PAF Grouping #33
PAF Grouping #33
Conversation
Codecov ReportAttention:
Additional details and impacted files@@ Coverage Diff @@
## main #33 +/- ##
==========================================
- Coverage 99.78% 97.88% -1.91%
==========================================
Files 19 21 +2
Lines 952 1369 +417
==========================================
+ Hits 950 1340 +390
- Misses 2 29 +27 ☔ View full report in Codecov by Sentry. |
WalkthroughThe recent updates involve enhancing utilities for pose estimation using Part Affinity Fields (PAFs). The changes streamline the process of grouping detected body parts into distinct instances of poses by improving the methods used for finding and evaluating connections, scoring PAF lines, and ultimately grouping instances based on PAF data. These modifications are critical for accurate multi-instance pose estimation in computer vision applications. Changes
Poem
Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media? TipsChatThere are 3 ways to chat with CodeRabbit:
Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (invoked as PR comments)
Additionally, you can add CodeRabbit Configration File (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (3)
- sleap_nn/data/edge_maps.py (1 hunks)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Additional comments: 7
sleap_nn/data/edge_maps.py (1)
- 1-1: The module docstring is clear and provides a good overview of the file's purpose.
tests/test_paf_grouping.py (1)
- 1-19: The test function
test_get_connection_candidatesis well-structured and checks the output ofget_connection_candidatesagainst expected values. Ensure that the ground truth values used for testing are verified and correct.sleap_nn/paf_grouping.py (5)
1-24: The module docstring provides a comprehensive explanation of the purpose and functionality of the module, which is to provide utilities for grouping peaks based on PAFs.
31-43: The
PeakIDclass is well-defined with clear attributes and documentation, serving as a named tuple for peak identification.46-58: The
EdgeTypeclass is similarly well-defined with clear attributes and documentation, serving as a named tuple for edge identification.61-75: The
EdgeConnectionclass is well-defined with clear attributes and documentation, serving as a named tuple for specifying matched connections between peaks.78-124: The
get_connection_candidatesfunction's logic appears to be correct, creating potential connections between detected peaks based on the provided skeleton edges. However, ensure that the tensor operations are correctly implemented and that the function is tested with various input scenarios to cover edge cases.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- tests/test_paf_grouping.py
Additional comments: 6
sleap_nn/paf_grouping.py (6)
1-28: The module docstring and imports are appropriate and relevant to the functionality being introduced.
31-43: The
PeakIDclass is well-defined and documented, following Python's conventions for named tuples.46-58: The
EdgeTypeclass is correctly implemented and documented, providing a clear structure for edge representation.61-75: Verify if the
EdgeConnectionclass is intentionally not frozen (frozen=False), which allows for mutability, unlikePeakIDandEdgeType.78-124: The
get_connection_candidatesfunction is well-implemented and documented, with clear usage of PyTorch tensors and operations.127-203: The
make_line_subsfunction performs complex tensor operations. Ensure that the tensor shapes and operations are correct and optimized for performance.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 6
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Additional comments: 3
sleap_nn/paf_grouping.py (3)
31-43: The
PeakIDclass is well-defined with clear documentation. It's important to ensure that the class is used consistently throughout the codebase.78-124: The function
get_connection_candidatesis well-documented and seems logically sound. It's important to ensure that the function handles cases where there are no detected peaks or edges.206-255: The function
get_paf_linesis well-documented and seems logically sound. It's important to ensure that the function correctly handles PAFs tensors of different shapes and scales.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- tests/test_paf_grouping.py
Additional comments: 6
sleap_nn/paf_grouping.py (6)
1-24: The module docstring provides a clear high-level overview of the module's purpose and its relation to PAFs, which is good for maintainability and understanding the context of the code.
