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Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
(cherry picked from commit b56c19af91a8c8a7e66bd4bd20b869aff5be3b92)

pytorch_lightning/metrics/classification/accuracy.py

@@ -38,7 +38,7 @@ class Accuracy(Metric):
     changed to subset accuracy (which requires all labels or sub-samples in the sample to
     be correctly predicted) by setting ``subset_accuracy=True``.
 
-    Accepts all input types listed in :ref:`metrics:Input types`.
+    Accepts all input types listed in :ref:`extensions/metrics:input types`.
 
     Args:
         threshold:
@@ -127,7 +127,7 @@ class Accuracy(Metric):
 
     def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
-        Update state with predictions and targets. See :ref:`metrics:Input types` for more information
+        Update state with predictions and targets. See :ref:`extensions/metrics:input types` for more information
         on input types.
 
         Args:

pytorch_lightning/metrics/classification/hamming_distance.py

@@ -35,7 +35,7 @@ class HammingDistance(Metric):
     treats each possible label separately - meaning that, for example, multi-class data is
     treated as if it were multi-label.
 
-    Accepts all input types listed in :ref:`metrics:Input types`.
+    Accepts all input types listed in :ref:`extensions/metrics:input types`.
 
     Args:
         threshold:
@@ -88,7 +88,7 @@ class HammingDistance(Metric):
 
     def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
-        Update state with predictions and targets. See :ref:`metrics:Input types` for more information
+        Update state with predictions and targets. See :ref:`extensions/metrics:input types` for more information
         on input types.
 
         Args:

pytorch_lightning/metrics/classification/helpers.py

@@ -250,7 +250,7 @@ def _check_classification_inputs(
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
 
@@ -376,7 +376,7 @@ def _input_format_classification(
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
 

pytorch_lightning/metrics/classification/precision_recall.py

@@ -31,7 +31,7 @@ class Precision(StatScores):
 
     The reduction method (how the precision scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`metrics:Input types`.
+    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         num_classes:
@@ -67,10 +67,11 @@ class Precision(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`metrics:Input types`) as the ``N`` dimension within the sample,
+              (see :ref:`extensions/metrics:input types`) as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
-            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs (see :ref:`metrics:Input types`)
+            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
+              (see :ref:`extensions/metrics:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -89,7 +90,7 @@ class Precision(StatScores):
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
         compute_on_step:
@@ -180,7 +181,7 @@ class Recall(StatScores):
 
     The reduction method (how the recall scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`metrics:Input types`.
+    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         num_classes:
@@ -216,10 +217,11 @@ class Recall(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`metrics:Input types`) as the ``N`` dimension within the sample,
+              (see :ref:`extensions/metrics:input types`) as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
-            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs (see :ref:`metrics:Input types`)
+            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
+              (see :ref:`extensions/metrics:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -239,7 +241,7 @@ class Recall(StatScores):
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
         compute_on_step:

pytorch_lightning/metrics/classification/stat_scores.py

@@ -28,7 +28,7 @@ class StatScores(Metric):
     ``reduce`` parameter, and additionally by the ``mdmc_reduce`` parameter in the
     multi-dimensional multi-class case.
 
-    Accepts all inputs listed in :ref:`metrics:Input types`.
+    Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         threshold:
@@ -71,7 +71,7 @@ class StatScores(Metric):
             one of the following:
 
             - ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
-              multi-class (see :ref:`metrics:Input types` for the definition of input types).
+              multi-class (see :ref:`extensions/metrics:input types` for the definition of input types).
 
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then the outputs are concatenated together. In each
@@ -86,7 +86,7 @@ class StatScores(Metric):
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
         compute_on_step:
@@ -175,7 +175,7 @@ class StatScores(Metric):
 
     def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
-        Update state with predictions and targets. See :ref:`metrics:Input types` for more information
+        Update state with predictions and targets. See :ref:`extensions/metrics:input types` for more information
         on input types.
 
         Args:

pytorch_lightning/metrics/functional/accuracy.py

@@ -72,7 +72,7 @@ def accuracy(
     changed to subset accuracy (which requires all labels or sub-samples in the sample to
     be correctly predicted) by setting ``subset_accuracy=True``.
 
-    Accepts all input types listed in :ref:`metrics:Input types`.
+    Accepts all input types listed in :ref:`extensions/metrics:input types`.
 
     Args:
         preds: Predictions from model (probabilities, or labels)

pytorch_lightning/metrics/functional/hamming_distance.py

@@ -49,7 +49,7 @@ def hamming_distance(preds: torch.Tensor, target: torch.Tensor, threshold: float
     treats each possible label separately - meaning that, for example, multi-class data is
     treated as if it were multi-label.
 
-    Accepts all input types listed in :ref:`metrics:Input types`.
+    Accepts all input types listed in :ref:`extensions/metrics:input types`.
 
     Args:
         preds: Predictions from model

pytorch_lightning/metrics/functional/precision_recall.py

@@ -60,7 +60,7 @@ def precision(
 
     The reduction method (how the precision scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`metrics:Input types`.
+    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         preds: Predictions from model (probabilities or labels)
@@ -94,10 +94,11 @@ def precision(
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`metrics:Input types`) as the ``N`` dimension within the sample,
+              (see :ref:`extensions/metrics:input types`) as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
-            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs (see :ref:`metrics:Input types`)
+            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
+              (see :ref:`extensions/metrics:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -122,7 +123,7 @@ def precision(
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
         class_reduction:
@@ -224,7 +225,7 @@ def recall(
 
     The reduction method (how the recall scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`metrics:Input types`.
+    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         preds: Predictions from model (probabilities, or labels)
@@ -255,10 +256,11 @@ def recall(
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`metrics:Input types`) as the ``N`` dimension within the sample,
+              (see :ref:`extensions/metrics:input types`) as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
-            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs (see :ref:`metrics:Input types`)
+            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
+              (see :ref:`extensions/metrics:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -283,7 +285,7 @@ def recall(
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
         class_reduction:
@@ -371,7 +373,7 @@ def precision_recall(
 
     The reduction method (how the recall scores are aggregated) is controlled by the
     ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`metrics:Input types`.
+    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         preds: Predictions from model (probabilities, or labels)
@@ -402,10 +404,11 @@ def precision_recall(
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`metrics:Input types`) as the ``N`` dimension within the sample,
+              (see :ref:`extensions/metrics:input types`) as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
-            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs (see :ref:`metrics:Input types`)
+            - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
+              (see :ref:`extensions/metrics:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -430,7 +433,7 @@ def precision_recall(
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
         class_reduction:

pytorch_lightning/metrics/functional/stat_scores.py

@@ -151,7 +151,7 @@ def stat_scores(
 
     The reduction method (how the statistics are aggregated) is controlled by the
     ``reduce`` parameter, and additionally by the ``mdmc_reduce`` parameter in the
-    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`metrics:Input types`.
+    multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`.
 
     Args:
         preds: Predictions from model (probabilities or labels)
@@ -196,7 +196,7 @@ def stat_scores(
             one of the following:
 
             - ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
-              multi-class (see :ref:`metrics:Input types` for the definition of input types).
+              multi-class (see :ref:`extensions/metrics:input types` for the definition of input types).
 
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then the outputs are concatenated together. In each
@@ -211,7 +211,7 @@ def stat_scores(
         is_multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
             than what they appear to be. See the parameter's
-            :ref:`documentation section <metrics:Using the is_multiclass parameter>`
+            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
             for a more detailed explanation and examples.
 
     Return: