Merge branch 'master' into bugfix/image_metrics

docs/source/pages/implement.rst

@@ -2,13 +2,15 @@
 
 .. testsetup:: *
 
-    from typing import Optional
+    from typing import Optional, Sequence, Union
+    from torch import Tensor
 
 *********************
 Implementing a Metric
 *********************
 
-To implement your own custom metric, subclass the base :class:`~torchmetrics.Metric` class and implement the following methods:
+To implement your own custom metric, subclass the base :class:`~torchmetrics.Metric` class and implement the following
+methods:
 
 - ``__init__()``: Each state variable should be called using ``self.add_state(...)``.
 - ``update()``: Any code needed to update the state given any inputs to the metric.
@@ -32,7 +34,7 @@ Example implementation:
             self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
             self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
 
-        def update(self, preds: torch.Tensor, target: torch.Tensor):
+        def update(self, preds: Tensor, target: Tensor):
             preds, target = self._input_format(preds, target)
             assert preds.shape == target.shape
 
@@ -48,21 +50,41 @@ Additionally you may want to set the class properties: `is_differentiable`, `hig
 
 .. testcode::
 
-   from torchmetrics import Metric
+    from torchmetrics import Metric
+
+    class MyMetric(Metric):
+        # Set to True if the metric is differentiable else set to False
+        is_differentiable: Optional[bool] = None
+
+        # Set to True if the metric reaches it optimal value when the metric is maximized.
+        # Set to False if it when the metric is minimized.
+        higher_is_better: Optional[bool] = True
+
+        # Set to True if the metric during 'update' requires access to the global metric
+        # state for its calculations. If not, setting this to False indicates that all
+        # batch states are independent and we will optimize the runtime of 'forward'
+        full_state_update: bool = True
 
-   class MyMetric(Metric):
-      # Set to True if the metric is differentiable else set to False
-      is_differentiable: Optional[bool] = None
+Finally, from torchmetrics v1.0.0 onwards, we also support plotting of metrics through the `.plot` method. By default
+this method will raise `NotImplementedError` but can be implemented by the user to provide a custom plot for the metric.
+For any metrics that returns a simple scalar tensor, or a dict of scalar tensors the internal `._plot` method can be
+used, that provides the common plotting functionality for most metrics in torchmetrics.
+
+.. testcode::
+
+    from torchmetrics import Metric
+    from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
-      # Set to True if the metric reaches it optimal value when the metric is maximized.
-      # Set to False if it when the metric is minimized.
-      higher_is_better: Optional[bool] = True
+    class MyMetric(Metric):
+        ...
 
-      # Set to True if the metric during 'update' requires access to the global metric
-      # state for its calculations. If not, setting this to False indicates that all
-      # batch states are independent and we will optimize the runtime of 'forward'
-      full_state_update: bool = True
+        def plot(
+            self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+        ) -> _PLOT_OUT_TYPE:
+            return self._plot(val, ax)
 
+If the metric returns a more complex output, a custom implementation of the `plot` method is required. For more details
+on the plotting API, see the this :ref:`page <plotting>` .
 
 Internal implementation details
 -------------------------------

docs/source/pages/plotting.rst

@@ -1,3 +1,5 @@
+.. _plotting:
+
 .. testsetup:: *
 
     import torch