8185 test refactor 2 #8405
8185 test refactor 2 #8405
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…combinations Signed-off-by: R. Garcia-Dias <[email protected]>
…eter combinations Signed-off-by: R. Garcia-Dias <[email protected]>
…nations Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
…tions Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: 129f778 I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: ebae4e3 Signed-off-by: R. Garcia-Dias <[email protected]>
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Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
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Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
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Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
Signed-off-by: R. Garcia-Dias <[email protected]>
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This reverts commit 1fc0a5b.
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Hi @garciadias it looks much better now compared to the current state of these test. I have made a few comments in places which are mostly code suggestions you can just accept and double check are correct, but in general it's good but there's a few places where fewer lines of code can be used or some things adjusted for clarity. Thanks!
WalkthroughThe test suite was refactored to replace deeply nested for-loops for test case generation with the utility function Changes
Sequence Diagram(s)sequenceDiagram
participant TestFile
participant dict_product
participant ParameterizedTest
TestFile->>dict_product: Provide parameter lists as kwargs
dict_product-->>TestFile: Return list of parameter dicts (Cartesian product)
loop For each param_dict in product
TestFile->>ParameterizedTest: Construct test case from param_dict
end
Note over TestFile,ParameterizedTest: Test logic and assertions remain unchanged
Poem
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Actionable comments posted: 1
♻️ Duplicate comments (7)
tests/networks/blocks/test_patchembedding.py (1)
29-46: Excellent refactoring usingdict_productutility!The refactoring successfully replaces nested loops with a concise list comprehension while preserving all test parameter combinations. The logic for computing input and expected shapes remains intact.
Note: The past review comment suggests a potential further optimization by using the dictionary from
dict_productmore directly to eliminate the need for creating a separateargsdictionary, but the current implementation is correct and functional.tests/networks/blocks/test_crossattention.py (1)
29-50: Successful refactoring maintaining conditional logic.The refactoring correctly preserves the conditional assignment of
rel_pos_embeddingbased on theflash_attnparameter while using thedict_productutility for cleaner test case generation. The parameter mapping fromflash_attntouse_flash_attentionis handled appropriately.This implementation aligns well with the suggested approach in the past review comments.
tests/transforms/test_gibbs_noise.py (1)
28-30: The refactoring is correct and maintains equivalent test coverage.The use of
dict_productsuccessfully replaces the previous nested loop approach while preserving the same parameter combinations. However, there's a past review comment suggestingitertools.productmight be more concise.tests/networks/blocks/test_dynunet_block.py (2)
24-54: The refactoring is functionally correct and maintains equivalent test coverage.The use of
dict_productsuccessfully replaces nested loops. The parameter extraction and computation logic (padding, output sizes) is preserved correctly.
58-89: Test case generation is correct but could be more concise.The refactoring maintains the same test coverage. The past review comment suggests a more compact approach that could reduce the verbosity of parameter extraction.
tests/networks/nets/test_vit.py (1)
38-39: Address the readability concerns with dictionary unpacking expressions.The ** bracketed expressions with conditional logic reduce readability, as previously noted. Consider using a for-loop approach or the
{**params,...}pattern to construct the dictionary more clearly.tests/transforms/utility/test_splitdimd.py (1)
26-31: Consider the simpler approach suggested in previous review.The current list comprehension can be simplified to just
dict_product(...)as suggested in the previous review, which would be more concise and direct.
