BinaryAccuracy support for bool target tensor (#136)

Summary:
Pull Request resolved: #136

Addressing github issue #134

Reviewed By: JKSenthil

Differential Revision: D44179119

fbshipit-source-id: 14d75bd3122776611b11649c50d12de871164f49

tests/metrics/functional/classification/test_accuracy.py

@@ -44,6 +44,16 @@ def test_binary_accuracy(self) -> None:
 
         self._test_binary_accuracy_with_input(input, target)
 
+    def test_binary_accuracy_with_bool_target(self) -> None:
+        num_classes = 2
+        input = torch.randint(high=num_classes, size=(BATCH_SIZE,))
+        target = torch.randint(high=num_classes, size=(BATCH_SIZE,))
+
+        output = binary_accuracy(input, target)
+        output_bool = binary_accuracy(input, target.bool())
+
+        torch.testing.assert_close(output, output_bool)
+
     def test_binary_accuracy_with_rounding(self) -> None:
         num_classes = 2
         input = torch.rand(size=(BATCH_SIZE,))

torcheval/metrics/functional/classification/accuracy.py

@@ -353,7 +353,16 @@ def _binary_accuracy_update(
     input = torch.where(input < threshold, 0, 1)
 
     num_correct = (input == target).sum()
-    num_total = target.new_tensor(target.shape[0])
+    if target.dtype == torch.bool:
+        num_total = torch.tensor(
+            target.shape[0],
+            dtype=torch.int64,
+            device=target.device,
+            requires_grad=False,
+        )
+    else:
+        # this is faster than using torch.tensor, but breaks for bool tensors because the shape will be cast to 1 in a bool tensor
+        num_total = target.new_tensor(target.shape[0])
     return num_correct, num_total