return self.end_value + (self.start_value - self.end_value) * abs(cycle_progress - 0.5) * 2
[docs]class CosineAnnealingScheduler(CyclicalScheduler):
"""Anneals 'start_value' to 'end_value' over each cycle.
The annealing takes the form of the first half of a cosine
wave (as suggested in [Smith17]_).
Args:
optimizer: torch optimizer or any object with attribute ``param_groups``
as a sequence.
param_name: name of optimizer's parameter to update.
start_value: value at start of cycle.
end_value: value at the end of the cycle.
cycle_size: length of cycle.
cycle_mult: ratio by which to change the cycle_size
at the end of each cycle (default=1).
start_value_mult: ratio by which to change the start value at the
end of each cycle (default=1.0).
end_value_mult: ratio by which to change the end value at the
end of each cycle (default=1.0).
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
param_group_index: optimizer's parameters group to use.
Note:
If the scheduler is bound to an 'ITERATION_*' event, 'cycle_size' should
usually be the number of batches in an epoch.
Examples:
.. code-block:: python
from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler
scheduler = CosineAnnealingScheduler(optimizer, 'lr', 1e-1, 1e-3, len(train_loader))
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
#
# Anneals the learning rate from 1e-1 to 1e-3 over the course of 1 epoch.
#
.. code-block:: python
from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler
from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler
optimizer = SGD(
[
{"params": model.base.parameters(), 'lr': 0.001},
{"params": model.fc.parameters(), 'lr': 0.01},
]
)
scheduler1 = LinearCyclicalScheduler(optimizer, 'lr', 1e-7, 1e-5, len(train_loader), param_group_index=0)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler1, "lr (base)")
scheduler2 = CosineAnnealingScheduler(optimizer, 'lr', 1e-5, 1e-3, len(train_loader), param_group_index=1)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler2, "lr (fc)")
.. [Smith17] Smith, Leslie N. "Cyclical learning rates for training neural networks."
Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017
"""
[docs] def get_param(self) -> float:
"""Method to get current optimizer's parameter value
"""
cycle_progress = self.event_index / self.cycle_size
return self.start_value + ((self.end_value - self.start_value) / 2) * (1 - math.cos(math.pi * cycle_progress))
[docs]class ConcatScheduler(ParamScheduler):
"""Concat a list of parameter schedulers.
The `ConcatScheduler` goes through a list of schedulers given by `schedulers`. Duration of each
scheduler is defined by `durations` list of integers.
Args:
schedulers: list of parameter schedulers.
durations: list of number of events that lasts a parameter scheduler from schedulers.
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
Examples:
.. code-block:: python
from ignite.contrib.handlers.param_scheduler import ConcatScheduler
from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler
from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.5, cycle_size=60)
scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.5, end_value=0.01, cycle_size=60)
combined_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=[30, ])
trainer.add_event_handler(Events.ITERATION_STARTED, combined_scheduler)
#
# Sets the Learning rate linearly from 0.1 to 0.5 over 30 iterations. Then
# starts an annealing schedule from 0.5 to 0.01 over 60 iterations.
# The annealing cycles are repeated indefinitely.
#
"""
def __init__(self, schedulers: List[ParamScheduler], durations: List[int], save_history: bool = False):
if not isinstance(schedulers, Sequence):
raise TypeError(f"Argument schedulers should be a sequence, but given {schedulers}")
if len(schedulers) < 2:
raise ValueError(
f"Argument schedulers should be of more than one parameter schedulers, but given {schedulers}"
)
if not isinstance(durations, (list, tuple)):
raise TypeError(f"Argument durations should be list/tuple, but given {durations}")
if not all([isinstance(t, numbers.Integral) for t in durations]):
raise ValueError(f"Argument durations should be list/tuple of integers, but given {durations}")
if len(schedulers) != len(durations) + 1:
raise ValueError(
"Incorrect number schedulers or duration values, " f"given {len(schedulers)} and {len(durations)}"
)
for i, scheduler in enumerate(schedulers):
if not isinstance(scheduler, ParamScheduler) and not isinstance(scheduler, ParamGroupScheduler):
raise TypeError(
f"Value at index {i} of schedulers should be a parameter scheduler, but given {type(scheduler)}"
)
self.schedulers = schedulers
self.durations = durations
tmp_optimizers = [s.optimizer for s in self.schedulers]
tmp_list_optimizers = [s if isinstance(s, list) else [s] for s in tmp_optimizers]
param_optimizers = list(itertools.chain(*tmp_list_optimizers))
optimizer = list(set(param_optimizers))
if len(optimizer) != 1:
raise ValueError("schedulers should be related to same optimizer")
tmp_param_names = [s.param_name for s in self.schedulers]
tmp_list_param_names = [s if isinstance(s, list) else [s] for s in tmp_param_names]
param_names = list(itertools.chain(*tmp_list_param_names))
param_name = list(set(param_names))
if len(param_name) != 1:
raise ValueError("schedulers should be related to same param_name")
# schedulers should have save_history sync with ParamGroupScheduler
for s in schedulers:
s.save_history = save_history
super(ConcatScheduler, self).__init__(
optimizer=optimizer[0], param_name=param_name[0], save_history=save_history
)
self._scheduler_index = 0
self._setup_scheduler()
self._state_attrs += ["_current_duration", "durations", "_scheduler_index"]
[docs] def state_dict(self) -> Dict[str, Any]:
"""Returns a dictionary containing a whole state of ConcatScheduler.
