Removed warning in DDP if Metric.reset/update are not decorated. (#2549)

* Removed warning in DDP if Metric.reset/update are not decorated.

* Removed unused import

Author: vfdev-5

ignite/metrics/metric.py

@@ -1,4 +1,3 @@
-import warnings
 from abc import ABCMeta, abstractmethod
 from collections.abc import Mapping
 from functools import wraps
@@ -210,17 +209,6 @@ def __init__(
     ):
         self._output_transform = output_transform
 
-        # Check device if distributed is initialized:
-        if idist.get_world_size() > 1:
-
-            # check if reset and update methods are decorated. Compute may not be decorated
-            if not (hasattr(self.reset, "_decorated") and hasattr(self.update, "_decorated")):
-                warnings.warn(
-                    f"{self.__class__.__name__} class does not support distributed setting. "
-                    "Computed result is not collected across all computing devices",
-                    RuntimeWarning,
-                )
-
         # Some metrics have a large performance regression when run on XLA devices, so for now, we disallow it.
         if torch.device(device).type == "xla":
             raise ValueError("Cannot create metric on an XLA device. Use device='cpu' instead.")

tests/ignite/metrics/test_metric.py

@@ -29,15 +29,6 @@ def update(self, output):
         assert output == self.true_output
 
 
-@pytest.mark.distributed
-@pytest.mark.skipif("WORLD_SIZE" not in os.environ, reason="Skip if WORLD_SIZE not in env vars")
-@pytest.mark.skipif(torch.cuda.is_available(), reason="Skip if GPU")
-def test_metric_warning(distributed_context_single_node_gloo):
-    y = torch.tensor([1.0])
-    with pytest.warns(RuntimeWarning, match=r"DummyMetric1 class does not support distributed setting"):
-        DummyMetric1((y, y))
-
-
 def test_no_transform():
     y_pred = torch.Tensor([[2.0], [-2.0]])
     y = torch.zeros(2)