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fix(losses): check exact input/target shape in MS_SSIMLoss - #6158
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The forward pass only verified that both tensors had the same number of dimensions, so an input of shape (2, C, H, W) against a target of shape (1, C, H, W) silently broadcast and produced a wrong loss and wrong per-image maps. Use KORNIA_CHECK_SAME_SHAPE so mismatched shapes raise immediately, and update the existing ndim-mismatch assertion to the shared check's message. Fixes kornia#6157 Signed-off-by: Charan Rathore <[email protected]>
Add a regression test for kornia#6157: a batch-2 input against a batch-1 target must raise instead of broadcasting silently. Signed-off-by: Charan Rathore <[email protected]>
Signed-off-by: Charan Rathore <[email protected]>
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| Check name | Status | Explanation | Resolution |
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| Docstring Coverage |  |
Docstring coverage is 16.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 6 functions across 2 files. | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Title check The title is concise, specific, and accurately describes the main change: exact input and target shape validation in MS_SSIMLoss. Description check The description covers the change, related issue, validation performed, test results, changelog status, and AI assistance. It is complete and relevant. Linked Issues check Issue #6157requiresMS_SSIMLossto reject non-4-D inputs and unequal input shapes, with both shapes in the error. The implementation adds separate 4-D checks and an unconditional exact-shape check …Out of Scope Changes check The changes are limited to kornia/losses/ms_ssim.py, focused regression tests intests/losses/test_ssim.py, and the related changelog fragment. Each change supports issue#6157. No unrelated chang…Full details: Linked Issues check
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Explanation
Issue
#6157requiresMS_SSIMLossto reject non-4-D inputs and unequal input shapes, with both shapes in the error. The implementation adds separate 4-D checks and an unconditional exact-shape check that raisesShapeError. Tests cover 3-D inputs, unequal 4-D shapes, and the checks-disabled case. The linked coding requirements are met.
Full details: Out of Scope Changes check
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Explanation
The changes are limited to
kornia/losses/ms_ssim.py, focused regression tests intests/losses/test_ssim.py, and the related changelog fragment. Each change supports issue#6157. No unrelated change is identified.
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Review comments at @kornia/losses/ms_ssim.py:
- Line 212: Update the MS-SSIM loss validation around KORNIA_CHECK_SAME_SHAPE to
reject either input unless it is 4-D before computing the loss, and add a
regression test confirming equal-shaped 3-D inputs are rejected.
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Equal-shaped 3-D inputs passed the shape-equality check and fell through to conv2d's unbatched path, failing deep inside the loss with a confusing unflatten error. Reject non-BxCxHxW inputs up front, the same validation the ssim metric applies. Signed-off-by: Charan Rathore <[email protected]>
…n sweep Signed-off-by: Charan Rathore <[email protected]>
Updated `MS_SSIMLoss` to enforce shape validation by rejecting mismatched input and target shapes, ensuring correct loss calculation.
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Good catch - adopted. Equal-shaped 3-D inputs did slip past the equality check into conv2d's unbatched path and died deep in |
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Review comments at @kornia/losses/ms_ssim.py:
- Line 218: Replace the configuration-dependent KORNIA_CHECK_SAME_SHAPE
validation with an unconditional shape comparison that raises an error
containing both input shapes when they differ, so MS-SSIM cannot broadcast
mismatched inputs when checks are disabled.
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… disabled KORNIA_CHECK_SAME_SHAPE is a no-op once Kornia checks are disabled globally, so a mismatched input/target pair could still broadcast silently. Compare the shapes unconditionally instead and raise with both shapes. Add a regression test for the checks-disabled path. Signed-off-by: Charan Rathore <[email protected]>
…ed-off-by: Charan Rathore <[email protected]> Added a test to ensure shape mismatch raises ValueError even when checks are disabled.
…ed-off-by: Charan Rathore <[email protected]> Remove license header and add tests for MS_SSIMLoss shape guard with checks disabled.