Returns:
dict:
a dictionary containing a whole state of ConcatScheduler
"""
state_dict = super(ConcatScheduler, self).state_dict()
state_dict["schedulers"] = []
for s in self.schedulers:
state_dict["schedulers"].append(s.state_dict())
return state_dict
[docs] def load_state_dict(self, state_dict: Mapping) -> None:
"""Copies parameters from :attr:`state_dict` into this ConcatScheduler.
Args:
state_dict: a dict containing parameters.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}")
if "schedulers" not in state_dict:
raise ValueError(
f"Required state attribute 'schedulers' is absent in provided state_dict '{state_dict.keys()}'"
)
sds = state_dict["schedulers"]
if len(sds) != len(self.schedulers):
raise ValueError(
f"Input state_dict contains {len(sds)} state_dicts of concatenated schedulers, "
f"but {len(self.schedulers)} needed"
)
for s, sd in zip(self.schedulers, sds):
s.load_state_dict(sd)
super(ConcatScheduler, self).load_state_dict(state_dict)
self._setup_scheduler()
def _setup_scheduler(self) -> None:
self._current_scheduler = self.schedulers[self._scheduler_index]
self._current_duration = (
self.durations[self._scheduler_index] if self._scheduler_index < len(self.durations) else -1
)
def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None:
if self._current_duration == 0:
self._scheduler_index += 1
self._setup_scheduler()
self._current_scheduler(engine, name)
self._current_duration -= 1
@property
def optimizer_param_groups(self) -> List[Dict[str, Any]]:
# We need to setup optimizer_param_groups as property
# to synchonize with the latest _current_scheduler and its internal optimizer_param_groups
return self._current_scheduler.optimizer_param_groups
@property
def save_history(self) -> bool:
return self._current_scheduler.save_history
@save_history.setter
def save_history(self, value: bool) -> None:
for s in self.schedulers:
s.save_history = value
[docs] def get_param(self) -> Union[List[float], float]:
return self._current_scheduler.get_param()
[docs] @classmethod
def simulate_values( # type: ignore[override]
cls,
num_events: int,
schedulers: List[ParamScheduler],
durations: List[int],
param_names: Optional[Union[List[str], Tuple[str]]] = None,
) -> List[List[int]]:
"""Method to simulate scheduled values during num_events events.
Args:
num_events: number of events during the simulation.
schedulers: list of parameter schedulers.
durations: list of number of events that lasts a parameter scheduler from schedulers.
param_names: parameter name or list of parameter names to simulate values.
By default, the first scheduler's parameter name is taken.
Returns:
list of [event_index, value_0, value_1, ...], where values correspond to `param_names`.
"""
if param_names is not None:
if not isinstance(param_names, (list, tuple)):
raise TypeError(f"Argument param_names should be list or tuple, but given {type(param_names)}")
if not all(isinstance(item, str) for item in param_names):
raise ValueError(f"Argument param_names should be list or tuple of strings, but given {param_names}")
tmp_param_optimizers = [s.optimizer for s in schedulers]
tmp_list_param_optimizers = [s if isinstance(s, list) else [s] for s in tmp_param_optimizers]
param_optimizers = list(itertools.chain(*tmp_list_param_optimizers))
tmp_optimizer = list(set(param_optimizers))
if len(tmp_optimizer) != 1:
raise ValueError("schedulers should be related to same optimizer")
optimizer = tmp_optimizer[0]
