a  dB@sddlmZmZmZmZddlZddlmZmZddlm Z ddl m Z ddl m Z ddlmZddlmZmZeszd gZGd d d e ZdS) )AnyOptionalSequenceUnionN)Tensortensor)_r2_score_update)_relative_squared_error_compute)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPERelativeSquaredError.plotcseZdZUdZdZdZdZeed<eed<eed<eed<de e e d d fd d Z eed d ddZ edddZdeeeeefeeedddZZS)RelativeSquaredErroraComputes the relative squared error (RSE). .. math:: \text{RSE} = \frac{\sum_i^N(y_i - \hat{y_i})^2}{\sum_i^N(y_i - \overline{y})^2} Where :math:`y` is a tensor of target values with mean :math:`\overline{y}`, and :math:`\hat{y}` is a tensor of predictions. If num_outputs > 1, the returned value is averaged over all the outputs. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predictions from model in float tensor with shape ``(N,)`` or ``(N, M)`` (multioutput) - ``target`` (:class:`~torch.Tensor`): Ground truth values in float tensor with shape ``(N,)`` or ``(N, M)`` (multioutput) As output of ``forward`` and ``compute`` the metric returns the following output: - ``rse`` (:class:`~torch.Tensor`): A tensor with the RSE score(s) Args: num_outputs: Number of outputs in multioutput setting squared: If True returns RSE value, if False returns RRSE value. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.regression import RelativeSquaredError >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> relative_squared_error = RelativeSquaredError() >>> relative_squared_error(preds, target) tensor(0.0514) TFsum_squared_error sum_errorresidualtotalN) num_outputssquaredkwargsreturnc s~tjfi|||_|jdt|jdd|jdt|jdd|jdt|jdd|jdtddd||_dS)Nrsum)defaultZdist_reduce_fxrrrr)super__init__rZ add_statetorchzerosrr)selfrrr __class__d/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/rse.pyrHszRelativeSquaredError.__init__)predstargetrcCsNt||\}}}}|j|7_|j|7_|j|7_|j|7_dS)z*Update state with predictions and targets.N)rrrrr)rr$r%rrrrr"r"r#updateXs zRelativeSquaredError.update)rcCst|j|j|j|j|jdS)z+Computes relative squared error over state.)r)r rrrrr)rr"r"r#computeaszRelativeSquaredError.compute)valaxrcCs |||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 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import RelativeSquaredError >>> metric = RelativeSquaredError() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import RelativeSquaredError >>> metric = RelativeSquaredError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )Z_plot)rr(r)r"r"r#plotgs(r)rT)NN)__name__ __module__ __qualname____doc__Zis_differentiableZhigher_is_betterZfull_state_updater__annotations__intboolrrr&r'rrrr r r* __classcell__r"r"r r#rs. " r)typingrrrrrrrZ%torchmetrics.functional.regression.r2rZ&torchmetrics.functional.regression.rser Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr r Z__doctest_skip__rr"r"r"r#s