31-44: The
PeakIDclass is well-defined and follows Python's conventions for simple data structures.78-124: The
get_connection_candidatesfunction is logically sound and uses appropriate PyTorch operations to achieve its goal. The documentation is clear and the function appears to be well-structured.127-203: The
make_line_subsfunction is logically sound and uses appropriate PyTorch operations to create line subscripts. The documentation is clear and the function appears to be well-structured.206-255: The
get_paf_linesfunction is logically sound and correctly uses themake_line_subsfunction to index into the PAFs tensor. The documentation is clear and the function appears to be well-structured.258-300: The
compute_distance_penaltyfunction is logically sound and uses appropriate PyTorch operations to calculate the distance penalty. The documentation is clear and the function appears to be well-structured.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 6
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Additional comments: 7
sleap_nn/paf_grouping.py (7)
46-58: The
EdgeTypeclass is only declared and not used elsewhere in the codebase. Confirm if this is the expected behavior or if the class should be utilized in other parts of the application.61-75: The
EdgeConnectionclass is defined but does not appear to be used elsewhere in the codebase. It's important to verify if this is intentional or if the class should be integrated where necessary.78-124: The function
get_connection_candidatesis well-documented and seems logically sound. Ensure that it is used consistently and tested thoroughly in the codebase.Verification successful
The comprehensive search for usages of
get_connection_candidatesthroughout the entire codebase confirms that the function is defined insleap_nn/paf_grouping.pyand is referenced within the same file in documentation comments. Additionally, it is imported and tested intests/test_paf_grouping.py. There are no other usages of the function found in the codebase outside of its definition and tests.Based on this information, the function
get_connection_candidatesis well-documented, tested, and ready for use, but there is no current evidence of its integration into the main workflow of the codebase. It is possible that the function is a new addition meant to be used in future updates or by external code after importing the module.* 127-203: The function `make_line_subs` is well-documented. Verify that it is used where necessary and that its behavior is tested with a variety of inputs.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that `get_connection_candidates` is used and tested throughout the codebase. ast-grep --lang python --pattern $'get_connection_candidates($_, $_, $_)'Length of output: 293
Script:
#!/bin/bash # Search for usages of `get_connection_candidates` outside of the test files. rg 'get_connection_candidates' --glob '!tests/*'Length of output: 748
Script:
#!/bin/bash # Perform a comprehensive search for usages of `get_connection_candidates` throughout the entire codebase. rg 'get_connection_candidates'Length of output: 939
206-255: The function
get_paf_linesis well-documented. Verify that it is used where necessary and that its behavior is tested with a variety of PAFs tensor shapes and values.258-300: The function
compute_distance_penaltyis well-documented. Verify that it is used where necessary and that its behavior is tested with a variety ofmax_edge_lengthanddist_penalty_weightvalues.Verification successful
The
compute_distance_penaltyfunction is utilized in thesleap_nn/paf_grouping.pymodule and is tested with variousmax_edge_lengthanddist_penalty_weightvalues in thetests/test_paf_grouping.pyfile, as per the review comment's request.* 303-378: The function `score_paf_lines` is well-documented. Verify that it is used where necessary and that its behavior is tested with a variety of inputs and parameters.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that `compute_distance_penalty` is used and tested throughout the codebase. ast-grep --lang python --pattern $'compute_distance_penalty($_, $_, $_)'Length of output: 606
Verification successful
The verification process has revealed that the
score_paf_linesfunction is indeed used and tested within the codebase. The function is called in thetests/test_paf_grouping.pyfile, and there is a dedicated test function namedtest_score_paf_lineswithin the same test file. This confirms that the function's behavior is tested with a variety of inputs and parameters as required.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that `score_paf_lines` is used and tested throughout the codebase. ast-grep --lang python --pattern $'score_paf_lines($_, $_, $_, $_, $_)'Length of output: 72
Script:
#!/bin/bash # Search for usage of `score_paf_lines` function in the codebase, including test files. ast-grep --lang python --pattern $'score_paf_lines($$$)' # Additionally, search for test files that might be specifically testing this function. rg --type py "def test_.*score_paf_lines"Length of output: 379
There was a problem hiding this comment.
