added ReduceTrait and FlattenSequence #8531
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WalkthroughAdds a new trait class ReduceTrait and exports it. Introduces FlattenSequence (array) and FlattenSequenced / FlattenSequenceD / FlattenSequenceDict (dictionary) transforms implementing ReduceTrait and exported from monai.transforms. apply_transform in monai/transforms/transform.py now guards to skip per-item list/tuple mapping when the transform is an instance of ReduceTrait. key_iterator KeyError message was collapsed into a single line. Tests for multi-sample and flattening behavior were added. Documentation and public exports updated to include the new transforms. Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Assessment against linked issues
Assessment against linked issues: Out-of-scope changesAdded documentation entries, public exports, and new public transforms (FlattenSequence, FlattenSequenced and aliases). These API additions are not required for the linked-issue fix but accompany the change. Pre-merge checks and finishing touches❌ Failed checks (1 warning)
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I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: e0cda55 Signed-off-by: Lukas Folle <lukas.folle@snke.com>
I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: e0cda55 Signed-off-by: Lukas Folle <lukas.folle@snke.com>
I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: e0cda55 Signed-off-by: Lukas Folle <lukas.folle@snke.com>
I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: e0cda55 Signed-off-by: Lukas Folle <lukas.folle@snke.com>
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🧹 Nitpick comments (5)
monai/transforms/transform.py (5)
127-137: Docstring: fix parameter name and clarify behavior“map_data” should be “map_items”, and mention that list/tuple returns are flattened one level.
- If `data` is a list or tuple and `map_data` is True, each item of `data` will be transformed + If `data` is a list or tuple and `map_items` is True, each item of `data` will be transformed and this method returns a list of outcomes. @@ - - If an integer is provided, it specifies the maximum level of nesting to which the transformation - should be recursively applied. This allows treating multi-sample transforms applied after another - multi-sample transform while controlling how deep the mapping goes. + - If an integer is provided, it specifies the maximum level of nesting to which the transformation + should be recursively applied. When a transform returns a list or tuple, the results are flattened + one level at each mapped depth.
174-199: Exception logging: style-only — OK; avoid mutatingdatafor loggingFormatting is good. Minor nit: don’t reassign
datawhen logging a first element; use a temp to avoid shadowing.- if isinstance(data, (list, tuple)): - data = data[0] + if isinstance(data, (list, tuple)): + _data_for_log = data[0] + else: + _data_for_log = data @@ - if isinstance(data, dict): - for k, v in data.items(): + if isinstance(_data_for_log, dict): + for k, v in _data_for_log.items(): _log_stats(data=v, prefix=k) else: - _log_stats(data=data) + _log_stats(data=_data_for_log)
459-461: TypeError message should report offending element type, notkeysInside the loop, report
type(key).__name__}for clarity.- raise TypeError( - f"keys must be one of (Hashable, Iterable[Hashable]) but is {type(keys).__name__}." - ) + raise TypeError( + f"keys must be one of (Hashable, Iterable[Hashable]); " + f"found element of type {type(key).__name__}." + )
515-518: key_iterator: signature formatting — OKOptional: annotate as Generator[Any, None, None] for completeness.
66-68: Docstring nits in _apply_transform
- Typo: “this class is function” → “this function”.
- Sphinx ref missing closing backtick: :ref:
Lazy Resampling topic<lazy_resampling>.Also applies to: 75-78
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🔇 Additional comments (8)
monai/transforms/transform.py (8)
93-108: _apply_transform: style-only refactor — OKMultiline call/returns improve readability; logic unchanged.
225-263: set_random_state: seeding cast and MAX_SEED mod — OKCasting to int64 and modding by MAX_SEED is appropriate for NumPy 2.0 compatibility; type checks/readability improved.
256-259: Type check message — OKClear, explicit message; no change requested.
277-279: NotImplemented message — OKConsistent and informative.
335-337: NotImplemented message — OKConsistent and informative.
441-448: Hook attachment formatting — OKNo behavioral change; readability improved.
481-483: sync_meta_info call — OKStyle change only; behavior preserved.
