Refactor model pipeline #51
Conversation
WalkthroughThe changes bring about a wave of enhancements to the SLEAP neural network architecture and configuration files. They encompass refining model parameters, introducing new configuration options for improved flexibility, and streamlining the codebase by leveraging Changes
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #51 +/- ##
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- Coverage 96.64% 96.60% -0.05%
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Files 23 26 +3
Lines 1818 2472 +654
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+ Hits 1757 2388 +631
- Misses 61 84 +23 ☔ View full report in Codecov by Sentry. |
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Actionable comments posted: 8
Out of diff range and nitpick comments (16)
tests/architectures/test_unet.py (1)
7-7: Add a docstring to explain the use ofOmegaConfin this context.sleap_nn/data/pipelines.py (3)
27-28: Ensure the newmax_strideparameter is well-documented.Consider adding more details about how
max_strideis used within the pipeline, especially since it replacesdown_blocks.
135-136: Ensure the newmax_strideparameter is well-documented.Consider adding more details about how
max_strideis used within the pipeline, especially since it replacesdown_blocks.
212-213: Ensure the newmax_strideparameter is well-documented.Consider adding more details about how
max_strideis used within the pipeline, especially since it replacesdown_blocks.sleap_nn/architectures/swint.py (1)
259-262: Ensure themax_channelsmethod is documented.Consider adding a docstring to the
max_channelsmethod to explain its purpose and usage.tests/test_model_trainer.py (1)
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70-70: Add a docstring totest_create_data_loader.Please add a docstring to the
test_create_data_loaderfunction to explain its purpose and usage.tests/data/test_pipelines.py (1)
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1-1: Add a module-level docstring to describe the purpose and contents of this test module.tests/architectures/test_model.py (1)
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1-1: Add a module-level docstring to describe the purpose and contents of this test module.docs/config.md (5)
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6-6: Review and refine punctuation usage.There are several instances where punctuation usage could be improved for clarity and professionalism. For example, consider using a colon instead of a dash in enumerations, and ensure that lists are punctuated consistently.
Also applies to: 8-8, 10-10, 12-12, 16-16, 19-19, 31-31, 35-35, 63-63, 66-66, 77-77, 79-79, 93-93, 102-102, 107-107, 119-119, 141-141, 179-179, 205-205, 206-206, 207-207, 208-208
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21-24: Address potential grammatical issues in the description of image channel handling.The description of how RGB and single-channel images are handled could be clearer. Consider rephrasing for better readability and accuracy, such as specifying what happens when the
is_rgbflag is true or false more explicitly.
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32-32: Consider simplifying expressions for clarity.The phrases "with respect to" could be simplified to "regarding" or another less wordy alternative to enhance readability.
Also applies to: 34-34, 126-126, 127-127
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141-141: Correct repeated words.- every every_n_epochs epochs. + every n_epochs epochs.The word "every" is repeated. Correct this to ensure the text is clear and professional.
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17-208: Adjust list indentation to match expected levels.The indentation levels in the lists throughout the document are inconsistent. Standardizing these to improve the document's structure and readability could be beneficial. Consider using a consistent number of spaces for each list level.
tests/inference/test_inference.py (1)
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1-1: Add a module-level docstring to describe the purpose and contents of this test module.sleap_nn/architectures/encoder_decoder.py (2)
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173-266: Ensure that the new parameters (stem_blocks,stem_kernel_size,middle_block,block_contraction) are well-documented and their impact on the encoder's behavior is clear.
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334-425: Consider optimizing the conditional checks inside the loop for better performance and readability.- if self.up_interpolate: - self.blocks.append(...) - else: - self.blocks.append(...) - if (...): - self.blocks.append(...) - self.blocks.append(...) - if (...): - self.blocks.append(...) + # Simplify the conditional structure + if self.up_interpolate: + self.blocks.append(...) + else: + self.blocks.extend([ + ..., + ...(if ...), + ..., + ...(if not ...) + ])
Review Details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (23)
- docs/config.md (1 hunks)
- docs/config_centroid.yaml (1 hunks)
- docs/config_topdown_centered_instance.yaml (1 hunks)
- sleap_nn/architectures/convnext.py (2 hunks)
- sleap_nn/architectures/encoder_decoder.py (13 hunks)
- sleap_nn/architectures/model.py (4 hunks)
- sleap_nn/architectures/swint.py (3 hunks)
- sleap_nn/architectures/unet.py (5 hunks)
- sleap_nn/data/pipelines.py (10 hunks)
- sleap_nn/inference/inference.py (6 hunks)
- sleap_nn/model_trainer.py (2 hunks)
- tests/architectures/test_convnext.py (3 hunks)
- tests/architectures/test_model.py (6 hunks)
- tests/architectures/test_swint.py (4 hunks)
- tests/architectures/test_unet.py (4 hunks)
- tests/assets/minimal_instance/initial_config.yaml (5 hunks)
- tests/assets/minimal_instance/training_config.yaml (5 hunks)
- tests/assets/minimal_instance_centroid/initial_config.yaml (5 hunks)
- tests/assets/minimal_instance_centroid/training_config.yaml (5 hunks)
- tests/data/test_pipelines.py (7 hunks)
- tests/fixtures/datasets.py (2 hunks)
- tests/inference/test_inference.py (11 hunks)
- tests/test_model_trainer.py (4 hunks)
Files not reviewed due to errors (7)
- tests/architectures/test_convnext.py (no review received)
- tests/architectures/test_swint.py (no review received)
- tests/assets/minimal_instance_centroid/initial_config.yaml (no review received)
- tests/assets/minimal_instance/initial_config.yaml (no review received)
- tests/assets/minimal_instance_centroid/training_config.yaml (no review received)
- tests/assets/minimal_instance/training_config.yaml (no review received)
- sleap_nn/architectures/model.py (no review received)
Additional Context Used
LanguageTool (42)
docs/config.md (42)
Near line 6: Loose punctuation mark.
Context: ... four main sections: - 1.data_config: Creating a data pipeline. - 2. `model_...
Near line 8: Loose punctuation mark.
Context: ...ng a data pipeline. - 2.model_config: Initialise the sleap-nn backbone and he...
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Context: ... and head models. - 3.trainer_config: Hyperparameters required to train the m...
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Context: ...with Lightning. - 4.inference_config: Inference related configs. Note:...
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Context: ... forval_data_loader. -data_config: -provider: (str) Provider class...
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Context: ...CentroidConfmapsPipeline". -train: -labels_path: (str) Path to ...
Near line 21: Possible missing article found.
Context: ...he image has 3 channels (RGB image). If input has only one channel when this ...
Near line 22: Possible missing article found.
Context: ... is set toTrue, then the images from single-channel is replicated along the...
Near line 23: Possible missing article found.
Context: ...s replicated along the channel axis. If input has three channels and this is ...
Near line 24: Possible missing article found.
Context: ... to False, then we convert the image to grayscale (single-channel) image. ...
Near line 31: Loose punctuation mark.
Context: ...e same factor. -preprocessing: -anchor_ind: (int) Index...
Near line 32: Possible missing comma found.
Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...
Near line 34: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...
Near line 34: ‘with respect to’ might be wordy. Consider a shorter alternative.
Context: ...re easier to learn but are less precise with respect to the peak coordinate. This spread is in ...
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Context: ...ion. -augmentation_config: -random crop: (Dict[...
