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Source code for ignite.contrib.handlers.param_scheduler

from __future__ import division

from collections import OrderedDict
from copy import copy

import math
import numbers

from abc import ABCMeta, abstractmethod

from collections.abc import Sequence, Mapping

import torch
from torch.optim.optimizer import Optimizer
from torch.optim.lr_scheduler import _LRScheduler


[docs]class ParamScheduler(metaclass=ABCMeta): """An abstract class for updating an optimizer's parameter value during training. Args: optimizer (`torch.optim.Optimizer`): optimizer param_name (str): name of optimizer's parameter to update. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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, param_name, save_history=False, param_group_index=None): if not isinstance(optimizer, Optimizer): raise TypeError("Argument optimizer should be torch.optim.Optimizer") self.optimizer = optimizer self.param_group_index = param_group_index self.param_name = param_name self.save_history = save_history self.event_index = 0 self._state_attrs = ['event_index', 'param_name', 'save_history', 'param_group_index'] def __call__(self, engine, name=None): value = self.get_param() for param_group in self.optimizer_param_groups: param_group[self.param_name] = value if name is None: name = self.param_name if self.save_history: if not hasattr(engine.state, 'param_history'): setattr(engine.state, 'param_history', {}) engine.state.param_history.setdefault(name, []) values = [pg[self.param_name] for pg in self.optimizer_param_groups] engine.state.param_history[name].append(values) self.event_index += 1 @property def optimizer_param_groups(self): if self.param_group_index is None: return self.optimizer.param_groups return [self.optimizer.param_groups[self.param_group_index], ]
[docs] def state_dict(self): """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): """Copies parameters from :attr:`state_dict` into this ParamScheduler. Args: state_dict (dict): a dict containing parameters. """ if not isinstance(state_dict, Mapping): raise TypeError("Argument state_dict should be a dictionary, but given {}".format(type(state_dict))) for name in self._state_attrs: if name not in state_dict: raise ValueError("Required state attribute '{}' is absent in provided state_dict '{}'" .format(name, 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): """Method to get current optimizer's parameter value """ pass
[docs] @classmethod def simulate_values(cls, num_events, **scheduler_kwargs): """Method to simulate scheduled values during `num_events` events. Args: num_events (int): number of events during the simulation. **scheduler_kwargs : parameter scheduler configuration kwargs. Returns: list of pairs: [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, **scheduler_kwargs): """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 (int): 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.optim.Optimizer`): optimizer param_name (str): name of optimizer's parameter to update. start_value (float): value at start of cycle. end_value (float): value at the middle of the cycle. cycle_size (int): length of cycle, value should be larger than 1. cycle_mult (float, optional): ratio by which to change the cycle_size. at the end of each cycle (default=1.0). start_value_mult (float, optional): ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult (float, optional): ratio by which to change the end value at the end of each cycle (default=1.0). save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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, param_name, start_value, end_value, cycle_size, cycle_mult=1.0, start_value_mult=1.0, end_value_mult=1.0, save_history=False, param_group_index=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("Argument cycle_size should be positive and larger than 1, " "but given {}".format(cycle_size)) self._state_attrs += ['start_value', 'end_value', 'cycle_size', 'cycle_mult', 'cycle', 'start_value_mult', 'end_value_mult'] def __call__(self, engine, name=None): if self.event_index != 0 and self.event_index % self.cycle_size == 0: self.event_index = 0 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.optim.Optimizer`): optimizer param_name (str): name of optimizer's parameter to update. start_value (float): value at start of cycle. end_value (float): value at the middle of the cycle. cycle_size (int): length of cycle. cycle_mult (float, optional): ratio by which to change the cycle_size at the end of each cycle (default=1). start_value_mult (float, optional): ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult (float, optional): ratio by