…ed-off-by: Charan Rathore <[email protected]> Added comprehensive tests for SSIM and MS-SSIM loss functions, including shape checks, gradient checks, and various input scenarios.
…s disabled Signed-off-by: Charan Rathore <[email protected]>
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@coderabbitai Good catch, adopted. Prepared with AI assistance (Instinct assisted). |
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Inline comments:
Review comments at @changelog.d/+ms-ssim-shape-check.fixed.md:
- Line 1: Rename the MS-SSIM shape-check changelog fragment to 6158.fixed.md so
it meets the changelog validator’s PR-numbered filename requirement.
Review comments at @tests/losses/test_ssim.py:
- Line 109: In the test containing enable_checks(), record the initial state
with are_checks_enabled() before the test changes it, and restore that state in
a finally block by re-enabling checks only when they were initially enabled.
Ensure cleanup also runs if the test fails.
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Review comments at @changelog.d/+ms-ssim-shape-check.fixed.md:
- Around line 1-3: Rename the changelog fragment to use the required PR-number
naming convention, using 6158 as the identifier, and leave its content
unchanged.
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…igned-off-by: Charan Rathore <[email protected]>
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ast-grep outline kornia/losses/ms_ssim.py --match 'MS_SSIMLoss|forward' --view expanded
ast-grep outline tests/losses/test_ssim.py --match 'TestMS|test_shape|test_exception|test_non|disabled' --view expanded
rg -n -C 7 'len\(.*shape|same shape|ShapeError|BxCxHxW' kornia/losses/ms_ssim.py
rg -n -C 12 'disable_checks|enable_checks|def test_shape_mismatch|def test_non_4d' tests/losses/test_ssim.pyLength of output: 7497
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…pe test CodeRabbit review on kornia#6158: the checks-disabled shape test re-enabled Kornia checks unconditionally in its finally block, which would flip a previously-disabled global state. Capture are_checks_enabled() first and restore only if checks were enabled. Signed-off-by: Charan Rathore <[email protected]>
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Review comments at @tests/losses/test_ssim.py:
- Around line 101-114: Update every input-shape mismatch test for SSIM and
MS_SSIMLoss to assert that the error message includes both concrete tensor
shapes, not just “same shape.” In the mismatch cases using shapes [2, 3, 16, 16]
and [1, 3, 16, 16], and [1, 1, 8, 8] and [2, 1, 8, 8], preserve each case’s
expected shape order in the match assertion.
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Strengthen the three shape-mismatch assertions to pin both concrete tensor shapes (in input/target order), not just "same shape". Verified: 80 passed, 1 skipped in tests/losses/test_ssim.py; order-flipped control fails as expected. Signed-off-by: Charan Rathore <[email protected]>
… shape mismatch; breaking fragment The checks-disabled test re-enabled Kornia checks unconditionally, so a run with KORNIA_CHECKS=0 left them on for every later test; restore the state it found. Parametrize the mismatch test over a batch-1, channel-1, height-1 and width-1 target, and the rank test over 3-D, 5-D and 2-D pairs, which kills a batch-only, a batch-and-channel-only and an ndim >= 4 variant of the check. On main a batch-1 image was broadcast over the other batch and returned the loss against the repeated image; it now raises, so the fragment is breaking and says what used to happen. The Convention block states the no-broadcast rule.
…d correctly; fixed fragment A batch of one used to be compared with every image of the other batch and returned the loss against the repeated image. Only the channel, height and width mismatches and unequal batch sizes other than 1 gave wrong losses or raised, so only those raise now. The fragment is back to fixed.
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Thanks. The check is right, with one change we pushed: a batch of one keeps broadcasting, because main computed it correctly.
