Fixing SSIM Test Method Names

[ericspod]

Description

This fixes erroneous test names flagged elsewhere by coderabbit.

Types of changes

• Non-breaking change (fix or new feature that would not break existing functionality).
• Breaking change (fix or new feature that would cause existing functionality to change).
• New tests added to cover the changes.
• Integration tests passed locally by running ./runtests.sh -f -u --net --coverage.
• Quick tests passed locally by running ./runtests.sh --quick --unittests --disttests.
• In-line docstrings updated.
• Documentation updated, tested make html command in the docs/ folder.

[coderabbitai]

📝 Walkthrough

Walkthrough

Six test methods in the multiscalessim metric test file were renamed to follow proper pytest naming conventions. Method names were updated to consistently use snake_case with the test_ prefix, enabling correct test discovery. No test logic or assertions were modified.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	Title accurately describes the main change: renaming test methods to follow pytest naming conventions.
Description check	✅ Passed	Description covers the essential information and follows the template structure with appropriate checkboxes selected.

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✨ Finishing Touches

• 📝 Generate docstrings (stacked PR)
• 📝 Generate docstrings (commit on current branch)

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[coderabbitai]

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

tests/metrics/test_compute_multiscalessim_metric.py (1)

75-79: ⚠️ Potential issue | 🟡 Minor

test_small_inputs is testing the wrong condition.

The input torch.randn(1, 1, 16, 16, 16) is a 5-D tensor (3 spatial dims) passed to a spatial_dims=2 metric. This raises ValueError due to a dimension mismatch — identical in kind to what test_input_ill_input_shape3d already covers. The intended scenario (a spatially-too-small 2-D input exhausted by MS-SSIM's downsampling cascade) is not exercised at all.

🛠️ Proposed fix

-    def test_small_inputs(self):
-        metric = MultiScaleSSIMMetric(spatial_dims=2)
-
-        with self.assertRaises(ValueError):
-            metric(torch.randn(1, 1, 16, 16, 16), torch.randn(1, 1, 16, 16, 16))
+    def test_small_inputs(self):
+        metric = MultiScaleSSIMMetric(spatial_dims=2)
+
+        with self.assertRaises(ValueError):
+            metric(torch.randn(1, 1, 4, 4), torch.randn(1, 1, 4, 4))

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@tests/metrics/test_compute_multiscalessim_metric.py` around lines 75 - 79,
The test_small_inputs currently passes 5-D tensors to
MultiScaleSSIMMetric(spatial_dims=2) causing a dimension-mismatch error (already
covered by test_input_ill_input_shape3d); change test_small_inputs to pass 4-D
tensors with valid rank but too-small spatial dimensions (e.g.,
torch.randn(1,1,8,8) or smaller) so the MS-SSIM downsampling cascade triggers a
ValueError; update the assertion to use those 4-D small spatial inputs and keep
the expect-ValueError assertion on MultiScaleSSIMMetric to avoid duplicating the
shape-mismatch case.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Outside diff comments:
In `@tests/metrics/test_compute_multiscalessim_metric.py`:
- Around line 75-79: The test_small_inputs currently passes 5-D tensors to
MultiScaleSSIMMetric(spatial_dims=2) causing a dimension-mismatch error (already
covered by test_input_ill_input_shape3d); change test_small_inputs to pass 4-D
tensors with valid rank but too-small spatial dimensions (e.g.,
torch.randn(1,1,8,8) or smaller) so the MS-SSIM downsampling cascade triggers a
ValueError; update the assertion to use those 4-D small spatial inputs and keep
the expect-ValueError assertion on MultiScaleSSIMMetric to avoid duplicating the
shape-mismatch case.

[benediktjohannes]

Hi @ericspod! Thanks for the fix, LGTM! I suggest that maybe changing this in tests/metrics/test_ssim_metric.py would maybe be a good addition for this in order to be consistent, how do you think about this? Thank you! And nice change!

[benediktjohannes]

Unforetunately, it is not possible to add a code suggestion to other things than those edited in the PR (or at least I do think so), so I'll just add another PR in order to fix these issues so that you don't have to worry about this. 👍 And thanks again for the fix!

[benediktjohannes]

Nice changes! Thanks!

[dzenanz]

LGTM