From a151c150e3062fa25319bce0c4ff42be4a9a098a Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Mon, 6 Mar 2023 15:51:33 +0100
Subject: [PATCH 1/8] boilerplate

---
 src/torchmetrics/image/fid.py       | 55 ++++++++++++++++++++++++++--
 src/torchmetrics/image/inception.py | 56 +++++++++++++++++++++++++++--
 src/torchmetrics/image/kid.py       | 55 ++++++++++++++++++++++++++--
 src/torchmetrics/image/lpip.py      | 55 ++++++++++++++++++++++++++--
 src/torchmetrics/image/rase.py      | 54 +++++++++++++++++++++++++++-
 src/torchmetrics/image/rmse_sw.py   | 54 +++++++++++++++++++++++++++-
 src/torchmetrics/image/tv.py        |  5 +++
 7 files changed, 324 insertions(+), 10 deletions(-)

diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index 352f7d0a647..a0ae9371622 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from copy import deepcopy
-from typing import Any, List, Optional, Union
+from typing import Any, List, Optional, Sequence, Union
 
 import numpy as np
 import torch
@@ -22,7 +22,11 @@
 
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_info
-from torchmetrics.utilities.imports import _SCIPY_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _SCIPY_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["FrechetInceptionDistance.plot"]
 
 if _TORCH_FIDELITY_AVAILABLE:
     from torch_fidelity.feature_extractor_inceptionv3 import FeatureExtractorInceptionV3 as _FeatureExtractorInceptionV3
@@ -303,3 +307,50 @@ def reset(self) -> None:
             self.real_features_num_samples = real_features_num_samples
         else:
             super().reset()
+
+    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
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/src/torchmetrics/image/inception.py b/src/torchmetrics/image/inception.py
index 9bf8cbe511f..ece4e74a373 100644
--- a/src/torchmetrics/image/inception.py
+++ b/src/torchmetrics/image/inception.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, List, Tuple, Union
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -21,7 +21,12 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
-from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["InceptionScore.plot"]
+
 
 __doctest_requires__ = {("InceptionScore", "IS"): ["torch_fidelity"]}
 
@@ -162,3 +167,50 @@ def compute(self) -> Tuple[Tensor, Tensor]:
 
         # return mean and std
         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
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/src/torchmetrics/image/kid.py b/src/torchmetrics/image/kid.py
index d52dfbfe0b8..8197296c5fc 100644
--- a/src/torchmetrics/image/kid.py
+++ b/src/torchmetrics/image/kid.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -21,7 +21,11 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
-from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["KernelInceptionDistance.plot"]
 
 __doctest_requires__ = {("KernelInceptionDistance", "KID"): ["torch_fidelity"]}
 
@@ -274,3 +278,50 @@ def reset(self) -> None:
             self._defaults["real_features"] = value
         else:
             super().reset()
+
+    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
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/src/torchmetrics/image/lpip.py b/src/torchmetrics/image/lpip.py
index ae5216a1d0a..95496e7c41e 100644
--- a/src/torchmetrics/image/lpip.py
+++ b/src/torchmetrics/image/lpip.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-from typing import Any, List
+from typing import Any, List, Optional, Sequence, Union
 
 import torch
 from torch import Tensor
@@ -21,7 +21,11 @@
 
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
-from torchmetrics.utilities.imports import _LPIPS_AVAILABLE
+from torchmetrics.utilities.imports import _LPIPS_AVAILABLE, _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["LearnedPerceptualImagePatchSimilarity.plot"]
 
 if _LPIPS_AVAILABLE:
     from lpips import LPIPS as _LPIPS
@@ -167,3 +171,50 @@ def compute(self) -> Tensor:
         if self.reduction == "sum":
             return self.sum_scores
         return None
+
+    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
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/src/torchmetrics/image/rase.py b/src/torchmetrics/image/rase.py
index e2b112a4671..08c45f13879 100644
--- a/src/torchmetrics/image/rase.py
+++ b/src/torchmetrics/image/rase.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 import torch
 from torch import Tensor
@@ -20,6 +20,11 @@
 from torchmetrics.functional.image.rase import relative_average_spectral_error
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["RelativeAverageSpectralError.plot"]
 
