class_docstr: 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.
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docstr: 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)
docstr_tok: ["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",")"]
code: 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)
  
file_path: torchmetrics/src/torchmetrics/detection/diou.py
imports: 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
label_desc: Use image node_id 4 for calling the DistanceIntersectionOverUnion obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self