108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
# 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 typing import Any, Optional
|
|
|
|
import torch
|
|
|
|
from pytorch_lightning.metrics.classification.confusion_matrix import ConfusionMatrix
|
|
from pytorch_lightning.metrics.functional.iou import _iou_from_confmat
|
|
|
|
|
|
class IoU(ConfusionMatrix):
|
|
r"""
|
|
Computes `Intersection over union, or Jaccard index calculation <https://en.wikipedia.org/wiki/Jaccard_index>`_:
|
|
|
|
.. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}
|
|
|
|
Where: :math:`A` and :math:`B` are both tensors of the same size, containing integer class values.
|
|
They may be subject to conversion from input data (see description below). Note that it is different from box IoU.
|
|
|
|
Works with binary, multiclass and multi-label data.
|
|
Accepts probabilities from a model output or integer class values in prediction.
|
|
Works with multi-dimensional preds and target.
|
|
|
|
Forward accepts
|
|
|
|
- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
|
|
- ``target`` (long tensor): ``(N, ...)``
|
|
|
|
If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
|
|
to convert into integer labels. This is the case for binary and multi-label probabilities.
|
|
|
|
If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
|
|
|
|
Args:
|
|
num_classes: Number of classes in the dataset.
|
|
ignore_index: optional int specifying a target class to ignore. If given, this class index does not contribute
|
|
to the returned score, regardless of reduction method. Has no effect if given an int that is not in the
|
|
range [0, num_classes-1]. By default, no index is ignored, and all classes are used.
|
|
absent_score: score to use for an individual class, if no instances of the class index were present in
|
|
`pred` AND no instances of the class index were present in `target`. For example, if we have 3 classes,
|
|
[0, 0] for `pred`, and [0, 2] for `target`, then class 1 would be assigned the `absent_score`.
|
|
threshold:
|
|
Threshold value for binary or multi-label probabilities.
|
|
reduction: a method to reduce metric score over labels.
|
|
|
|
- ``'elementwise_mean'``: takes the mean (default)
|
|
- ``'sum'``: takes the sum
|
|
- ``'none'``: no reduction will be applied
|
|
|
|
compute_on_step:
|
|
Forward only calls ``update()`` and return None if this is set to False.
|
|
dist_sync_on_step:
|
|
Synchronize metric state across processes at each ``forward()``
|
|
before returning the value at the step.
|
|
process_group:
|
|
Specify the process group on which synchronization is called. default: None (which selects the entire world)
|
|
|
|
Example:
|
|
>>> from pytorch_lightning.metrics import IoU
|
|
>>> target = torch.randint(0, 2, (10, 25, 25))
|
|
>>> pred = torch.tensor(target)
|
|
>>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
|
|
>>> iou = IoU(num_classes=2)
|
|
>>> iou(pred, target)
|
|
tensor(0.9660)
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
num_classes: int,
|
|
ignore_index: Optional[int] = None,
|
|
absent_score: float = 0.0,
|
|
threshold: float = 0.5,
|
|
reduction: str = 'elementwise_mean',
|
|
compute_on_step: bool = True,
|
|
dist_sync_on_step: bool = False,
|
|
process_group: Optional[Any] = None,
|
|
):
|
|
super().__init__(
|
|
num_classes=num_classes,
|
|
normalize=None,
|
|
threshold=threshold,
|
|
compute_on_step=compute_on_step,
|
|
dist_sync_on_step=dist_sync_on_step,
|
|
process_group=process_group,
|
|
)
|
|
self.reduction = reduction
|
|
self.ignore_index = ignore_index
|
|
self.absent_score = absent_score
|
|
|
|
def compute(self) -> torch.Tensor:
|
|
"""
|
|
Computes intersection over union (IoU)
|
|
"""
|
|
return _iou_from_confmat(self.confmat, self.num_classes, self.ignore_index, self.absent_score, self.reduction)
|