function descriptions

pytorch_lightning/metrics/functional/classification.py

@@ -9,6 +9,16 @@
 
 def to_onehot(tensor: torch.Tensor,
               n_classes: Optional[int] = None) -> torch.Tensor:
+    """ Converts a dense label tensor to one-hot format
+
+    Args:
+        tensor: dense label tensor, with shape [N, d1, d2, ...]
+
+        n_classes: number of classes C
+
+    Output:
+        A sparse label tensor with shape [N, C, d1, d2, ...]
+    """
     if n_classes is None:
         n_classes = int(tensor.max().detach().item() + 1)
     dtype, device, shape = tensor.dtype, tensor.device, tensor.shape
@@ -19,11 +29,15 @@ def to_onehot(tensor: torch.Tensor,
 
 
 def to_categorical(tensor: torch.Tensor, argmax_dim: int = 1) -> torch.Tensor:
+    """ Converts a tensor of probabilities to a dense label tensor """
     return torch.argmax(tensor, dim=argmax_dim)
 
 
 def get_num_classes(pred: torch.Tensor, target: torch.Tensor,
                     num_classes: Optional[int]) -> int:
+    """ Returns the number of classes for a given prediction and
+        target tensor
+    """
     if num_classes is None:
         if pred.ndim > target.ndim:
             num_classes = pred.size(1)
@@ -36,6 +50,20 @@ def stat_scores(pred: torch.Tensor, target: torch.Tensor,
                 class_index: int, argmax_dim: int = 1
                 ) -> Tuple[torch.Tensor, torch.Tensor,
                            torch.Tensor, torch.Tensor]:
+    """ Calculates the number of true postive, false postive, true negative
+    and false negative for a specfic class
+
+    Args:
+        pred: prediction tensor
+
+        target: target tensor
+
+        class_index: class to calculate over
+
+        argmax_dim: if pred is a tensor of probabilities, this indicates the
+            axis the argmax transformation will be applied over
+
+    """
     if pred.ndim == target.ndim + 1:
         pred = to_categorical(pred, argmax_dim=argmax_dim)
 
@@ -52,6 +80,21 @@ def stat_scores_multiple_classes(pred: torch.Tensor, target: torch.Tensor,
                                  argmax_dim: int = 1
                                  ) -> Tuple[torch.Tensor, torch.Tensor,
                                             torch.Tensor, torch.Tensor]:
+    """ Calls the stat_scores function iteratively for all classes, thus
+        calculating the number of true postive, false postive, true negative
+        and false negative for each class
+
+    Args:
+        pred: prediction tensor
+
+        target: target tensor
+
+        class_index: class to calculate over
+
+        argmax_dim: if pred is a tensor of probabilities, this indicates the
+            axis the argmax transformation will be applied over
+
+    """
     num_classes = get_num_classes(pred=pred, target=target,
                                   num_classes=num_classes)