 
 class RelativeAverageSpectralError(Metric):
@@ -85,3 +90,50 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return relative_average_spectral_error(preds, target, self.window_size)
+
+    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
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/src/torchmetrics/image/rmse_sw.py b/src/torchmetrics/image/rmse_sw.py
index 9f61b490ef3..80faa661b19 100644
--- a/src/torchmetrics/image/rmse_sw.py
+++ b/src/torchmetrics/image/rmse_sw.py
@@ -12,13 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
 
 from torchmetrics.functional.image.rmse_sw import _rmse_sw_compute, _rmse_sw_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["RootMeanSquaredErrorUsingSlidingWindow.plot"]
 
 
 class RootMeanSquaredErrorUsingSlidingWindow(Metric):
@@ -85,3 +90,50 @@ def compute(self) -> Optional[Tensor]:
         assert self.rmse_map is not None
         rmse, _ = _rmse_sw_compute(self.rmse_val_sum, self.rmse_map, self.total_images)
         return rmse
+
+    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
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/src/torchmetrics/image/tv.py b/src/torchmetrics/image/tv.py
index 9d30148cd33..d843a8f7676 100644
--- a/src/torchmetrics/image/tv.py
+++ b/src/torchmetrics/image/tv.py
@@ -20,6 +20,11 @@
 from torchmetrics.functional.image.tv import _total_variation_compute, _total_variation_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["TotalVariation.plot"]
 
 
 class TotalVariation(Metric):

From 0659d591af073923efdc5763ba30595b9831d62c Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Tue, 7 Mar 2023 12:21:56 +0100
Subject: [PATCH 2/8] add tests + fix code

---
 src/torchmetrics/image/fid.py          | 30 ++++++----
 src/torchmetrics/image/inception.py    | 24 ++++----
 src/torchmetrics/image/kid.py          | 29 +++++----
 src/torchmetrics/image/lpip.py         | 24 +++-----
 src/torchmetrics/image/rase.py         | 17 ++----
 src/torchmetrics/image/rmse_sw.py      | 18 ++----
 src/torchmetrics/image/tv.py           | 43 +++++++++++++-
 tests/unittests/utilities/test_plot.py | 82 +++++++++++++++++++++++++-
 8 files changed, 186 insertions(+), 81 deletions(-)

diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index a0ae9371622..b3b92c53c31 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -330,12 +330,12 @@ def plot(
 
             >>> # Example plotting a single value
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
-            >>> metric.update(preds, target)
+            >>> 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::
@@ -343,14 +343,18 @@ def plot(
 
             >>> # Example plotting multiple values
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
+            >>> from torchmetrics.image.kid import KernelInceptionDistance
+            >>> 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(10):
-            ...     values.append(metric(preds, target))
+            >>> 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)
diff --git a/src/torchmetrics/image/inception.py b/src/torchmetrics/image/inception.py
index ece4e74a373..c8034af54af 100644
--- a/src/torchmetrics/image/inception.py
+++ b/src/torchmetrics/image/inception.py
@@ -190,27 +190,23 @@ def plot(
 
             >>> # Example plotting a single value
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
-            >>> metric.update(preds, target)
-            >>> fig_, ax_ = metric.plot()
+            >>> 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
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
+            >>> from torchmetrics.image.inception import InceptionScore
+            >>> metric = InceptionScore()
             >>> values = [ ]
-            >>> for _ in range(10):
-            ...     values.append(metric(preds, target))
+            >>> 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)
diff --git a/src/torchmetrics/image/kid.py b/src/torchmetrics/image/kid.py
index 8197296c5fc..0540e95aa16 100644
--- a/src/torchmetrics/image/kid.py
+++ b/src/torchmetrics/image/kid.py
@@ -301,12 +301,12 @@ def plot(
 
             >>> # Example plotting a single value
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
-            >>> metric.update(preds, target)
+            >>> from torchmetrics.image.kid import KernelInceptionDistance
+            >>> imgs_dist1 = torch.randint(0, 200, (30, 3, 299, 299), dtype=torch.uint8)
+            >>> imgs_dist2 = torch.randint(100, 255, (30, 3, 299, 299), dtype=torch.uint8)
+            >>> metric = KernelInceptionDistance(subsets=3, subset_size=20)
+            >>> metric.update(imgs_dist1, real=True)
+            >>> metric.update(imgs_dist2, real=False)
             >>> fig_, ax_ = metric.plot()
 
