Method plot

utils/torchmetric_sfid.py:438–488

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 out

    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.fid import FrechetInceptionDistance
            >>> imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
            >>> imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
            >>> metric = FrechetInceptionDistance(feature=64)
            >>> metric.update(imgs_dist1, real=True)
            >>> metric.update(imgs_dist2, real=False)
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.image.fid import FrechetInceptionDistance
            >>> imgs_dist1 = lambda: torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
            >>> imgs_dist2 = lambda: torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
            >>> metric = FrechetInceptionDistance(feature=64)
            >>> values = [ ]
            >>> for _ in range(3):
            ...     metric.update(imgs_dist1(), real=True)
            ...     metric.update(imgs_dist2(), real=False)
            ...     values.append(metric.compute())
            ...     metric.reset()
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)


if __name__ == "__main__":