🧹 Nitpick comments (1)
tests/networks/blocks/test_CABlock.py (1)
27-40: Good refactoring of test case generation with room for further improvement.The replacement of nested for-loops with
dict_productimproves code readability and maintainability. The implementation correctly preserves all original test parameter combinations and maintains the same test case structure.As suggested in the past review comment, you could make this even more concise using Python's
**syntax:TEST_CASES_CAB = [ [ - { - "spatial_dims": params["spatial_dims"], - "dim": params["dim"], - "num_heads": params["num_heads"], - "bias": params["bias"], - "flash_attention": False, - }, + {**params, "flash_attention": False}, (2, params["dim"], *([16] * params["spatial_dims"])), (2, params["dim"], *([16] * params["spatial_dims"])), ] for params in dict_product(spatial_dims=[2, 3], dim=[32, 64, 128], num_heads=[2, 4, 8], bias=[True, False]) ]
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tests/apps/detection/networks/test_retinanet.py(2 hunks)tests/data/meta_tensor/test_meta_tensor.py(1 hunks)tests/networks/blocks/test_CABlock.py(1 hunks)tests/networks/blocks/test_crossattention.py(1 hunks)tests/networks/blocks/test_dynunet_block.py(1 hunks)tests/networks/blocks/test_patchembedding.py(1 hunks)tests/networks/blocks/test_segresnet_block.py(1 hunks)tests/networks/blocks/test_transformerblock.py(1 hunks)tests/networks/blocks/test_unetr_block.py(1 hunks)tests/networks/nets/test_dynunet.py(2 hunks)tests/networks/nets/test_mednext.py(1 hunks)tests/networks/nets/test_segresnet.py(1 hunks)tests/networks/nets/test_segresnet_ds.py(1 hunks)tests/networks/nets/test_swin_unetr.py(2 hunks)tests/networks/nets/test_transchex.py(1 hunks)tests/networks/nets/test_unetr.py(1 hunks)tests/networks/nets/test_vit.py(1 hunks)tests/networks/nets/test_vitautoenc.py(1 hunks)tests/test_masked_autoencoder_vit.py(1 hunks)tests/test_utils.py(2 hunks)tests/transforms/spatial/test_spatial_resampled.py(3 hunks)tests/transforms/test_gibbs_noise.py(1 hunks)tests/transforms/test_spacing.py(1 hunks)tests/transforms/test_spatial_resample.py(2 hunks)tests/transforms/utility/test_splitdimd.py(1 hunks)tests/utils/test_pad_mode.py(1 hunks)
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tests/networks/blocks/test_CABlock.py (1)
tests/test_utils.py (3)
SkipIfBeforePyTorchVersion(266-277)assert_allclose(100-140)dict_product(868-885)
tests/networks/nets/test_transchex.py (1)
tests/test_utils.py (3)
dict_product(868-885)skip_if_downloading_fails(144-169)skip_if_quick(204-211)
tests/networks/blocks/test_transformerblock.py (1)
tests/test_utils.py (1)
dict_product(868-885)
tests/transforms/test_gibbs_noise.py (1)
tests/test_utils.py (1)
dict_product(868-885)
tests/networks/nets/test_vitautoenc.py (1)
tests/test_utils.py (3)
dict_product(868-885)skip_if_quick(204-211)skip_if_windows(252-256)
tests/networks/blocks/test_dynunet_block.py (2)
tests/test_utils.py (2)
dict_product(868-885)test_script_save(739-758)monai/networks/blocks/dynunet_block.py (1)
get_padding(304-312)
tests/networks/blocks/test_segresnet_block.py (1)
tests/test_utils.py (1)
dict_product(868-885)
tests/networks/blocks/test_patchembedding.py (1)
tests/test_utils.py (2)
SkipIfBeforePyTorchVersion(266-277)dict_product(868-885)
tests/networks/nets/test_vit.py (1)
tests/test_utils.py (4)
SkipIfBeforePyTorchVersion(266-277)dict_product(868-885)skip_if_quick(204-211)test_script_save(739-758)
tests/networks/nets/test_segresnet_ds.py (1)
tests/test_utils.py (3)
SkipIfBeforePyTorchVersion(266-277)dict_product(868-885)test_script_save(739-758)
tests/networks/nets/test_mednext.py (1)
tests/test_utils.py (1)
dict_product(868-885)
tests/transforms/test_spatial_resample.py (1)
tests/test_utils.py (2)
assert_allclose(100-140)dict_product(868-885)
tests/apps/detection/networks/test_retinanet.py (2)
tests/test_utils.py (5)
SkipIfBeforePyTorchVersion(266-277)dict_product(868-885)skip_if_quick(204-211)test_onnx_save(761-782)test_script_save(739-758)monai/networks/nets/resnet.py (7)
resnet10(536-545)resnet18(548-557)resnet34(560-569)resnet50(572-581)resnet101(584-593)resnet152(596-605)resnet200(608-617)
tests/networks/blocks/test_crossattention.py (1)
tests/test_utils.py (3)
SkipIfBeforePyTorchVersion(266-277)assert_allclose(100-140)dict_product(868-885)
tests/networks/nets/test_unetr.py (1)
tests/test_utils.py (4)
SkipIfBeforePyTorchVersion(266-277)dict_product(868-885)skip_if_quick(204-211)test_script_save(739-758)
tests/networks/nets/test_swin_unetr.py (1)