         .. plot::
@@ -314,14 +314,17 @@ def plot(
 
             >>> # Example plotting multiple values
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
+            >>> from torchmetrics.image.kid import KernelInceptionDistance
+            >>> imgs_dist1 = lambda: torch.randint(0, 200, (30, 3, 299, 299), dtype=torch.uint8)
+            >>> imgs_dist2 = lambda: torch.randint(100, 255, (30, 3, 299, 299), dtype=torch.uint8)
+            >>> metric = KernelInceptionDistance(subsets=3, subset_size=20)
             >>> values = [ ]
-            >>> for _ in range(10):
-            ...     values.append(metric(preds, target))
+            >>> for _ in range(3):
+            ...     metric.update(imgs_dist1(), real=True)
+            ...     metric.update(imgs_dist2(), real=False)
+            ...     values.append(metric.compute()[0])
+            ...     metric.reset()
             >>> fig_, ax_ = metric.plot(values)
         """
+        val = val or self.compute()[0]  # by default we select the mean to plot
         return self._plot(val, ax)
diff --git a/src/torchmetrics/image/lpip.py b/src/torchmetrics/image/lpip.py
index 95496e7c41e..2e7133a27e5 100644
--- a/src/torchmetrics/image/lpip.py
+++ b/src/torchmetrics/image/lpip.py
@@ -34,13 +34,13 @@ def _download_lpips() -> None:
         _LPIPS(pretrained=True, net="vgg")
 
     if _SKIP_SLOW_DOCTEST and not _try_proceed_with_timeout(_download_lpips):
-        __doctest_skip__ = ["LearnedPerceptualImagePatchSimilarity", "LPIPS"]
+        __doctest_skip__ = ["LearnedPerceptualImagePatchSimilarity", "LearnedPerceptualImagePatchSimilarity.plot"]
 else:
 
     class _LPIPS(Module):
         pass
 
-    __doctest_skip__ = ["LearnedPerceptualImagePatchSimilarity", "LPIPS"]
+    __doctest_skip__ = ["LearnedPerceptualImagePatchSimilarity", "LearnedPerceptualImagePatchSimilarity.plot"]
 
 
 class NoTrainLpips(_LPIPS):
@@ -194,12 +194,9 @@ def plot(
 
             >>> # Example plotting a single value
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
-            >>> metric.update(preds, target)
+            >>> from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+            >>> metric = LearnedPerceptualImagePatchSimilarity()
+            >>> metric.update(torch.rand(10, 3, 100, 100), torch.rand(10, 3, 100, 100))
             >>> fig_, ax_ = metric.plot()
 
         .. plot::
@@ -207,14 +204,11 @@ def plot(
 
             >>> # Example plotting multiple values
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
+            >>> from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+            >>> metric = LearnedPerceptualImagePatchSimilarity()
             >>> values = [ ]
-            >>> for _ in range(10):
-            ...     values.append(metric(preds, target))
+            >>> for _ in range(3):
+            ...     values.append(metric(torch.rand(10, 3, 100, 100), torch.rand(10, 3, 100, 100)))
             >>> fig_, ax_ = metric.plot(values)
         """
         return self._plot(val, ax)
diff --git a/src/torchmetrics/image/rase.py b/src/torchmetrics/image/rase.py
index 08c45f13879..7ed80c19afc 100644
--- a/src/torchmetrics/image/rase.py
+++ b/src/torchmetrics/image/rase.py
@@ -113,12 +113,9 @@ def plot(
 
             >>> # Example plotting a single value
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
-            >>> metric.update(preds, target)
+            >>> from torchmetrics import RelativeAverageSpectralError
+            >>> metric = RelativeAverageSpectralError()
+            >>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16))
             >>> fig_, ax_ = metric.plot()
 
         .. plot::
@@ -127,13 +124,11 @@ def plot(
             >>> # Example plotting multiple values
             >>> import torch
             >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
+            >>> from torchmetrics import RelativeAverageSpectralError
+            >>> metric = RelativeAverageSpectralError()
             >>> values = [ ]
             >>> for _ in range(10):
-            ...     values.append(metric(preds, target))
+            ...     values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
             >>> fig_, ax_ = metric.plot(values)
         """
         return self._plot(val, ax)
diff --git a/src/torchmetrics/image/rmse_sw.py b/src/torchmetrics/image/rmse_sw.py
index 80faa661b19..4352ddca2e6 100644
--- a/src/torchmetrics/image/rmse_sw.py
+++ b/src/torchmetrics/image/rmse_sw.py
@@ -113,12 +113,9 @@ def plot(
 