511-513: NotImplemented message — OKConsistent across classes.
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🧹 Nitpick comments (2)
monai/transforms/transform.py (2)
156-165: Type annotation tighteningThe function currently declares
-> list[Any] | ReturnTypebut constructslist[ReturnType]. Prefer narrowing tolist[ReturnType] | ReturnTypefor consistency.Proposed signature (outside this hunk):
def apply_transform(...) -> list[ReturnType] | ReturnType: ...
515-518: Return type hint could be more explicitkey_iterator yields either a Hashable or a tuple when extra iterables are present. Consider tightening the annotation for better tooling.
- ) -> Generator: + ) -> Generator[Hashable | tuple[Hashable, ...], None, None]:
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🔇 Additional comments (14)
monai/transforms/transform.py (14)
93-96: Refactor-only: OKCall-site reformatting reads cleaner. No functional change.
98-103: Refactor-only: OKTuple-unpack branch formatting is fine. No logic change.
104-108: Refactor-only: OKSingle-arg branch formatting is fine. No logic change.
174-199: Refactor-only: OKError-logging reformatting is clear; logic unchanged.
225-227: Refactor-only: OKSignature reflow is fine; types unchanged.
245-251: Seed normalization: OKModulo against MAX_SEED post np.int64 cast is reasonable for NumPy 2.0 compat.
256-258: Refactor-only: OKTypeError message formatting is fine.
277-279: Refactor-only: OKNotImplementedError reflow is fine.
335-337: Refactor-only: OKAbstract call error message reflow is fine.
441-443: Refactor-only: OKHook attachment formatting is clearer.
447-448: Refactor-only: OKInverse hook formatting is clearer.
459-461: Refactor-only: OKType check error formatting is fine.
481-483: Refactor-only: OKsync_meta_info call formatting is fine.
511-513: Refactor-only: OKNotImplementedError reflow is fine.
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All remaining failing CI runs are not coming from this change. |
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Hi @lukas-folle-snkeos thanks for the contribution! Please check the logic I flagged and we do need tests to check that this behaviour is correct. You can take the mentioned issue as an example of what to check for but I think we need to test this logic thoroughly and more clearly document what's going on in comments. |
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Taking a look. |
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Thanks for the review @ericspod! I added a test with a real transform and refactored the code as suggested. |
I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: 2d58774 Signed-off-by: Lukas Folle <lukas.folle@snke.com>
I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: 2d58774 Signed-off-by: Lukas Folle <lukas.folle@snke.com>
I, Lukas Folle <lukas.folle@snke.com>, hereby add my Signed-off-by to this commit: 2d58774 Signed-off-by: Lukas Folle <lukas.folle@snke.com> Merge branch 'dev' of github.com:lukas-folle-snkeos/MONAI into dev
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🧹 Nitpick comments (3)
tests/transforms/compose/test_compose.py (3)
286-290: Use existingmtimport for consistency.Avoid method-local imports; leverage
import monai.transforms as mtalready present.- from monai.transforms import CenterSpatialCrop, RandSpatialCropSamples - - center_crop = CenterSpatialCrop([128, 128]) - multi_sample_transform = RandSpatialCropSamples([64, 64], 1) + center_crop = mt.CenterSpatialCrop([128, 128]) + multi_sample_transform = mt.RandSpatialCropSamples([64, 64], 1)
293-305: Prefer unittest assertions over bareassert.Gives richer failure messages and isn’t optimized away with
-O. Also add explicit tensor-type checks.- assert execute_compose(img, [center_crop]).shape == torch.Size([1, 128, 128]) + self.assertEqual(execute_compose(img, [center_crop]).shape, torch.Size([1, 128, 128])) single_multi_sample_trait_result = execute_compose(img, [multi_sample_transform, center_crop]) - assert ( - isinstance(single_multi_sample_trait_result, list) - and len(single_multi_sample_trait_result) == 1 - and single_multi_sample_trait_result[0].shape == torch.Size([1, 64, 64]) - ) + self.assertIsInstance(single_multi_sample_trait_result, list) + self.assertEqual(len(single_multi_sample_trait_result), 1) + self.assertIsInstance(single_multi_sample_trait_result[0], torch.Tensor) + self.assertEqual(single_multi_sample_trait_result[0].shape, torch.Size([1, 64, 64])) double_multi_sample_trait_result = execute_compose(img, [multi_sample_transform, multi_sample_transform, center_crop]) - assert ( - isinstance(double_multi_sample_trait_result, list) - and len(double_multi_sample_trait_result) == 1 - and double_multi_sample_trait_result[0].shape == torch.Size([1, 64, 64]) - ) + self.assertIsInstance(double_multi_sample_trait_result, list) + self.assertEqual(len(double_multi_sample_trait_result), 1) + self.assertIsInstance(double_multi_sample_trait_result[0], torch.Tensor) + self.assertEqual(double_multi_sample_trait_result[0].shape, torch.Size([1, 64, 64]))
291-306: Add 2 more assertions to harden behavior.