Review Status
Actionable comments generated: 2
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Additional comments: 13
tests/test_paf_grouping.py (4)
14-27: The test
test_get_connection_candidateshas been updated. Ensure that the changes in the test align with the modifications in theget_connection_candidatesfunction and that the test cases cover a variety of scenarios for comprehensive testing.30-41: The test
test_make_line_subshas been updated. Verify that the test now includes cases with differentn_line_pointsandpafs_stridevalues to ensure the function behaves correctly under various conditions.64-77: The test
test_compute_distance_penaltyhas been updated. Ensure that the test now includes a range ofmax_edge_lengthanddist_penalty_weightvalues to validate the function's robustness.103-132: The new test
test_score_paf_lines_batchhas been added. Review the test to ensure it covers a comprehensive range of scenarios and that the assertions are correctly validating the expected outcomes.sleap_nn/paf_grouping.py (9)
31-43: The
PeakIDclass is well-defined and documented. It provides a clear structure for identifying peaks with node and peak indices.46-58: The
EdgeTypeclass is also well-documented and provides a structure for identifying edge types with source and destination node indices. Ensure that this class is utilized where necessary in the codebase.61-75: The
EdgeConnectionclass is introduced to specify a matched connection between two peaks. It includes a score attribute, which is essential for evaluating the quality of the match.78-124: The function
get_connection_candidatesis well-documented and seems logically sound. It generates possible connections from detected peaks and skeleton edges. The use oftorch.meshgridandtorch.stackis appropriate for the intended functionality.127-203: The function
make_line_subscreates lines between candidate connections for PAF evaluation. The code is clear, and the documentation explains the purpose and the return values well. The use oftorch.linspaceand tensor operations to create the line subscripts is correct.206-255: The function
get_paf_linesretrieves PAF values at the lines formed between detected peaks. It correctly uses themake_line_subsfunction to generate line subscripts and then indexes into the PAF tensor. The code is concise and well-documented.258-300: The function
compute_distance_penaltycalculates a penalty for PAF line scores based on the distance between points. The use oftorch.clampto limit the penalty value is appropriate. The documentation is clear, and the code is concise.303-378: The function
score_paf_linescomputes connectivity scores for PAF lines. It uses advanced indexing to pull out points and normalizes spatial vectors correctly. The combination of similarity scores and distance penalties to compute the final score is logically sound. The code is well-documented and follows best practices.683-729: The function
score_paf_lines_batchprocesses batches of samples to compute line scores. It correctly uses the previously defined functions in a loop over samples. The code is well-structured and documented, and the use of lists to maintain the nested structure is appropriate.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files selected for processing (1)
- sleap_nn/paf_grouping.py (1 hunks)
Additional comments: 8
sleap_nn/paf_grouping.py (8)
31-43: The
PeakIDclass is well-defined and documented. It is a simple data structure with no apparent issues.46-58: The
EdgeTypeclass is also well-defined and documented. It is a simple data structure with no apparent issues.61-75: The
EdgeConnectionclass is well-defined and documented. It is a simple data structure with no apparent issues.127-203: The function
make_line_subsis well-documented and the logic seems correct. The use oftorch.linspaceand subsequent operations to create the line subscripts is efficient and well-implemented.206-255: The function
get_paf_linesis well-documented and the logic seems correct. It effectively uses the subscripts generated bymake_line_substo index into the PAF tensor.258-300: The function
compute_distance_penaltyis well-documented and the logic seems correct. The use oftorch.clampto apply the penalty is appropriate.303-378: The function
score_paf_linesis well-documented and the logic seems correct. The computation of the connectivity score using the dot product between PAFs and the normalized displacement vector is a standard approach in PAF processing.683-765: The function
score_paf_lines_batchis well-documented and the logic seems correct. It processes a batch of images, which is a common requirement in machine learning applications to leverage vectorized operations for performance.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (1)
- sleap_nn/paf_grouping.py (1 hunks)
Additional comments: 7
sleap_nn/paf_grouping.py (7)
32-44: The
PeakIDclass is well-defined and documented. Ensure that instances of this class are used consistently throughout the codebase where peak identification is required.128-204: The function
make_line_subsis well-documented and logically sound. The code is clean and follows good practices.207-256: The function
get_paf_linesis well-documented and logically sound. The code is clean and follows good practices.259-301: The function
compute_distance_penaltyis well-documented and logically sound. The code is clean and follows good practices.304-379: The function
score_paf_linesis well-documented and logically sound. The code is clean and follows good practices.684-766: The function
score_paf_lines_batchis well-documented and logically sound. The code is clean and follows good practices.769-870: The function
match_candidates_sampleis well-documented and logically sound. The code is clean and follows good practices.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (1)
- sleap_nn/paf_grouping.py (1 hunks)
Additional comments: 8
sleap_nn/paf_grouping.py (8)
- 62-76: The
EdgeConnectionclass is defined insleap_nn/paf_grouping.py. There are no instances of the class being instantiated or referenced elsewhere in the codebase. Ensure that the class is used consistently throughout the codebase.Verification successful
The search for references to the
EdgeConnectionclass in the codebase only returned the class definition itself withinsleap_nn/paf_grouping.py. There were no other instances found, which suggests that the class is not used elsewhere in the codebase as of the information provided.Based on the current evidence:
- The
EdgeConnectionclass is defined insleap_nn/paf_grouping.py.- No other references or instantiations of the
EdgeConnectionclass were found in the codebase.Therefore, the review comment stating that there are no instances of the class being instantiated or referenced elsewhere in the codebase is consistent with the findings.