             >>> # Example plotting a single value
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
-            >>> metric.update(preds, target)
+            >>> from torchmetrics import RootMeanSquaredErrorUsingSlidingWindow
+            >>> metric = RootMeanSquaredErrorUsingSlidingWindow()
+            >>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16))
             >>> fig_, ax_ = metric.plot()
 
         .. plot::
@@ -126,14 +123,11 @@ def plot(
 
             >>> # Example plotting multiple values
             >>> import torch
-            >>> _ = torch.manual_seed(42)
-            >>> from torchmetrics import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
-            >>> metric = SpectralDistortionIndex()
+            >>> from torchmetrics import RootMeanSquaredErrorUsingSlidingWindow
+            >>> metric = RootMeanSquaredErrorUsingSlidingWindow()
             >>> values = [ ]
             >>> for _ in range(10):
-            ...     values.append(metric(preds, target))
+            ...     values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
             >>> fig_, ax_ = metric.plot(values)
         """
         return self._plot(val, ax)
diff --git a/src/torchmetrics/image/tv.py b/src/torchmetrics/image/tv.py
index d843a8f7676..40b92297ba0 100644
--- a/src/torchmetrics/image/tv.py
+++ b/src/torchmetrics/image/tv.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any
+from typing import Any, Optional, Sequence, Union
 
 import torch
 from torch import Tensor, tensor
@@ -91,3 +91,44 @@ def compute(self) -> Tensor:
         """Compute final total variation."""
         score = dim_zero_cat(self.score) if self.reduction is None or self.reduction == "none" else self.score
         return _total_variation_compute(score, self.num_elements, self.reduction)
+
+    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 import TotalVariation
+            >>> metric = TotalVariation()
+            >>> metric.update(torch.rand(5, 3, 28, 28))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics import TotalVariation
+            >>> metric = TotalVariation()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.rand(5, 3, 28, 28)))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        return self._plot(val, ax)
diff --git a/tests/unittests/utilities/test_plot.py b/tests/unittests/utilities/test_plot.py
index 030a3dcaf31..2613731c6ca 100644
--- a/tests/unittests/utilities/test_plot.py
+++ b/tests/unittests/utilities/test_plot.py
@@ -50,11 +50,18 @@
 from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
 from torchmetrics.image import (
     ErrorRelativeGlobalDimensionlessSynthesis,
+    FrechetInceptionDistance,
+    InceptionScore,
+    KernelInceptionDistance,
+    LearnedPerceptualImagePatchSimilarity,
     MultiScaleStructuralSimilarityIndexMeasure,
     PeakSignalNoiseRatio,
+    RelativeAverageSpectralError,
+    RootMeanSquaredErrorUsingSlidingWindow,
     SpectralAngleMapper,
     SpectralDistortionIndex,
     StructuralSimilarityIndexMeasure,
+    TotalVariation,
     UniversalImageQualityIndex,
 )
 from torchmetrics.nominal import CramersV, PearsonsContingencyCoefficient, TheilsU, TschuprowsT
@@ -224,13 +231,26 @@
             _multilabel_randint_input,
             id="multilabel average precision",
         ),
+        pytest.param(TotalVariation, _image_input, None, id="total variation"),
+        pytest.param(
+            RootMeanSquaredErrorUsingSlidingWindow,
+            _image_input,
+            _image_input,
+            id="root mean squared error using sliding window",
+        ),
+        pytest.param(RelativeAverageSpectralError, _image_input, _image_input, id="relative average spectral error"),
+        pytest.param(
+            LearnedPerceptualImagePatchSimilarity,
+            lambda: torch.rand(10, 3, 100, 100),
+            lambda: torch.rand(10, 3, 100, 100),
+            id="learned perceptual image patch similarity",
+        ),
     ],
 )
 @pytest.mark.parametrize("num_vals", [1, 5])
-def test_single_multi_val_plot_methods(metric_class: object, preds: Callable, target: Callable, num_vals: int):
+def test_plot_methods(metric_class: object, preds: Callable, target: Callable, num_vals: int):
     """Test the plot method of metrics that only output a single tensor scalar."""
     metric = metric_class()
-
     input = (lambda: (preds(),)) if target is None else lambda: (preds(), target())
 
     if num_vals == 1:
@@ -246,6 +266,64 @@ def test_single_multi_val_plot_methods(metric_class: object, preds: Callable, ta
     assert isinstance(ax, matplotlib.axes.Axes)
 