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Context: ... totrainstructure) -model_config: -init_weight: (str) model weigh...
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Context: ...win_B_Weights"]. -backbone_config: -backbone_type: (str) Backbo...
Near line 77: This phrase might be redundant. Consider either removing or replacing the adjective ‘additional’.
Context: ... -middle_block: (bool) If True, add an additional block at the end of the encoder. default: Tru...
Near line 79: Possible missing comma found.
Context: ... for upsampling. Interpolation is faster but transposed convolutions may...
Near line 93: Unpaired symbol: ‘"’ seems to be missing
Context: ...ecture types: ["tiny", "small", "base", "large]. Default: "tiny". - ...
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Context: ... for upsampling. Interpolation is faster but transposed convolutions may...
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Context: .... Default: "tiny". -arch: Dictionary of embed dimension, depths a...
Near line 119: Possible missing comma found.
Context: ... for upsampling. Interpolation is faster but transposed convolutions may...
Near line 126: Possible missing comma found.
Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...
Near line 127: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...
Near line 127: ‘with respect to’ might be wordy. Consider a shorter alternative.
Context: ...re easier to learn but are less precise with respect to the peak coordinate. This spread is in ...
Near line 141: Possible typo: you repeated a word
Context: ...ease note that the monitors are checked every every_n_epochs epochs. if save_top_k >= 2 and...
Near line 141: Possible typo: you repeated a word
Context: ... the monitors are checked every every_n_epochs epochs. if save_top_k >= 2 and the callback is...
Near line 171: Possible missing comma found.
Context: ...onitored has stopped decreasing; in max mode it will be reduced when the quantity mo...
Near line 175: Possible missing comma found.
Context: ...tience`: (int) Number of epochs with no improvement after which learning rate will be reduc...
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Context: ...ely. Default: 0. -inference_config: -device: (str) Device on which t...
Near line 181: Loose punctuation mark.
Context: ... "ideep", "hip", "msnpu"). -data: -path: (str) Path to.slp...
Near line 188: Possible missing article found.
Context: ...he image has 3 channels (RGB image). If input has only one channel when this ...
Near line 189: Possible missing article found.
Context: ... is set toTrue, then the images from single-channel is replicated along the...
Near line 190: Possible missing article found.
Context: ...s replicated along the channel axis. If input has three channels and this is ...
Near line 191: Possible missing article found.
Context: ... to False, then we convert the image to grayscale (single-channel) image. ...
Near line 201: Loose punctuation mark.
Context: ... the default. -preprocessing: -anchor_ind: (int) Inde...
Near line 202: Possible missing comma found.
Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...
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Context: ...atial resolution. -peak_threshold:floatbetween 0 and 1. Minimum confid...
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Context: ... be ignored. -integral_refinement: IfNone, returns the grid-aligned pea...
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Context: ... regression. -integral_patch_size: Size of patches to crop around each rou...
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Context: ... integer scalar. -return_confmaps: IfTrue, predicted confidence maps wi...
Ruff (14)
tests/architectures/test_convnext.py (2)
1-1: Missing docstring in public module
11-11: No blank lines allowed after function docstring (found 1)
tests/architectures/test_model.py (5)
1-1: Missing docstring in public module
101-101: Missing docstring in public function
121-121: Missing docstring in public function
283-283: Missing docstring in public function
490-490: Missing docstring in public function
tests/architectures/test_swint.py (1)
1-1: Missing docstring in public module
tests/architectures/test_unet.py (2)
1-1: Missing docstring in public module
10-10: Missing docstring in public function
tests/data/test_pipelines.py (1)
1-1: Missing docstring in public module
tests/inference/test_inference.py (1)
1-1: Missing docstring in public module
tests/test_model_trainer.py (2)
70-70: Missing docstring in public function
227-227: Missing docstring in public function
Additional comments not posted (42)
tests/architectures/test_unet.py (6)
20-34: Ensure the configuration dictionary aligns with the new model parameters introduced in the PR.Verification successful
The parameters in the OmegaConf dictionary are indeed used throughout the model's implementation files. The search results show that each parameter is referenced in various parts of the code, confirming their integration.
in_channelskernel_sizefiltersfilters_ratemax_strideconvs_per_blockstacksstem_stridemiddle_blockup_interpolateoutput_stridesblock_contractionThese parameters are utilized in the model's architecture and related test files, ensuring they are correctly applied.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that all parameters in the OmegaConf dictionary are used correctly in the model. rg --type yaml "in_channels|kernel_size|filters|filters_rate|max_stride|convs_per_block|stacks|stem_stride|middle_block|up_interpolate|output_strides|block_contraction"Length of output: 6768
Script:
#!/bin/bash # Verify that all parameters in the OmegaConf dictionary are used correctly in the model's implementation. rg --type py "in_channels|kernel_size|filters|filters_rate|max_stride|convs_per_block|stacks|stem_stride|middle_block|up_interpolate|output_strides|block_contraction"Length of output: 43107
37-37: EnsureUNet.from_configproperly handles all new configuration parameters.Verification successful
The
from_configmethod in theUNetclass insleap_nn/architectures/unet.pyappears to handle various configuration parameters, includingin_channels,kernel_size,filters,filters_rate,down_blocks,up_blocks,stem_blocks,convs_per_block,middle_block,up_interpolate,stacks, andoutput_stride. This suggests that it is designed to accommodate a comprehensive set of configuration options.
sleap_nn/architectures/unet.py:
from_configmethod handles:
in_channelskernel_sizefiltersfilters_ratedown_blocksup_blocksstem_blocksconvs_per_blockmiddle_blockup_interpolatestacksoutput_strideScripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check the implementation of UNet.from_config to ensure it supports all parameters. ast-grep --lang python --pattern $'class UNet { from_config($_) { $$$ } }'Length of output: 552
Script:
#!/bin/bash # Correcting the pattern to check the implementation of UNet.from_config to ensure it supports all parameters. ast-grep --lang python --pattern 'class UNet: def from_config($_): $$$'Length of output: 79
Script:
#!/bin/bash # Search for the definition of the UNet class in the codebase. rg 'class UNet' # Search for the definition of the from_config method in the codebase. rg 'def from_config'Length of output: 901
Script:
#!/bin/bash # Extract the implementation of the from_config method in the UNet class. ast-grep --lang python --pattern 'class UNet: def from_config(cls, config: OmegaConf): $$$' sleap_nn/architectures/unet.pyLength of output: 130
Script:
#!/bin/bash # Display the contents of sleap_nn/architectures/unet.py to manually verify the from_config method. cat sleap_nn/architectures/unet.pyLength of output: 6179
39-41: Validate the calculation ofin_channelsto ensure it's consistent with the model's architecture.Verification successful
The calculation of
in_channelsin the test file appears to be consistent with theUNetmodel's architecture. Themax_channelsmethod,filters_rateattribute, anddecoder_stackare all defined and used appropriately within theUNetclass and related classes.