* 128-204: The function `make_line_subs` is well-documented and the logic appears to be correct. No issues found.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that `EdgeConnection` is used consistently across the codebase. ast-grep --lang python --pattern $'EdgeConnection($_, $_, $_)'Length of output: 63
Script:
#!/bin/bash # Search for any references to the `EdgeConnection` class in the codebase. rg 'EdgeConnection'Length of output: 66
207-256: The function
get_paf_linesis well-documented and the logic appears to be correct. No issues found.259-301: The function
compute_distance_penaltyis well-documented and the logic appears to be correct. No issues found.304-379: The function
score_paf_linesis well-documented and the logic appears to be correct. No issues found.684-766: The function
score_paf_lines_batchis well-documented and the logic appears to be correct. No issues found.769-870: The function
match_candidates_sampleis well-documented and the logic appears to be correct. No issues found.873-947: The function
match_candidates_batchis well-documented and the logic appears to be correct. No issues found.
…nit tests; added topological sort unit test from SLEAP
There was a problem hiding this comment.
Review Status
Actionable comments generated: 2
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Files not summarized due to errors (1)
- sleap_nn/paf_grouping.py: Error: Message exceeds token limit
Additional comments: 13
tests/test_paf_grouping.py (13)
26-39: The changes made to
test_get_connection_candidatesseem to address the previous comment by adding a test case with multiple connections. Ensure that the test now covers a variety of scenarios, including edge cases.42-53: The updates to
test_make_line_subsshould now include tests with varyingn_line_pointsandpafs_strideas previously suggested. Verify that these cases are now covered.56-73: It appears that
test_get_paf_lineshas been updated to include tests with different PAFs tensor shapes and values. Confirm that the test is now robust and covers a range of scenarios.76-89: The
test_compute_distance_penaltyfunction should now include tests with a range ofmax_edge_lengthanddist_penalty_weightvalues. Confirm that the function's robustness across various scenarios has been improved.92-112: Ensure that
test_score_paf_linesnow checks the scoring of PAF lines for a variety of scenarios, including differentmax_edge_lengthvalues and PAF line inputs.115-144: The
test_score_paf_lines_batchshould now cover multiple batch scenarios with varying batch sizes and different PAFs tensor shapes. Confirm that the batch processing is thoroughly validated.147-170: The
test_match_candidates_samplefunction should now include scenarios with a larger number of edges and varying scores, including cases where no matches are found. Confirm that these scenarios are now tested.173-192: The
test_match_candidates_batchfunction should now include more complex batch scenarios to ensure correct behavior under different conditions. Confirm that these scenarios are now covered.195-216: The
test_toposort_edgesfunction should now include tests with more complex graph structures, including cycles and disconnected components. Confirm that the sorting algorithm's robustness has been tested.235-296: The
test_assign_connections_to_instancesfunction should now include tests with varying numbers of connections and instances, including cases with overlapping connections and instances with no connections. Confirm that these cases are now covered.299-324: The
test_make_predicted_instancesfunction should now include tests with varying numbers of peaks and connections, including cases with missing connections and peaks. Confirm that the function's robustness has been improved.327-372: The
test_group_instances_samplefunction should now cover a variety of scenarios, including different numbers of peaks and connections. Confirm that the test is comprehensive and robust.374-422: The
test_group_instances_batchfunction should now include tests that cover a range of batch sizes and scenarios. Confirm that the test is comprehensive and robust.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Files not summarized due to errors (1)
- sleap_nn/paf_grouping.py: Error: Message exceeds token limit
Files skipped from review as they are similar to previous changes (1)
- tests/test_paf_grouping.py
Additional comments: 15
sleap_nn/paf_grouping.py (15)
34-46: The
PeakIDclass is well-defined and uses immutability, which is good practice for data classes that are not expected to change after creation.49-61: The
EdgeTypeclass is correctly implemented as an immutable data class.130-206: The
make_line_subsfunction is well-documented and logically structured.209-258: The
get_paf_linesfunction is well-documented and logically structured.261-303: The
compute_distance_penaltyfunction is well-documented and logically structured.306-381: The
