a  d* @sddlmZmZmZmZddlmZddlmZddl m Z m Z ddl m Z mZddlmZmZesnddgZn e sxdgZGd ddeZd S) )AnyOptionalSequenceUnion)Tensor)IntersectionOverUnion) _ciou_compute _ciou_update)_MATPLOTLIB_AVAILABLE_TORCHVISION_GREATER_EQUAL_0_13)_AX_TYPE_PLOT_OUT_TYPECompleteIntersectionOverUnion"CompleteIntersectionOverUnion.plotcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <de ee eeed d fdd ZeeeedddZeeeedddZdeeeeefeeedddZZS)ra Computes Complete Intersection Over Union (`CIoU`_). 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 detection 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: (:class:`~torch.Tensor`) with overall ciou value over all classes and samples. - ciou/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 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(0.8611)} Raises: ModuleNotFoundError: If torchvision is not installed with version 0.13.0 or newer. Fis_differentiableThigher_is_betterfull_state_updateZciou _iou_typeg _invalid_valxyxyN) box_format iou_threshold class_metricsrespect_labelskwargsreturnc s8tstd|jdtj||||fi|dS)NzMetric `z` requires that `torchvision` version 0.13.0 or newer is installed. Please install with `pip install torchvision>=0.13` or `pip install torchmetrics[detection]`.)r ModuleNotFoundErrorruppersuper__init__)selfrrrrr __class__d/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/detection/ciou.pyrhs z&CompleteIntersectionOverUnion.__init__)argsrrcOst|i|SN)r r%rr#r#r$_iou_update_fnwsz,CompleteIntersectionOverUnion._iou_update_fncOst|i|Sr&)rr'r#r#r$_iou_compute_fn{sz-CompleteIntersectionOverUnion._iou_compute_fn)valaxrcCs |||S)az 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 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() .. plot:: :scale: 75 >>> # 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) )Z_plot)r r*r+r#r#r$plotsBr)rNFT)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrrstrrfloatrr staticmethodrr(r)rrr r r, __classcell__r#r#r!r$rs6 C    N)typingrrrrZtorchrZtorchmetrics.detection.iourZ&torchmetrics.functional.detection.ciourr Ztorchmetrics.utilities.importsr r Ztorchmetrics.utilities.plotr r Z__doctest_skip__rr#r#r#r$s