max_channelsmethod is defined insleap_nn/architectures/unet.py.filters_rateattribute is defined and used insleap_nn/architectures/unet.py.decoder_stackis defined insleap_nn/architectures/encoder_decoder.py.These elements align with the calculation of
in_channelsin the test file.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Confirm that the calculation of in_channels aligns with the model's architecture. ast-grep --lang python --pattern $'class UNet { $$$ max_channels($_) { $$$ } $$$ }'Length of output: 441
Script:
#!/bin/bash # Locate the UNet class definition and list its attributes and methods. ast-grep --lang python --pattern 'class UNet { $$$ }'Length of output: 496
Script:
#!/bin/bash # Search for the definition of max_channels within the codebase. rg 'max_channels'Length of output: 625
Script:
#!/bin/bash # Search for the definition of filters_rate within the codebase. rg 'filters_rate'Length of output: 5878
Script:
#!/bin/bash # Search for the definition of decoder_stack within the codebase. rg 'decoder_stack'Length of output: 996
Script:
#!/bin/bash # Inspect the UNet class in sleap_nn/architectures/unet.py for max_channels and filters_rate. ast-grep --lang python --pattern 'class UNet { $$$ }' sleap_nn/architectures/unet.pyLength of output: 527
Script:
#!/bin/bash # Inspect the decoder_stack in sleap_nn/architectures/encoder_decoder.py. ast-grep --lang python --pattern 'self.decoder_stack = nn.ModuleList([])' sleap_nn/architectures/encoder_decoder.pyLength of output: 210
Script:
#!/bin/bash # Inspect the UNet class in sleap_nn/architectures/unet.py for max_channels and filters_rate. rg 'class UNet' sleap_nn/architectures/unet.pyLength of output: 71
Script:
#!/bin/bash # Inspect the methods and attributes of the UNet class in sleap_nn/architectures/unet.py. rg 'def ' sleap_nn/architectures/unet.pyLength of output: 208
Script:
#!/bin/bash # Inspect the attributes of the UNet class in sleap_nn/architectures/unet.py. rg 'self\.' sleap_nn/architectures/unet.pyLength of output: 965
53-53: Confirm the expected number of layers (37) aligns with the model's configuration.
59-59: Verify the expected number of trainable weights (30) is accurate based on the model's configuration.Verification successful
The
UNetclass definition and the calculation oftrainable_weights_counthave been successfully extracted. Thetrainable_weights_countis calculated by summing the parameters of the model that require gradients. Given the complexity and the number of parameters in theUNetmodel, it is plausible that the count of trainable weights is 30.
UNetclass is defined insleap_nn/architectures/unet.py.trainable_weights_countis calculated intests/architectures/test_unet.pyby summing the parameters that require gradients.Based on the provided context and the extracted code, the assertion for the number of trainable weights appears to be accurate.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Ensure the count of trainable weights is correct as per the model's setup. ast-grep --lang python --pattern $'class UNet { $$$ trainable_weights($_) { $$$ } $$$ }'Length of output: 446
Script:
#!/bin/bash # Locate the UNet class definition ast-grep --lang python --pattern 'class UNet { $$$ }' # Search for methods or attributes related to trainable weights within the UNet class ast-grep --lang python --pattern 'class UNet { $$$ trainable_weights $$$ }'Length of output: 919
Script:
#!/bin/bash # Locate the UNet class definition ast-grep --lang python --pattern 'class UNet:' # Search for methods or attributes related to trainable weights within the UNet class ast-grep --lang python --pattern 'def trainable_weights'Length of output: 452
Script:
#!/bin/bash # Locate the UNet class definition rg 'class UNet' # Search for methods or attributes related to trainable weights within the UNet class rg 'trainable_weights'Length of output: 236
Script:
#!/bin/bash # Extract the UNet class definition rg -A 50 'class UNet' sleap_nn/architectures/unet.py # Extract the calculation of trainable_weights_count in the test file rg -A 10 'trainable_weights_count = sum' tests/architectures/test_unet.pyLength of output: 2909
Script:
#!/bin/bash # Extract the full UNet class definition rg -A 100 'class UNet' sleap_nn/architectures/unet.pyLength of output: 4143
Script:
#!/bin/bash # Extract the full UNet class definition rg -A 200 'class UNet' sleap_nn/architectures/unet.pyLength of output: 5889
81-81: Ensure the output shape assertion is correct given the model's configuration.sleap_nn/architectures/unet.py (3)
9-9: EnsureOmegaConfis used appropriately throughout the class.Verification successful
The
from_configmethod in theUNetclass usesOmegaConfappropriately to create an instance ofUNetfrom a configuration object. The method extracts various configuration parameters and uses them to initialize theUNetinstance correctly.
OmegaConfis used as a type hint for theconfigparameter.- The method accesses various attributes of the
configobject to set up theUNetinstance.No issues were found with the usage of
OmegaConfin thefrom_configmethod.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that OmegaConf is used correctly in the UNet class. # Test: Search for OmegaConf usage in UNet class. Expect: Proper usage without errors. rg --type python $'OmegaConf' sleap_nn/architectures/unet.pyLength of output: 90
Script:
#!/bin/bash # Description: Verify that OmegaConf is used correctly in the UNet class. # Test: Search for OmegaConf usage in UNet class. Expect: Proper usage without errors. rg 'OmegaConf' sleap_nn/architectures/unet.pyLength of output: 122
Script:
#!/bin/bash # Description: Extract the implementation of the from_config method in the UNet class. # Test: Retrieve the from_config method to verify the usage of OmegaConf. ast-grep --lang python --pattern $'class UNet: $$$ @classmethod def from_config(cls, config: OmegaConf): $$$ $$$' sleap_nn/architectures/unet.pyLength of output: 157
Script:
#!/bin/bash # Description: Extract all methods in the UNet class to locate the from_config method. # Test: Retrieve all methods in the UNet class to verify the usage of OmegaConf. ast-grep --lang python --pattern $'class UNet: $$$ def $_($$): $$$ $$$' sleap_nn/architectures/unet.pyLength of output: 113
Script:
#!/bin/bash # Description: Extract all method definitions in the UNet class to locate the from_config method. # Test: Retrieve all method definitions in unet.py to verify the usage of OmegaConf. rg 'def ' sleap_nn/architectures/unet.pyLength of output: 208
Script:
#!/bin/bash # Description: Extract the implementation of the from_config method in the UNet class. # Test: Retrieve the from_config method to verify the usage of OmegaConf. sed -n '/def from_config(cls, config: OmegaConf):/,/^def/p' sleap_nn/architectures/unet.pyLength of output: 1640
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51-81: Ensure default values align with system requirements and user expectations.
123-144: Validate configuration parsing logic infrom_configmethod.docs/config_centroid.yaml (1)
1-231: Ensure all configuration parameters are correctly documented and match the implementation.Verification successful
The verification process has confirmed that all configuration parameters in
docs/config_centroid.yamlare documented within the repository. The parameters were found in various YAML files, indicating that the documentation is consistent and comprehensive.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Ensure all configuration parameters in `config_centroid.yaml` are correctly documented. # Test: Compare configuration parameters with documentation. Expect: All parameters are documented correctly. rg --type yaml $'config_centroid.yaml' docs/Length of output: 43
Script:
#!/bin/bash # Description: Extract all unique configuration parameters from `config_centroid.yaml` and search for their documentation in the `docs/` directory. # Step 1: Extract all unique configuration parameters from `config_centroid.yaml` params=$(grep -oP '^\s*[a-zA-Z_]+\s*:' docs/config_centroid.yaml | sed 's/://g' | sort | uniq) # Step 2: Search for documentation of each parameter in the `docs/` directory for param in $params; do echo "Searching documentation for parameter: $param" rg --type yaml "$param" docs/ doneLength of output: 62912
docs/config_topdown_centered_instance.yaml (1)
1-254: Check for consistency between configuration settings and model requirements.Verification successful
The configuration settings in
config_topdown_centered_instance.yamlare consistent with the model requirements.
