Complete Intersection Over Union (cIoU)

Computes Complete Intersection Over Union (CIoU) <https://arxiv.org/abs/2005.03572>`_.

As input to forward and update the metric accepts the following input:

• preds (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: (FloatTensor) 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.

  • scores: FloatTensor of shape (num_boxes) containing detection scores for the boxes.

  • labels: IntTensor of shape (num_boxes) containing 0-indexed detection classes for the boxes.

• target (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: FloatTensor 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: IntTensor 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:

• ciou_dict: A dictionary containing the following key-values:

  • ciou: (Tensor)

  • ciou/cl_{cl}: (Tensor), if argument class metrics=True

Parameters:

• box_format (str) – 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 (bool) – Option to enable per-class metrics for IoU. Has a performance impact.

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> import torch
>>> from torchmetrics.detection import CompleteIntersectionOverUnion
>>> 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 = CompleteIntersectionOverUnion()
>>> metric(preds, target)
{'ciou': tensor(-0.5694)}

Raises:

ModuleNotFoundError – If torchvision is not installed with version 0.13.0 or newer.

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – 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 (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>>

>>> # Example plotting single value
>>> import torch
>>> from torchmetrics.detection import CompleteIntersectionOverUnion
>>> 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 = CompleteIntersectionOverUnion()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>>

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.detection import CompleteIntersectionOverUnion
>>> 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 = CompleteIntersectionOverUnion()
>>> vals = []
>>> for _ in range(20):
...     vals.append(metric(preds, target()))
>>> fig_, ax_ = metric.plot(vals)