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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
if not _TORCHVISION_AVAILABLE:
__doctest_skip__ = ["complete_intersection_over_union"]
def _ciou_update(
preds: torch.Tensor, target: torch.Tensor, iou_threshold: Optional[float], replacement_val: float = 0
) -> torch.Tensor:
if preds.ndim != 2 or preds.shape[-1] != 4:
raise ValueError(f"Expected preds to be of shape (N, 4) but got {preds.shape}")
if target.ndim != 2 or target.shape[-1] != 4:
raise ValueError(f"Expected target to be of shape (N, 4) but got {target.shape}")
from torchvision.ops import complete_box_iou
if preds.numel() == 0: # if no boxes are predicted
return torch.zeros(target.shape[0], target.shape[0], device=target.device, dtype=torch.float32)
if target.numel() == 0: # if no boxes are true
return torch.zeros(preds.shape[0], preds.shape[0], device=preds.device, dtype=torch.float32)
iou = complete_box_iou(preds, target)
if iou_threshold is not None:
iou[iou < iou_threshold] = replacement_val
return iou
def _ciou_compute(iou: torch.Tensor, aggregate: bool = True) -> torch.Tensor:
if not aggregate:
return iou
return iou.diag().mean() if iou.numel() > 0 else torch.tensor(0.0, device=iou.device)
def complete_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 Complete Intersection over Union (`CIOU`_) 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 full matrix of values
Example::
By default iou is aggregated across all box pairs e.g. mean along the diagonal of the IoU matrix:
>>> import torch
>>> from torchmetrics.functional.detection import complete_intersection_over_union
>>> preds = torch.tensor(
... [
... [296.55, 93.96, 314.97, 152.79],
... [328.94, 97.05, 342.49, 122.98],
... [356.62, 95.47, 372.33, 147.55],
... ]
... )
>>> target = torch.tensor(
... [
... [300.00, 100.00, 315.00, 150.00],
... [330.00, 100.00, 350.00, 125.00],
... [350.00, 100.00, 375.00, 150.00],
... ]
... )
>>> complete_intersection_over_union(preds, target)
tensor(0.5790)
Example::
By setting `aggregate=False` the IoU score per prediction and target boxes is returned:
>>> import torch
>>> from torchmetrics.functional.detection import complete_intersection_over_union
>>> preds = torch.tensor(
... [
... [296.55, 93.96, 314.97, 152.79],
... [328.94, 97.05, 342.49, 122.98],
... [356.62, 95.47, 372.33, 147.55],
... ]
... )
>>> target = torch.tensor(
... [
... [300.00, 100.00, 315.00, 150.00],
... [330.00, 100.00, 350.00, 125.00],
... [350.00, 100.00, 375.00, 150.00],
... ]
... )
>>> complete_intersection_over_union(preds, target, aggregate=False)
tensor([[ 0.6883, -0.2072, -0.3352],
[-0.2217, 0.4881, -0.1913],
[-0.3971, -0.1543, 0.5606]])
"""
if not _TORCHVISION_AVAILABLE:
raise ModuleNotFoundError(
f"`{complete_intersection_over_union.__name__}` requires that `torchvision` is installed."
" Please install with `pip install torchmetrics[detection]`."
)
iou = _ciou_update(preds, target, iou_threshold, replacement_val)
return _ciou_compute(iou, aggregate)
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