Merge branch 'master' into curve_average

docs/source/pages/lightning.rst

@@ -187,10 +187,11 @@ The following contains a list of pitfalls to be aware of:
             self.val_acc[dataloader_idx](preds, y)
             self.log('val_acc', self.val_acc[dataloader_idx])
 
-* Mixing the two logging methods by calling ``self.log("val", self.metric)`` in ``{training}/{val}/{test}_step`` method and
-  then calling ``self.log("val", self.metric.compute())`` in the corresponding ``{training}/{val}/{test}_epoch_end`` method.
-  Because the object is logged in the first case, Lightning will reset the metric before calling the second line leading to
-  errors or nonsense results.
+* Mixing the two logging methods by calling ``self.log("val", self.metric)`` in ``{training|validation|test}_step``
+  method and then calling ``self.log("val", self.metric.compute())`` in the corresponding
+  ``on_{train|validation|test}_epoch_end`` method.
+  Because the object is logged in the first case, Lightning will reset the metric before calling the second line leading
+  to errors or nonsense results.
 
 * Calling ``self.log("val", self.metric(preds, target))`` with the intention of logging the metric object. Because
   ``self.metric(preds, target)`` corresponds to calling the forward method, this will return a tensor and not the
@@ -209,4 +210,4 @@ The following contains a list of pitfalls to be aware of:
 * Using :class:`~torchmetrics.wrappers.MetricTracker` wrapper with Lightning is a special case, because the wrapper in itself is not a metric
   i.e. it does not inherit from the base :class:`~torchmetrics.Metric` class but instead from :class:`~torch.nn.ModuleList`. Thus,
   to log the output of this metric one needs to manually log the returned values (not the object) using ``self.log``
-  and for epoch level logging this should be done in the appropriate ``on_***_epoch_end`` method.
+  and for epoch level logging this should be done in the appropriate ``on_{train|validation|test}_epoch_end`` method.

docs/source/pages/overview.rst

@@ -331,7 +331,7 @@ inside your LightningModule. In most cases we just have to replace ``self.log``
             # ...
             self.valid_metrics.update(logits, y)
 
-        def validation_epoch_end(self, outputs):
+        def on_validation_epoch_end(self):
             # use log_dict instead of log
             # metrics are logged with keys: val_Accuracy, val_Precision and val_Recall
             output = self.valid_metrics.compute()

tests/integrations/lightning/boring_model.py

@@ -68,7 +68,7 @@ def training_step(...):
     or:
 
     model = BaseTestModel()
-    model.training_epoch_end = None
+    model.validation_step = None
 
     """