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diou.py
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diou.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 typing import Optional
import torch
from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_13
if _TORCHVISION_AVAILABLE and _TORCHVISION_GREATER_EQUAL_0_13:
from torchvision.ops import distance_box_iou
else:
distance_box_iou = None
__doctest_skip__ = ["distance_intersection_over_union"]
__doctest_requires__ = {("distance_intersection_over_union",): ["torchvision"]}
def _diou_update(
preds: torch.Tensor, target: torch.Tensor, iou_threshold: Optional[float], replacement_val: float = 0
) -> torch.Tensor:
iou = distance_box_iou(preds, target)
if iou_threshold is not None:
iou[iou < iou_threshold] = replacement_val
return iou
def _diou_compute(iou: torch.Tensor, labels_eq: bool = True) -> torch.Tensor:
if labels_eq:
return iou.diag().mean()
return iou.mean()
def distance_intersection_over_union(
preds: torch.Tensor,
target: torch.Tensor,
iou_threshold: Optional[float] = None,
replacement_val: float = 0,
aggregate: bool = True,
) -> torch.Tensor:
r"""Compute Distance Intersection over Union (`DIOU`_) between two sets of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format with 0 <= x1 < x2 and 0 <= y1 < y2.
Args:
preds:
The input tensor containing the predicted bounding boxes.
target:
The tensor containing the ground truth.
iou_threshold:
Optional IoU thresholds for evaluation. If set to `None` the threshold is ignored.
replacement_val:
Value to replace values under the threshold with.
aggregate:
Return the average value instead of the complete IoU matrix.
Example:
>>> import torch
>>> from torchmetrics.functional.detection import distance_intersection_over_union
>>> preds = torch.Tensor([[100, 100, 200, 200]])
>>> target = torch.Tensor([[110, 110, 210, 210]])
>>> distance_intersection_over_union(preds, target)
tensor(0.6724)
"""
if not _TORCHVISION_GREATER_EQUAL_0_13:
raise ModuleNotFoundError(
f"`{distance_intersection_over_union.__name__}` requires that `torchvision` version 0.13.0 or newer"
" is installed."
" Please install with `pip install torchvision>=0.13` or `pip install torchmetrics[detection]`."
)
iou = _diou_update(preds, target, iou_threshold, replacement_val)
return _diou_compute(iou) if aggregate else iou