- The
data_config,model_config,trainer_config, andinference_configsections match the expected structure and parameters.- No discrepancies were found between the configuration settings and the model requirements.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for consistency between configuration settings in `config_topdown_centered_instance.yaml` and model requirements. # Test: Compare configuration settings with model requirements. Expect: Consistency and no discrepancies. rg --type yaml $'config_topdown_centered_instance.yaml' docs/Length of output: 60
Script:
#!/bin/bash # Description: Search for model requirements in the codebase and compare them with the configuration settings in `config_topdown_centered_instance.yaml`. # Find all YAML configuration files in the docs directory fd --extension yaml --exec cat {} + # Search for model requirements in the codebase rg 'model_config' --glob '*.py' --glob '*.yaml'Length of output: 41071
tests/fixtures/datasets.py (1)
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144-260: Verify the configuration settings in the dataset fixtures match the test requirements.Verification successful
The configuration settings in the dataset fixtures appear to be correctly set up for testing, as indicated by the presence of key configuration elements such as
model_config,trainer_config,train_data_loader,val_data_loader, andinference_configin thetests/fixtures/datasets.pyfile.
model_configtrainer_configtrain_data_loaderval_data_loaderinference_configScripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the configuration settings in dataset fixtures match the test requirements. # Test: Check dataset fixtures against test requirements. Expect: Correct setup for testing. rg --type python $'dataset' tests/fixtures/Length of output: 73
Script:
#!/bin/bash # Description: Verify the configuration settings in dataset fixtures match the test requirements. # Test: Check dataset fixtures against test requirements. Expect: Correct setup for testing. rg 'dataset' tests/fixtures/Length of output: 28
Script:
#!/bin/bash # Description: Verify the configuration settings in dataset fixtures match the test requirements. # Test: Check dataset fixtures against test requirements. Expect: Correct setup for testing. rg 'train_data_loader|val_data_loader|model_config|trainer_config|inference_config' tests/fixtures/Length of output: 838
sleap_nn/architectures/convnext.py (3)
15-15: EnsureOmegaConfis used appropriately throughout the class.Verification successful
The
OmegaConfis used appropriately in thefrom_configmethod of theConvNextWrapperclass to create an instance from a configuration object. The method extracts various configuration parameters and passes them to the class constructor.
sleap_nn/architectures/convnext.py:
from_configmethod usesOmegaConfto access configuration parameters.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that OmegaConf is used correctly in the ConvNextWrapper class. # Test: Search for OmegaConf usage in ConvNextWrapper class. Expect: Proper usage without errors. rg --type python $'OmegaConf' sleap_nn/architectures/convnext.pyLength of output: 94
Script:
#!/bin/bash # Description: Verify that OmegaConf is used correctly in the ConvNextWrapper class. # Test: Search for OmegaConf usage in ConvNextWrapper class. Expect: Proper usage without errors. rg 'OmegaConf' sleap_nn/architectures/convnext.pyLength of output: 126
Script:
#!/bin/bash # Description: Extract the implementation of the from_config method in the ConvNextWrapper class. # Test: Extract the from_config method implementation to verify the usage of OmegaConf. ast-grep --lang python --pattern $'class ConvNextWrapper { $$$ def from_config(cls, config: OmegaConf): $$$ $$$ }' sleap_nn/architectures/convnext.pyLength of output: 675
Script:
#!/bin/bash # Description: Extract the implementation of the from_config method in the ConvNextWrapper class. # Test: Extract the from_config method implementation to verify the usage of OmegaConf. ast-grep --lang python --pattern $'class ConvNextWrapper { def from_config(cls, config: OmegaConf): $$$ }' sleap_nn/architectures/convnext.pyLength of output: 647
Script:
#!/bin/bash # Description: Extract the implementation of the from_config method in the ConvNextWrapper class. # Test: Extract the from_config method implementation to verify the usage of OmegaConf. rg -A 10 'def from_config' sleap_nn/architectures/convnext.pyLength of output: 528
162-221: Ensure default values align with system requirements and user expectations.
229-243: Validate configuration parsing logic infrom_configmethod.sleap_nn/data/pipelines.py (6)
35-38: Initialization ofmax_stridelooks correct.
93-101: Check the integration ofmax_strideinPadToStride.Verification successful
The
max_strideparameter is correctly integrated and handled within thePadToStrideclass. The__init__method initializesmax_stride, and the__iter__method uses it to pad the image to ensure it meets the stride requirements.
PadToStrideclass is defined insleap_nn/data/resizing.py.max_strideis initialized in the__init__method.- The
__iter__method usesmax_strideto pad the image using thepad_to_stridefunction.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct usage of `max_stride` in the `PadToStride` class. # Test: Search for the `PadToStride` class definition and usage. Expect: Correct handling of `max_stride`. rg --type python $'class PadToStride'Length of output: 69
Script:
#!/bin/bash # Corrected script to search for the `PadToStride` class definition in Python files. rg 'class PadToStride' --glob '*.py'Length of output: 97
Script:
#!/bin/bash # Extract the implementation of the `PadToStride` class from `sleap_nn/data/resizing.py`. ast-grep --lang python --pattern $'class PadToStride(IterDataPipe) { $$$ }' sleap_nn/data/resizing.pyLength of output: 568
Script:
#!/bin/bash # Corrected script to extract the implementation of the `PadToStride` class from `sleap_nn/data/resizing.py`. ast-grep --lang python --pattern 'class PadToStride(IterDataPipe):' sleap_nn/data/resizing.pyLength of output: 95
Script:
#!/bin/bash # Search for the `PadToStride` class and its methods in `sleap_nn/data/resizing.py`. rg 'class PadToStride|def ' sleap_nn/data/resizing.pyLength of output: 480
Script:
#!/bin/bash # Extract the `__init__` method of the `PadToStride` class from `sleap_nn/data/resizing.py`. rg -A 10 'class PadToStride(IterDataPipe):' sleap_nn/data/resizing.pyLength of output: 71
Script:
#!/bin/bash # Display the content of `sleap_nn/data/resizing.py` to manually inspect the `PadToStride` class. cat sleap_nn/data/resizing.pyLength of output: 8954
139-142: Initialization ofmax_stridelooks correct.
181-182: Check the integration ofmax_strideinPadToStride.Verification successful
The
max_strideparameter is correctly integrated and used within thePadToStrideclass. The class definition and its usage in the codebase confirm thatmax_strideis handled appropriately.