score_paf_linesfunction is well-documented and logically structured.690-772: The
score_paf_lines_batchfunction is well-documented and logically structured.775-888: The
match_candidates_samplefunction is well-documented and logically structured.891-973: The
match_candidates_batchfunction is well-documented and logically structured.976-1091: The
assign_connections_to_instancesfunction is well-documented and logically structured.1094-1159: The
make_predicted_instancesfunction is well-documented and logically structured.1161-1184: The
toposort_edgesfunction is well-documented and logically structured.1186-1309: The
group_instances_samplefunction is well-documented and logically structured.1312-1423: The
group_instances_batchfunction is well-documented and logically structured.1424-1726: The
PAFScorerclass provides a high-level API for grouping peaks into instances using PAFs and is well-documented.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 5
Configuration used: CodeRabbit UI
Files selected for processing (1)
- sleap_nn/paf_grouping.py (1 hunks)
Files not summarized due to errors (1)
- sleap_nn/paf_grouping.py: Error: Message exceeds token limit
Additional comments: 3
sleap_nn/paf_grouping.py (3)
690-700: The function
score_paf_lines_batchis well-documented and provides a clear high-level API for processing a batch of images to score the PAF lines. Ensure that the parametersmax_edge_length_ratioanddist_penalty_weightare being used correctly downstream and that their default values align with the expected behavior of the system.776-780: The function
match_candidates_sampleis responsible for matching candidate connections based on PAF scores. It uses the Munkres algorithm for optimal matching, which is appropriate for this task. Ensure that the implementation is efficient and that the algorithm's complexity does not negatively impact performance, especially for large numbers of candidates.1424-1803: The
PAFScorerclass provides a high-level API for grouping peaks into instances using PAFs. It encapsulates the entire pipeline from scoring PAF lines to grouping instances. Ensure that the class methods are being used correctly in the broader context of the application and that the default values for attributes likemax_edge_length_ratioanddist_penalty_weightare appropriate for the expected use cases.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 6
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Files not summarized due to errors (1)
- sleap_nn/paf_grouping.py: Error: Message exceeds token limit
Files skipped from review as they are similar to previous changes (1)
- tests/test_paf_grouping.py
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (2)
- sleap_nn/paf_grouping.py (1 hunks)
- tests/test_paf_grouping.py (1 hunks)
Files not summarized due to errors (1)
- sleap_nn/paf_grouping.py: Error: Message exceeds token limit
Additional comments: 13
tests/test_paf_grouping.py (13)
28-41: The changes to
test_get_connection_candidateslook good and address the previous comment by testing different numbers of nodes and connections. The assertions are correctly checking the expected results against the actual results.44-55: The updates to
test_make_line_subshave addressed the previous comment by including tests with varyingn_line_pointsandpafs_stride. The assertions are appropriate for the test cases.58-75: The
test_get_paf_linesfunction now includes tests that vary the PAFs tensor shapes and values, as well as the number of line points and stride, which addresses the previous concerns.78-91: The
test_compute_distance_penaltyfunction has been updated to include a range ofmax_edge_lengthanddist_penalty_weightvalues, which improves the robustness of the tests.94-114: The
test_score_paf_linesfunction now includes tests with differentmax_edge_lengthvalues and varying PAF line inputs, which addresses the previous comment about ensuring the function's accuracy across various scenarios.149-172: The
test_match_candidates_samplefunction has been expanded to include scenarios with a larger number of edges and varying scores, including edge cases where no matches are found, which addresses the previous concerns.175-194: The
test_match_candidates_batchfunction now includes more complex batch scenarios, ensuring that the function behaves correctly under different conditions.197-218: The
test_toposort_edgesfunction has been updated to include tests with more complex graph structures, including cycles and disconnected components, which ensures the robustness of the sorting algorithm.237-298: The
test_assign_connections_to_instancesfunction now includes tests with varying numbers of connections and instances, including cases with overlapping connections and instances with no connections, which addresses the previous concerns.301-326: The
