Add error on wrong shape in CosineSimilarity metric (#2241)

CHANGELOG.md

@@ -72,8 +72,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed bug in `Metric._reduce_states(...)` when using `dist_sync_fn="cat"` ([#2226](https://github.com/Lightning-AI/torchmetrics/pull/2226))
 
 
+- Fixed bug in `CosineSimilarity` where 2d is expected but 1d input was given ([#2241](https://github.com/Lightning-AI/torchmetrics/pull/2241))
+
+
 - Fixed bug in `MetricCollection` when using compute groups and `compute` is called more than once ([#2211](https://github.com/Lightning-AI/torchmetrics/pull/2211))
 
+
 ## [1.2.0] - 2023-09-22
 
 ### Added

src/torchmetrics/functional/regression/cosine_similarity.py

@@ -31,6 +31,11 @@ def _cosine_similarity_update(
 
  """
  _check_same_shape(preds, target)
+ if preds.ndim != 2:
+ raise ValueError(
+ "Expected input to cosine similarity to be 2D tensors of shape `[N,D]` where `N` is the number of samples"
+ f" and `D` is the number of dimensions, but got tensor of shape {preds.shape}"
+ )
  preds = preds.float()
  target = target.float()
 

src/torchmetrics/regression/cosine_similarity.py

@@ -118,7 +118,7 @@ def plot(
  >>> # Example plotting a single value
  >>> from torchmetrics.regression import CosineSimilarity
  >>> metric = CosineSimilarity()
- >>> metric.update(randn(10,), randn(10,))
+ >>> metric.update(randn(10,2), randn(10,2))
  >>> fig_, ax_ = metric.plot()
 
  .. plot::
@@ -130,7 +130,7 @@ def plot(
  >>> metric = CosineSimilarity()
  >>> values = []
  >>> for _ in range(10):
- ... values.append(metric(randn(10,), randn(10,)))
+ ... values.append(metric(randn(10,2), randn(10,2)))
  >>> fig, ax = metric.plot(values)
 
  """

tests/unittests/regression/test_cosine_similarity.py

@@ -30,8 +30,8 @@
 
 
 _single_target_inputs = _Input(
- preds=torch.rand(NUM_BATCHES, BATCH_SIZE),
- target=torch.rand(NUM_BATCHES, BATCH_SIZE),
+ preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1),
+ target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1),
 )
 
 _multi_target_inputs = _Input(
@@ -40,23 +40,10 @@
 )
 
 
-def _multi_target_ref_metric(preds, target, reduction, sk_fn=sk_cosine):
- sk_preds = preds.view(-1, num_targets).numpy()
- sk_target = target.view(-1, num_targets).numpy()
- result_array = sk_fn(sk_target, sk_preds)
- col = np.diagonal(result_array)
- col_sum = col.sum()
- if reduction == "sum":
- return col_sum
- if reduction == "mean":
- return col_sum / len(col)
- return col
-
-
-def _single_target_ref_metric(preds, target, reduction, sk_fn=sk_cosine):
- sk_preds = preds.view(-1).numpy()
- sk_target = target.view(-1).numpy()
- result_array = sk_fn(np.expand_dims(sk_preds, axis=0), np.expand_dims(sk_target, axis=0))
+def _ref_metric(preds, target, reduction):
+ sk_preds = preds.numpy()
+ sk_target = target.numpy()
+ result_array = sk_cosine(sk_target, sk_preds)
  col = np.diagonal(result_array)
  col_sum = col.sum()
  if reduction == "sum":
@@ -70,8 +57,8 @@ def _single_target_ref_metric(preds, target, reduction, sk_fn=sk_cosine):
 @pytest.mark.parametrize(
  "preds, target, ref_metric",
  [
- (_single_target_inputs.preds, _single_target_inputs.target, _single_target_ref_metric),
- (_multi_target_inputs.preds, _multi_target_inputs.target, _multi_target_ref_metric),
+ (_single_target_inputs.preds, _single_target_inputs.target, _ref_metric),
+ (_multi_target_inputs.preds, _multi_target_inputs.target, _ref_metric),
  ],
 )
 class TestCosineSimilarity(MetricTester):
@@ -104,4 +91,11 @@ def test_error_on_different_shape(metric_class=CosineSimilarity):
  """Test that error is raised on different shapes of input."""
  metric = metric_class()
  with pytest.raises(RuntimeError, match="Predictions and targets are expected to have the same shape"):
- metric(torch.randn(100), torch.randn(50))
+ metric(torch.randn(100, 2), torch.randn(50, 2))
+
+
+def test_error_on_non_2d_input():
+ """Test that error is raised if input is not 2-dimensional."""
+ metric = CosineSimilarity()
+ with pytest.raises(ValueError, match="Expected input to cosine similarity to be 2D tensors of shape.*"):
+ metric(torch.randn(100), torch.randn(100))

tests/unittests/utilities/test_plot.py

@@ -480,7 +480,7 @@
  id="learned perceptual image patch similarity",
  ),
  pytest.param(ConcordanceCorrCoef, _rand_input, _rand_input, id="concordance corr coef"),
- pytest.param(CosineSimilarity, _rand_input, _rand_input, id="cosine similarity"),
+ pytest.param(CosineSimilarity, _multilabel_rand_input, _multilabel_rand_input, id="cosine similarity"),
  pytest.param(ExplainedVariance, _rand_input, _rand_input, id="explained variance"),
  pytest.param(KendallRankCorrCoef, _rand_input, _rand_input, id="kendall rank corr coef"),
  pytest.param(