o .wif@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z ddlmZdd lmZmZes?d gZGd d d e ZdS) )Sequence)AnyOptionalUnionN)Tensor)Literal)Metric)procrustes_disparity)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEProcrustesDisparity.plotcseZdZUdZeed<eed<dZeed<dZeed<dZ eed<dZ e ed <d Z e ed <dd e ddeddffdd ZdejdejddfddZdejfddZddeeeedfdeedefddZZS) ProcrustesDisparityakCompute the `Procrustes Disparity`_. The Procrustes Disparity is defined as the sum of the squared differences between two datasets after applying a Procrustes transformation. The Procrustes Disparity is useful to compare two datasets that are similar but not aligned. The metric works similar to ``scipy.spatial.procrustes`` but for batches of data points. The disparity is aggregated over the batch, thus to get the individual disparities please use the functional version of this metric: ``torchmetrics.functional.shape.procrustes.procrustes_disparity``. As input to ``forward`` and ``update`` the metric accepts the following input: - ``point_cloud1`` (torch.Tensor): A tensor of shape ``(N, M, D)`` with ``N`` being the batch size, ``M`` the number of data points and ``D`` the dimensionality of the data points. - ``point_cloud2`` (torch.Tensor): A tensor of shape ``(N, M, D)`` with ``N`` being the batch size, ``M`` the number of data points and ``D`` the dimensionality of the data points. As output to ``forward`` and ``compute`` the metric returns the following output: - ``gds`` (:class:`~torch.Tensor`): A scalar tensor with the Procrustes Disparity. Args: reduction: Determines whether to return the mean disparity or the sum of the disparities. Can be one of ``"mean"`` or ``"sum"``. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``average`` is not one of ``"mean"`` or ``"sum"``. Example: >>> from torch import randn >>> from torchmetrics.shape import ProcrustesDisparity >>> metric = ProcrustesDisparity() >>> point_cloud1 = randn(10, 50, 2) >>> point_cloud2 = randn(10, 50, 2) >>> metric(point_cloud1, point_cloud2) tensor(0.9770) disparitytotalFfull_state_updateis_differentiablehigher_is_betterplot_lower_boundg?plot_upper_boundmean reductionrsumkwargsreturnNc s^tjd i||dvrtd|||_|jdtddd|jdtddddS) Nrz9Argument `reduction` must be one of ['mean', 'sum'], got rrr)defaultdist_reduce_fxrr)super__init__ ValueErrorr add_statetorchtensor)selfrr __class__rZ/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/shape/procrustes.pyr!Ps zProcrustesDisparity.__init__ point_cloud1 point_cloud2cCs2t||}|j|7_|j|7_dS)z8Update the Procrustes Disparity with the given datasets.N)r rrrnumel)r&r*r+rrrr)updateXs zProcrustesDisparity.updatecCs|jdkr |j|jS|jS)z"Computes the Procrustes Disparity.r)rrr)r&rrr)compute^s  zProcrustesDisparity.computevalaxcCs |||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 and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.shape import ProcrustesDisparity >>> metric = ProcrustesDisparity() >>> metric.update(torch.randn(10, 50, 2), torch.randn(10, 50, 2)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.shape import ProcrustesDisparity >>> metric = ProcrustesDisparity() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randn(10, 50, 2), torch.randn(10, 50, 2))) >>> fig_, ax_ = metric.plot(values) )_plot)r&r/r0rrr)plotds &r )r)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrfloatrrrr!r$r-r.rrrr r r2 __classcell__rrr'r)rs )      2r)collections.abcrtypingrrrr$rtyping_extensionsr torchmetricsr(torchmetrics.functional.shape.procrustesr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrrrr)s