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

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Merged
vfdev-5 merged 2 commits into from
Apr 16, 2022
Merged

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

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989ead6
Removed warning in DDP if Metric.reset/update are not decorated.
vfdev-5 Apr 14, 2022
60ab102
Removed unused import
vfdev-5 Apr 14, 2022
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12 changes: 0 additions & 12 deletions ignite/metrics/metric.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
import warnings
from abc import ABCMeta, abstractmethod
from collections.abc import Mapping
from functools import wraps
Expand Down Expand Up @@ -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.")
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9 changes: 0 additions & 9 deletions tests/ignite/metrics/test_metric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down