update running_average, ssim, top_k_categorical_accuracy

ignite/metrics/running_average.py

@@ -27,6 +27,9 @@ class RunningAverage(Metric):
             None when ``src`` is an instance of :class:`~ignite.metrics.metric.Metric`, as the running average will
             use the ``src``'s device. Otherwise, defaults to CPU. Only applicable when the computed value
             from the metric is a tensor.
+        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.
 
     Examples:
 
@@ -84,6 +87,9 @@ def log_running_avg_metrics():
             0.039208...
             0.038423...
             0.057655...
+
+    .. versionchanged:: 0.5.1
+        ``skip_unrolling`` argument is added.
     """
 
     required_output_keys = None
@@ -96,6 +102,7 @@ def __init__(
         output_transform: Optional[Callable] = None,
         epoch_bound: Optional[bool] = None,
         device: Optional[Union[str, torch.device]] = None,
+        skip_unrolling: bool = False,
     ):
         if not (isinstance(src, Metric) or src is None):
             raise TypeError("Argument src should be a Metric or None.")
@@ -131,7 +138,9 @@ def output_transform(x: Any) -> Any:
             )
         self.epoch_bound = epoch_bound
         self.alpha = alpha
-        super(RunningAverage, self).__init__(output_transform=output_transform, device=device)
+        super(RunningAverage, self).__init__(
+            output_transform=output_transform, device=device, skip_unrolling=skip_unrolling
+        )
 
     @reinit__is_reduced
     def reset(self) -> None:

ignite/metrics/ssim.py

@@ -33,6 +33,9 @@ class SSIM(Metric):
         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.
 
     Examples:
         To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
@@ -62,6 +65,9 @@ class SSIM(Metric):
             0.9218971...
 
     .. versionadded:: 0.4.2
+
+    .. versionchanged:: 0.5.1
+        ``skip_unrolling`` argument is added.
     """
 
     _state_dict_all_req_keys = ("_sum_of_ssim", "_num_examples", "_kernel")
@@ -76,6 +82,7 @@ def __init__(
         gaussian: bool = True,
         output_transform: Callable = lambda x: x,
         device: Union[str, torch.device] = torch.device("cpu"),
+        skip_unrolling: bool = False,
     ):
         if isinstance(kernel_size, int):
             self.kernel_size: Sequence[int] = [kernel_size, kernel_size]
@@ -97,7 +104,7 @@ def __init__(
         if any(y <= 0 for y in self.sigma):
             raise ValueError(f"Expected sigma to have positive number. Got {sigma}.")
 
-        super(SSIM, self).__init__(output_transform=output_transform, device=device)
+        super(SSIM, self).__init__(output_transform=output_transform, device=device, skip_unrolling=skip_unrolling)
         self.gaussian = gaussian
         self.data_range = data_range
         self.c1 = (k1 * data_range) ** 2

ignite/metrics/top_k_categorical_accuracy.py

@@ -24,6 +24,9 @@ class TopKCategoricalAccuracy(Metric):
         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.
 
     Examples:
         To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
@@ -71,6 +74,9 @@ def one_hot_to_binary_output_transform(output):
         .. testoutput::
 
             0.75
+
+    .. versionchanged:: 0.5.1
+        ``skip_unrolling`` argument is added.
     """
 
     _state_dict_all_req_keys = ("_num_correct", "_num_examples")
@@ -80,8 +86,9 @@ def __init__(
         k: int = 5,
         output_transform: Callable = lambda x: x,
         device: Union[str, torch.device] = torch.device("cpu"),
+        skip_unrolling: bool = False,
     ) -> None:
-        super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device)
+        super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device, skip_unrolling=skip_unrolling)
         self._k = k
 
     @reinit__is_reduced

tests/ignite/metrics/test_mean_pairwise_distance.py

@@ -5,7 +5,6 @@
 import torch
 
 import ignite.distributed as idist
-
 from ignite.exceptions import NotComputableError
 from ignite.metrics import MeanPairwiseDistance
 

tests/ignite/metrics/test_multilabel_confusion_matrix.py

@@ -4,7 +4,6 @@
 from sklearn.metrics import multilabel_confusion_matrix
 
 import ignite.distributed as idist
-
 from ignite.exceptions import NotComputableError
 from ignite.metrics.multilabel_confusion_matrix import MultiLabelConfusionMatrix