# This scheduler uses `ParamScheduler` which
# should be replicated in order to simulate LR values and
# not perturb original scheduler.
with tempfile.TemporaryDirectory() as tmpdirname:
cache_filepath = Path(tmpdirname) / "ignite_lr_scheduler_cache.pt"
objs = {f"lr_scheduler_{i}": s.state_dict() for i, s in enumerate(schedulers)}
# all schedulers should be related to the same optimizer
objs["optimizer"] = optimizer.state_dict()
torch.save(objs, cache_filepath.as_posix())
# do not save_history
for s in schedulers:
s.save_history = False
output = []
scheduler = cls(schedulers=schedulers, save_history=False, durations=durations)
if param_names is None:
param_names = [scheduler.param_name]
for i in range(num_events):
scheduler(engine=None)
values = [i]
for param_name in param_names:
params = [p[param_name] for p in scheduler.optimizer_param_groups]
values = values + params
output.append(values)
objs = torch.load(cache_filepath.as_posix())
for i, s in enumerate(schedulers):
s.load_state_dict(objs[f"lr_scheduler_{i}"])
optimizer.load_state_dict(objs["optimizer"])
return output
[docs]class LRScheduler(ParamScheduler):
"""A wrapper class to call `torch.optim.lr_scheduler` objects as `ignite` handlers.
Args:
lr_scheduler: lr_scheduler object to wrap.
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
.. code-block:: python
from ignite.contrib.handlers.param_scheduler import LRScheduler
from torch.optim.lr_scheduler import StepLR
step_scheduler = StepLR(optimizer, step_size=3, gamma=0.1)
scheduler = LRScheduler(step_scheduler)
# In this example, we assume to have installed PyTorch>=1.1.0
# (with new `torch.optim.lr_scheduler` behaviour) and
# we attach scheduler to Events.ITERATION_COMPLETED
# instead of Events.ITERATION_STARTED to make sure to use
# the first lr value from the optimizer, otherwise it is will be skipped:
trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler)
"""
def __init__(self, lr_scheduler: _LRScheduler, save_history: bool = False):
if not isinstance(lr_scheduler, _LRScheduler):
raise TypeError(
"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, "
f"but given {type(lr_scheduler)}"
)
self.lr_scheduler = lr_scheduler
super(LRScheduler, self).__init__(
optimizer=self.lr_scheduler.optimizer, # type: ignore[attr-defined]
param_name="lr",
save_history=save_history,
)
self._state_attrs += ["lr_scheduler"]
def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None:
self.lr_scheduler.last_epoch += 1 # type: ignore[attr-defined]
super(LRScheduler, self).__call__(engine, name)
[docs] def get_param(self) -> Union[float, List[float]]:
"""Method to get current optimizer's parameter value
"""
# Emulate context manager for pytorch>=1.4
self.lr_scheduler._get_lr_called_within_step = True # type: ignore[attr-defined]
lr_list = cast(List[float], self.lr_scheduler.get_lr())
self.lr_scheduler._get_lr_called_within_step = False # type: ignore[attr-defined]
if len(lr_list) == 1:
return lr_list[0]
else:
return lr_list
[docs] @classmethod
def simulate_values( # type: ignore[override]
cls, num_events: int, lr_scheduler: _LRScheduler, **kwargs: Any
) -> List[List[int]]:
"""Method to simulate scheduled values during num_events events.
Args:
num_events: number of events during the simulation.
lr_scheduler: lr_scheduler object to wrap.
Returns:
event_index, value
"""
if not isinstance(lr_scheduler, _LRScheduler):
raise TypeError(
"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, "
f"but given {type(lr_scheduler)}"
)
# This scheduler uses `torch.optim.lr_scheduler._LRScheduler` which
# should be replicated in order to simulate LR values and
# not perturb original scheduler.
with tempfile.TemporaryDirectory() as tmpdirname:
cache_filepath = Path(tmpdirname) / "ignite_lr_scheduler_cache.pt"
obj = {
"lr_scheduler": lr_scheduler.state_dict(),
"optimizer": lr_scheduler.optimizer.state_dict(), # type: ignore[attr-defined]
}
torch.save(obj, cache_filepath.as_posix())
values = []
scheduler = cls(save_history=False, lr_scheduler=lr_scheduler, **kwargs) # type: ignore[call-arg]
for i in range(num_events):
params = [p[scheduler.param_name] for p in scheduler.optimizer_param_groups]
values.append([i] + params)
scheduler(engine=None)
obj = torch.load(cache_filepath.as_posix())
lr_scheduler.load_state_dict(obj["lr_scheduler"])
lr_scheduler.optimizer.load_state_dict(obj["optimizer"]) # type: ignore[attr-defined]
return values
[docs]def create_lr_scheduler_with_warmup(
lr_scheduler: Union[ParamScheduler, _LRScheduler],
warmup_start_value: float,
warmup_duration: int,
warmup_end_value: Optional[float] = None,
save_history: bool = False,
output_simulated_values: Optional[List] = None,
) -> "ConcatScheduler":
"""
Helper method to create a learning rate scheduler with a linear warm-up.
