Source code for avalanche.evaluation.metrics.accuracy

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# Copyright (c) 2021 ContinualAI.                                              #
# Copyrights licensed under the MIT License.                                   #
# See the accompanying LICENSE file for terms.                                 #
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# Date: 30-12-2020                                                             #
# Author(s): Lorenzo Pellegrini                                                #
# E-mail: contact@continualai.org                                              #
# Website: www.continualai.org                                                 #
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from typing import List, Union, Dict

import torch
from torch import Tensor
from avalanche.evaluation import Metric, PluginMetric, GenericPluginMetric
from avalanche.evaluation.metrics.mean import Mean
from avalanche.evaluation.metric_utils import phase_and_task
from collections import defaultdict


[docs]class Accuracy(Metric[float]): """Accuracy metric. This is a standalone metric. The update method computes the accuracy incrementally by keeping a running average of the <prediction, target> pairs of Tensors provided over time. The "prediction" and "target" tensors may contain plain labels or one-hot/logit vectors. Each time `result` is called, this metric emits the average accuracy across all predictions made since the last `reset`. The reset method will bring the metric to its initial state. By default this metric in its initial state will return an accuracy value of 0. """
[docs] def __init__(self): """Creates an instance of the standalone Accuracy metric. By default this metric in its initial state will return an accuracy value of 0. The metric can be updated by using the `update` method while the running accuracy can be retrieved using the `result` method. """ self._mean_accuracy = Mean() """The mean utility that will be used to store the running accuracy."""
@torch.no_grad() def update( self, predicted_y: Tensor, true_y: Tensor, ) -> None: """Update the running accuracy given the true and predicted labels. :param predicted_y: The model prediction. Both labels and logit vectors are supported. :param true_y: The ground truth. Both labels and one-hot vectors are supported. :return: None. """ true_y = torch.as_tensor(true_y) predicted_y = torch.as_tensor(predicted_y) if len(true_y) != len(predicted_y): raise ValueError("Size mismatch for true_y and predicted_y tensors") # Check if logits or labels if len(predicted_y.shape) > 1: # Logits -> transform to labels predicted_y = torch.max(predicted_y, 1)[1] if len(true_y.shape) > 1: # Logits -> transform to labels true_y = torch.max(true_y, 1)[1] true_positives = float(torch.sum(torch.eq(predicted_y, true_y))) total_patterns = len(true_y) self._mean_accuracy.update( true_positives / total_patterns, total_patterns ) def result(self) -> float: """Retrieves the running accuracy. Calling this method will not change the internal state of the metric. :return: The current running accuracy, which is a float value between 0 and 1. """ return self._mean_accuracy.result() def reset(self) -> None: """Resets the metric. :return: None. """ self._mean_accuracy.reset()
[docs]class TaskAwareAccuracy(Metric[float]): """The task-aware Accuracy metric. The metric computes a dictionary of <task_label, accuracy value> pairs. update/result/reset methods are all task-aware. See :class:`avalanche.evaluation.Accuracy` for the documentation about the `Accuracy` metric. """
[docs] def __init__(self): """Creates an instance of the task-aware Accuracy metric.""" self._mean_accuracy = defaultdict(Accuracy) """ The mean utility that will be used to store the running accuracy for each task label. """
@torch.no_grad() def update( self, predicted_y: Tensor, true_y: Tensor, task_labels: Union[float, Tensor], ) -> None: """Update the running accuracy given the true and predicted labels. Parameter `task_labels` is used to decide how to update the inner dictionary: if Float, only the dictionary value related to that task is updated. If Tensor, all the dictionary elements belonging to the task labels will be updated. :param predicted_y: The model prediction. Both labels and logit vectors are supported. :param true_y: The ground truth. Both labels and one-hot vectors are supported. :param task_labels: the int task label associated to the current experience or the task labels vector showing the task label for each pattern. :return: None. """ if len(true_y) != len(predicted_y): raise ValueError("Size mismatch for true_y and predicted_y tensors") if isinstance(task_labels, Tensor) and len(task_labels) != len(true_y): raise ValueError("Size mismatch for true_y and task_labels tensors") if isinstance(task_labels, int): self._mean_accuracy[task_labels].update(predicted_y, true_y) elif isinstance(task_labels, Tensor): for pred, true, t in zip(predicted_y, true_y, task_labels): if isinstance(t, Tensor): t = t.item() self._mean_accuracy[t].update( pred.unsqueeze(0), true.unsqueeze(0) ) else: raise ValueError( f"Task label type: {type(task_labels)}, " f"expected int/float or Tensor" ) def result(self, task_label=None) -> Dict[int, float]: """ Retrieves the running accuracy. Calling this method will not change the internal state of the metric. task label is ignored if `self.split_by_task=False`. :param task_label: if None, return the entire dictionary of accuracies for each task. Otherwise return the dictionary `{task_label: accuracy}`. :return: A dict of running accuracies for each task label, where each value is a float value between 0 and 1. """ assert task_label is None or isinstance(task_label, int) if task_label is None: return {k: v.result() for k, v in self._mean_accuracy.items()} else: return {task_label: self._mean_accuracy[task_label].result()} def reset(self, task_label=None) -> None: """ Resets the metric. task label is ignored if `self.split_by_task=False`. :param task_label: if None, reset the entire dictionary. Otherwise, reset the value associated to `task_label`. :return: None. """ assert task_label is None or isinstance(task_label, int) if task_label is None: self._mean_accuracy = defaultdict(Accuracy) else: self._mean_accuracy[task_label].reset()