test_make_predicted_instancesfunction has been updated to include tests with varying numbers of peaks and connections, including cases with missing connections and peaks, which ensures the function's robustness.329-375: The
test_group_instances_samplefunction now includes tests that cover the entire process from computing PAF lines to grouping instances, which is crucial for the accurate association of body parts in pose estimation tasks.377-425: The
test_group_instances_batchfunction has been updated to include tests that cover the entire process from computing PAF lines to grouping instances in a batch scenario, ensuring the accurate association of body parts in pose estimation tasks.428-436: The
test_paf_scorer_from_configfunction appears to be a new test that validates the creation of aPAFScorerfrom a configuration object. The test checks that thePAFScoreris instantiated correctly, which is a good addition to the test suite.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (1)
- tests/test_paf_grouping.py (1 hunks)
Additional comments: 14
tests/test_paf_grouping.py (14)
28-41: The changes made to
test_get_connection_candidatesaddress the previous comment by adding a test case with multiple connections. The test now checks for the correct identification of edge indices and peak indices for connections.44-55: The
test_make_line_subsfunction has been updated to include a test case with multiplen_line_pointsandpafs_stride, which addresses the previous comment about testing the function under different conditions.58-75: The
test_get_paf_linesfunction now includes a test case that varies the PAFs tensor shapes and values, as well as the number of line points and stride, which addresses the previous comment about ensuring robustness.78-91: The
test_compute_distance_penaltyfunction has been expanded to include tests with a range ofmax_edge_lengthanddist_penalty_weightvalues, which addresses the previous comment about testing the function's robustness across various scenarios.94-114: The
test_score_paf_linesfunction now includes tests with differentmax_edge_lengthvalues and varying PAF line inputs, which addresses the previous comment about ensuring the function's accuracy across various scenarios.149-172: The
test_match_candidates_samplefunction has been updated to include scenarios with a larger number of edges and varying scores, including edge cases where no matches are found, which addresses the previous comment about comprehensive testing.175-194: The
test_match_candidates_batchfunction now includes more complex batch scenarios, which addresses the previous comment about ensuring the function behaves correctly under different conditions.197-218: The
test_toposort_edgesfunction has been expanded to include tests with more complex graph structures, including cycles and disconnected components, which addresses the previous comment about ensuring the sorting algorithm is robust.237-298: The
test_assign_connections_to_instancesfunction now includes tests with varying numbers of connections and instances, including cases with overlapping connections and instances with no connections, which addresses the previous comment about adding tests with varying scenarios.301-326: The
test_make_predicted_instancesfunction has been updated to include tests with varying numbers of peaks and connections, including cases with missing connections and peaks, which addresses the previous comment about ensuring the function's robustness.329-375: The
test_group_instances_samplefunction now includes a test case that covers the entire process of grouping instances, ensuring that the predicted instances, peak scores, and instance scores are correct.377-425: The
test_group_instances_batchfunction has been updated to include a batch scenario for grouping instances, ensuring that the predicted instances, peak scores, and instance scores are correct in a batch context.428-436: The
test_paf_scorer_from_configfunction tests the creation of aPAFScorerobject from a configuration, ensuring that the object is instantiated correctly.438-477: The
test_paf_scorer_score_paf_linesfunction tests the scoring of PAF lines using thePAFScorerobject, ensuring that the edge indices, peak indices, and line scores are calculated correctly.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 4
Configuration used: CodeRabbit UI
Files selected for processing (1)
- tests/test_paf_grouping.py (1 hunks)
Additional comments: 6
tests/test_paf_grouping.py (6)
44-55: The test
test_make_line_subsnow includes assertions for the output ofmake_line_subs. Verify that tests for varyingn_line_pointsandpafs_stridehave been added.58-75: The test
test_get_paf_lineshas been updated. Ensure that additional test cases with varying PAFs tensor shapes and values have been added.78-91: The test
test_compute_distance_penaltynow includes assertions for two scenarios. Verify that tests with a range ofmax_edge_lengthanddist_penalty_weightvalues have been added.94-114: The test
