o .wÖiÛã@s”ddlmZddlmZmZmZddlmZddlm Z ddl m Z m Z ddl mZmZddlmZmZes;dd gZnes@d gZGd d„de ƒZd S) é)ÚSequence)ÚAnyÚOptionalÚUnion)ÚTensor)ÚIntersectionOverUnion)Ú _giou_computeÚ _giou_update)Ú_MATPLOTLIB_AVAILABLEÚ_TORCHVISION_AVAILABLE)Ú_AX_TYPEÚ_PLOT_OUT_TYPEÚ GeneralizedIntersectionOverUnionú%GeneralizedIntersectionOverUnion.plotc sìeZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <  dd e dee dedededd f ‡fdd„ Zedededefdd„ƒZedededefdd„ƒZ ddeeeeefdeedefdd„Z‡ZS) ra¢ Compute Generalized Intersection Over Union (`GIoU`_). 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: - ``giou_dict``: A dictionary containing the following key-values: - giou: (:class:`~torch.Tensor`) with overall giou value over all classes and samples. - giou/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 GeneralizedIntersectionOverUnion >>> 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 = GeneralizedIntersectionOverUnion() >>> metric(preds, target) {'giou': tensor(0.8613)} Raises: ModuleNotFoundError: If torchvision is not installed with version 0.8.0 or newer. FÚis_differentiableTÚhigher_is_betterÚfull_state_updateÚgiouÚ _iou_typegð¿Ú _invalid_valÚxyxyNÚ box_formatÚ iou_thresholdÚ class_metricsÚrespect_labelsÚkwargsÚreturnc stƒj||||fi|¤ŽdS©N)ÚsuperÚ__init__)Úselfrrrrr©Ú __class__©úX/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/detection/giou.pyrjsz)GeneralizedIntersectionOverUnion.__init__ÚargscOót|i|¤ŽSr)r ©r%rr#r#r$Ú_iou_update_fntóz/GeneralizedIntersectionOverUnion._iou_update_fncOr&r)rr'r#r#r$Ú_iou_compute_fnxr)z0GeneralizedIntersectionOverUnion._iou_compute_fnÚvalÚaxcCs | ||¡S)a† 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 GeneralizedIntersectionOverUnion >>> 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 = GeneralizedIntersectionOverUnion() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.detection import GeneralizedIntersectionOverUnion >>> 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, 335.00, 150.00]]) + torch.randint(-10, 10, (1, 4)), ... "labels": torch.tensor([5]), ... } ... ] >>> metric = GeneralizedIntersectionOverUnion() >>> vals = [] >>> for _ in range(20): ... vals.append(metric(preds, target())) >>> fig_, ax_ = metric.plot(vals) )Ú_plot)r r+r,r#r#r$Úplot|s Br)rNFT)NN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚboolÚ__annotations__rrrrÚstrrÚfloatrrÚ staticmethodrr(r*rrr r r.Ú __classcell__r#r#r!r$rsH  D   ûþýüûúù ÿÿÿþN)Úcollections.abcrÚtypingrrrÚtorchrÚtorchmetrics.detection.iourÚ&torchmetrics.functional.detection.giourr Útorchmetrics.utilities.importsr r Útorchmetrics.utilities.plotr r Ú__doctest_skip__rr#r#r#r$Ús