 
+@pytest.mark.parametrize(
+    ("metric_class", "preds", "target", "index_0"),
+    [
+        pytest.param(
+            partial(KernelInceptionDistance, feature=64, subsets=3, subset_size=20),
+            lambda: torch.randint(0, 200, (30, 3, 299, 299), dtype=torch.uint8),
+            lambda: torch.randint(0, 200, (30, 3, 299, 299), dtype=torch.uint8),
+            True,
+            id="kernel inception distance",
+        ),
+        pytest.param(
+            partial(FrechetInceptionDistance, feature=64),
+            lambda: torch.randint(0, 200, (30, 3, 299, 299), dtype=torch.uint8),
+            lambda: torch.randint(0, 200, (30, 3, 299, 299), dtype=torch.uint8),
+            False,
+            id="frechet inception distance",
+        ),
+        pytest.param(
+            partial(InceptionScore, feature=64),
+            lambda: torch.randint(0, 255, (50, 3, 299, 299), dtype=torch.uint8),
+            None,
+            True,
+            id="inception score",
+        ),
+    ],
+)
+@pytest.mark.parametrize("num_vals", [1, 2])
+def test_plot_methods_special_image_metrics(metric_class, preds, target, index_0, num_vals):
+    """Test the plot method of metrics that only output a single tensor scalar.
+
+    This takes care of FID, KID and inception score image metrics as these have a slightly different call and update
+    signature than other metrics.
+    """
+    metric = metric_class()
+
+    if num_vals == 1:
+        if target is None:
+            metric.update(preds())
+        else:
+            metric.update(preds(), real=True)
+            metric.update(target(), real=False)
+        fig, ax = metric.plot()
+    else:
+        vals = []
+        for _ in range(num_vals):
+            if target is None:
+                vals.append(metric(preds()))
+            else:
+                metric.update(preds(), real=True)
+                metric.update(target(), real=False)
+                vals.append(metric.compute() if not index_0 else metric.compute()[0])
+                metric.reset()
+        fig, ax = metric.plot(vals)
+
+    assert isinstance(fig, plt.Figure)
+    assert isinstance(ax, matplotlib.axes.Axes)
+
+
 @pytest.mark.parametrize(
     ("metric_class", "preds", "target", "labels"),
     [

From 27f874aa42f001a711309f4524662462a22b2dee Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Tue, 7 Mar 2023 12:54:53 +0100
Subject: [PATCH 3/8] fix

---
 tests/unittests/utilities/test_plot.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/unittests/utilities/test_plot.py b/tests/unittests/utilities/test_plot.py
index 2613731c6ca..ab8e8ff1107 100644
--- a/tests/unittests/utilities/test_plot.py
+++ b/tests/unittests/utilities/test_plot.py
@@ -285,7 +285,7 @@ def test_plot_methods(metric_class: object, preds: Callable, target: Callable, n
         ),
         pytest.param(
             partial(InceptionScore, feature=64),
-            lambda: torch.randint(0, 255, (50, 3, 299, 299), dtype=torch.uint8),
+            lambda: torch.randint(0, 255, (30, 3, 299, 299), dtype=torch.uint8),
             None,
             True,
             id="inception score",
@@ -312,7 +312,7 @@ def test_plot_methods_special_image_metrics(metric_class, preds, target, index_0
         vals = []
         for _ in range(num_vals):
             if target is None:
-                vals.append(metric(preds()))
+                vals.append(metric(preds())[0])
             else:
                 metric.update(preds(), real=True)
                 metric.update(target(), real=False)

