# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from copy import deepcopy from typing import Any, Dict, List, Tuple, Union import torch from torch import Tensor from torch.nn import ModuleList from torchmetrics.collections import MetricCollection from torchmetrics.metric import Metric from torchmetrics.utilities.prints import rank_zero_warn class MetricTracker(ModuleList): """A wrapper class that can help keeping track of a metric or metric collection over time and implement useful methods. The wrapper implements the standard ``.update()``, ``.compute()``, ``.reset()`` methods that just calls corresponding method of the currently tracked metric. However, the following additional methods are provided: -``MetricTracker.n_steps``: number of metrics being tracked -``MetricTracker.increment()``: initialize a new metric for being tracked -``MetricTracker.compute_all()``: get the metric value for all steps -``MetricTracker.best_metric()``: returns the best value Args: metric: instance of a ``torchmetrics.Metric`` or ``torchmetrics.MetricCollection`` to keep track of at each timestep. maximize: either single bool or list of bool indicating if higher metric values are better (``True``) or lower is better (``False``). Example (single metric): >>> from torchmetrics import MetricTracker >>> from torchmetrics.classification import MulticlassAccuracy >>> _ = torch.manual_seed(42) >>> tracker = MetricTracker(MulticlassAccuracy(num_classes=10, average='micro')) >>> for epoch in range(5): ... tracker.increment() ... for batch_idx in range(5): ... preds, target = torch.randint(10, (100,)), torch.randint(10, (100,)) ... tracker.update(preds, target) ... print(f"current acc={tracker.compute()}") current acc=0.1120000034570694 current acc=0.08799999952316284 current acc=0.12600000202655792 current acc=0.07999999821186066 current acc=0.10199999809265137 >>> best_acc, which_epoch = tracker.best_metric(return_step=True) >>> best_acc # doctest: +ELLIPSIS 0.1260... >>> which_epoch 2 >>> tracker.compute_all() tensor([0.1120, 0.0880, 0.1260, 0.0800, 0.1020]) Example (multiple metrics using MetricCollection): >>> from torchmetrics import MetricTracker, MetricCollection, MeanSquaredError, ExplainedVariance >>> _ = torch.manual_seed(42) >>> tracker = MetricTracker(MetricCollection([MeanSquaredError(), ExplainedVariance()]), maximize=[False, True]) >>> for epoch in range(5): ... tracker.increment() ... for batch_idx in range(5): ... preds, target = torch.randn(100), torch.randn(100) ... tracker.update(preds, target) ... print(f"current stats={tracker.compute()}") # doctest: +NORMALIZE_WHITESPACE current stats={'MeanSquaredError': tensor(1.8218), 'ExplainedVariance': tensor(-0.8969)} current stats={'MeanSquaredError': tensor(2.0268), 'ExplainedVariance': tensor(-1.0206)} current stats={'MeanSquaredError': tensor(1.9491), 'ExplainedVariance': tensor(-0.8298)} current stats={'MeanSquaredError': tensor(1.9800), 'ExplainedVariance': tensor(-0.9199)} current stats={'MeanSquaredError': tensor(2.2481), 'ExplainedVariance': tensor(-1.1622)} >>> from pprint import pprint >>> best_res, which_epoch = tracker.best_metric(return_step=True) >>> pprint(best_res) # doctest: +ELLIPSIS {'ExplainedVariance': -0.829..., 'MeanSquaredError': 1.821...} >>> which_epoch {'MeanSquaredError': 0, 'ExplainedVariance': 2} >>> pprint(tracker.compute_all()) {'ExplainedVariance': tensor([-0.8969, -1.0206, -0.8298, -0.9199, -1.1622]), 'MeanSquaredError': tensor([1.8218, 2.0268, 1.9491, 1.9800, 2.2481])} """ def __init__(self, metric: Union[Metric, MetricCollection], maximize: Union[bool, List[bool]] = True) -> None: super().__init__() if not isinstance(metric, (Metric, MetricCollection)): raise TypeError( "Metric arg need to be an instance of a torchmetrics" f" `Metric` or `MetricCollection` but got {metric}" ) self._base_metric = metric if not isinstance(maximize, (bool, list)): raise ValueError("Argument `maximize` should either be a single bool or list of bool") if isinstance(maximize, list) and isinstance(metric, MetricCollection) and len(maximize) != len(metric): raise ValueError("The len of argument `maximize` should match the length of the metric collection") if isinstance(metric, Metric) and not isinstance(maximize, bool): raise ValueError("Argument `maximize` should be a single bool when `metric` is a single Metric") self.maximize = maximize self._increment_called = False @property def n_steps(self) -> int: """Returns the number of times the tracker has been incremented.""" return len(self) - 1 # subtract the base metric def increment(self) -> None: """Creates a new instance of the input metric that will be updated next.""" self._increment_called = True self.append(deepcopy(self._base_metric)) def forward(self, *args, **kwargs) -> None: # type: ignore """Calls forward of the current metric being tracked.""" self._check_for_increment("forward") return self[-1](*args, **kwargs) def update(self, *args, **kwargs) -> None: # type: ignore """Updates the current metric being tracked.""" self._check_for_increment("update") self[-1].update(*args, **kwargs) def compute(self) -> Any: """Call compute of the current metric being tracked.""" self._check_for_increment("compute") return self[-1].compute() def compute_all(self) -> Union[Tensor, Dict[str, Tensor]]: """Compute the metric value for all tracked metrics. Return: Either a single tensor if the tracked base object is a single metric, else if a metric collection is provide a dict of tensors will be returned Raises: ValueError: If `self.increment` have not been called before this method is called. """ self._check_for_increment("compute_all") # The i!=0 accounts for the self._base_metric should be ignored res = [metric.compute() for i, metric in enumerate(self) if i != 0] if isinstance(self._base_metric, MetricCollection): keys = res[0].keys() return {k: torch.stack([r[k] for r in res], dim=0) for k in keys} return torch.stack(res, dim=0) def reset(self) -> None: """Resets the current metric being tracked.""" self[-1].reset() def reset_all(self) -> None: """Resets all metrics being tracked.""" for metric in self: metric.reset() def best_metric( self, return_step: bool = False ) -> Union[ None, float, Tuple[float, int], Tuple[None, None], Dict[str, Union[float, None]], Tuple[Dict[str, Union[float, None]], Dict[str, Union[int, None]]], ]: """Returns the highest metric out of all tracked. Args: return_step: If ``True`` will also return the step with the highest metric value. Returns: Either a single value or a tuple, depends on the value of ``return_step`` and the object being tracked. - If a single metric is being tracked and ``return_step=False`` then a single tensor will be returned - If a single metric is being tracked and ``return_step=True`` then a 2-element tuple will be returned, where the first value is optimal value and second value is the corresponding optimal step - If a metric collection is being tracked and ``return_step=False`` then a single dict will be returned, where keys correspond to the different values of the collection and the values are the optimal metric value - If a metric collection is being bracked and ``return_step=True`` then a 2-element tuple will be returned where each is a dict, with keys corresponding to the different values of th collection and the values of the first dict being the optimal values and the values of the second dict being the optimal step In addtion the value in all cases may be ``None`` if the underlying metric does have a proper defined way of being optimal. """ if isinstance(self._base_metric, Metric): fn = torch.max if self.maximize else torch.min try: value, idx = fn(self.compute_all(), 0) if return_step: return value.item(), idx.item() return value.item() except ValueError as error: rank_zero_warn( f"Encountered the following error when trying to get the best metric: {error}" "this is probably due to the 'best' not being defined for this metric." "Returning `None` instead.", UserWarning, ) if return_step: return None, None return None else: # this is a metric collection res = self.compute_all() maximize = self.maximize if isinstance(self.maximize, list) else len(res) * [self.maximize] value, idx = {}, {} for i, (k, v) in enumerate(res.items()): try: fn = torch.max if maximize[i] else torch.min out = fn(v, 0) value[k], idx[k] = out[0].item(), out[1].item() except ValueError as error: rank_zero_warn( f"Encountered the following error when trying to get the best metric for metric {k}:" f"{error} this is probably due to the 'best' not being defined for this metric." "Returning `None` instead.", UserWarning, ) value[k], idx[k] = None, None if return_step: return value, idx return value def _check_for_increment(self, method: str) -> None: """Method for checking that a metric that can be updated/used for computations has been intialized.""" if not self._increment_called: raise ValueError(f"`{method}` cannot be called before `.increment()` has been called")