Source code for ignite.handlers.lr_finder

# coding: utf-8
import contextlib
import logging
import tempfile
import warnings
from math import ceil
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Union

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

import ignite.distributed as idist
from ignite.engine import Engine, Events
from ignite.handlers import Checkpoint
from ignite.handlers.param_scheduler import LRScheduler, PiecewiseLinear

[docs]class FastaiLRFinder: """Learning rate finder handler for supervised trainers. While attached, the handler increases the learning rate in between two boundaries in a linear or exponential manner. It provides valuable information on how well the network can be trained over a range of learning rates and what can be an optimal learning rate. Examples: .. code-block:: python from ignite.handlers import FastaiLRFinder trainer = ... model = ... optimizer = ... lr_finder = FastaiLRFinder() to_save = {"model": model, "optimizer": optimizer} with lr_finder.attach(trainer, to_save=to_save) as trainer_with_lr_finder: # Get lr_finder results lr_finder.get_results() # Plot lr_finder results (requires matplotlib) lr_finder.plot() # get lr_finder suggestion for lr lr_finder.lr_suggestion() Note: When context manager is exited all LR finder's handlers are removed. Note: Please, also keep in mind that all other handlers attached the trainer will be executed during LR finder's run. Note: This class may require `matplotlib` package to be installed to plot learning rate range test: .. code-block:: bash pip install matplotlib References: Cyclical Learning Rates for Training Neural Networks: fastai/lr_find: .. versionadded:: 0.4.6 """ def __init__(self) -> None: self._diverge_flag = False self._history = {} # type: Dict[str, List[Any]] self._best_loss = None self._lr_schedule = None # type: Optional[Union[LRScheduler, PiecewiseLinear]] self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__) def _run( self, trainer: Engine, optimizer: Optimizer, output_transform: Callable, num_iter: int, start_lr: float, end_lr: float, step_mode: str, smooth_f: float, diverge_th: float, ) -> None: self._history = {"lr": [], "loss": []} self._best_loss = None self._diverge_flag = False # attach LRScheduler to trainer. if num_iter is None: num_iter = trainer.state.epoch_length * trainer.state.max_epochs else: max_iter = trainer.state.epoch_length * trainer.state.max_epochs # type: ignore[operator] if max_iter < num_iter: max_iter = num_iter trainer.state.max_iters = num_iter trainer.state.max_epochs = ceil(num_iter / trainer.state.epoch_length) # type: ignore[operator] if not trainer.has_event_handler(self._reached_num_iterations): trainer.add_event_handler(Events.ITERATION_COMPLETED, self._reached_num_iterations, num_iter) # attach loss and lr logging if not trainer.has_event_handler(self._log_lr_and_loss): trainer.add_event_handler( Events.ITERATION_COMPLETED, self._log_lr_and_loss, output_transform, smooth_f, diverge_th ) self.logger.debug(f"Running LR finder for {num_iter} iterations") if start_lr is None: start_lr = optimizer.param_groups[0]["lr"] # Initialize the proper learning rate policy if step_mode.lower() == "exp": start_lr = [start_lr] * len(optimizer.param_groups) # type: ignore self._lr_schedule = LRScheduler(_ExponentialLR(optimizer, start_lr, end_lr, num_iter)) else: self._lr_schedule = PiecewiseLinear( optimizer, param_name="lr", milestones_values=[(0, start_lr), (num_iter, end_lr)] ) if not trainer.has_event_handler(self._lr_schedule): trainer.add_event_handler(Events.ITERATION_COMPLETED, self._lr_schedule, num_iter) def _reset(self, trainer: Engine) -> None: self.logger.debug("Completed LR finder run") trainer.remove_event_handler(self._lr_schedule, Events.ITERATION_COMPLETED) # type: ignore[arg-type] trainer.remove_event_handler(self._log_lr_and_loss, Events.ITERATION_COMPLETED) trainer.remove_event_handler(self._reached_num_iterations, Events.ITERATION_COMPLETED) def _log_lr_and_loss(self, trainer: Engine, output_transform: Callable, smooth_f: float, diverge_th: float) -> None: output = trainer.state.output loss = output_transform(output) if not isinstance(loss, float): if isinstance(loss, torch.Tensor): if (loss.ndimension() == 0) or (loss.ndimension() == 1 and len(loss) == 1): loss = loss.item() else: raise ValueError( "if output of the engine is torch.Tensor, then " "it must be 0d torch.Tensor or 1d torch.Tensor with 1 element, " f"but got torch.Tensor of shape {loss.shape}" ) else: raise TypeError( "output of the engine should be of type float or 0d torch.Tensor " "or 1d torch.Tensor with 1 element, " f"but got output of type {type(loss).__name__}" ) loss = idist.all_reduce(loss) lr = self._lr_schedule.get_param() # type: ignore[union-attr] self._history["lr"].append(lr) if trainer.state.iteration == 1: self._best_loss = loss else: if smooth_f > 0: loss = smooth_f * loss + (1 - smooth_f) * self._history["loss"][-1] if loss < self._best_loss: self._best_loss = loss self._history["loss"].append(loss) # Check if the loss has diverged; if it has, stop the trainer if self._history["loss"][-1] > diverge_th * self._best_loss: # type: ignore[operator] self._diverge_flag = True"Stopping early, the loss has diverged") trainer.terminate() def _reached_num_iterations(self, trainer: Engine, num_iter: int) -> None: if trainer.state.iteration > num_iter: trainer.terminate() def _warning(self, _: Any) -> None: if not self._diverge_flag: warnings.warn( "Run completed without loss diverging, increase end_lr, decrease diverge_th or look" " at lr_finder.plot()", UserWarning, ) def _detach(self, trainer: Engine) -> None: """ Detaches lr_finder from trainer. Args: trainer: the trainer to detach form. """ if trainer.has_event_handler(self._run, Events.STARTED): trainer.remove_event_handler(self._run, Events.STARTED) if trainer.has_event_handler(self._warning, Events.COMPLETED): trainer.remove_event_handler(self._warning, Events.COMPLETED) if trainer.has_event_handler(self._reset, Events.COMPLETED): trainer.remove_event_handler(self._reset, Events.COMPLETED)
[docs] def get_results(self) -> Dict[str, List[Any]]: """ Returns: Dictionary with loss and lr logs from the previous run """ return self._history
[docs] def plot( self, skip_start: int = 10, skip_end: int = 5, log_lr: bool = True, display_suggestion: bool = True, ax: Optional[Any] = None, **kwargs: Any, ) -> None: """Plots the learning rate range test. This method requires ``matplotlib`` package to be installed: .. code-block:: bash pip install matplotlib Args: skip_start: number of batches to trim from the start. Default: 10. skip_end: number of batches to trim from the start. Default: 5. log_lr: True to plot the learning rate in a logarithmic scale; otherwise, plotted in a linear scale. Default: True. display_suggestion: if True, red dot shows the suggested learning rate. ax: Pre-existing axes for the plot. Default: None. kwargs: optional kwargs passed to ``plt.subplots`` if ``ax`` is not provided. .. code-block:: python ax = lr_finder.plot(skip_end=0) ax.figure.savefig("output.jpg") """ try: from matplotlib import pyplot as plt except ImportError: raise RuntimeError( "This method requires matplotlib to be installed. " "Please install it with command: \n pip install matplotlib" ) if not self._history: raise RuntimeError("learning rate finder didn't run yet so results can't be plotted") if skip_start < 0: raise ValueError("skip_start cannot be negative") if skip_end < 0: raise ValueError("skip_end cannot be negative") # Get the data to plot from the history dictionary. lrs = self._history["lr"] losses = self._history["loss"] num_groups = len(lrs[0]) if isinstance(lrs[0], list) else 1 legends = [f"suggested lr for param_groups {i}" for i in