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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 collections.abc import Sequence
from typing import Any, Optional, Union
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.segmentation.mean_iou import _mean_iou_compute, _mean_iou_update, _mean_iou_validate_args
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__ = ["MeanIoU.plot"]
class MeanIoU(Metric):
"""Computes Mean Intersection over Union (mIoU) for semantic segmentation.
The metric is defined by the overlap between the predicted segmentation and the ground truth, divided by the
total area covered by the union of the two. The metric can be computed for each class separately or for all
classes at once. The metric is optimal at a value of 1 and worst at a value of 0.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An one-hot boolean tensor of shape ``(N, C, ...)`` with ``N`` being
the number of samples and ``C`` the number of classes. Alternatively, an integer tensor of shape ``(N, ...)``
can be provided, where the integer values correspond to the class index. The input type can be controlled
with the ``input_format`` argument.
- ``target`` (:class:`~torch.Tensor`): An one-hot boolean tensor of shape ``(N, C, ...)`` with ``N`` being
the number of samples and ``C`` the number of classes. Alternatively, an integer tensor of shape ``(N, ...)``
can be provided, where the integer values correspond to the class index. The input type can be controlled
with the ``input_format`` argument.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``miou`` (:class:`~torch.Tensor`): The mean Intersection over Union (mIoU) score. If ``per_class`` is set to
``True``, the output will be a tensor of shape ``(C,)`` with the IoU score for each class. If ``per_class`` is
set to ``False``, the output will be a scalar tensor.
Args:
num_classes: The number of classes in the segmentation problem.
include_background: Whether to include the background class in the computation
per_class: Whether to compute the IoU for each class separately. If set to ``False``, the metric will
compute the mean IoU over all classes.
input_format: What kind of input the function receives. Choose between ``"one-hot"`` for one-hot encoded tensors
or ``"index"`` for index tensors
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If ``num_classes`` is not a positive integer
ValueError:
If ``include_background`` is not a boolean
ValueError:
If ``per_class`` is not a boolean
ValueError:
If ``input_format`` is not one of ``"one-hot"`` or ``"index"``
Example:
>>> from torch import randint
>>> from torchmetrics.segmentation import MeanIoU
>>> miou = MeanIoU(num_classes=3)
>>> preds = randint(0, 2, (10, 3, 128, 128))
>>> target = randint(0, 2, (10, 3, 128, 128))
>>> miou(preds, target)
tensor(0.3326)
>>> miou = MeanIoU(num_classes=3, per_class=True)
>>> miou(preds, target)
tensor([0.3334, 0.3327, 0.3318])
>>> miou = MeanIoU(num_classes=3, per_class=True, include_background=False)
>>> miou(preds, target)
tensor([0.3327, 0.3318])
"""
score: Tensor
num_batches: Tensor
full_state_update: bool = False
is_differentiable: bool = False
higher_is_better: bool = True
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
def __init__(
self,
num_classes: int,
include_background: bool = True,
per_class: bool = False,
input_format: Literal["one-hot", "index"] = "one-hot",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
_mean_iou_validate_args(num_classes, include_background, per_class, input_format)
self.num_classes = num_classes
self.include_background = include_background
self.per_class = per_class
self.input_format = input_format
num_classes = num_classes - 1 if not include_background else num_classes
self.add_state("score", default=torch.zeros(num_classes if per_class else 1), dist_reduce_fx="sum")
self.add_state("num_batches", default=torch.tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update the state with the new data."""
intersection, union = _mean_iou_update(
preds, target, self.num_classes, self.include_background, self.input_format
)
score = _mean_iou_compute(intersection, union, per_class=self.per_class)
self.score += score.mean(0) if self.per_class else score.mean()
self.num_batches += 1
def compute(self) -> Tensor:
"""Compute the final Mean Intersection over Union (mIoU)."""
return self.score / self.num_batches
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = 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.audio import PerceptualEvaluationSpeechQuality
>>> metric = PerceptualEvaluationSpeechQuality(8000, 'nb')
>>> metric.update(torch.rand(8000), torch.rand(8000))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import PerceptualEvaluationSpeechQuality
>>> metric = PerceptualEvaluationSpeechQuality(8000, 'nb')
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(8000), torch.rand(8000)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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