Add docstring for skip_unrolling, modify skip_unrolling clause

ignite/metrics/metric.py

@@ -233,6 +233,9 @@ class Metric(Serializable, metaclass=ABCMeta):
         device: specifies which device updates are accumulated on. Setting the
             metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
             non-blocking. By default, CPU.
+        skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
+            true for multi-output model, for example, if``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
+            Alternatively, output_transform can be used to handle this.
 
     Attributes:
         required_output_keys: dictionary defines required keys to be found in ``engine.state.output`` if the
@@ -290,8 +293,8 @@ def compute(self):
 
             res = evaluator.run(data)
 
-    .. versionchanged:: 0.4.2
-        ``required_output_keys`` became public attribute.
+    .. versionchanged:: 0.5.1
+        ``skip_unrolling`` attribute is introduced for Metric class.
     """
 
     # public class attribute
@@ -300,7 +303,10 @@ def compute(self):
     _required_output_keys = required_output_keys
 
     def __init__(
-        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling : bool =False
+        self,
+        output_transform: Callable = lambda x: x,
+        device: Union[str, torch.device] = torch.device("cpu"),
+        skip_unrolling: bool = False,
     ):
         self._output_transform = output_transform
 
@@ -378,33 +384,34 @@ def iteration_completed(self, engine: Engine) -> None:
         """
 
         output = self._output_transform(engine.state.output)
-        if self._skip_unrolling is False:
-            if isinstance(output, Mapping):
-                if self.required_output_keys is None:
-                    raise TypeError(
-                        f"Transformed engine output for {self.__class__.__name__} metric should be a tuple/list, "
-                        f"but given {type(output)}"
-                    )
-                if not all([k in output for k in self.required_output_keys]):
-                    raise ValueError(
-                        "When transformed engine's output is a mapping, "
-                        f"it should contain {self.required_output_keys} keys, but given {list(output.keys())}"
-                    )
-                output = tuple(output[k] for k in self.required_output_keys)
-
-            if isinstance(output, Sequence) and all([_is_list_of_tensors_or_numbers(o) for o in output]):
-                if not (len(output) == 2 and len(output[0]) == len(output[1])):
-                    raise ValueError(
-                        f"Output should have 2 items of the same length, "
-                        f"got {len(output)} and {len(output[0])}, {len(output[1])}"
-                    )
-                for o1, o2 in zip(output[0], output[1]):
-                    # o1 and o2 are list of tensors or numbers
-                    tensor_o1 = _to_batched_tensor(o1)
-                    tensor_o2 = _to_batched_tensor(o2, device=tensor_o1.device)
-                    self.update((tensor_o1, tensor_o2))
-            else:
-                self.update(output)
+        if isinstance(output, Mapping):
+            if self.required_output_keys is None:
+                raise TypeError(
+                    f"Transformed engine output for {self.__class__.__name__} metric should be a tuple/list, "
+                    f"but given {type(output)}"
+                )
+            if not all([k in output for k in self.required_output_keys]):
+                raise ValueError(
+                    "When transformed engine's output is a mapping, "
+                    f"it should contain {self.required_output_keys} keys, but given {list(output.keys())}"
+                )
+            output = tuple(output[k] for k in self.required_output_keys)
+
+        if (
+            (not self._skip_unrolling)
+            and isinstance(output, Sequence)
+            and all([_is_list_of_tensors_or_numbers(o) for o in output])
+        ):
+            if not (len(output) == 2 and len(output[0]) == len(output[1])):
+                raise ValueError(
+                    f"Output should have 2 items of the same length, "
+                    f"got {len(output)} and {len(output[0])}, {len(output[1])}"
+                )
+            for o1, o2 in zip(output[0], output[1]):
+                # o1 and o2 are list of tensors or numbers
+                tensor_o1 = _to_batched_tensor(o1)
+                tensor_o2 = _to_batched_tensor(o2, device=tensor_o1.device)
+                self.update((tensor_o1, tensor_o2))
         else:
             self.update(output)