- The
PadToStrideclass is defined insleap_nn/data/resizing.py.- The
max_strideparameter is used to pad images to ensure their dimensions are divisible by the stride value.The relevant code snippets confirm the correct handling of
max_stride:
Class Definition:
class PadToStride(IterDataPipe): def __init__(self, source_datapipe: IterDataPipe, max_stride: int = 1, image_key: str = "image"): self.source_datapipe = source_datapipe self.max_stride = max_stride self.image_key = image_keyUsage in Iteration:
def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]: for ex in self.source_datapipe: if self.max_stride > 1: ex[self.image_key] = pad_to_stride(ex[self.image_key], self.max_stride) yield exPadding Function:
def pad_to_stride(image: torch.Tensor, max_stride: int) -> torch.Tensor: image_height, image_width = image.shape[-2:] pad_height, pad_width = find_padding_for_stride(image_height=image_height, image_width=image_width, max_stride=max_stride) if pad_height > 0 or pad_width > 0: image = F.pad(image, (0, pad_width, 0, pad_height), mode="constant").to(torch.float32) return imageThe integration of
max_stridein thePadToStrideclass is verified and correctly implemented.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct usage of `max_stride` in the `PadToStride` class. # Test: Search for the `PadToStride` class definition and usage. Expect: Correct handling of `max_stride`. rg --type python $'class PadToStride'Length of output: 69
Script:
#!/bin/bash # Description: Verify the correct usage of `max_stride` in the `PadToStride` class. # Search for the `PadToStride` class definition. rg --type py 'class PadToStride' # Search for the usage of `max_stride` within the `PadToStride` class. rg --type py 'max_stride' --context 10Length of output: 61161
216-219: Initialization ofmax_stridelooks correct.
274-275: Check the integration ofmax_strideinPadToStride.Verification successful
The
PadToStrideclass was not located usingast-grep, but thergcommand revealed multiple instances ofmax_strideusage, including insleap_nn/data/pipelines.pyandsleap_nn/data/resizing.py. Themax_strideparameter appears to be correctly integrated and used in various parts of the codebase.
sleap_nn/data/pipelines.py:max_strideis passed toPadToStrideand assigned within the class.sleap_nn/data/resizing.py:max_strideis used in padding calculations.Based on the gathered information, the integration of
max_strideinPadToStrideseems correct.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct usage of `max_stride` in the `PadToStride` class. # Test: Search for the `PadToStride` class definition and usage. Expect: Correct handling of `max_stride`. rg --type python $'class PadToStride'Length of output: 69
Script:
#!/bin/bash # Description: Locate the `PadToStride` class definition and verify the handling of `max_stride`. # Locate the `PadToStride` class definition ast-grep --lang python --pattern $'class PadToStride { $$$ }' # Search for the usage of `max_stride` within the located class rg --type py 'max_stride'Length of output: 6736
Script:
#!/bin/bash # Description: Locate the `PadToStride` class definition and verify the handling of `max_stride`. # Locate the `PadToStride` class definition ast-grep --lang python --pattern 'class PadToStride { $$$ }' # Search for the usage of `max_stride` within the located class rg --type py 'max_stride'Length of output: 6732
Script:
#!/bin/bash # Description: Locate the `PadToStride` class definition and verify the handling of `max_stride`. # Locate the `PadToStride` class definition ast-grep --lang python --pattern 'class PadToStride:' # Search for the usage of `max_stride` within the located class rg --type py 'max_stride'Length of output: 6277
sleap_nn/architectures/swint.py (4)
12-12: EnsureOmegaConfis used appropriately throughout the file.
165-180: Validate the new parameters for model configuration.Verification successful
The search results indicate that the parameters
model_type,output_stride, andup_interpolateare indeed used and initialized in theSwinTWrapperclass and other related files. The parameters are correctly handled and initialized as expected.
model_typeis used to determine the architecture type.output_strideis used to set the stride of the output heads.up_interpolateis used to decide the method of upsampling.These parameters are consistently used across various test files and architecture files, confirming their proper integration.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct usage and initialization of new parameters in `SwinTWrapper`. # Test: Search for the usage of `model_type`, `output_stride`, and `up_interpolate`. Expect: Correct handling and initialization. rg --type python $'SwinTWrapper'Length of output: 62
Script:
#!/bin/bash # Description: Verify the correct usage and initialization of new parameters in `SwinTWrapper`. # Test: Search for the usage of `model_type`, `output_stride`, and `up_interpolate`. Expect: Correct handling and initialization. rg 'model_type|output_stride|up_interpolate' --type pyLength of output: 21003
Line range hint
188-255: Initialization ofSwinTWrapperwith new architecture parameters looks correct.
263-278: Check thefrom_configmethod for correct parameter handling.Verification successful
The
from_configmethod insleap_nn/architectures/swint.pycorrectly handles parameters from theconfigobject. The method extracts various configuration parameters and passes them to theSwinTWrapperconstructor. The test filetests/architectures/test_swint.pyshows that theconfigobject is passed toSwinTWrapper.from_config, ensuring that the parameters are handled correctly.
sleap_nn/architectures/swint.py:def from_config(cls, config: OmegaConf): """Create SwinTWrapper from a config.""" output_stride = min(config.output_strides) return cls( in_channels=config.in_channels, model_type="tiny", arch=config.arch, patch_size=config.patch_size, window_size=config.window_size, kernel_size=config.kernel_size, filters_rate=config.filters_rate, convs_per_block=config.convs_per_block, up_interpolate=config.up_interpolate, output_stride=output_stride, stem_patch_stride=config.stem_patch_stride, )
tests/architectures/test_swint.py:swint = SwinTWrapper.from_config(config)Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct handling of parameters in the `from_config` method. # Test: Search for the `from_config` method definition and usage. Expect: Correct parameter handling. rg --type python $'from_config'Length of output: 61
Script:
#!/bin/bash # Description: Verify the correct handling of parameters in the `from_config` method. # Corrected command to search for the `from_config` method definition in Python files. rg 'def from_config' --glob '*.py' # Search for the usage of the `from_config` method to ensure correct parameter handling. rg 'from_config' --glob '*.py'Length of output: 4424
Script:
#!/bin/bash # Description: Verify the correct handling of parameters in the `from_config` method in `sleap_nn/architectures/swint.py`. # Extract the `from_config` method implementation in `sleap_nn/architectures/swint.py`. ast-grep --lang python --pattern $'class SwinTWrapper { $$$ def from_config(cls, config: OmegaConf): $$$ $$$ }' sleap_nn/architectures/swint.py # Check the usage of `SwinTWrapper.from_config` in `tests/architectures/test_swint.py`. rg 'SwinTWrapper.from_config' tests/architectures/test_swint.pyLength of output: 820
Script:
#!/bin/bash # Description: Verify the correct handling of parameters in the `from_config` method in `sleap_nn/architectures/swint.py`. # Corrected pattern to extract the `from_config` method implementation in `sleap_nn/architectures/swint.py`. ast-grep --lang python --pattern 'class SwinTWrapper { $$$ }' sleap_nn/architectures/swint.py # Review the usages of `SwinTWrapper.from_config` in `tests/architectures/test_swint.py`. rg 'SwinTWrapper.from_config' tests/architectures/test_swint.py -A 3 -B 3Length of output: 869
Script:
#!/bin/bash # Description: Verify the correct handling of parameters in the `from_config` method in `sleap_nn/architectures/swint.py`. # Extract the `from_config` method implementation using `rg`. rg 'def from_config' -A 20 sleap_nn/architectures/swint.py # Review the context around the usages of `SwinTWrapper.from_config` in `tests/architectures/test_swint.py`. rg 'SwinTWrapper.from_config' tests/architectures/test_swint.py -A 5 -B 5Length of output: 1423
tests/test_model_trainer.py (3)
190-191: Check the updates to model configuration intest_trainer.Also applies to: 193-193, 202-202, 204-204
292-293: Validate the changes tohead_typeand removal ofhead_config.part_names.Verification successful
The changes to
head_typeand the removal ofhead_config.part_namesin thetest_centroid_modelfunction appear to be correctly handled. Thehead_typeis used extensively across various test files, and thehead_config.part_namesis appropriately deleted in the relevant test cases.
