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iou.py
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iou.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, Dict, List, Optional, Sequence, Union
import torch
from torch import Tensor
from torchmetrics.detection.helpers import _fix_empty_tensors, _input_validator
from torchmetrics.functional.detection.iou import _iou_compute, _iou_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_8
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if _TORCHVISION_GREATER_EQUAL_0_8:
from torchvision.ops import box_convert
else:
box_convert = None
if not _TORCHVISION_GREATER_EQUAL_0_8:
__doctest_skip__ = ["IntersectionOverUnion", "IntersectionOverUnion.plot"]
elif not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["IntersectionOverUnion.plot"]
class IntersectionOverUnion(Metric):
r"""Computes Intersection Over Union (IoU).
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: ``IntTensor`` 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:
- ``iou_dict``: A dictionary containing the following key-values:
- iou: (:class:`~torch.Tensor`)
- iou/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 IntersectionOverUnion
>>> preds = [
... {
... "boxes": torch.tensor([
... [296.55, 93.96, 314.97, 152.79],
... [298.55, 98.96, 314.97, 151.79]]),
... "labels": torch.tensor([4, 5]),
... }
... ]
>>> target = [
... {
... "boxes": torch.tensor([[300.00, 100.00, 315.00, 150.00]]),
... "labels": torch.tensor([5]),
... }
... ]
>>> metric = IntersectionOverUnion()
>>> metric(preds, target)
{'iou': tensor(0.8614)}
Example::
The metric can also return the score per class:
>>> import torch
>>> from torchmetrics.detection import IntersectionOverUnion
>>> preds = [
... {
... "boxes": torch.tensor([
... [296.55, 93.96, 314.97, 152.79],
... [298.55, 98.96, 314.97, 151.79]]),
... "labels": torch.tensor([4, 5]),
... }
... ]
>>> target = [
... {
... "boxes": torch.tensor([
... [300.00, 100.00, 315.00, 150.00],
... [300.00, 100.00, 315.00, 150.00]
... ]),
... "labels": torch.tensor([4, 5]),
... }
... ]
>>> metric = IntersectionOverUnion(class_metrics=True)
>>> metric(preds, target)
{'iou': tensor(0.7756), 'iou/cl_4': tensor(0.6898), 'iou/cl_5': tensor(0.8614)}
Raises:
ModuleNotFoundError:
If torchvision is not installed with version 0.8.0 or newer.
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = True
full_state_update: bool = True
groundtruth_labels: List[Tensor]
iou_matrix: List[Tensor]
_iou_type: str = "iou"
_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:
super().__init__(**kwargs)
if not _TORCHVISION_GREATER_EQUAL_0_8:
raise ModuleNotFoundError(
f"Metric `{self._iou_type.upper()}` requires that `torchvision` version 0.8.0 or newer is installed."
" Please install with `pip install torchvision>=0.8` or `pip install torchmetrics[detection]`."
)
allowed_box_formats = ("xyxy", "xywh", "cxcywh")
if box_format not in allowed_box_formats:
raise ValueError(f"Expected argument `box_format` to be one of {allowed_box_formats} but got {box_format}")
self.box_format = box_format
self.iou_threshold = iou_threshold
if not isinstance(class_metrics, bool):
raise ValueError("Expected argument `class_metrics` to be a boolean")
self.class_metrics = class_metrics
if not isinstance(respect_labels, bool):
raise ValueError("Expected argument `respect_labels` to be a boolean")
self.respect_labels = respect_labels
self.add_state("groundtruth_labels", default=[], dist_reduce_fx=None)
self.add_state("iou_matrix", default=[], dist_reduce_fx=None)
@staticmethod
def _iou_update_fn(*args: Any, **kwargs: Any) -> Tensor:
return _iou_update(*args, **kwargs)
@staticmethod
def _iou_compute_fn(*args: Any, **kwargs: Any) -> Tensor:
return _iou_compute(*args, **kwargs)
def update(self, preds: List[Dict[str, Tensor]], target: List[Dict[str, Tensor]]) -> None:
"""Update state with predictions and targets."""
_input_validator(preds, target, ignore_score=True)
for p, t in zip(preds, target):
det_boxes = self._get_safe_item_values(p["boxes"])
gt_boxes = self._get_safe_item_values(t["boxes"])
self.groundtruth_labels.append(t["labels"])
iou_matrix = self._iou_update_fn(det_boxes, gt_boxes, self.iou_threshold, self._invalid_val) # N x M
if self.respect_labels:
label_eq = p["labels"].unsqueeze(1) == t["labels"].unsqueeze(0) # N x M
iou_matrix[~label_eq] = self._invalid_val
self.iou_matrix.append(iou_matrix)
def _get_safe_item_values(self, boxes: Tensor) -> Tensor:
boxes = _fix_empty_tensors(boxes)
if boxes.numel() > 0:
boxes = box_convert(boxes, in_fmt=self.box_format, out_fmt="xyxy")
return boxes
def _get_gt_classes(self) -> List:
"""Returns a list of unique classes found in ground truth and detection data."""
if len(self.groundtruth_labels) > 0:
return torch.cat(self.groundtruth_labels).unique().tolist()
return []
def compute(self) -> dict:
"""Computes IoU based on inputs passed in to ``update`` previously."""
score = torch.cat([mat[mat != self._invalid_val] for mat in self.iou_matrix], 0).mean()
results: Dict[str, Tensor] = {f"{self._iou_type}": score}
if self.class_metrics:
gt_labels = dim_zero_cat(self.groundtruth_labels)
classes = gt_labels.unique().tolist() if len(gt_labels) > 0 else []
for cl in classes:
masked_iou, observed = torch.zeros_like(score), torch.zeros_like(score)
for mat, gt_lab in zip(self.iou_matrix, self.groundtruth_labels):
scores = mat[:, gt_lab == cl]
masked_iou += scores[scores != self._invalid_val].sum()
observed += scores[scores != self._invalid_val].numel()
results.update({f"{self._iou_type}/cl_{cl}": masked_iou / observed})
return results
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
>>> import torch
>>> from torchmetrics.detection import IntersectionOverUnion
>>> 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 = IntersectionOverUnion()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.detection import IntersectionOverUnion
>>> 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 = IntersectionOverUnion()
>>> vals = []
>>> for _ in range(20):
... vals.append(metric(preds, target()))
>>> fig_, ax_ = metric.plot(vals)
"""
return self._plot(val, ax)