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minmax.py
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minmax.py
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# Copyright The 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 typing import Any, Dict, Optional, Sequence, Union
import torch
from torch import Tensor
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["MinMaxMetric.plot"]
class MinMaxMetric(Metric):
"""Wrapper metric that tracks both the minimum and maximum of a scalar/tensor across an experiment.
The min/max value will be updated each time ``.compute`` is called.
Args:
base_metric:
The metric of which you want to keep track of its maximum and minimum values.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError
If ``base_metric` argument is not a subclasses instance of ``torchmetrics.Metric``
Example::
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> from pprint import pprint
>>> base_metric = BinaryAccuracy()
>>> minmax_metric = MinMaxMetric(base_metric)
>>> preds_1 = torch.Tensor([[0.1, 0.9], [0.2, 0.8]])
>>> preds_2 = torch.Tensor([[0.9, 0.1], [0.2, 0.8]])
>>> labels = torch.Tensor([[0, 1], [0, 1]]).long()
>>> pprint(minmax_metric(preds_1, labels))
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> minmax_metric.update(preds_2, labels)
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(0.7500), 'raw': tensor(0.7500)}
"""
full_state_update: Optional[bool] = True
min_val: Tensor
max_val: Tensor
def __init__(
self,
base_metric: Metric,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not isinstance(base_metric, Metric):
raise ValueError(
f"Expected base metric to be an instance of `torchmetrics.Metric` but received {base_metric}"
)
self._base_metric = base_metric
self.min_val = torch.tensor(float("inf"))
self.max_val = torch.tensor(float("-inf"))
def update(self, *args: Any, **kwargs: Any) -> None:
"""Update the underlying metric."""
self._base_metric.update(*args, **kwargs)
def compute(self) -> Dict[str, Tensor]:
"""Compute the underlying metric as well as max and min values for this metric.
Returns a dictionary that consists of the computed value (``raw``), as well as the minimum (``min``) and maximum
(``max``) values.
"""
val = self._base_metric.compute()
if not self._is_suitable_val(val):
raise RuntimeError(
f"Returned value from base metric should be a scalar (int, float or tensor of size 1, but got {val}"
)
self.max_val = val if self.max_val.to(val.device) < val else self.max_val.to(val.device)
self.min_val = val if self.min_val.to(val.device) > val else self.min_val.to(val.device)
return {"raw": val, "max": self.max_val, "min": self.min_val}
def reset(self) -> None:
"""Set ``max_val`` and ``min_val`` to the initialization bounds and resets the base metric."""
super().reset()
self._base_metric.reset()
@staticmethod
def _is_suitable_val(val: Union[int, float, Tensor]) -> bool:
"""Check whether min/max is a scalar value."""
if isinstance(val, (int, float)):
return True
if isinstance(val, Tensor):
return val.numel() == 1
return False
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> metric = MinMaxMetric(BinaryAccuracy())
>>> metric.update(torch.randint(2, (20,)), torch.randint(2, (20,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> metric = MinMaxMetric(BinaryAccuracy())
>>> values = [ ]
>>> for _ in range(3):
... values.append(metric(torch.randint(2, (20,)), torch.randint(2, (20,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)