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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 Any, Optional, Sequence, Union
from torch import Tensor
from torchmetrics.detection.iou import IntersectionOverUnion
from torchmetrics.functional.detection.diou import _diou_compute, _diou_update
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_13
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _TORCHVISION_GREATER_EQUAL_0_13:
__doctest_skip__ = ["DistanceIntersectionOverUnion", "DistanceIntersectionOverUnion.plot"]
elif not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["DistanceIntersectionOverUnion.plot"]
class DistanceIntersectionOverUnion(IntersectionOverUnion):
r"""Computes Distance Intersection Over Union (`DIoU`_).
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~List`): A list consisting of dictionaries each containing the key-values
(each dictionary corresponds to a single image). Parameters that should be provided per dict:
- ``boxes`` (:class:`~torch.Tensor`): float tensor of shape ``(num_boxes, 4)`` containing ``num_boxes``
detection boxes of the format specified in the constructor.
By default, this method expects ``(xmin, ymin, xmax, ymax)`` in absolute image coordinates.
- ``labels`` (:class:`~torch.Tensor`): integer tensor of shape ``(num_boxes)`` containing 0-indexed detection
classes for the boxes.
- ``target`` (:class:`~List`): A list consisting of dictionaries each containing the key-values
(each dictionary corresponds to a single image). Parameters that should be provided per dict:
- ``boxes`` (:class:`~torch.Tensor`): float tensor of shape ``(num_boxes, 4)`` containing ``num_boxes`` ground
truth boxes of the format specified in the constructor.
By default, this method expects ``(xmin, ymin, xmax, ymax)`` in absolute image coordinates.
- ``labels`` (:class:`~torch.Tensor`): integer tensor of shape ``(num_boxes)`` containing 0-indexed ground truth
classes for the boxes.
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``diou_dict``: A dictionary containing the following key-values:
- diou: (:class:`~torch.Tensor`) with overall diou value over all classes and samples.
- diou/cl_{cl}: (:class:`~torch.Tensor`), if argument ``class_metrics=True``
Args:
box_format:
Input format of given boxes. Supported formats are ``['xyxy', 'xywh', 'cxcywh']``.
iou_thresholds:
Optional IoU thresholds for evaluation. If set to `None` the threshold is ignored.
class_metrics:
Option to enable per-class metrics for IoU. Has a performance impact.
respect_labels:
Ignore values from boxes that do not have the same label as the ground truth box. Else will compute Iou
between all pairs of boxes.
kwargs:
Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> import torch
>>> from torchmetrics.detection import DistanceIntersectionOverUnion
>>> preds = [
... {
... "boxes": torch.tensor([[296.55, 93.96, 314.97, 152.79], [298.55, 98.96, 314.97, 151.79]]),
... "scores": torch.tensor([0.236, 0.56]),
... "labels": torch.tensor([4, 5]),
... }
... ]
>>> target = [
... {
... "boxes": torch.tensor([[300.00, 100.00, 315.00, 150.00]]),
... "labels": torch.tensor([5]),
... }
... ]
>>> metric = DistanceIntersectionOverUnion()
>>> metric(preds, target)
{'diou': tensor(0.8611)}
Raises:
ModuleNotFoundError:
If torchvision is not installed with version 0.13.0 or newer.
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = True
_iou_type: str = "diou"
_invalid_val: float = -1.0
def __init__(
self,
box_format: str = "xyxy",
iou_threshold: Optional[float] = None,
class_metrics: bool = False,
respect_labels: bool = True,
**kwargs: Any,
) -> None:
if not _TORCHVISION_GREATER_EQUAL_0_13:
raise ModuleNotFoundError(
f"Metric `{self._iou_type.upper()}` requires that `torchvision` version 0.13.0 or newer is installed."
" Please install with `pip install torchvision>=0.13` or `pip install torchmetrics[detection]`."
)
super().__init__(box_format, iou_threshold, class_metrics, respect_labels, **kwargs)
@staticmethod
def _iou_update_fn(*args: Any, **kwargs: Any) -> Tensor:
return _diou_update(*args, **kwargs)
@staticmethod
def _iou_compute_fn(*args: Any, **kwargs: Any) -> Tensor:
return _diou_compute(*args, **kwargs)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 object and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting single value
>>> import torch
>>> from torchmetrics.detection import DistanceIntersectionOverUnion
>>> preds = [
... {
... "boxes": torch.tensor([[296.55, 93.96, 314.97, 152.79], [298.55, 98.96, 314.97, 151.79]]),
... "scores": torch.tensor([0.236, 0.56]),
... "labels": torch.tensor([4, 5]),
... }
... ]
>>> target = [
... {
... "boxes": torch.tensor([[300.00, 100.00, 315.00, 150.00]]),
... "labels": torch.tensor([5]),
... }
... ]
>>> metric = DistanceIntersectionOverUnion()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.detection import DistanceIntersectionOverUnion
>>> preds = [
... {
... "boxes": torch.tensor([[296.55, 93.96, 314.97, 152.79], [298.55, 98.96, 314.97, 151.79]]),
... "scores": torch.tensor([0.236, 0.56]),
... "labels": torch.tensor([4, 5]),
... }
... ]
>>> target = lambda : [
... {
... "boxes": torch.tensor([[300.00, 100.00, 315.00, 150.00]]) + torch.randint(-10, 10, (1, 4)),
... "labels": torch.tensor([5]),
... }
... ]
>>> metric = DistanceIntersectionOverUnion()
>>> vals = []
>>> for _ in range(20):
... vals.append(metric(preds, target()))
>>> fig_, ax_ = metric.plot(vals)
"""
return self._plot(val, ax)