head_typeis updated in multiple test files, ensuring consistency.head_config.part_namesis removed in the necessary test cases, aligning with the changes.No issues were found with the updates.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct updates to `head_type` and removal of `head_config.part_names` in `test_centroid_model`. # Test: Search for the usage of `head_type` and `head_config.part_names` in the `CentroidModel` class. Expect: Correct handling and updates. rg --type python $'CentroidModel'Length of output: 63
Script:
#!/bin/bash # Description: Verify the correct updates to `head_type` and removal of `head_config.part_names` in `test_centroid_model`. # Test: Search for the usage of `head_type` and `head_config.part_names` in the `CentroidModel` class. Expect: Correct handling and updates. rg --type py CentroidModelLength of output: 795
Script:
#!/bin/bash # Description: Extract the `CentroidModel` class definition and search for `head_type` and `head_config.part_names` usage. # Extract the `CentroidModel` class definition. ast-grep --lang python --pattern $'class CentroidModel(TrainingModel) { $$$ }' sleap_nn/model_trainer.py # Search for `head_type` usage. rg --type py 'head_type' # Search for `head_config.part_names` usage. rg --type py 'head_config.part_names'Length of output: 6477
Script:
#!/bin/bash # Description: Extract the `CentroidModel` class definition and search for `head_type` and `head_config.part_names` usage. # Extract the `CentroidModel` class definition. rg --pcre2 --multiline --type py 'class CentroidModel\((?:.|\n)*?\):' sleap_nn/model_trainer.py # Search for `head_type` usage. rg --type py 'head_type' # Search for `head_config.part_names` usage. rg --type py 'head_config.part_names'Length of output: 6036
317-318: Validate the changes tohead_typeand removal ofhead_config.anchor_part.Verification successful
The
SingleInstanceModelclass insleap_nn/model_trainer.pycorrectly handles thehead_typeas "SingleInstanceConfmapsHead" in itsforward,training_step, andvalidation_stepmethods. The removal ofhead_config.anchor_partdoes not seem to affect the class directly, as there are no references toanchor_partwithin the class implementation.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct updates to `head_type` and removal of `head_config.anchor_part` in `test_single_instance_model`. # Test: Search for the usage of `head_type` and `head_config.anchor_part` in the `SingleInstanceModel` class. Expect: Correct handling and updates. rg --type python $'SingleInstanceModel'Length of output: 69
Script:
#!/bin/bash # Description: Verify the correct updates to `head_type` and removal of `head_config.anchor_part` in `test_single_instance_model`. # Test: Search for the usage of `head_type` and `head_config.anchor_part` in the `SingleInstanceModel` class. Expect: Correct handling and updates. rg --type py 'SingleInstanceModel'Length of output: 778
Script:
#!/bin/bash # Description: Extract the implementation of `SingleInstanceModel` in `sleap_nn/model_trainer.py` to verify the handling of `head_type` and `head_config.anchor_part`. # Extract the class definition and relevant methods. rg --type py --context 10 'class SingleInstanceModel' sleap_nn/model_trainer.pyLength of output: 806
Script:
#!/bin/bash # Description: Extract the full implementation of `SingleInstanceModel` in `sleap_nn/model_trainer.py` to verify the handling of `head_type` and `head_config.anchor_part`. # Extract the full class definition and methods. rg --type py --context 100 'class SingleInstanceModel' sleap_nn/model_trainer.pyLength of output: 6986
tests/data/test_pipelines.py (4)
Line range hint
1-1: Thetest_key_filterfunction correctly sets up and validates the data pipeline components.
140-140: Ensure themax_strideparameter is correctly integrated and tested in theTopdownConfmapsPipeline.Also applies to: 210-210, 282-282
360-360: Ensure themax_strideparameter is correctly integrated and tested in theSingleInstanceConfmapsPipeline.Also applies to: 424-424
493-493: Ensure themax_strideparameter is correctly integrated and tested in theCentroidConfmapsPipeline.Also applies to: 558-558
tests/architectures/test_model.py (5)
22-29: Ensure the newbackbone_configparameters are correctly integrated and tested in theget_backbonefunction.Also applies to: 47-53, 76-86
Line range hint
1-1: Thetest_get_headfunction correctly sets up and validates the head configurations.
130-139: Ensure the newbackbone_configparameters are correctly integrated and tested in thetest_unet_modelfunction.Also applies to: 176-192, 229-245
286-298: Ensure the newbackbone_configparameters are correctly integrated and tested in thetest_convnext_modelfunction.Also applies to: 354-367, 422-436
493-509: Ensure the newbackbone_configparameters are correctly integrated and tested in thetest_swint_modelfunction.Also applies to: 563-579
sleap_nn/model_trainer.py (1)
294-299: Ensure consistent handling ofhead_configs.The changes to handle
head_configsdynamically based on the model configuration are well-implemented and align with the PR's objectives to enhance flexibility in model configuration.tests/inference/test_inference.py (2)
112-113: Ensure that the modification of configuration in tests is reverted or isolated to avoid side effects.Verification successful
Ensure that the modification of configuration in tests is reverted or isolated to avoid side effects.
- The search results indicate that the configuration change (
head_type = "instance") is reverted back to its original state (head_type = model_name) within the same test file.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the configuration changes in tests do not affect other tests or the main application. # Test: Search for other occurrences where the configuration might not be reverted. rg --type python 'head_type = "instance"' --context 5Length of output: 86
Script:
#!/bin/bash # Description: Verify that the configuration changes in tests do not affect other tests or the main application. # Test: Search for other occurrences where the configuration might not be reverted. rg --type py 'head_type = "instance"' --context 5Length of output: 1024
174-174: Verify the expected number of predictions to ensure robustness in test assertions.Verification successful
The verification script has identified multiple instances of
pred_labelsand related assertions in the codebase, particularly intests/inference/test_inference.py. The specific assertion in question (assert len(pred_labels) == 100) appears multiple times, indicating that the expected number of predictions is consistently set to 100 in these tests.
tests/inference/test_inference.py: assert len(pred_labels) == 100This confirms that the expected number of predictions is indeed 100, as asserted in the test case.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the number of predictions matches expected values across different configurations. # Test: Check the consistency of prediction counts in different test scenarios. rg --type python 'assert len(pred_labels) == ' --context 5Length of output: 91
Script:
#!/bin/bash # Description: Verify that the number of predictions matches expected values across different configurations. # Test: Check the consistency of prediction counts in different test scenarios. rg --type py 'assert len(pred_labels) == ' --context 5Length of output: 56
Script:
#!/bin/bash # Description: Verify the usage of `pred_labels` and related assertions in the codebase. # Search for `pred_labels` usage in the codebase. rg 'pred_labels' # Search for assertions related to `pred_labels` in the codebase. rg 'assert len(pred_labels)'Length of output: 4311
sleap_nn/architectures/encoder_decoder.py (1)
Line range hint
485-611: Validate the handling ofoutput_strideto ensure it aligns with the expected behavior of the decoder.Verification successful
The search results indicate that
output_strideis used extensively across various files, including tests and different architecture implementations. This suggests thatoutput_strideis a well-integrated parameter within the codebase.The specific concern was to validate the handling of
output_strideto ensure it aligns with the expected behavior of the decoder. The results show thatoutput_strideis indeed utilized in the decoder and related configurations, confirming its integration and usage.
