o .wi @sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z m Z ddlmZddlmZdd lmZmZesCd gZGd d d eZdS) )Sequence)AnyOptionalUnionN)Tensor)Literal)_generalized_dice_compute_generalized_dice_update_generalized_dice_validate_args)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEGeneralizedDiceScore.plotcseZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e ed <d Z e ed <   d%de dedededdeddeddffdd ZdededdfddZdefdd Zd&d!eeeedfd"eedefd#d$ZZS)'GeneralizedDiceScoreaCompute `Generalized Dice Score`_. The metric can be used to evaluate the performance of image segmentation models. The Generalized Dice Score is defined as: .. math:: GDS = \frac{2 \\sum_{i=1}^{N} w_i \\sum_{j} t_{ij} p_{ij}}{ \\sum_{i=1}^{N} w_i \\sum_{j} t_{ij} + \\sum_{i=1}^{N} w_i \\sum_{j} p_{ij}} where :math:`N` is the number of classes, :math:`t_{ij}` is the target tensor, :math:`p_{ij}` is the prediction tensor, and :math:`w_i` is the weight for class :math:`i`. The weight can be computed in three different ways: - `square`: :math:`w_i = 1 / (\\sum_{j} t_{ij})^2` - `simple`: :math:`w_i = 1 / \\sum_{j} t_{ij}` - `linear`: :math:`w_i = 1` Note that the generalized dice loss can be computed as one minus the generalized dice score. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An one-hot boolean tensor of shape ``(N, C, ...)`` with ``N`` being the number of samples and ``C`` the number of classes. Alternatively, an integer tensor of shape ``(N, ...)`` can be provided, where the integer values correspond to the class index. The input type can be controlled with the ``input_format`` argument. - ``target`` (:class:`~torch.Tensor`): An one-hot boolean tensor of shape ``(N, C, ...)`` with ``N`` being the number of samples and ``C`` the number of classes. Alternatively, an integer tensor of shape ``(N, ...)`` can be provided, where the integer values correspond to the class index. The input type can be controlled with the ``input_format`` argument. As output to ``forward`` and ``compute`` the metric returns the following output: - ``gds`` (:class:`~torch.Tensor`): The generalized dice score. If ``per_class`` is set to ``True``, the output will be a tensor of shape ``(C,)`` with the generalized dice score for each class. If ``per_class`` is set to ``False``, the output will be a scalar tensor. Args: num_classes: The number of classes in the segmentation problem. include_background: Whether to include the background class in the computation per_class: Whether to compute the metric for each class separately. weight_type: The type of weight to apply to each class. Can be one of ``"square"``, ``"simple"``, or ``"linear"``. input_format: What kind of input the function receives. Choose between ``"one-hot"`` for one-hot encoded tensors or ``"index"`` for index tensors kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``num_classes`` is not a positive integer ValueError: If ``include_background`` is not a boolean ValueError: If ``per_class`` is not a boolean ValueError: If ``weight_type`` is not one of ``"square"``, ``"simple"``, or ``"linear"`` ValueError: If ``input_format`` is not one of ``"one-hot"`` or ``"index"`` Example: >>> from torch import randint >>> from torchmetrics.segmentation import GeneralizedDiceScore >>> gds = GeneralizedDiceScore(num_classes=3) >>> preds = randint(0, 2, (10, 3, 128, 128)) >>> target = randint(0, 2, (10, 3, 128, 128)) >>> gds(preds, target) tensor(0.4992) >>> gds = GeneralizedDiceScore(num_classes=3, per_class=True) >>> gds(preds, target) tensor([0.5001, 0.4993, 0.4982]) >>> gds = GeneralizedDiceScore(num_classes=3, per_class=True, include_background=False) >>> gds(preds, target) tensor([0.4993, 0.4982]) scoresamplesFfull_state_updateis_differentiableThigher_is_bettergplot_lower_boundg?plot_upper_boundsquareone-hot num_classesinclude_background per_class weight_type)rsimplelinear input_format)rindexkwargsreturnNc stjdi|t|||||||_||_||_||_||_|s&|dn|}|jdt |r1|nddd|jdt ddddS)Nrsum)defaultdist_reduce_fxr) super__init__r rrrrr add_statetorchzeros)selfrrrrr r" __class__r(g/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/segmentation/generalized_dice.pyr*us zGeneralizedDiceScore.__init__predstargetcCsVt|||j|j|j|j\}}|jt|||jjdd7_|j |j d7_ dS)zUpdate the state with new data.r)dimN) r rrrr rrrr%rshape)r.r2r3 numerator denominatorr(r(r1updates  zGeneralizedDiceScore.updatecCs |j|jS)z)Compute the final generalized dice score.)rr)r.r(r(r1computes zGeneralizedDiceScore.computevalaxcCs |||S)aPlot 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 and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.segmentation import GeneralizedDiceScore >>> metric = GeneralizedDiceScore(num_classes=3) >>> metric.update(torch.randint(0, 2, (10, 3, 128, 128)), torch.randint(0, 2, (10, 3, 128, 128))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.segmentation import GeneralizedDiceScore >>> metric = GeneralizedDiceScore(num_classes=3) >>> values = [ ] >>> for _ in range(10): ... values.append( ... metric(torch.randint(0, 2, (10, 3, 128, 128)), torch.randint(0, 2, (10, 3, 128, 128))) ... ) >>> fig_, ax_ = metric.plot(values) )_plot)r.r:r;r(r(r1plots (r)TFrr)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrfloatrintrrr*r8r9rrrr rr= __classcell__r(r(r/r1r"s> J     2r)collections.abcrtypingrrrr,rtyping_extensionsr5torchmetrics.functional.segmentation.generalized_dicerr r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rr(r(r(r1s