Merge branch 'master' into dependabot-github_actions-pypa-gh-action-p…

…ypi-publish-1.6.4

docs/source/pages/lightning.rst

@@ -32,9 +32,9 @@ The example below shows how to use a metric in your `LightningModule <https://py
 
     class MyModel(LightningModule):
 
-        def __init__(self):
+        def __init__(self, num_classes):
             ...
-            self.accuracy = torchmetrics.Accuracy(task='multiclass')
+            self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -78,10 +78,10 @@ value by calling ``.compute()``.
 
     class MyModule(LightningModule):
 
-        def __init__(self):
+        def __init__(self, num_classes):
             ...
-            self.train_acc = torchmetrics.Accuracy(task='multiclass')
-            self.valid_acc = torchmetrics.Accuracy(task='multiclass')
+            self.train_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
+            self.valid_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -105,8 +105,8 @@ of the metrics.
 
         def __init__(self):
             ...
-            self.train_acc = torchmetrics.Accuracy(task='multiclass')
-            self.valid_acc = torchmetrics.Accuracy(task='multiclass')
+            self.train_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
+            self.valid_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -141,9 +141,9 @@ mixed as it can lead to wrong results.
 
         class MyModule(LightningModule):
 
-            def __init__(self):
+            def __init__(self, num_classes):
                 ...
-                self.valid_acc = torchmetrics.Accuracy(task='multiclass')
+                self.valid_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
             def validation_step(self, batch, batch_idx):
                 logits = self(x)
@@ -185,9 +185,9 @@ The following contains a list of pitfalls to be aware of:
 
     class MyModule(LightningModule):
 
-        def __init__(self):
+        def __init__(self, num_classes):
             ...
-            self.val_acc = nn.ModuleList([torchmetrics.Accuracy(task='multiclass') for _ in range(2)])
+            self.val_acc = nn.ModuleList([torchmetrics.Accuracy(task="multiclass", num_classes=num_classes) for _ in range(2)])
 
         def val_dataloader(self):
             return [DataLoader(...), DataLoader(...)]

docs/source/pages/overview.rst

@@ -282,9 +282,9 @@ inside your LightningModule. In most cases we just have to replace ``self.log``
     from torchmetrics.classification import MulticlassAccuracy, MulticlassPrecision, MulticlassRecall
 
     class MyModule(LightningModule):
-        def __init__(self):
+        def __init__(self, num_classes):
             metrics = MetricCollection([
-                MulticlassAccuracy(), MulticlassPrecision(), MulticlassRecall()
+                MulticlassAccuracy(num_classes), MulticlassPrecision(num_classes), MulticlassRecall(num_classes)
             ])
             self.train_metrics = metrics.clone(prefix='train_')
             self.valid_metrics = metrics.clone(prefix='val_')

docs/source/pages/quickstart.rst

@@ -64,7 +64,7 @@ The code-snippet below shows a simple example for calculating the accuracy using
     preds = torch.randn(10, 5).softmax(dim=-1)
     target = torch.randint(5, (10,))
 
-    acc = torchmetrics.functional.accuracy(preds, target, task='multiclass', num_classes=5)
+    acc = torchmetrics.functional.accuracy(preds, target, task="multiclass", num_classes=5)
 
 Module metrics
 ~~~~~~~~~~~~~~
@@ -86,7 +86,7 @@ The code below shows how to use the class-based interface:
     import torchmetrics
 
     # initialize metric
-    metric = torchmetrics.Accuracy(task='multiclass', num_classes=5)
+    metric = torchmetrics.Accuracy(task="multiclass", num_classes=5)
 
     n_batches = 10
     for i in range(n_batches):