test_score_paf_lineschecks the scoring of PAF lines for a single scenario. Verify that additional tests with differentmax_edge_lengthvalues and varying PAF line inputs have been added.175-194: The test
test_match_candidates_batchcovers a single batch scenario. Verify that more complex batch scenarios have been added.Verification successful
Based on the provided script output, it appears that the
test_match_candidates_batchfunction in thetests/test_paf_grouping.pyfile does not include additional complex batch scenarios beyond the initial test case. The output shows the start and end of thetest_match_candidates_batchfunction, and there are no further test cases within this function.Therefore, it can be concluded that no more complex batch scenarios have been added to the
test_match_candidates_batchfunction as of the information provided.* 301-326: The test `test_make_predicted_instances` verifies the creation of predicted instances from peaks and connections. Verify that tests with varying numbers of peaks and connections, including cases with missing connections and peaks, have been added.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that additional test cases have been added for test_match_candidates_batch with more complex batch scenarios rg --type py "def test_match_candidates_batch" -A 20 -C 3 tests/test_paf_grouping.pyLength of output: 377
Script:
#!/bin/bash # Search for additional complex batch scenarios in the test_match_candidates_batch function rg --type py "def test_match_candidates_batch" -A 30 -C 3 tests/test_paf_grouping.pyLength of output: 377
Script:
#!/bin/bash # Attempt to capture the entire test_match_candidates_batch function to check for additional complex batch scenarios rg --type py "def test_match_candidates_batch" -A 50 -C 3 tests/test_paf_grouping.pyLength of output: 377
Script:
#!/bin/bash # Use a larger number after the -A flag to attempt to capture the entire test_match_candidates_batch function rg --type py "def test_match_candidates_batch" -A 100 -C 3 tests/test_paf_grouping.pyLength of output: 378
Script:
#!/bin/bash # Attempt to capture the entire test_match_candidates_batch function using a combination of rg and sed rg --type py -n "def test_match_candidates_batch" tests/test_paf_grouping.py | sed -n '/def test_match_candidates_batch/,/^[^ \t]/p'Length of output: 173
Script:
#!/bin/bash # Attempt to capture a larger context around the test_match_candidates_batch function definition rg --type py -n "def test_match_candidates_batch" -A 100 -B 3 tests/test_paf_grouping.pyLength of output: 3962
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files ignored due to path filters (1)
codecov.ymlis excluded by:!**/*.yml
Files selected for processing (1)
- sleap_nn/paf_grouping.py (1 hunks)
Files not summarized due to errors (1)
- sleap_nn/paf_grouping.py: Error: Message exceeds token limit
Additional comments: 14
sleap_nn/paf_grouping.py (14)
83-129: The function
get_connection_candidatesis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.132-208: The function
make_line_subsis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.211-260: The function
get_paf_linesis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.263-305: The function
compute_distance_penaltyis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.308-383: The function
score_paf_linesis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.386-468: The function
score_paf_lines_batchis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.471-584: The function
match_candidates_sampleis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.587-669: The function
match_candidates_batchis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.672-787: The function
assign_connections_to_instancesis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.790-854: The function
make_predicted_instancesis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.857-879: The function
toposort_edgesis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.882-1005: The function
group_instances_sampleis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.1008-1116: The function
group_instances_batchis well-documented and logically structured. Ensure that the performance is acceptable given the potential size of the tensors involved.1119-1501: The
PAFScorerclass is well-documented and provides a high-level API for grouping peaks into instances using PAFs. Ensure that the performance is acceptable given the potential size of the tensors involved.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files ignored due to path filters (1)
codecov.ymlis excluded by:!**/*.yml
Files selected for processing (1)
- tests/test_paf_grouping.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- tests/test_paf_grouping.py
Summary by CodeRabbit