Use centerline_extraction_3d_cuda in SoftclDiceLoss#8904
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📝 WalkthroughWalkthroughAdds optional hard CUDA 3D skeletonization paths to clDice losses, wires the new flags through the composite loss, updates packaging extras for the CUDA extension, and adds CUDA-gated hard-target tests. Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 4
♻️ Duplicate comments (1)
monai/losses/cldice.py (1)
321-322:⚠️ Potential issue | 🟡 Minor | ⚡ Quick winClarify dependency name in docstring.
Same issue as SoftclDiceLoss: docstring mentions "MONAI C++ extensions" but should specify
binary_thinning_3d_cuda.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@monai/losses/cldice.py` around lines 321 - 322, Update the docstring for the use_hard_target parameter to explicitly name the required extension: replace the vague "MONAI C++ extensions" text with a clear reference to `binary_thinning_3d_cuda` (the MONAI C++ extension providing 3D binary thinning); ensure the description on use_hard_target in clDiceLoss (and mirror the same wording used for SoftclDiceLoss) states that `binary_thinning_3d_cuda` is required for CUDA 3D target thinning and that this option defaults to False.
🧹 Nitpick comments (2)
monai/losses/cldice.py (1)
239-239: ⚡ Quick winDocument the second argument to binary_thinning.
The second argument
0tobinary_thinning_3d.binary_thinningis undocumented. Add a comment explaining its purpose.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@monai/losses/cldice.py` at line 239, Add a short inline comment explaining the purpose of the magic numeric second argument passed to binary_thinning_3d.binary_thinning(skel_true[b, c], 0); specifically state what the value 0 represents (e.g., the connectivity/foreground label/iteration mode used by binary_thinning) and why that choice is appropriate here; locate the call in cldice.py where binary_thinning_3d.binary_thinning is invoked (on skel_true and skel_pred slices) and append a concise comment clarifying the semantic meaning of the second parameter and its expected values.tests/losses/test_cldice_loss.py (1)
88-93: 🏗️ Heavy liftExpand test coverage for hard target mode.
Test only covers trivial case where input equals target. Add test cases with imperfect overlap to verify hard skeletonization produces expected loss values compared to soft mode.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@tests/losses/test_cldice_loss.py` around lines 88 - 93, Add additional assertions to test_hard_target in tests/losses/test_cldice_loss.py: create non-trivial 3D examples where prediction and target have imperfect overlap (e.g., single-voxel shifts or partial overlap using the same ONES_3D shape convention), compute loss using SoftclDiceLoss(use_hard_target=True) and compare against SoftclDiceLoss(use_hard_target=False) (or known numeric expectations) to verify hard skeletonization changes the value; ensure calls use the loss(...) invocation on CUDA tensors (as with ONES_3D["input"].cuda()) and assert the hard-mode loss is different and matches the expected relation to soft-mode (e.g., greater or lower depending on your algorithm) with np.testing.assert_allclose or assert_not_equal as appropriate.Source: Coding guidelines
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@monai/losses/cldice.py`:
- Around line 157-158: The docstring for the use_hard_target parameter in
monai.losses.cldice.py is ambiguous about the required dependency; update the
parameter description (the use_hard_target docstring in the cLDice/CLDice loss
implementation) to explicitly name the dependency package
`binary_thinning_3d_cuda` and add a brief install hint (e.g., "install
binary_thinning_3d_cuda to enable CUDA 3D binary thinning"). Keep the existing
note about requiring a 3D target and MONAI C++ extensions if still relevant, but
ensure the exact package name `binary_thinning_3d_cuda` is present so users can
install the correct dependency.
- Around line 235-242: When use_hard_target is True but the code falls back to
soft_skel (because target.dim() != 5, _has_thinning is False, or target.is_cuda
is False), emit a clear warning instead of silently continuing; locate the
branch that computes skel_true (the if using binary_thinning_3d.binary_thinning
and the else calling soft_skel) and add a warnings.warn (or use the module
logger) indicating that hard-target thinning was requested but unavailable and
that soft_skel is being used as fallback, including the reason (which condition
failed) and referencing use_hard_target, binary_thinning_3d.binary_thinning, and
soft_skel in the message.
In `@tests/losses/test_cldice_loss.py`:
- Around line 88-93: The test test_hard_target currently may pass via the soft
fallback if binary_thinning_3d is unavailable; modify the test to first check
for the binary_thinning_3d dependency and skip the test when it's not
importable, or explicitly verify the hard-target branch is used by asserting
that SoftclDiceLoss(use_hard_target=True) actually calls/relies on
binary_thinning_3d (e.g., by checking importability or observing a
side-effect/flag from the hard-skeletonization path). Reference SoftclDiceLoss
and binary_thinning_3d in the test_hard_target test and either skip when
binary_thinning_3d is missing or add an assertion that confirms the hard-target
implementation was executed.
- Line 89: Add a PEP 257 docstring to the test_hard_target method in
tests/losses/test_cldice_loss.py: open the test_hard_target function and add a
one-line or short multi-line docstring that succinctly describes the behavior
being tested (e.g., what inputs are provided, expected outcome, and any edge
conditions like hard targets), ensuring it follows project docstring style
guidelines.
---
Duplicate comments:
In `@monai/losses/cldice.py`:
- Around line 321-322: Update the docstring for the use_hard_target parameter to
explicitly name the required extension: replace the vague "MONAI C++ extensions"
text with a clear reference to `binary_thinning_3d_cuda` (the MONAI C++
extension providing 3D binary thinning); ensure the description on
use_hard_target in clDiceLoss (and mirror the same wording used for
SoftclDiceLoss) states that `binary_thinning_3d_cuda` is required for CUDA 3D
target thinning and that this option defaults to False.
---
Nitpick comments:
In `@monai/losses/cldice.py`:
- Line 239: Add a short inline comment explaining the purpose of the magic
numeric second argument passed to
binary_thinning_3d.binary_thinning(skel_true[b, c], 0); specifically state what
the value 0 represents (e.g., the connectivity/foreground label/iteration mode
used by binary_thinning) and why that choice is appropriate here; locate the
call in cldice.py where binary_thinning_3d.binary_thinning is invoked (on
skel_true and skel_pred slices) and append a concise comment clarifying the
semantic meaning of the second parameter and its expected values.
In `@tests/losses/test_cldice_loss.py`:
- Around line 88-93: Add additional assertions to test_hard_target in
tests/losses/test_cldice_loss.py: create non-trivial 3D examples where
prediction and target have imperfect overlap (e.g., single-voxel shifts or
partial overlap using the same ONES_3D shape convention), compute loss using
SoftclDiceLoss(use_hard_target=True) and compare against
SoftclDiceLoss(use_hard_target=False) (or known numeric expectations) to verify
hard skeletonization changes the value; ensure calls use the loss(...)
invocation on CUDA tensors (as with ONES_3D["input"].cuda()) and assert the
hard-mode loss is different and matches the expected relation to soft-mode
(e.g., greater or lower depending on your algorithm) with
np.testing.assert_allclose or assert_not_equal as appropriate.
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🧹 Nitpick comments (1)
monai/losses/cldice.py (1)
193-202: ⚡ Quick winDocument
ValueErrorforuse_hard_targetconditions in Raises section.The forward method now raises
ValueErrorwhenuse_hard_target=Truebut conditions aren't met (lines 237-240). Add this to the docstring's Raises section.📝 Suggested docstring update
Raises: AssertionError: When input and target (after one hot transform if set) have different shapes. + ValueError: When ``use_hard_target=True`` but target is not a 5D CUDA tensor + or binary_thinning_3d_cuda is unavailable. """🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@monai/losses/cldice.py` around lines 193 - 202, Update the forward method docstring in cldice.py (function forward) to document the new ValueError raised when use_hard_target=True and the target does not meet required conditions; specifically add a Raises entry that states a ValueError is raised if use_hard_target is True but the target tensor is not binary/hard (or not of expected shape/type) so the hard-target conversion cannot be applied. Reference the forward method and the use_hard_target branch where the ValueError is thrown and ensure the Raises section lists both AssertionError (existing) and this ValueError.Source: Coding guidelines
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Nitpick comments:
In `@monai/losses/cldice.py`:
- Around line 193-202: Update the forward method docstring in cldice.py
(function forward) to document the new ValueError raised when
use_hard_target=True and the target does not meet required conditions;
specifically add a Raises entry that states a ValueError is raised if
use_hard_target is True but the target tensor is not binary/hard (or not of
expected shape/type) so the hard-target conversion cannot be applied. Reference
the forward method and the use_hard_target branch where the ValueError is thrown
and ensure the Raises section lists both AssertionError (existing) and this
ValueError.