class AccuracyPluginMetric(GenericPluginMetric[float]): """ Base class for all accuracies plugin metrics """ def __init__(self, reset_at, emit_at, mode, split_by_task=False): """Creates the Accuracy plugin :param reset_at: :param emit_at: :param mode: :param split_by_task: whether to compute task-aware accuracy or not. """ self.split_by_task = split_by_task if self.split_by_task: self._accuracy = TaskAwareAccuracy() else: self._accuracy = Accuracy() super(AccuracyPluginMetric, self).__init__( self._accuracy, reset_at=reset_at, emit_at=emit_at, mode=mode ) def reset(self, strategy=None) -> None: self._metric.reset() def result(self, strategy=None) -> float: return self._metric.result() def update(self, strategy): if isinstance(self._accuracy, Accuracy): self._accuracy.update(strategy.mb_output, strategy.mb_y) elif isinstance(self._accuracy, TaskAwareAccuracy): self._accuracy.update( strategy.mb_output, strategy.mb_y, strategy.mb_task_id ) else: assert False, "should never get here."
[docs]class MinibatchAccuracy(AccuracyPluginMetric): """ The minibatch plugin accuracy metric. This metric only works at training time. This metric computes the average accuracy over patterns from a single minibatch. It reports the result after each iteration. If a more coarse-grained logging is needed, consider using :class:`EpochAccuracy` instead. """
[docs] def __init__(self): """ Creates an instance of the MinibatchAccuracy metric. """ super(MinibatchAccuracy, self).__init__( reset_at="iteration", emit_at="iteration", mode="train" )
def __str__(self): return "Top1_Acc_MB"
[docs]class EpochAccuracy(AccuracyPluginMetric): """ The average accuracy over a single training epoch. This plugin metric only works at training time. The accuracy will be logged after each training epoch by computing the number of correctly predicted patterns during the epoch divided by the overall number of patterns encountered in that epoch. """
[docs] def __init__(self): """ Creates an instance of the EpochAccuracy metric. """ super(EpochAccuracy, self).__init__( reset_at="epoch", emit_at="epoch", mode="train" )
def __str__(self): return "Top1_Acc_Epoch"
[docs]class RunningEpochAccuracy(AccuracyPluginMetric): """ The average accuracy across all minibatches up to the current epoch iteration. This plugin metric only works at training time. At each iteration, this metric logs the accuracy averaged over all patterns seen so far in the current epoch. The metric resets its state after each training epoch. """
[docs] def __init__(self): """ Creates an instance of the RunningEpochAccuracy metric. """ super(RunningEpochAccuracy, self).__init__( reset_at="epoch", emit_at="iteration", mode="train" )
def __str__(self): return "Top1_RunningAcc_Epoch"
[docs]class ExperienceAccuracy(AccuracyPluginMetric): """ At the end of each experience, this plugin metric reports the average accuracy over all patterns seen in that experience. This metric only works at eval time. """
[docs] def __init__(self): """ Creates an instance of ExperienceAccuracy metric """ super(ExperienceAccuracy, self).__init__( reset_at="experience", emit_at="experience", mode="eval" )
def __str__(self): return "Top1_Acc_Exp"
[docs]class StreamAccuracy(AccuracyPluginMetric): """ At the end of the entire stream of experiences, this plugin metric reports the average accuracy over all patterns seen in all experiences. This metric only works at eval time. """
[docs] def __init__(self): """ Creates an instance of StreamAccuracy metric """ super(StreamAccuracy, self).__init__( reset_at="stream", emit_at="stream", mode="eval" )
def __str__(self): return "Top1_Acc_Stream"
[docs]class TrainedExperienceAccuracy(AccuracyPluginMetric): """ At the end of each experience, this plugin metric reports the average accuracy for only the experiences that the model has been trained on so far. This metric only works at eval time. """
[docs] def __init__(self): """ Creates an instance of TrainedExperienceAccuracy metric by first constructing AccuracyPluginMetric """ super(TrainedExperienceAccuracy, self).__init__( reset_at="stream", emit_at="stream", mode="eval" ) self._current_experience = 0
def after_training_exp(self, strategy) -> None: self._current_experience = strategy.experience.current_experience # Reset average after learning from a new experience AccuracyPluginMetric.reset(self, strategy) return AccuracyPluginMetric.after_training_exp(self, strategy) def update(self, strategy): """ Only update the accuracy with results from experiences that have been trained on """ if strategy.experience.current_experience <= self._current_experience: AccuracyPluginMetric.update(self, strategy) def __str__(self): return "Accuracy_On_Trained_Experiences"
[docs]def accuracy_metrics( *, minibatch=False, epoch=False, epoch_running=False, experience=False, stream=False, trained_experience=False, ) -> List[PluginMetric]: """ Helper method that can be used to obtain the desired set of plugin metrics. :param minibatch: If True, will return a metric able to log the minibatch accuracy at training time. :param epoch: If True, will return a metric able to log the epoch accuracy at training time. :param epoch_running: If True, will return a metric able to log the running epoch accuracy at training time. :param experience: If True, will return a metric able to log the accuracy on each evaluation experience. :param stream: If True, will return a metric able to log the accuracy averaged over the entire evaluation stream of experiences. :param trained_experience: If True, will return a metric able to log the average evaluation accuracy only for experiences that the model has been trained on :return: A list of plugin metrics. """ metrics = [] if minibatch: metrics.append(MinibatchAccuracy()) if epoch: metrics.append(EpochAccuracy()) if epoch_running: metrics.append(RunningEpochAccuracy()) if experience: metrics.append(ExperienceAccuracy()) if stream: metrics.append(StreamAccuracy()) if trained_experience: metrics.append(TrainedExperienceAccuracy()) return metrics
__all__ = [ "Accuracy", "TaskAwareAccuracy", "MinibatchAccuracy", "EpochAccuracy", "RunningEpochAccuracy", "ExperienceAccuracy", "StreamAccuracy", "TrainedExperienceAccuracy", "accuracy_metrics", ]