The #6157 repro reproduces on main e057dd459. I went through every kind of mismatch on main. A batch-1 image against a batch-2 or batch-3 one, on either side and with every reduction, returned exactly the loss against the expanded image (bitwise), with only torch's l1_loss warning. That is a working call, not a wrong loss, so it keeps working here, and test_batch_of_one_broadcasts pins it in both argument orders. The wrong results were elsewhere: a height or width of 1 was broadcast and returned a wrong loss, more than 100 off the expanded target, and a 3-D (1, 5, W) pair returned a wrong value with a (5, W) map. Channel, width, height and other batch mismatches, and 5-D, other 3-D, 2-D and 1-D pairs, ended in a torch RuntimeError or IndexError. Only a rank mismatch raised ValueError, and it named the types. Here every one of those raises ValueError (ShapeError for a shape mismatch) and names the shapes. For valid inputs, the loss and both gradients are bitwise main over 240 CPU configurations (float16, bfloat16, float32 and float64; five shapes, three reductions, two sigma sets, two seeds) and 180 on MPS, and the batch-1 broadcast gives main's bits too. TorchScript still compiles the module and now reports the real message. torch.compile(fullgraph=True) builds the same number of frames as main for dynamic, automatic and static shapes, the broadcast included.
On the plain raise versus KORNIA_CHECK_SAME_SHAPE: ssim checks the rank and the shape with plain raises, and so does this function's own type check, so the plain raise follows the closest convention. It also keeps the check under python -O, where Kornia checks are off by default.
We pushed a main merge and two commits:
- The shape check accepts equal batch sizes or a batch size of 1 on either side, and requires the same channels, height and width. The message says so, and a Convention bullet states the rule.
test_shape_mismatch_raises_when_checks_disabledre-enabled checks unconditionally in itsfinally. 029884e had fixed that, and 20efaf3 brought it back, so a run withKORNIA_CHECKS=0left checks on for every later test. It now restores the state it found, astests/contrib/test_extract_tensor_patch.pydoes.- The mismatch test covered only the batch axis, so a check comparing only
shape[0], or onlyshape[:2], passed every test. It now uses a batch-3, channel-1, height-1 and width-1 target. The rank test now covers 3-D, 5-D and 2-D pairs, because anndim >= 4check passed before. All the new mismatch cases fail on main. - The fragment stays
fixedand now says what each kind of input used to do: no call that returned a right value raises.
The two open CodeRabbit threads about the fragment name are stale (it has carried the PR number since 3dbb360), so we resolved them. The description still says the check uses KORNIA_CHECK_SAME_SHAPE; it is plain raises now, so you may want to update that sentence.
Posted on behalf of @ducha-aiki by Claude (Opus 5.5).
Stale: every requested change is in at the current head (4-D check, shape check that also holds with checks disabled, PR-numbered fragment, concrete-shape assertions, prior check state restored in the disabled-checks test).
Posted on behalf of @ducha-aiki by Claude (Opus 5.5).
What does this pull request do?
MS_SSIMLoss.forwardonly checked that both tensors had the same number of dimensions, so an input of shape(2, C, H, W)against a target of shape(1, C, H, W)silently broadcast and returned a wrong loss (and wrong per-image maps withreduction="none"). The old error message also printed the tensor types instead of the shapes.This replaces the ndim-only check with
KORNIA_CHECK_SAME_SHAPE(the same check used byCharbonnierLossandMutualInformationLoss), so mismatched shapes raise immediately with a clear message, and updates the existing ndim-mismatch assertion to the shared check's message. A changelog fragment is included.Related issue or discussion
Fixes #6157
What did you check?
New regression test
test_shape_mismatch_raisesfails before the fix (DID NOT RAISE) and passes after it; the fulltests/losses/test_ssim.pymodule passes with no regressions.How did AI help?
Prepared with AI assistance (Instinct assisted): it drafted the fix, the regression test, and this description. I reviewed the diff and ran the tests above myself.
Summary by CodeRabbit
MS_SSIMLossvalidation: inputs must be 4D inBxCxHxWformat and have matching shapes. Mismatched shapes are rejected even when Kornia checks are disabled.