Fix restrictive input check in pearson corr coef

CHANGELOG.md

@@ -120,6 +120,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed support in `MetricTracker` for `MultioutputWrapper` and nested structures ([#1608](https://github.com/Lightning-AI/metrics/pull/1608))
 
 
+- Fixed restrictive check in `PearsonCorrCoef` ([#1649](https://github.com/Lightning-AI/metrics/pull/1649))
+
+
 - Fixed integration with `jsonargparse` and `LightningCLI` ([#1651](https://github.com/Lightning-AI/metrics/pull/1651))
 
 

src/torchmetrics/functional/regression/utils.py

@@ -21,7 +21,9 @@ def _check_data_shape_to_num_outputs(preds: Tensor, target: Tensor, num_outputs:
             f"Expected both predictions and target to be either 1- or 2-dimensional tensors,"
             f" but got {target.ndim} and {preds.ndim}."
         )
-    if (num_outputs == 1 and preds.ndim != 1) or (num_outputs > 1 and num_outputs != preds.shape[1]):
+    cond1 = num_outputs == 1 and not (preds.ndim == 1 or preds.shape[1] == 1)
+    cond2 = num_outputs > 1 and num_outputs != preds.shape[1]
+    if cond1 or cond2:
         raise ValueError(
             f"Expected argument `num_outputs` to match the second dimension of input, but got {num_outputs}"
             f" and {preds.shape[1]}."

tests/unittests/regression/test_pearson.py

@@ -129,6 +129,12 @@ def test_error_on_different_shape():
         metric(torch.randn(100, 5), torch.randn(100, 5))
 
 
+def test_1d_input_allowed():
+    """Check that both input of the form [N,] and [N,1] is allowed with default num_outputs argument."""
+    assert isinstance(pearson_corrcoef(torch.randn(10, 1), torch.randn(10, 1)), torch.Tensor)
+    assert isinstance(pearson_corrcoef(torch.randn(10), torch.randn(10)), torch.Tensor)
+
+
 @pytest.mark.parametrize("shapes", [(5,), (1, 5), (2, 5)])
 def test_final_aggregation_function(shapes):
     """Test that final aggregation function can take various shapes of input."""