Merge branch 'master' into ssim_kernel_order

CHANGELOG.md

@@ -37,6 +37,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed bug where compositional metrics where unable to sync because of type mismatch ([#454](https://github.com/PyTorchLightning/metrics/pull/454))
 
 
+- Fixed metric hashing ([#478](https://github.com/PyTorchLightning/metrics/pull/478))
+
+
 - Fixed the semantic ordering of kernel height and width in `SSIM` metric ([#474](https://github.com/PyTorchLightning/metrics/pull/474))
 
 

tests/bases/test_hashing.py

@@ -0,0 +1,22 @@
+import pytest
+
+from tests.helpers.testers import DummyListMetric, DummyMetric
+
+
+@pytest.mark.parametrize(
+ "metric_cls",
+ [
+ DummyMetric,
+ DummyListMetric,
+ ],
+)
+def test_metric_hashing(metric_cls):
+ """Tests that hases are different.
+
+ See the Metric's hash function for details on why this is required.
+ """
+ instance_1 = metric_cls()
+ instance_2 = metric_cls()
+
+ assert hash(instance_1) != hash(instance_2)
+ assert id(instance_1) != id(instance_2)

tests/bases/test_metric.py

@@ -160,7 +160,7 @@ class B(DummyListMetric):
 
  b1 = B()
  b2 = B()
- assert hash(b1) == hash(b2)
+ assert hash(b1) != hash(b2) # different ids
  assert isinstance(b1.x, list) and len(b1.x) == 0
  b1.x.append(tensor(5))
  assert isinstance(hash(b1), int) # <- check that nothing crashes

torchmetrics/functional/classification/precision_recall_curve.py

@@ -153,7 +153,7 @@ def _precision_recall_curve_compute_single_class(
 
  recall = torch.cat([reversed(recall[sl]), torch.zeros(1, dtype=recall.dtype, device=recall.device)])
 
- thresholds = tensor(reversed(thresholds[sl]))
+ thresholds = reversed(thresholds[sl]).detach().clone() # type: ignore
 
  return precision, recall, thresholds
 

torchmetrics/metric.py

@@ -502,7 +502,12 @@ def _filter_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
  return filtered_kwargs
 
  def __hash__(self) -> int:
- hash_vals = [self.__class__.__name__]
+ # we need to add the id here, since PyTorch requires a module hash to be unique.
+ # Internally, PyTorch nn.Module relies on that for children discovery
+ # (see https://github.com/pytorch/pytorch/blob/v1.9.0/torch/nn/modules/module.py#L1544)
+ # For metrics that include tensors it is not a problem,
+ # since their hash is unique based on the memory location but we cannot rely on that for every metric.
+ hash_vals = [self.__class__.__name__, id(self)]
 
  for key in self._defaults:
  val = getattr(self, key)