Args:
lr_scheduler: learning rate scheduler
after the warm-up.
warmup_start_value: learning rate start value of the warm-up phase.
warmup_duration: warm-up phase duration, number of events.
warmup_end_value: learning rate end value of the warm-up phase, (default=None). If None,
warmup_end_value is set to optimizer initial lr.
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
output_simulated_values: optional output of simulated learning rate values.
If output_simulated_values is a list of None, e.g. `[None] * 100`, after the execution it will be filled
by 100 simulated learning rate values.
Returns:
ConcatScheduler
Note:
If the first learning rate value provided by `lr_scheduler` is different from `warmup_end_value`, an additional
event is added after the warm-up phase such that the warm-up ends with `warmup_end_value` value and then
`lr_scheduler` provides its learning rate values as normally.
Examples:
.. code-block:: python
torch_lr_scheduler = ExponentialLR(optimizer=optimizer, gamma=0.98)
lr_values = [None] * 100
scheduler = create_lr_scheduler_with_warmup(torch_lr_scheduler,
warmup_start_value=0.0,
warmup_end_value=0.1,
warmup_duration=10,
output_simulated_values=lr_values)
lr_values = np.array(lr_values)
# Plot simulated values
plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate")
# Attach to the trainer
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
"""
if not isinstance(lr_scheduler, (ParamScheduler, _LRScheduler)):
raise TypeError(
"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler or "
f"ParamScheduler, but given {type(lr_scheduler)}"
)
if not isinstance(warmup_duration, numbers.Integral):
raise TypeError(f"Argument warmup_duration should be integer, but given {warmup_duration}")
if not (warmup_duration > 1):
raise ValueError(f"Argument warmup_duration should be at least 2 events, but given {warmup_duration}")
warmup_schedulers = [] # type: List[ParamScheduler]
for param_group_index, param_group in enumerate(lr_scheduler.optimizer.param_groups):
if warmup_end_value is None:
param_group_warmup_end_value = param_group["lr"]
else:
param_group_warmup_end_value = warmup_end_value
milestones_values = [(0, warmup_start_value), (warmup_duration - 1, param_group_warmup_end_value)]
if isinstance(lr_scheduler, _LRScheduler):
init_lr = param_group["lr"]
if init_lr != param_group_warmup_end_value:
milestones_values.append((warmup_duration, init_lr))
lr_scheduler = LRScheduler(lr_scheduler, save_history=save_history)
else:
init_lr = lr_scheduler.get_param()
if init_lr == param_group_warmup_end_value:
if warmup_duration > 2:
d = (param_group_warmup_end_value - warmup_start_value) / (warmup_duration - 1)
milestones_values[-1] = (warmup_duration - 2, param_group_warmup_end_value - d)
else:
milestones_values.pop(-1)
warmup_schedulers.append(
PiecewiseLinear(
lr_scheduler.optimizer,
param_name="lr",
milestones_values=milestones_values,
param_group_index=param_group_index,
save_history=save_history,
)
)
warmup_scheduler = ParamGroupScheduler(warmup_schedulers, save_history=save_history)
schedulers = [
warmup_scheduler,
lr_scheduler,
] # type: List[Union[ParamScheduler, ParamGroupScheduler, _LRScheduler]]
durations = [milestones_values[-1][0] + 1]
combined_scheduler = ConcatScheduler(schedulers, durations=durations, save_history=save_history)
if output_simulated_values is not None:
if not isinstance(output_simulated_values, list):
raise TypeError(
"Argument output_simulated_values should be a list of None, e.g. `[None] * 100`, "
f"but given {type(output_simulated_values)}."
)
num_events = len(output_simulated_values)
result = ConcatScheduler.simulate_values(num_events=num_events, schedulers=schedulers, durations=durations)
for i in range(num_events):
output_simulated_values[i] = result[i]
return combined_scheduler
[docs]class PiecewiseLinear(ParamScheduler):
"""
Piecewise linear parameter scheduler
Args:
optimizer: torch optimizer or any object with attribute ``param_groups``
as a sequence.
param_name: name of optimizer's parameter to update.
milestones_values: list of tuples (event index, parameter value)
represents milestones and parameter. Milestones should be increasing integers.
save_history: whether to log the parameter values to
`engine.state.param_history`, (default=False).
param_group_index: optimizer's parameters group to use.