From ea11b30d86c6d65ed71aaadf88a1e11d64649f53 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Tue, 7 Mar 2023 12:57:33 +0100
Subject: [PATCH 4/8] changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ac070334422..f4e1f38d79a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -25,6 +25,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     [#1581](https://github.com/Lightning-AI/metrics/pull/1581),
     [#1585](https://github.com/Lightning-AI/metrics/pull/1585),
     [#1593](https://github.com/Lightning-AI/metrics/pull/1593),
+    [#1600](https://github.com/Lightning-AI/metrics/pull/1600),
 )
 
 

From 575c7cd9c997b215ef89b402b2ce86da8cd8fb14 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Tue, 7 Mar 2023 14:55:17 +0100
Subject: [PATCH 5/8] req

---
 requirements/docs.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements/docs.txt b/requirements/docs.txt
index 4dd5da84959..f81bcda95d0 100644
--- a/requirements/docs.txt
+++ b/requirements/docs.txt
@@ -16,3 +16,4 @@ sphinx-copybutton>=0.3
 -r visual.txt
 -r audio.txt
 -r detection.txt
+-r image

From 29d598b9aa44ccc344188e0be5965a0d4ccfcc74 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Thu, 9 Mar 2023 09:16:30 +0100
Subject: [PATCH 6/8] fix

---
 requirements/docs.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/docs.txt b/requirements/docs.txt
index f81bcda95d0..1209e6d16b0 100644
--- a/requirements/docs.txt
+++ b/requirements/docs.txt
@@ -16,4 +16,4 @@ sphinx-copybutton>=0.3
 -r visual.txt
 -r audio.txt
 -r detection.txt
--r image
+-r image.txt

From 3a57e64010eab4d625688fafb531d30a32ea3037 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Thu, 9 Mar 2023 10:40:23 +0100
Subject: [PATCH 7/8] fix

---
 src/torchmetrics/image/fid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index b3b92c53c31..df3f228cafd 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -343,7 +343,7 @@ def plot(
 
             >>> # Example plotting multiple values
             >>> import torch
-            >>> from torchmetrics.image.kid import KernelInceptionDistance
+            >>> 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)

From e526c55a4d420d5606c77a59cd00acd4aeba6103 Mon Sep 17 00:00:00 2001
From: SkafteNicki <skaftenicki@gmail.com>
Date: Thu, 9 Mar 2023 11:03:29 +0100
Subject: [PATCH 8/8] skip on missing import

---
 src/torchmetrics/image/fid.py       | 2 +-
 src/torchmetrics/image/inception.py | 2 +-
 src/torchmetrics/image/kid.py       | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index df3f228cafd..cd0fe874a60 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -35,7 +35,7 @@
     class _FeatureExtractorInceptionV3(Module):
         pass
 
-    __doctest_skip__ = ["FrechetInceptionDistance", "FID"]
+    __doctest_skip__ = ["FrechetInceptionDistance", "FrechetInceptionDistance.plot"]
 
 
 if _SCIPY_AVAILABLE:
diff --git a/src/torchmetrics/image/inception.py b/src/torchmetrics/image/inception.py
index c8034af54af..8ce90fb777d 100644
--- a/src/torchmetrics/image/inception.py
+++ b/src/torchmetrics/image/inception.py
@@ -28,7 +28,7 @@
     __doctest_skip__ = ["InceptionScore.plot"]
 
 
-__doctest_requires__ = {("InceptionScore", "IS"): ["torch_fidelity"]}
+__doctest_requires__ = {("InceptionScore", "InceptionScore.plot"): ["torch_fidelity"]}
 
 
 class InceptionScore(Metric):
diff --git a/src/torchmetrics/image/kid.py b/src/torchmetrics/image/kid.py
index 0540e95aa16..b7b9906185e 100644
--- a/src/torchmetrics/image/kid.py
+++ b/src/torchmetrics/image/kid.py
@@ -27,7 +27,7 @@
 if not _MATPLOTLIB_AVAILABLE:
     __doctest_skip__ = ["KernelInceptionDistance.plot"]
 
-__doctest_requires__ = {("KernelInceptionDistance", "KID"): ["torch_fidelity"]}
+__doctest_requires__ = {("KernelInceptionDistance", "KernelInceptionDistance.plot"): ["torch_fidelity"]}
 
 
 def maximum_mean_discrepancy(k_xx: Tensor, k_xy: Tensor, k_yy: Tensor) -> Tensor: