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tracker.py
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tracker.py
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# 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, Tuple, Union
import torch
from torch import Tensor, nn
from torchmetrics.metric import Metric
class MetricTracker(nn.ModuleList):
"""A wrapper class that can help keeping track of a metric 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 torchmetric modular to keep track of at each timestep.
maximize: bool indicating if higher metric values are better (`True`) or lower
is better (`False`)
Example:
>>> from torchmetrics import Accuracy, MetricTracker
>>> _ = torch.manual_seed(42)
>>> tracker = MetricTracker(Accuracy(num_classes=10))
>>> 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()}") # doctest: +NORMALIZE_WHITESPACE
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)
>>> tracker.compute_all()
tensor([0.1120, 0.0880, 0.1260, 0.0800, 0.1020])
"""
def __init__(self, metric: Metric, maximize: bool = True) -> None:
super().__init__()
if not isinstance(metric, Metric):
raise TypeError("metric arg need to be an instance of a torchmetrics metric" f" but got {metric}")
self._base_metric = 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 instace 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) -> Tensor:
"""Compute the metric value for all tracked metrics."""
self._check_for_increment("compute_all")
return torch.stack([metric.compute() for i, metric in enumerate(self) if i != 0], 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[float, Tuple[int, float]]:
"""Returns the highest metric out of all tracked.
Args:
return_step: If `True` will also return the step with the highest metric value.
Returns:
The best metric value, and optionally the timestep.
"""
fn = torch.max if self.maximize else torch.min
idx, max = fn(self.compute_all(), 0)
if return_step:
return idx.item(), max.item()
return max.item()
def _check_for_increment(self, method: str) -> None:
if not self._increment_called:
raise ValueError(f"`{method}` cannot be called before `.increment()` has been called")