.. code-block:: python
scheduler = PiecewiseLinear(optimizer, "lr",
milestones_values=[(10, 0.5), (20, 0.45), (21, 0.3), (30, 0.1), (40, 0.1)])
# Attach to the trainer
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
#
# Sets the learning rate to 0.5 over the first 10 iterations, then decreases linearly from 0.5 to 0.45 between
# 10th and 20th iterations. Next there is a jump to 0.3 at the 21st iteration and LR decreases linearly
# from 0.3 to 0.1 between 21st and 30th iterations and remains 0.1 until the end of the iterations.
#
"""
def __init__(
self,
optimizer: Optimizer,
param_name: str,
milestones_values: List[Tuple[int, float]],
save_history: bool = False,
param_group_index: Optional[int] = None,
):
super(PiecewiseLinear, self).__init__(optimizer, param_name, save_history, param_group_index=param_group_index)
if not isinstance(milestones_values, Sequence):
raise TypeError(
f"Argument milestones_values should be a list or tuple, but given {type(milestones_values)}"
)
if len(milestones_values) < 1:
raise ValueError(
f"Argument milestones_values should be with at least one value, but given {milestones_values}"
)
values = [] # type: List[float]
milestones = [] # type: List[int]
for pair in milestones_values:
if not isinstance(pair, tuple) or len(pair) != 2:
raise ValueError("Argument milestones_values should be a list of pairs (milestone, param_value)")
if not isinstance(pair[0], numbers.Integral):
raise TypeError(f"Value of a milestone should be integer, but given {type(pair[0])}")
if len(milestones) > 0 and pair[0] < milestones[-1]:
raise ValueError(
f"Milestones should be increasing integers, but given {pair[0]} is smaller "
f"than the previous milestone {milestones[-1]}"
)
milestones.append(pair[0])
values.append(pair[1])
self.values = values
self.milestones = milestones
self._index = 0
self._state_attrs += ["values", "milestones", "_index"]
def _get_start_end(self) -> Tuple[int, int, float, float]:
if self.milestones[0] > self.event_index:
return self.event_index - 1, self.event_index, self.values[0], self.values[0]
elif self.milestones[-1] <= self.event_index:
return (self.event_index, self.event_index + 1, self.values[-1], self.values[-1])
elif self.milestones[self._index] <= self.event_index < self.milestones[self._index + 1]:
return (
self.milestones[self._index],
self.milestones[self._index + 1],
self.values[self._index],
self.values[self._index + 1],
)
else:
self._index += 1
return self._get_start_end()
[docs] def get_param(self) -> float:
start_index, end_index, start_value, end_value = self._get_start_end()
return start_value + (end_value - start_value) * (self.event_index - start_index) / (end_index - start_index)
[docs]class ParamGroupScheduler:
"""
Scheduler helper to group multiple schedulers into one.
Args:
schedulers: list/tuple of parameter schedulers.
names: list of names of schedulers.
save_history: whether to save history or not.