which to change the end value at the end of each cycle (default=1.0). save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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): 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.optim.Optimizer`): optimizer param_name (str): name of optimizer's parameter to update. start_value (float): value at start of cycle. end_value (float): value at the end of the cycle. cycle_size (int): length of cycle. cycle_mult (float, optional): ratio by which to change the cycle_size at the end of each cycle (default=1). start_value_mult (float, optional): ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult (float, optional): ratio by which to change the end value at the end of each cycle (default=1.0). save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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): """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 ParamScheduler): list of parameter schedulers. durations (list of int): list of number of events that lasts a parameter scheduler from schedulers. save_history (bool, optional): 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, durations, save_history=False): if not isinstance(schedulers, Sequence) or len(schedulers) < 2: raise ValueError("Argument schedulers should be a sequence of more than one parameter schedulers, " "but given {}".format(schedulers)) if not isinstance(durations, Sequence) or \ not all([isinstance(t, numbers.Integral) for t in durations]): raise ValueError("Argument durations should be list/tuple of integers, " "but given {}".format(durations)) if len(schedulers) != len(durations) + 1: raise ValueError("Incorrect number schedulers or duration values, " "given {} and {}".format(len(schedulers), len(durations))) for i, scheduler in enumerate(schedulers): if not isinstance(scheduler, ParamScheduler): raise TypeError("Value at index {} of schedulers should be a parameter scheduler, " "but given {}".format(i, type(scheduler))) self.schedulers = schedulers self.durations = durations super(ConcatScheduler, self).__init__(optimizer=_get_fake_optimizer(), param_name="", save_history=save_history) self._scheduler_index = 0 self._current_scheduler = None self._current_duration = None self._setup_scheduler() self._state_attrs += ['_current_duration', 'durations', '_scheduler_index']
[docs] def state_dict(self): """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): """Copies parameters from :attr:`state_dict` into this ConcatScheduler. Args: state_dict (dict): a dict containing parameters. """ if not isinstance(state_dict, Mapping): raise TypeError("Argument state_dict should be a dictionary, but given {}".format(type(state_dict))) if 'schedulers' not in state_dict: raise ValueError("Required state attribute '{}' is absent in provided state_dict '{}'" .format('schedulers', state_dict.keys())) sds = state_dict['schedulers'] if len(sds) != len(self.schedulers): raise ValueError("Input state_dict contains {} state_dicts of concatenated schedulers, " "but {} needed".format(len(sds), len(self.schedulers))) 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): 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 self.param_name = self._current_scheduler.param_name self.optimizer = self._current_scheduler.optimizer def __call__(self, engine, name=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): # 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): return self._current_scheduler.save_history @save_history.setter def save_history(self, value): for s in self.schedulers: s.save_history = value
[docs] def get_param(self): return self._current_scheduler.get_param()
[docs] @classmethod def simulate_values(cls, num_events, schedulers, durations, param_names=None, **kwargs): """Method to simulate scheduled values during num_events events. Args: num_events (int): number of events during the simulation. schedulers (list of ParamScheduler): list of parameter schedulers. durations (list of int): list of number of events that lasts a parameter scheduler from schedulers. param_names (list or tuple of str, optional): 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 and not isinstance(param_names, (list, tuple)): raise ValueError("Argument param_names should be list or tuple of strings") output = [] # Need to copy all schedulers otherwise unsafe copy_schedulers = [_replicate_scheduler(s) for s in schedulers] scheduler = cls(copy_schedulers, durations, save_history=False) if param_names is None: param_names = [scheduler.param_name] for i in range(num_events): scheduler(engine=None) values = [scheduler.optimizer_param_groups[0][param_name] for param_name in param_names] output.append([i, ] + values) return output