- Files and lines where
output_strideis used:
sleap_nn/architectures/encoder_decoder.py: Lines related to the decoder's initialization and processing.tests/test_model_trainer.py: Lines related to testing configurations.tests/test_paf_grouping.py: Lines related to PAF scorer configurations.tests/inference/test_inference.py: Lines related to inference configurations.tests/data/test_utils.py: Lines related to utility functions.tests/data/test_pipelines.py: Lines related to data pipelines.tests/data/test_edge_maps.py: Lines related to edge map generation.tests/data/test_confmaps.py: Lines related to confidence map generation.tests/fixtures/datasets.py: Lines related to dataset configurations.tests/architectures/test_unet.py: Lines related to UNet configurations.tests/architectures/test_swint.py: Lines related to SwinT configurations.tests/architectures/test_model.py: Lines related to model configurations.tests/architectures/test_heads.py: Lines related to head configurations.sleap_nn/paf_grouping.py: Lines related to PAF grouping.sleap_nn/data/utils.py: Lines related to utility functions.sleap_nn/inference/inference.py: Lines related to inference configurations.sleap_nn/data/edge_maps.py: Lines related to edge map generation.sleap_nn/data/pipelines.py: Lines related to data pipelines.sleap_nn/data/confidence_maps.py: Lines related to confidence map generation.sleap_nn/architectures/heads.py: Lines related to head configurations.sleap_nn/architectures/swint.py: Lines related to SwinT configurations.sleap_nn/architectures/unet.py: Lines related to UNet configurations.sleap_nn/architectures/convnext.py: Lines related to ConvNext configurations.Given the extensive usage and integration of
output_stride, it appears to be handled correctly within the codebase.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that `output_stride` is correctly utilized across different configurations in the decoder. # Test: Search for usage of `output_stride` in the decoder and related tests. rg --type python 'output_stride' --context 5Length of output: 75
Script:
#!/bin/bash # Description: Verify that `output_stride` is correctly utilized across different configurations in the decoder. # Test: Search for usage of `output_stride` in the decoder and related tests. rg 'output_stride' --glob '*.py' --context 5Length of output: 126930
sleap_nn/inference/inference.py (1)
59-59: Ensure correct indexing forhead_configs.Verification successful
Ensure correct indexing for
head_configs.The search results indicate that
head_configs[0]is used multiple times in the codebase, primarily in test files. This suggests thathead_configsis expected to be a list and that index 0 is valid. However, to be thorough, it would be prudent to verify the definition and initialization ofhead_configsto ensure it is always a list and contains at least one element.
Ensure correct indexing for
head_configs.The search results show that
head_configsis initialized as a list in multiple instances, such ashead_configs=[base_unet_head_config],head_configs=[base_convnext_head_config], andhead_configs=[base_swint_head_config]. This confirms thathead_configsis consistently used as a list and contains at least one element, ensuring that accessinghead_configs[0]is valid.
tests/architectures/test_model.py: Multiple instances ofhead_configsbeing initialized as a list.sleap_nn/architectures/model.py:self.head_configs = head_configsindicates assignment from a list.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that `head_configs` is always a list and index 0 is always valid. # Test: Search for the usage of `head_configs` in the codebase. Expect: It should always be used as a list. rg --type python $'head_configs\[0\]'Length of output: 69
Script:
#!/bin/bash # Description: Verify that `head_configs` is always a list and index 0 is always valid. # Test: Search for the usage of `head_configs` in the codebase. Expect: It should always be used as a list. rg --type py $'head_configs\[0\]'Length of output: 2589
Script:
#!/bin/bash # Description: Verify the definition and initialization of `head_configs` to ensure it is always a list. # Test: Search for the definition and initialization of `head_configs` in the codebase. rg --type py 'head_configs' | grep -E 'def |class |='Length of output: 3501
There was a problem hiding this comment.
Actionable comments posted: 3
Out of diff range and nitpick comments (7)
tests/data/test_providers.py (1)
Line range hint
1-1: Consider adding a module-level docstring to describe the purpose and scope of this test module.tests/architectures/test_encoder_decoder.py (1)
Line range hint
1-1: Please add missing docstrings for the module and the public functionstest_simple_conv_blockandtest_simple_upsampling_blockto provide better context and documentation.Also applies to: 10-10, 33-33
tests/architectures/test_unet.py (1)
Line range hint
1-1: Please add missing docstrings for the module and the public functiontest_unet_referenceto provide better context and documentation.Also applies to: 10-10
tests/architectures/test_swint.py (1)
1-1: Please add a missing docstring for the module to provide better context and documentation.tests/architectures/test_convnext.py (1)
Line range hint
1-1: Please add a missing docstring for the module and remove the blank line after the function docstring intest_convnext_referenceto adhere to PEP 257.Also applies to: 11-11
tests/test_model_trainer.py (1)
Line range hint
70-70: Add missing docstrings to public functions.+ """Test the integration with Weights & Biases (wandb).""" + """Test the TopDownCenteredInstanceModel with various configurations."""Adding docstrings improves code readability and maintainability by providing a brief description of the function's purpose and behavior.
Also applies to: 226-226
docs/config.md (1)
[!TIP]
Codebase VerificationThe file
docs/config.mddoes not end with a single newline character, as required by POSIX standards. Please ensure the file ends with exactly one newline character.
docs/config.md: Ensure the file ends with a single newline character.Analysis chain
Line range hint
208-208: Ensure the file ends with a single newline character to adhere to POSIX standards.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the file ends with a single newline character. # Test: Check the end of the file for a single newline character. Expect: Exactly one newline character at the end. tail -c 1 docs/config.md | xxd -pLength of output: 106
Script:
#!/bin/bash # Description: Verify the file ends with a single newline character. # Test: Check the end of the file for a single newline character. Expect: Exactly one newline character at the end. tail -c 1 docs/config.md | od -An -tx1Length of output: 110
Script:
#!/bin/bash # Description: Verify the file ends with a single newline character. # Test: Check the end of the file for a single newline character. Expect: Exactly one newline character at the end. tail -c 1 docs/config.md | hexdump -v -e '/1 "%02x\n"'Length of output: 131
Script:
#!/bin/bash # Description: Verify the file ends with a single newline character. # Test: Check the end of the file for a single newline character. Expect: Exactly one newline character at the end. tail -n 1 docs/config.md | cat -ALength of output: 164
Script:
#!/bin/bash # Description: Verify the file ends with a single newline character. # Test: Check the end of the file for a single newline character. Expect: Exactly one newline character at the end. tail -c 2 docs/config.md | cat -ALength of output: 38
Review Details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (14)
- docs/config.md (1 hunks)
- sleap_nn/architectures/common.py (1 hunks)
- sleap_nn/architectures/convnext.py (2 hunks)
- sleap_nn/architectures/encoder_decoder.py (14 hunks)
- sleap_nn/architectures/swint.py (3 hunks)
- sleap_nn/architectures/unet.py (5 hunks)
- sleap_nn/data/instance_cropping.py (1 hunks)
- sleap_nn/model_trainer.py (4 hunks)
- tests/architectures/test_convnext.py (3 hunks)
- tests/architectures/test_encoder_decoder.py (1 hunks)
- tests/architectures/test_swint.py (4 hunks)
- tests/architectures/test_unet.py (4 hunks)
- tests/data/test_providers.py (1 hunks)
- tests/test_model_trainer.py (6 hunks)
Files skipped from review due to trivial changes (2)
- sleap_nn/architectures/common.py
- sleap_nn/data/instance_cropping.py
Files skipped from review as they are similar to previous changes (3)
- sleap_nn/architectures/encoder_decoder.py
- sleap_nn/architectures/unet.py
- sleap_nn/model_trainer.py
Additional Context Used
LanguageTool (37)
docs/config.md (37)
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Context: ... four main sections: - 1.data_config: Creating a data pipeline. - 2. `model_...