- Cardinality when chaining (e.g., 2 samples then 2 samples => 4).
- Preservation when the original input is a list (no over-flatten).
Proposed test to add nearby:
def test_multi_sample_trait_cardinality_and_preserve_nested(self): img = torch.zeros([1, 128, 128]) t2 = mt.RandSpatialCropSamples([32, 32], num_samples=2) # chaining should multiply counts: 2 x 2 = 4, flattened res = execute_compose(img, [t2, t2]) self.assertIsInstance(res, list) self.assertEqual(len(res), 4) for r in res: self.assertEqual(r.shape, torch.Size([1, 32, 32])) # original list input should preserve nested structure res2 = execute_compose([img], [t2]) self.assertIsInstance(res2, list) self.assertEqual(len(res2), 1) self.assertIsInstance(res2[0], list) self.assertEqual(len(res2[0]), 2)
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tests/transforms/compose/test_compose.py (1)
285-306: Good targeted coverage for MultiSampleTrait flattening.This guards against list-of-lists when chaining multi-sample transforms. Nice.
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Hi folks. I've taken an initial look at this. I'm always a little afraid of these kind of changes that mandate a particular behaviour. Are there no situations where someone might multi-sample something and then multi-sample the multi-sampled thing and expect the lists to be nested? Should this be fixed in execute_compose instead? Please give me a couple of hours today to take a look at that. |
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@atbenmurray @ericspod could you please check the status of this PR again? |
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@lukas-folle-snkeos My apologies; taking a look now |
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Okay, so apologies for the long wait. I finally managed to get my head into it properly: I'd like to suggest a slight change. I think that, if a user wants to run multisamples on top of multisamples there will be scenarios in which they don't want to next the output and scenarios in which they do. I'd rather we didn't remove the ability to do so: I propose that we do the following instead:
The footprint has a slightly larger change but then we don't lose the flexibility It would be used as follows: We can make interesting uses of ReduceTrait, allowing it to be a trait that can do things like aggregations, catting, etc, as well as flattening lists. WDYT? |
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@atbenmurray I like that idea; looks a whole lot cleaner to me compared to the initially proposed change! I will bring the changes into this PR. |
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🔇 Additional comments (3)
monai/transforms/__init__.py (3)
509-509: LGTM!ReduceTrait import is correctly placed with other trait imports.
524-524: LGTM!FlattenSequence import is correctly placed and alphabetically sorted.
597-599: LGTM!Dictionary variant imports are correctly placed and follow naming conventions.
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1976-1976: Unused function argument: self
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🔇 Additional comments (2)
monai/transforms/utility/array.py (2)
46-46: LGTM!Import of
ReduceTraitis correct and necessary for the newFlattenSequenceclass.
113-113: LGTM!Export declaration properly adds
FlattenSequenceto the public API.
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@atbenmurray do you want to have a look at the proposed changes? |
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Fixes #8528.
Description
This PR adds the
FlatttenSequencetransform (a flavor of the also newly addedReduceTrait) which can flatten a nested data structure by one level. This way, #8528 can be tackled without the need to change theapply_transformofComposesignificantly.Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.