range(num_groups)] if ax is None: fig, ax = plt.subplots(**kwargs) # Check to show the suggested learning rate if display_suggestion: sug_lr = self.lr_suggestion() idx = self._history["lr"].index(sug_lr) if skip_start >= idx: warnings.warn( "skip_start is larger than the suggested LR found" " and it will not be visible on the plot. Please, make the value smaller.", UserWarning, ) corresponding_loss = self._history["loss"][int(idx)] # Check if optimizer has multiple param_groups if not isinstance(sug_lr, list): sug_lr = [ sug_lr, ] for lr in sug_lr: ax.scatter( lr, corresponding_loss, color="red" if len(sug_lr) == 1 else None, s=75, marker="o", zorder=3 ) # handle skip_end=0 properly if skip_end == 0: lrs = lrs[skip_start:] losses = losses[skip_start:] else: lrs = lrs[skip_start:-skip_end] losses = losses[skip_start:-skip_end] plt.legend(legends) # Plot loss as a function of the learning rate ax.plot(lrs, losses) if log_lr: ax.set_xscale("log") lr_min = min(lrs[0]) if isinstance(lrs[0], list) else lrs[0] lr_max = max(lrs[-1]) if isinstance(lrs[-1], list) else lrs[-1] ax.set_xlim([lr_min, lr_max]) ax.set_xlabel("Learning rate") ax.set_ylabel("Loss") return ax
[docs] def lr_suggestion(self) -> Any: """ Returns: Learning rate at the minimum numerical gradient (ignoring the increasing part of the curve) """ if not self._history: raise RuntimeError("learning rate finder didn't run yet so lr_suggestion can't be returned") loss = self._history["loss"] min_loss_idx = torch.tensor(loss).argmin() # Ignore the increasing part of the curve decreasing_losses = self._history["loss"][: int(min_loss_idx.item()) + 1] if len(decreasing_losses) < 3: raise RuntimeError( "FastaiLRFinder got unexpected curve shape, the curve should be somehow U-shaped, " "please decrease start_lr or increase end_lr to resolve this issue." ) losses = torch.tensor(decreasing_losses) grads = torch.tensor([0.5 * (losses[i + 1] - losses[i - 1]) for i in range(1, len(losses) - 1)]) min_grad_idx = grads.argmin() + 1 return self._history["lr"][int(min_grad_idx)]
[docs] def apply_suggested_lr(self, optimizer: Optimizer) -> None: """ Applying the suggested learning rate(s) on the given optimizer. Args: optimizer: the optimizer to apply the suggested learning rate(s) on. Note: The given optimizer must be the same as the one we before found the suggested learning rate for. """ sug_lr = self.lr_suggestion() if not isinstance(sug_lr, list): sug_lr = [ sug_lr, ] if len(sug_lr) != len(optimizer.param_groups): raise RuntimeError( "The number of parameter groups does not match between " "given optimizer and the one used for estimating the " f"learning rate: {len(sug_lr)} vs {len(optimizer.param_groups)}" ) for i, lr in enumerate(sug_lr): optimizer.param_groups[i]["lr"] = lr
[docs] @contextlib.contextmanager def attach( self, trainer: Engine, to_save: Mapping, output_transform: Callable = lambda output: output, num_iter: Optional[int] = None, start_lr: Optional[float] = None, end_lr: float = 10.0, step_mode: str = "exp", smooth_f: float = 0.05, diverge_th: float = 5.0, ) -> Any: """Attaches lr_finder to a given trainer. It also resets model and optimizer at the end of the run. Args: trainer: lr_finder is attached to this trainer. Please, keep in mind that all attached handlers will be executed. to_save: dictionary with optimizer and other objects that needs to be restored after running the LR finder. For example, ``to_save={'optimizer': optimizer, 'model': model}``. It should contain "optimizer" key for the optimizer. Also all objects should implement ``state_dict`` and ``load_state_dict`` methods. output_transform: function that transforms the trainer's ``state.output`` after each iteration. It must return the loss of that iteration. num_iter: number of iterations for lr schedule between base lr and end_lr. Default, it will run for ``trainer.state.epoch_length * trainer.state.max_epochs``. start_lr: lower bound for lr search. Default, Learning Rate specified with the optimizer. end_lr: upper bound for lr search. Default, 10.0. step_mode: "exp" or "linear", which way should the lr be increased from ``start_lr`` to ``end_lr``. Default, "exp". smooth_f: loss smoothing factor in range ``[0, 1)``. Default, 0.05 diverge_th: Used for stopping the search when ``current loss > diverge_th * best_loss``. Default, 5.0. Returns: trainer_with_lr_finder (trainer used for finding the lr) Examples: .. code-block:: python to_save = {"model": model, "optimizer": optimizer} with lr_finder.attach(trainer, to_save=to_save) as trainer_with_lr_finder: Note: lr_finder cannot be attached to more than one trainer at a time. """ if not isinstance(to_save, Mapping): raise TypeError(f"Argument to_save should be a mapping, but given {type(to_save)}") Checkpoint._check_objects(to_save, "state_dict") Checkpoint._check_objects(to_save, "load_state_dict") if "optimizer" not in to_save: raise ValueError("Mapping to_save should contain 'optimizer' key") if not isinstance(to_save["optimizer"], torch.optim.Optimizer): raise TypeError( f"Object to_save['optimizer'] should be torch optimizer, but given {type(to_save['optimizer'])}" ) if smooth_f < 0 or smooth_f >= 1: raise ValueError("smooth_f is outside the range [0, 1]") if diverge_th < 1: raise ValueError("diverge_th should be larger than 1") if step_mode not in ["exp", "linear"]: raise ValueError(f"step_mode should be 'exp' or 'linear', but given {step_mode}") if num_iter is not None: if not isinstance(num_iter, int): raise TypeError(f"if provided, num_iter should be an integer, but give {num_iter}") if num_iter <= 0: raise ValueError(f"if provided, num_iter should be positive, but give {num_iter}") if isinstance(start_lr, (float, int)) and start_lr >= end_lr: raise ValueError(f"start_lr must be less than end_lr, start_lr={start_lr} vs end_lr={end_lr}") # store to_save with tempfile.TemporaryDirectory() as tmpdirname: obj = {k: o.state_dict() for k, o in to_save.items()} # add trainer obj["trainer"] = trainer.state_dict() cache_filepath = Path(tmpdirname) / "", cache_filepath.as_posix()) optimizer = to_save["optimizer"] # Attach handlers if not trainer.has_event_handler(self._run): trainer.add_event_handler( Events.STARTED, self._run, optimizer, output_transform, num_iter, start_lr, end_lr, step_mode, smooth_f, diverge_th, ) if not trainer.has_event_handler(self._warning): trainer.add_event_handler(Events.COMPLETED, self._warning) if not trainer.has_event_handler(self._reset): trainer.add_event_handler(Events.COMPLETED, self._reset) yield trainer self._detach(trainer) # restore to_save and reset trainer's state obj = torch.load(cache_filepath.as_posix()) trainer.load_state_dict(obj["trainer"]) for k, o in obj.items(): if k in to_save: to_save[k].load_state_dict(o)
class _ExponentialLR(_LRScheduler): """Exponentially increases the learning rate between two boundaries over a number of iterations. Args: optimizer: wrapped optimizer. end_lr: the initial learning rate which is the lower boundary of the test. Default: 10. num_iter: the number of iterations over which the test occurs. Default: 100. last_epoch: the index of last epoch. Default: -1. """ def __init__(self, optimizer: Optimizer, start_lr: float, end_lr: float, num_iter: int, last_epoch: int = -1): self.end_lr = end_lr self.num_iter = num_iter super(_ExponentialLR, self).__init__(optimizer, last_epoch) # override base_lrs self.base_lrs = start_lr def get_lr(self) -> List[float]: # type: ignore curr_iter = self.last_epoch + 1 # type: ignore[attr-defined] r = curr_iter / self.num_iter return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs] # type: ignore[attr-defined]

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 06/15/2022, 1:21:32 PM.

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