tests/test_utils.py (1)
dict_product(868-885)
tests/networks/nets/test_segresnet.py (2)
tests/test_utils.py (1)
dict_product(868-885)monai/utils/enums.py (1)
UpsampleMode(174-182)
tests/networks/nets/test_dynunet.py (1)
tests/test_utils.py (5)
assert_allclose(100-140)dict_product(868-885)skip_if_no_cuda(245-249)skip_if_windows(252-256)test_script_save(739-758)
tests/transforms/spatial/test_spatial_resampled.py (1)
tests/test_utils.py (2)
assert_allclose(100-140)dict_product(868-885)
tests/transforms/test_spacing.py (1)
tests/test_utils.py (3)
assert_allclose(100-140)dict_product(868-885)skip_if_quick(204-211)
tests/data/meta_tensor/test_meta_tensor.py (1)
tests/test_utils.py (4)
SkipIfBeforePyTorchVersion(266-277)assert_allclose(100-140)dict_product(868-885)skip_if_no_cuda(245-249)
tests/networks/blocks/test_unetr_block.py (2)
tests/test_utils.py (2)
dict_product(868-885)test_script_save(739-758)monai/networks/blocks/dynunet_block.py (1)
get_padding(304-312)
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🔇 Additional comments (41)
tests/test_utils.py (2)
868-885: Excellent refactoring of the dict_product utility function!The new implementation is significantly cleaner and more maintainable than the previous version. The function correctly generates the Cartesian product of input iterables and returns a list of dictionaries, which is exactly what's needed for the test case refactoring across the codebase.
The implementation is correct:
- Uses
itertools.productfor efficient Cartesian product generation- Clear type hints with
Iterable[Any]inputs andlist[dict]output- Comprehensive docstring with example usage
- Simple list comprehension makes the logic easy to understand
31-31: Import changes align perfectly with the dict_product refactoring.The addition of
Iterablefromcollections.abcandAnyfromtypingsupports the new function signature, while removing the unusedLiteralimport keeps the imports clean.Also applies to: 37-37
tests/networks/blocks/test_transformerblock.py (2)
24-24: Correct import addition for the refactoring.The
dict_productimport is properly added to support the test case generation refactoring.
27-46: Excellent refactoring of test case generation!The replacement of nested for-loops with
dict_productsignificantly improves code readability and maintainability. The implementation correctly:
- Preserves all original test parameter combinations
- Maintains the same test case structure with parameter dictionaries and shape tuples
- Uses
dict_productappropriately to generate the Cartesian product- Aligns with the past review comment suggesting this exact pattern
The refactored code is much more concise and declarative than the previous nested loop approach.
tests/networks/blocks/test_CABlock.py (1)
23-23: Correct import addition for the refactoring.The
dict_productimport is properly added to support the test case generation refactoring.tests/data/meta_tensor/test_meta_tensor.py (2)
35-35: Correct import addition for the refactoring.The
dict_productimport is properly added along with other test utilities to support the test case generation refactoring.
39-41: Perfect refactoring of test parameter generation!The replacement of nested loops with
dict_productsignificantly improves code readability. The implementation correctly:
- Uses
dict_productto generate the Cartesian product of device and dtype combinations- Unpacks the dictionary values with
*p["device"], *p["dtype"]to maintain the expected tuple format- Preserves all original test parameter combinations
- Aligns exactly with the past review comment suggestion
The refactored code is much more concise and declarative than the previous nested loop approach.
tests/networks/blocks/test_segresnet_block.py (2)
21-21: Correct import addition for the refactoring.The
dict_productimport is properly added to support the test case generation refactoring.
23-40: Excellent refactoring of test case generation!The replacement of nested for-loops with
dict_productsignificantly improves code readability and maintainability. The implementation correctly:
- Preserves all original test parameter combinations including complex norm configurations
- Uses
dict_productappropriately to generate the Cartesian product- Maintains the same test case structure with parameter dictionaries and shape tuples
- Aligns perfectly with the past review comment suggesting this exact pattern
The refactored code is much more concise and declarative, making it easier to understand the test parameter space at a glance.
