Method plot

utils/torchmetric_inception.py:175–217

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. a

(
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
    )

        return kl.mean(), kl.std()

    def plot(
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
    ) -> _PLOT_OUT_TYPE:
        """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.image.inception import InceptionScore
            >>> metric = InceptionScore()
            >>> metric.update(torch.randint(0, 255, (50, 3, 299, 299), dtype=torch.uint8))
            >>> fig_, ax_ = metric.plot()  # the returned plot only shows the mean value by default

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.image.inception import InceptionScore
            >>> metric = InceptionScore()
            >>> values = [ ]
            >>> for _ in range(3):
            ...     # we index by 0 such that only the mean value is plotted
            ...     values.append(metric(torch.randint(0, 255, (50, 3, 299, 299), dtype=torch.uint8))[0])
            >>> fig_, ax_ = metric.plot(values)

        """
        val = val or self.compute()[0]  # by default we select the mean to plot
        return self._plot(val, ax)