.. code-block:: python
optimizer = SGD(
[
{"params": model.base.parameters(), 'lr': 0.001),
{"params": model.fc.parameters(), 'lr': 0.01),
]
)
scheduler1 = LinearCyclicalScheduler(optimizer, 'lr', 1e-7, 1e-5, len(train_loader), param_group_index=0)
scheduler2 = CosineAnnealingScheduler(optimizer, 'lr', 1e-5, 1e-3, len(train_loader), param_group_index=1)
lr_schedulers = [scheduler1, scheduler2]
names = ["lr (base)", "lr (fc)"]
scheduler = ParamGroupScheduler(schedulers=lr_schedulers, names=names)
# Attach single scheduler to the trainer
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
"""
def __init__(self, schedulers: List[ParamScheduler], names: Optional[List[str]] = None, save_history: bool = False):
if not isinstance(schedulers, Sequence):
raise TypeError(f"Argument schedulers should be a list/tuple, but given {schedulers}")
if not all(isinstance(scheduler, ParamScheduler) for scheduler in schedulers):
raise ValueError(
f"Argument schedulers should be a list/tuple of parameter schedulers, but given {schedulers}"
)
if names is None:
names = [s.param_name for s in schedulers]
if not isinstance(names, (list, tuple)):
raise TypeError(f"Argument names should be a list/tuple, but given {names}")
if not all(isinstance(n, str) for n in names):
raise ValueError(f"Argument names should be a list/tuple of parameter scheduler's names, but given {names}")
if len(names) != len(schedulers):
raise ValueError(f"{len(schedulers)} should be equal {len(names)}")
self.schedulers = schedulers
self.names = names
# schedulers should have save_history sync with ParamGroupScheduler
for s in schedulers:
s.save_history = save_history
self.optimizer = [s.optimizer for s in self.schedulers]
self.param_name = [s.param_name for s in self.schedulers]
def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None:
for scheduler, name in zip(self.schedulers, self.names):
scheduler(engine, name)
@property
def optimizer_param_groups(self) -> List[Dict[str, Any]]:
return [pg for scheduler in self.schedulers for pg in scheduler.optimizer_param_groups]
@property
def save_history(self) -> bool:
return self.schedulers[0].save_history
@save_history.setter
def save_history(self, value: bool) -> None:
for s in self.schedulers:
s.save_history = value
[docs] def state_dict(self) -> Dict[str, List[Any]]:
"""Returns a dictionary containing a whole state of ParamGroupScheduler.
Returns:
dict:
a dictionary containing a whole state of ParamGroupScheduler
"""
state_dict = OrderedDict() # type: Dict[str, List[Any]]
state_dict["schedulers"] = []
for n, s in zip(self.names, self.schedulers):
state_dict["schedulers"].append((n, s.state_dict()))
return state_dict
[docs] def load_state_dict(self, state_dict: Mapping) -> None:
"""Copies parameters from :attr:`state_dict` into this ParamScheduler.
Args:
state_dict: a dict containing parameters.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}")
if "schedulers" not in state_dict:
raise ValueError(
f"Required state attribute '{'schedulers'}' is absent in provided state_dict '{state_dict.keys()}'"
)
sds = state_dict["schedulers"]
if len(sds) != len(self.schedulers):
raise ValueError(
f"Input state_dict contains {len(sds)} state_dicts of param group schedulers, "
f"but {len(self.schedulers)} needed"
)
for req_n, s, (n, sd) in zip(self.names, self.schedulers, sds):
if req_n != n:
raise ValueError(
f"Name of scheduler from input state dict does not correspond to required one, {n} vs {req_n}"
)
s.load_state_dict(sd)
[docs] @classmethod
def simulate_values(cls, num_events: int, schedulers: List[_LRScheduler], **kwargs: Any) -> List[List[int]]:
"""Method to simulate scheduled values during num_events events.
Args:
num_events: number of events during the simulation.
schedulers: lr_scheduler object to wrap.
kwargs: kwargs passed to construct an instance of
:class:`ignite.contrib.handlers.param_scheduler.ParamGroupScheduler`.
Returns:
event_index, value
"""
# This scheduler uses `torch.optim.lr_scheduler._LRScheduler` which
# should be replicated in order to simulate LR values and
# not perturb original scheduler.
with tempfile.TemporaryDirectory() as tmpdirname:
cache_filepath = Path(tmpdirname) / "ignite_lr_scheduler_cache.pt"
objs = {f"lr_scheduler_{i}": s.state_dict() for i, s in enumerate(schedulers)}
# all schedulers should be related to the same optimizer
objs["optimizer"] = schedulers[0].optimizer.state_dict() # type: ignore[attr-defined]
torch.save(objs, cache_filepath.as_posix())
values = []
scheduler = cls(schedulers=schedulers, **kwargs) # type: ignore[arg-type]
for i in range(num_events):
params = [scheduler.get_param() for scheduler in schedulers] # type: ignore[attr-defined]
values.append([i] + params)
scheduler(engine=None)
objs = torch.load(cache_filepath.as_posix())
for i, s in enumerate(schedulers):
s.load_state_dict(objs[f"lr_scheduler_{i}"])
s.optimizer.load_state_dict(objs["optimizer"]) # type: ignore[attr-defined]
return values
def _get_fake_optimizer(
optimizer_cls: Optional[Union[Type[Optimizer], Type[torch.optim.SGD]]] = None, **kwargs: Any
) -> Union[Optimizer, torch.optim.SGD]:
t = torch.zeros([1], requires_grad=True)
if optimizer_cls is None:
optimizer_cls = torch.optim.SGD
kwargs["lr"] = 0.01
return optimizer_cls([t], **kwargs)