tests/networks/blocks/test_patchembedding.py (1)
48-58: Clean refactoring with consistent pattern.The
TEST_CASE_PATCHEMBEDgeneration follows the same improved pattern as the previous test case, maintaining all parameter combinations while using the more readabledict_productapproach.tests/apps/detection/networks/test_retinanet.py (1)
89-94: Effective refactoring usingdict_productfor test case combinations.The refactoring successfully replaces nested loops with a cleaner approach using
dict_product. The logic correctly generates all combinations of models and test cases while preserving the original test case structure through proper unpacking syntax.tests/test_masked_autoencoder_vit.py (1)
25-66: Well-structured refactoring handling complex parameter combinations.The refactoring successfully uses
dict_productto generate base parameter combinations while preserving the more complex logic for handling different spatial dimensions. The renaming of parameters toimg_size_scalarandpatch_size_scalarimproves clarity, and the subsequent processing correctly handles the tuple expansion and conditional parameter addition.tests/networks/nets/test_transchex.py (1)
23-47: Clean and straightforward refactoring withdict_product.The refactoring successfully replaces nested loops with a concise list comprehension using
dict_product. All parameter combinations are preserved, and the tuple expansion logic forimg_sizeandpatch_sizeis correctly maintained.tests/networks/nets/test_segresnet_ds.py (2)
25-44: Excellent refactoring that improves code readability and maintainability.The conversion from nested loops to
dict_productwith list comprehension is well-executed. The parameter combinations are clearly defined and the test case structure is preserved correctly.
46-58: Clean and concise test case generation.The second test case list follows the same pattern effectively, maintaining all parameter combinations while being more readable than nested loops.
tests/utils/test_pad_mode.py (1)
28-41: Excellent refactoring that improves code clarity and reduces nesting.The conversion from nested loops to
dict_productwith a single loop is well-executed. All parameter combinations are preserved, and the test logic remains unchanged while being more readable.tests/networks/nets/test_vitautoenc.py (1)
22-42: Clean and maintainable refactoring that preserves test coverage.The use of
dict_productwith list comprehension is well-implemented. The parameter combinations are clearly defined, and the test case structure correctly handles the broadcasting of image and patch sizes to match spatial dimensions.tests/networks/nets/test_vit.py (1)
23-63: Refactoring successfully simplifies test case generation.The use of
dict_producteffectively replaces the nested for-loops with a more concise and maintainable approach. The parameter combinations and test coverage remain unchanged.tests/transforms/test_spatial_resample.py (1)
71-87: Clean refactoring improves code maintainability.The replacement of nested for-loops with
dict_productcreates a more concise and readable test case generation. The parameter combinations and test coverage are preserved.tests/networks/nets/test_mednext.py (1)
25-67: Comprehensive refactoring enhances code consistency.All three test case lists have been successfully refactored using
dict_product, creating a consistent and maintainable approach to test case generation. The parameter combinations and test coverage remain unchanged.tests/networks/nets/test_swin_unetr.py (1)
41-69: Elegant refactoring with clever parameter handling.The use of
enumeratewith modulo operation to handle cyclic assignment of thedownsampleparameter is well-implemented. The refactoring maintains the original parameter combinations while significantly improving code readability.tests/networks/nets/test_dynunet.py (4)
23-23: Import addition looks good.The addition of
dict_productto the imports is necessary for the refactoring and correctly sourced fromtests.test_utils.
35-67: Excellent refactoring of 2D test case generation.The replacement of nested loops with
dict_productsignificantly improves code readability and maintainability while preserving all original test parameters and logic.
69-93: Good refactoring of 3D test case generation.The use of
dict_productfor 3D test cases is consistent with the 2D approach and maintains all original test parameters while improving code clarity.
95-124: Well-executed refactoring of deep supervision test cases.The refactoring successfully handles the more complex parameter combinations for deep supervision tests, maintaining all original logic while improving code organization and readability.
tests/transforms/spatial/test_spatial_resampled.py (3)
25-25: Import addition is correct.The addition of
dict_productto the imports is necessary for the refactoring and properly sourced fromtests.test_utils.
44-68: Excellent implementation following past review guidance.The refactoring successfully implements the approach suggested in the past review comment, using
dict_productto generate parameter combinations in a more concise and maintainable way.
77-100: Good consistency in 2D test case refactoring.The 2D test case generation follows the same pattern as the 3D cases, maintaining consistency while correctly handling the hardcoded padding mode for 2D scenarios.
tests/networks/nets/test_segresnet.py (4)
22-22: Import addition is appropriate.The addition of
dict_productto the imports is necessary for the refactoring and correctly sourced fromtests.test_utils.