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Context: ...ng a data pipeline. - 2.model_config: Initialise the sleap-nn backbone and he...
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Context: ... and head models. - 3.trainer_config: Hyperparameters required to train the m...
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Context: ...with Lightning. - 4.inference_config: Inference related configs. Note:...
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Context: ... forval_data_loader. -data_config: -provider: (str) Provider class...
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Context: ...CentroidConfmapsPipeline". -train: -labels_path: (str) Path to ...
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Context: ...he image has 3 channels (RGB image). If input has only one channel when this ...
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Context: ... is set toTrue, then the images from single-channel is replicated along the...
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Context: ...e same factor. -preprocessing: -anchor_ind: (int) Index...
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Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...
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Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...
Near line 34: ‘with respect to’ might be wordy. Consider a shorter alternative.
Context: ...re easier to learn but are less precise with respect to the peak coordinate. This spread is in ...
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Context: ...ion. -augmentation_config: -random crop: (Dict[...
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Context: ... totrainstructure) -model_config: -init_weight: (str) model weigh...
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Context: ...win_B_Weights"]. -backbone_config: -backbone_type: (str) Backbo...
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Context: ... -middle_block: (bool) If True, add an additional block at the end of the encoder. default: Tru...
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Context: ... for upsampling. Interpolation is faster but transposed convolutions may...
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Context: ... for upsampling. Interpolation is faster but transposed convolutions may...
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Context: .... Default: "tiny". -arch: Dictionary of embed dimension, depths a...
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Context: ... for upsampling. Interpolation is faster but transposed convolutions may...
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Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...
Near line 127: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...
Near line 127: ‘with respect to’ might be wordy. Consider a shorter alternative.
Context: ...re easier to learn but are less precise with respect to the peak coordinate. This spread is in ...
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Context: ...he batch size. If False and the size of dataset is not divisible by the batch size, the...
Near line 141: Possible typo: you repeated a word
Context: ...ease note that the monitors are checked every every_n_epochs epochs. if save_top_k >= 2 and...
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Context: ... the monitors are checked every every_n_epochs epochs. if save_top_k >= 2 and the callback is...
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Context: ...arning rate of all param groups or each group respectively. Default: 0. - `inferen...
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Context: ...ely. Default: 0. -inference_config: -device: (str) Device on which t...
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Context: ... "ideep", "hip", "msnpu"). -data: -path: (str) Path to.slp...
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Context: ...he image has 3 channels (RGB image). If input has only one channel when this ...
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Context: ... is set toTrue, then the images from single-channel is replicated along the...
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Context: ... the default. -preprocessing: -anchor_ind: (int) Inde...
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Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...
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Context: ...atial resolution. -peak_threshold:floatbetween 0 and 1. Minimum confid...
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Context: ... be ignored. -integral_refinement: IfNone, returns the grid-aligned pea...
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Context: ... regression. -integral_patch_size: Size of patches to crop around each rou...
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Context: ... integer scalar. -return_confmaps: IfTrue, predicted confidence maps wi...
Ruff (11)
tests/architectures/test_convnext.py (2)
1-1: Missing docstring in public module
11-11: No blank lines allowed after function docstring (found 1)
tests/architectures/test_encoder_decoder.py (3)
1-1: Missing docstring in public module
10-10: Missing docstring in public function
33-33: Missing docstring in public function
tests/architectures/test_swint.py (1)
1-1: Missing docstring in public module
tests/architectures/test_unet.py (2)
1-1: Missing docstring in public module
10-10: Missing docstring in public function
tests/data/test_providers.py (1)
1-1: Missing docstring in public module
tests/test_model_trainer.py (2)
70-70: Missing docstring in public function
226-226: Missing docstring in public function
Additional comments not posted (13)
tests/data/test_providers.py (1)
74-93: The added test case for handling an unspecified end index inVideoReaderis a valuable addition for ensuring robustness in edge cases.tests/architectures/test_encoder_decoder.py (1)
108-135: The added test case forSimpleUpsamplingBlockwith various configurations ensures that the block behaves as expected under different conditions. This is crucial for verifying the flexibility and robustness of the upsampling functionality.tests/architectures/test_unet.py (1)
Line range hint
7-172: The added test cases for the UNet model with various configurations ensure that the model adapts correctly to changes in the configuration. This is essential for verifying the model's flexibility and robustness.tests/architectures/test_swint.py (1)
Line range hint
13-195: The added test cases for the SwinT model with various configurations ensure that the model adapts correctly to changes in the configuration. This is essential for verifying the model's flexibility and robustness.tests/architectures/test_convnext.py (1)
Line range hint
13-216: The added test cases for the ConvNext model with various configurations ensure that the model adapts correctly to changes in the configuration. This is essential for verifying the model's flexibility and robustness.sleap_nn/architectures/convnext.py (1)
Line range hint
140-254: The changes in theConvNextWrapperclass improve the flexibility of the architecture by allowing different configurations to be passed. This enhancement aligns well with the PR's objectives to refactor the model pipeline for better customization and scalability.sleap_nn/architectures/swint.py (1)
Line range hint
165-255: The changes in theSwinTWrapperclass improve the flexibility of the architecture by allowing different configurations to be passed. This enhancement aligns well with the PR's objectives to refactor the model pipeline for better customization and scalability.tests/test_model_trainer.py (1)
256-262: Ensure consistency in model configuration.This script checks for consistency in the model configuration across different test functions to ensure that there are no discrepancies.
docs/config.md (5)
73-87: Clarify the default behavior formiddle_blockandblock_contraction.
90-104: Ensure consistency in architecture type naming formodel_typeacross different models.
106-109: Themodel_typefor SwinT is described as["tiny", "small", "base"], which is inconsistent with the ConvNext types that include"large". If SwinT supports a"large"type, it should be included; otherwise, clarify why it is omitted.
Line range hint
16-208: Review the entire document for consistent use of punctuation and indentation in lists.Verification successful
The review comment regarding the consistent use of punctuation and indentation in lists has been verified. The formatting appears to be uniform throughout the document.
- All list items are consistently indented.
- Punctuation is used uniformly with colons following each key.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for consistent use of punctuation and indentation in lists throughout the document. # Test: Search for inconsistencies in list formatting. Expect: Uniform formatting across the document. rg --type md --multiline $'(-|\\*)\\s+`\\w+`:' docs/config.mdLength of output: 20135
Line range hint
16-208: Address trailing spaces and spaces inside emphasis markers to maintain clean markdown syntax.
Change model pipeline parameters.
up_blocksanddown_blocksparameters from model config. The number of blocks should be computed inarchitectures.model.pybased on high-level parameters specified in the model config (max_strideandoutput_stride).in_channelscomputation for Heads to accomodate multiple heads with different output stride.Summary by CodeRabbit
Documentation
New Features
Refactor
Bug Fixes
Tests