[metrics] Accuracy num_classes error fix (#3764)

* change accuracy error to warning

* changelog

CHANGELOG.md

@@ -89,6 +89,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `ModelCheckpoint` with `save_top_k=-1` option not tracking the best models when a monitor metric is available ([3735](https://github.com/PyTorchLightning/pytorch-lightning/pull/3735))
 
+- Fixed counter-intuitive error being thrown in `Accuracy` metric for zero target tensor ([#3764](https://github.com/PyTorchLightning/pytorch-lightning/pull/3764))
+
 ## [0.9.0] - YYYY-MM-DD
 
 ### Added

pytorch_lightning/metrics/functional/classification.py

@@ -87,8 +87,10 @@ def get_num_classes(
     if num_classes is None:
         num_classes = num_all_classes
     elif num_classes != num_all_classes:
-        rank_zero_warn(f'You have set {num_classes} number of classes if different from'
-                       f' predicted ({num_pred_classes}) and target ({num_target_classes}) number of classes')
+        rank_zero_warn(f'You have set {num_classes} number of classes which is'
+                       f' different from predicted ({num_pred_classes}) and'
+                       f' target ({num_target_classes}) number of classes',
+                       RuntimeWarning)
     return num_classes
 
 
@@ -266,9 +268,6 @@ def accuracy(
         tensor(0.7500)
 
     """
-    if not (target > 0).any() and num_classes is None:
-        raise RuntimeError("cannot infer num_classes when target is all zero")
-
     tps, fps, tns, fns, sups = stat_scores_multiple_classes(
         pred=pred, target=target, num_classes=num_classes)
 

tests/metrics/functional/test_classification.py

@@ -194,8 +194,12 @@ def test_multilabel_accuracy():
     assert torch.allclose(accuracy(y2, torch.logical_not(y2), class_reduction='none'), torch.tensor([0., 0.]))
     assert torch.allclose(accuracy(y1, torch.logical_not(y1), class_reduction='none'), torch.tensor([0., 0.]))
 
-    with pytest.raises(RuntimeError):
-        accuracy(y2, torch.zeros_like(y2), class_reduction='none')
+    # num_classes does not match extracted number from input we expect a warning
+    with pytest.warns(RuntimeWarning,
+                      match=r'You have set .* number of classes which is'
+                            r' different from predicted (.*) and'
+                            r' target (.*) number of classes'):
+        _ = accuracy(y2, torch.zeros_like(y2), num_classes=3)
 
 
 def test_accuracy():