[docs]class LRScheduler(ParamScheduler): """A wrapper class to call `torch.optim.lr_scheduler` objects as `ignite` handlers. Args: lr_scheduler (subclass of `torch.optim.lr_scheduler._LRScheduler`): lr_scheduler object to wrap. save_history (bool, optional): 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, save_history=False, **kwds): if not isinstance(lr_scheduler, _LRScheduler): raise TypeError("Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, " "but given {}".format(type(lr_scheduler))) if len(lr_scheduler.optimizer.param_groups) > 1: raise ValueError("Optimizer passed to lr_scheduler should have a single param group, " "but currently there are {} param groups".format(len(lr_scheduler.optimizer.param_groups))) self.lr_scheduler = lr_scheduler super(LRScheduler, self).__init__( optimizer=self.lr_scheduler.optimizer, param_name='lr', save_history=save_history ) self._state_attrs += ['lr_scheduler', ] def __call__(self, engine, name=None): self.lr_scheduler.last_epoch += 1 super(LRScheduler, self).__call__(engine, name)
[docs] def get_param(self): """Method to get current optimizer's parameter value """ # Emulate context manager for pytorch>=1.4 self.lr_scheduler._get_lr_called_within_step = True lr_list = self.lr_scheduler.get_lr() self.lr_scheduler._get_lr_called_within_step = False if len(lr_list) > 1: raise ValueError("Optimizer passed to lr_scheduler should have a single param group, " "but currently there are {} param groups".format(len(lr_list))) return lr_list[0]
[docs] @classmethod def simulate_values(cls, num_events, lr_scheduler, **kwargs): """Method to simulate scheduled values during num_events events. Args: num_events (int): number of events during the simulation. lr_scheduler (subclass of `torch.optim.lr_scheduler._LRScheduler`): lr_scheduler object to wrap. Returns: list of pairs: [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. copy_lr_scheduler = LRScheduler._replicate_lr_scheduler(lr_scheduler) values = [] scheduler = cls(save_history=False, lr_scheduler=copy_lr_scheduler) for i in range(num_events): values.append([i, scheduler.optimizer_param_groups[0][scheduler.param_name]]) scheduler(engine=None) return values
@staticmethod def _replicate_lr_scheduler(lr_scheduler): if not isinstance(lr_scheduler, _LRScheduler): raise TypeError("lr_scheduler should inherit of _LRScheduler") lr_scheduler_cls = lr_scheduler.__class__ optimizer_cls = lr_scheduler.optimizer.__class__ dummy_optimizer = _get_fake_optimizer(optimizer_cls, **lr_scheduler.optimizer.defaults) for group in dummy_optimizer.param_groups: group.setdefault('initial_lr', group['lr']) kwargs = lr_scheduler.state_dict() for k in [_k for _k in kwargs.keys() if "_" == _k[0]] + ['base_lrs', 'last_epoch']: del kwargs[k] copy_lr_scheduler = lr_scheduler_cls(optimizer=dummy_optimizer, **kwargs) copy_lr_scheduler.load_state_dict(lr_scheduler.state_dict()) return copy_lr_scheduler
[docs]def create_lr_scheduler_with_warmup(lr_scheduler, warmup_start_value, warmup_end_value, warmup_duration, save_history=False, output_simulated_values=None): """ Helper method to create a learning rate scheduler with a linear warm-up. Args: lr_scheduler (ParamScheduler or subclass of `torch.optim.lr_scheduler._LRScheduler`): learning rate scheduler after the warm-up. warmup_start_value (float): learning rate start value of the warm-up phase. warmup_end_value (float): learning rate end value of the warm-up phase. warmup_duration (int): warm-up phase duration, number of events. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). output_simulated_values (list, optional): 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: learning rate scheduler with linear warm-up. 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 " "ParamScheduler, but given {}".format(type(lr_scheduler))) if not (isinstance(warmup_duration, numbers.Integral) and warmup_duration > 1): raise ValueError("Argument warmup_duration should be at least 2 events, but given {}" .format(warmup_duration)) milestones_values = [(0, warmup_start_value), (warmup_duration - 1, warmup_end_value)] if isinstance(lr_scheduler, _LRScheduler): init_lrs = [g['lr'] for g in lr_scheduler.optimizer.param_groups] if len(init_lrs) < 1: raise RuntimeError("Number of parameter groups of input `lr_scheduler.optimizer` is less than one.") if init_lrs[0] != warmup_end_value: milestones_values.append((warmup_duration, init_lrs[0])) lr_scheduler = LRScheduler(lr_scheduler) else: init_lr = lr_scheduler.get_param() if init_lr == warmup_end_value: if warmup_duration > 2: d = (warmup_end_value - warmup_start_value) / (warmup_duration - 1) milestones_values[-1] = (warmup_duration - 2, warmup_end_value - d) else: milestones_values.pop(-1) warmup_scheduler = PiecewiseLinear(lr_scheduler.optimizer, param_name="lr", milestones_values=milestones_values, param_group_index=lr_scheduler.param_group_index) schedulers = [warmup_scheduler, lr_scheduler] 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`, " "but given {}.".format(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.optim.Optimizer`): optimizer. param_name (str): name of optimizer's parameter to update. milestones_values (list of tuples (int, float)): list of tuples (event index, parameter value) represents milestones and parameter. Milestones should be increasing integers. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): optimizer's parameters group to use. Returns: PiecewiseLinear: piecewise linear scheduler .. 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, param_name, milestones_values, save_history=False, param_group_index=None): super(PiecewiseLinear, self).__init__(optimizer, param_name, save_history, param_group_index=param_group_index) if not isinstance(milestones_values, Sequence) or len(milestones_values) < 1: raise ValueError("Argument milestones_values should be a list or tuple with at least one value, " "but given {}".format(type(milestones_values))) values = [] milestones = [] for pair in milestones_values: if not isinstance(pair, Sequence) 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 ValueError("Value of a milestone should be integer, but given {}".format(type(pair[0]))) if len(milestones) > 0 and pair[0] < milestones[-1]: raise ValueError("Milestones should be increasing integers, but given {} is smaller " "than the previous milestone {}".format(pair[0], 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): 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): 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 ParamScheduler): list/tuple of parameter schedulers. names (list of str): list of names of schedulers. .. 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, names): if not (isinstance(schedulers, Sequence) and all(isinstance(scheduler, ParamScheduler) for scheduler in schedulers)): raise ValueError("Argument schedulers should be a list/tuple of parameter schedulers") if not (isinstance(names, (list, tuple)) and all(isinstance(n, str) for n in names)): raise ValueError("Argument names should be a list/tuple of parameter scheduler's names") if len(names) != len(schedulers): raise ValueError("{} should be equal {}".format(len(schedulers), len(names))) self.schedulers = schedulers self.names = names def __call__(self, engine): for scheduler, name in zip(self.schedulers, self.names): scheduler(engine, name=name)
[docs] def state_dict(self): """Returns a dictionary containing a whole state of ParamGroupScheduler. Returns: dict: a dictionary containing a whole state of ParamGroupScheduler """ state_dict = OrderedDict() 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): """Copies parameters from :attr:`state_dict` into this ParamScheduler. Args: state_dict (dict): a dict containing parameters. """ if not isinstance(state_dict, Mapping): raise TypeError("Argument state_dict should be a dictionary, but given {}".format(type(state_dict))) if 'schedulers' not in state_dict: raise ValueError("Required state attribute '{}' is absent in provided state_dict '{}'" .format('schedulers', state_dict.keys())) sds = state_dict['schedulers'] if len(sds) != len(self.schedulers): raise ValueError("Input state_dict contains {} state_dicts of param group schedulers, " "but {} needed".format(len(sds), len(self.schedulers))) for req_n, s, (n, sd) in zip(self.names, self.schedulers, sds): if req_n != n: raise ValueError("Name of scheduler from input state dict does not correspond to required one," " {} vs {}".format(n, req_n)) s.load_state_dict(sd)
def _replicate_scheduler(scheduler, save_history=False): if isinstance(scheduler, LRScheduler): return LRScheduler(LRScheduler._replicate_lr_scheduler(scheduler.lr_scheduler), save_history=save_history) elif isinstance(scheduler, ConcatScheduler): copy_schedulers = [_replicate_scheduler(s, save_history=save_history) for s in scheduler.schedulers] return ConcatScheduler(copy_schedulers, durations=scheduler.durations, save_history=save_history) elif isinstance(scheduler, ParamScheduler): new_scheduler = copy(scheduler) new_scheduler.optimizer = _get_fake_optimizer() new_scheduler.save_history = save_history return new_scheduler else: raise TypeError("Unknown scheduler type {}".format(type(scheduler))) def _get_fake_optimizer(optimizer_cls=None, **kwargs): 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)

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