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@monai/losses/cldice.py`:
- Around line 378-379: Add unit test coverage for SoftDiceclDiceLoss hard-mode
forwarding: the current tests in test_cldice_loss.py only exercise
SoftclDiceLoss, so create tests that instantiate SoftDiceclDiceLoss and verify
use_hard_target and use_hard_prob are passed through to the underlying dice
logic. Focus on the SoftDiceclDiceLoss wrapper and its dice computation path so
the new tests confirm both flags affect behavior as expected.
- Around line 236-255: The new hard-mode guards in clDiceLoss.forward now raise
ValueError, but the method docstring still only mentions AssertionError. Update
the forward() docstring in cldice.py to document the ValueError cases for
use_hard_prob and use_hard_target, including the 5D CUDA tensor and
centerline_extraction_3d_cuda requirements, so the raised exceptions match the
implementation.
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Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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🧹 Nitpick comments (1)
tests/losses/test_cldice_loss.py (1)
88-118: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winAdd negative-path coverage for the hard-mode contract.
These tests only cover the CUDA happy path.
monai/losses/cldice.pynow also guarantees aValueErrorwhenuse_hard_target/use_hard_probis used without 5D CUDA tensors or without the thinning extension, and that new contract is untested here. A small CPU/4D failure case per flag would lock that behavior down. As per path instructions, "Ensure new or modified definitions will be covered by existing or new unit tests."Also applies to: 162-188
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@tests/losses/test_cldice_loss.py` around lines 88 - 118, Add negative-path unit coverage for the hard-mode contract in SoftclDiceLoss: the current tests only verify the CUDA success cases in test_hard_target and test_hard_prob. Add small failure assertions that exercise the new ValueError behavior when use_hard_target or use_hard_prob is enabled with unsupported inputs, such as CPU or non-5D tensors, and also when the thinning extension is unavailable. Use the existing SoftclDiceLoss test methods as the anchor points so the new cases cover the contract now enforced in monai.losses.cldice.Source: Path instructions
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Nitpick comments:
In `@tests/losses/test_cldice_loss.py`:
- Around line 88-118: Add negative-path unit coverage for the hard-mode contract
in SoftclDiceLoss: the current tests only verify the CUDA success cases in
test_hard_target and test_hard_prob. Add small failure assertions that exercise
the new ValueError behavior when use_hard_target or use_hard_prob is enabled
with unsupported inputs, such as CPU or non-5D tensors, and also when the
thinning extension is unavailable. Use the existing SoftclDiceLoss test methods
as the anchor points so the new cases cover the contract now enforced in
monai.losses.cldice.
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for both prob and target Signed-off-by: yang <sychen52@gmail.com>
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This PR introduces the
use_hard_targetanduse_hard_probparameters toSoftclDiceLossandSoftDiceclDiceLoss, allowing users to optionally compute topological skeletons using exact, CUDA-accelerated 3D algorithms from thecenterline_extraction_3d_cudapackage.Currently, the default clDice loss relies on
soft_skel(morphological max-pooling) for both the prediction and the target. Whilesoft_skelprovides a necessary approximation of a centerline, it often results in a thick, fuzzy skeleton.By dynamically importing the
centerline_extraction_3d_cudapackage, this PR brings three major advantages:tsens) against an exact, 1-pixel-wide, topologically-correct hard centerline for the target ground truth (use_hard_target=True).tprec) by extracting a differentiable centerline from the continuous probability map using an inward max-pooling sweep with a fully supported backward pass (use_hard_prob=True).Changes:
centerline_extraction_3d_cudatosetup.cfgas an optional dependency (in theallandcenterline_extractiongroups).monai/losses/cldice.pyto optionally importcenterline_extraction_3dand use it for the ground truth target whenuse_hard_target=True, and for the probability map whenuse_hard_prob=True.test_hard_targetandtest_hard_probtotests/losses/test_cldice_loss.pyto validate both forward and backward pass functionalities.Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.