112 lines
4.5 KiB
Python
112 lines
4.5 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional
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import torch
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from pytorch_lightning.metrics.functional.confusion_matrix import _confusion_matrix_update
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from pytorch_lightning.metrics.functional.reduction import reduce
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from pytorch_lightning.metrics.utils import get_num_classes
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def _iou_from_confmat(
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confmat: torch.Tensor,
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num_classes: int,
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ignore_index: Optional[int] = None,
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absent_score: float = 0.0,
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reduction: str = 'elementwise_mean',
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):
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intersection = torch.diag(confmat)
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union = confmat.sum(0) + confmat.sum(1) - intersection
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# If this class is absent in both target AND pred (union == 0), then use the absent_score for this class.
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scores = intersection.float() / union.float()
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scores[union == 0] = absent_score
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# Remove the ignored class index from the scores.
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if ignore_index is not None and ignore_index >= 0 and ignore_index < num_classes:
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scores = torch.cat([
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scores[:ignore_index],
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scores[ignore_index + 1:],
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])
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return reduce(scores, reduction=reduction)
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def iou(
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pred: torch.Tensor,
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target: torch.Tensor,
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ignore_index: Optional[int] = None,
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absent_score: float = 0.0,
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threshold: float = 0.5,
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num_classes: Optional[int] = None,
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reduction: str = 'elementwise_mean',
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) -> torch.Tensor:
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r"""
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Computes `Intersection over union, or Jaccard index calculation <https://en.wikipedia.org/wiki/Jaccard_index>`_:
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.. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}
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Where: :math:`A` and :math:`B` are both tensors of the same size,
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containing integer class values. They may be subject to conversion from
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input data (see description below).
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Note that it is different from box IoU.
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If pred and target are the same shape and pred is a float tensor,
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we use the ``threshold`` argument. This is the case for binary and multi-label logits.
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If pred has an extra dimension as in the case of multi-class scores we
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perform an argmax on ``dim=1``.
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Args:
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pred: Tensor containing integer predictions, with shape [N, d1, d2, ...]
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target: Tensor containing integer targets, with shape [N, d1, d2, ...]
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ignore_index: optional int specifying a target class to ignore. If given,
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this class index does not contribute to the returned score, regardless
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of reduction method. Has no effect if given an int that is not in the
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range [0, num_classes-1], where num_classes is either given or derived
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from pred and target. By default, no index is ignored, and all classes are used.
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absent_score: score to use for an individual class, if no instances of
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the class index were present in `pred` AND no instances of the class
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index were present in `target`. For example, if we have 3 classes,
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[0, 0] for `pred`, and [0, 2] for `target`, then class 1 would be
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assigned the `absent_score`.
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threshold:
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Threshold value for binary or multi-label logits. default: 0.5
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num_classes:
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Optionally specify the number of classes
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reduction: a method to reduce metric score over labels.
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- ``'elementwise_mean'``: takes the mean (default)
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- ``'sum'``: takes the sum
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- ``'none'``: no reduction will be applied
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Return:
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IoU score : Tensor containing single value if reduction is
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'elementwise_mean', or number of classes if reduction is 'none'
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Example:
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>>> target = torch.randint(0, 2, (10, 25, 25))
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>>> pred = torch.tensor(target)
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>>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
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>>> iou(pred, target)
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tensor(0.9660)
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"""
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num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes)
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confmat = _confusion_matrix_update(pred, target, num_classes, threshold)
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return _iou_from_confmat(confmat, num_classes, ignore_index, absent_score, reduction)
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