26-46: Excellent refactoring that reduces code complexity.The refactoring successfully uses
dict_productto generate all parameter combinations in a much more concise way, addressing the past review comment about reducing the number of lines while maintaining full test coverage.
48-62: Consistent refactoring pattern applied.The second SegResNet test case follows the same refactoring pattern as the first, maintaining consistency and readability while preserving all original test parameters.
64-85: Well-executed VAE test case refactoring.The SegResNetVAE test cases are properly refactored using
dict_product, maintaining all original parameters including the complex VAE-specific configurations while improving code organization.tests/networks/nets/test_unetr.py (2)
21-21: Import addition is correct.The addition of
dict_productto the imports is necessary for the refactoring and properly sourced fromtests.test_utils.
23-56: Excellent refactoring with clever conditional parameter handling.The refactoring successfully uses
dict_productto generate parameter combinations while employing a clever conditional dictionary unpacking pattern**({"spatial_dims": 2} if params["nd"] == 2 else {})to handle the 2D spatial dimensions case. This maintains all original test logic while significantly improving code readability.tests/networks/blocks/test_unetr_block.py (4)
22-22: Import addition is appropriate.The addition of
dict_productto the imports is necessary for the refactoring and correctly sourced fromtests.test_utils.
25-32: Well-implemented helper function for output size calculation.The
_get_out_sizehelper function correctly implements the standard convolution output size formula and properly handles both integer and tuple padding values. This follows the past review comment suggestion to break up the code into reusable pieces.
35-55: Excellent refactoring with improved organization.The refactoring successfully uses
dict_productfor parameter combinations and leverages the helper function for output size calculations, following the past review comment suggestion to improve code organization and readability.
85-123: Complex refactoring handled well.The refactoring successfully manages the complex output size calculation through multiple upsampling layers while using
dict_productfor parameter combinations. The descriptive variable names and preserved logic maintain clarity despite the complexity.tests/transforms/test_spacing.py (3)
27-27: LGTM - Import addition aligns with refactoring objective.The addition of
dict_productimport is consistent with the PR's goal of replacing nested for-loops with this utility function.
29-206: LGTM - Well-structured test data reorganization.The refactoring successfully consolidates test case definitions into a clear, declarative structure:
- The
_template_5_expected_outputvariable extraction improves readability by removing inline conditional logic- The
all_template_partslist provides a clean separation of test case parameters- Each test case is well-documented with clear parameter dictionaries
- The structure maintains all original test logic while improving maintainability
This approach makes it easier to add new test cases and understand existing ones.
207-220: LGTM - Excellent use ofdict_productfor test case generation.The refactoring successfully replaces manual test case construction with clean, declarative list comprehensions:
TESTSgeneration usesdict_productto create Cartesian products of templates and devicesTESTS_TORCHandTEST_INVERSEfollow the same pattern for their respective parameter combinations- The approach eliminates nested loops while maintaining identical test coverage
- Code is more readable and maintainable
This perfectly aligns with the PR objective of standardizing test case generation across the codebase.
Co-authored-by: Eric Kerfoot <[email protected]> Signed-off-by: Rafael Garcia-Dias <[email protected]>
Co-authored-by: Eric Kerfoot <[email protected]> Signed-off-by: Rafael Garcia-Dias <[email protected]>
Co-authored-by: Eric Kerfoot <[email protected]> Signed-off-by: Rafael Garcia-Dias <[email protected]>
for more information, see https://pre-commit.ci
DCO Remediation Commit for R. Garcia-Dias <[email protected]> I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: c594e27 I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: 6580ee4 I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: 14a58c0 I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: 69f5133 I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: 5c6171d I, R. Garcia-Dias <[email protected]>, hereby add my Signed-off-by to this commit: 2762cd5 Signed-off-by: R. Garcia-Dias <[email protected]>
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Hi @ericspod, I think now this PR is ready. Can you take a look, please? |
Fixes #8185
Description
This PR solves items 2 and 3 on #8185 for a few test folders.
I would merge these and proceed with the same type of change in other files if @ericspod approves.
I would like to keep these PRs small, so even if they have the same pattern of changes, merging them bit by bit would make them more manageable.
A few sentences describing the changes proposed in this pull request.
Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.Summary by CodeRabbit
Refactor
Style
Chores