Add minimal pretrained checkpoints for tests and fix PAF grouping interpolation #73
Conversation
* Refactor preprocessing config * Merge train and val data configs * Remove pipeline name * Modify backbone_config * Modify ckpts * Fix inference tests * Fix device for inference * Fix scale in inference * Fix Predictor * Modify `bottom_up` to `bottomup` * Fix bottomup inference * Fix scale in augmentation
* Refactor preprocessing config * Merge train and val data configs * Remove pipeline name * Modify backbone_config * Modify ckpts * Fix inference tests * Fix device for inference * Fix scale in inference * Fix Predictor * Modify `bottom_up` to `bottomup` * Fix bottomup inference * Fix scale in augmentation * Test remove image * Fix instance cropping * Fix tests * Remove scale in pipelines
WalkthroughThe configuration structure has been extensively reorganized to enhance clarity and usability across various files. Key updates include the renaming of label paths, the introduction of streamlined augmentation settings, and an improved organization of model configurations. Additionally, new parameters for resuming training from checkpoints have been added, alongside enhancements to the augmentation and data processing logic. These changes facilitate easier management of training parameters, provide a better understanding of model architecture, and support effective application of data augmentations. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Config
participant Model
participant Augmentor
User->>Config: Load configuration
Config->>Model: Set up model parameters
Config->>Augmentor: Configure data augmentations
Augmentor-->>Config: Return augmentation settings
Model-->>User: Ready for training
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Actionable comments posted: 12
Outside diff range, codebase verification and nitpick comments (10)
docs/config_topdown_centered_instance.yaml (2)
87-87: Review and update device and accelerator settings.The
trainer_acceleratoris set togpuindocs/config_topdown_centered_instance.yaml, but there's a comment indicating that the device and trainer accelerator settings need to be redone. Please review and update these settings to ensure they align with your hardware and training objectives.
- File:
docs/config_topdown_centered_instance.yaml- Line: Contains a TODO comment regarding device and accelerator settings.
Analysis chain
Verify device and accelerator settings.
The
trainer_acceleratoris set togpu. Ensure that this aligns with your available hardware and training objectives.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the device and accelerator settings. # Test: Check if the trainer accelerator is correctly specified. Expect: The trainer accelerator should be `gpu`. rg --type yaml "trainer_accelerator: gpu"Length of output: 209
3-4: Label paths are missingThe specified paths for
train_labels_pathandval_labels_path(docs/minimal_instance.pkg.slp) do not exist in the repository. Please verify the correct paths or ensure that the necessary files are included in the repository.
- Paths checked:
docs/minimal_instance.pkg.slpfor both train and validation labels.Analysis chain
Verify the correctness of label paths.
Ensure that the paths
train_labels_path: minimal_instance.pkg.slpandval_labels_path: minimal_instance.pkg.slpare correct and accessible.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence of the specified label paths. # Test: Check if the specified label paths exist. Expect: Files should exist. test -f "docs/minimal_instance.pkg.slp" && echo "Train labels path exists." || echo "Train labels path missing." test -f "docs/minimal_instance.pkg.slp" && echo "Validation labels path exists." || echo "Validation labels path missing."Length of output: 215
docs/config_bottomup.yaml (1)
3-4: Label paths are missing or incorrect.The specified paths
train_labels_path: minimal_instance.pkg.slpandval_labels_path: minimal_instance.pkg.slpdo not exist in the repository. Please verify the paths or ensure the files are added to the repository.
- Check if
docs/minimal_instance.pkg.slpis the correct path.- Ensure the necessary files are included in the repository.
Analysis chain
Verify the correctness of label paths.
Ensure that the paths
train_labels_path: minimal_instance.pkg.slpandval_labels_path: minimal_instance.pkg.slpare correct and accessible.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence of the specified label paths. # Test: Check if the specified label paths exist. Expect: Files should exist. test -f "docs/minimal_instance.pkg.slp" && echo "Train labels path exists." || echo "Train labels path missing." test -f "docs/minimal_instance.pkg.slp" && echo "Validation labels path exists." || echo "Validation labels path missing."Length of output: 215
tests/inference/test_predictors.py (1)
Line range hint
49-49: Avoid using broad exception handling.Using
pytest.raises(Exception)is discouraged as it can catch unintended exceptions, making debugging difficult. Consider specifying a more precise exception type.- with pytest.raises(Exception): + with pytest.raises(ValueError):docs/config.md (3)
16-18: Fix unordered list indentation.The indentation of list items is inconsistent with markdownlint guidelines.
- - `train_labels_path`: (str) Path to training data (`.slp` file) - - `val_labels_path`: (str) Path to validation data (`.slp` file) - - `scale`: (float or List[float]) Factor to resize the image dimensions by, specified as either a float scalar or as a 2-tuple of [scale_x, scale_y]. If a scalar is provided, both dimensions are resized by the same factor. + - `train_labels_path`: (str) Path to training data (`.slp` file) + - `val_labels_path`: (str) Path to validation data (`.slp` file) + - `scale`: (float or List[float]) Factor to resize the image dimensions by, specified as either a float scalar or as a 2-tuple of [scale_x, scale_y]. If a scalar is provided, both dimensions are resized by the same factor.Also applies to: 28-29, 30-31
Tools
Markdownlint
16-16: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
17-17: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
18-18: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
63-64: Fix unordered list indentation.The indentation of list items is inconsistent with markdownlint guidelines.
- - `backbone_type`: (str) Backbone architecture for the model to be trained. One of "unet", "convnext" or "swint". - - `backbone_config`: (for UNet) - - `backbone_config`: (for ConvNext) - - `head_configs`: (Dict) Dictionary with the following keys having head configs for the model to be trained. **Note**: Configs should be provided only for the model to train and others should be `None`. + - `backbone_type`: (str) Backbone architecture for the model to be trained. One of "unet", "convnext" or "swint". + - `backbone_config`: (for UNet) + - `backbone_config`: (for ConvNext) + - `head_configs`: (Dict) Dictionary with the following keys having head configs for the model to be trained. **Note**: Configs should be provided only for the model to train and others should be `None`.Also applies to: 80-81, 95-96, 111-112
Tools
Markdownlint
63-63: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
64-64: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
159-159: Fix unordered list indentation.The indentation of list items is inconsistent with markdownlint guidelines.
- - `wandb`: (Only if `use_wandb` is `True`, else skip this) + - `wandb`: (Only if `use_wandb` is `True`, else skip this)Tools
Markdownlint
159-159: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
sleap_nn/training/model_trainer.py (2)
70-70: Initializemodel_typewith a default value.Consider setting a default value for
model_typeduring initialization to prevent potential issues if it remains unset.- self.model_type = None + self.model_type = "unknown"
Line range hint
308-321: Document themodel_typeparameter in the constructor.Ensure that the
model_typeparameter is clearly documented in the constructor's docstring for better understanding.""" Initialise the configs and the model. Args: config: OmegaConf dictionary with model configurations. skeletons: List of sio.Skeleton objects. + model_type: Type of the model. One of `single_instance`, `centered_instance`, `centroid`, `bottomup`. """sleap_nn/inference/predictors.py (1)
Line range hint
86-106: Update docstring forpeak_thresholdparameter.Ensure the docstring reflects the new functionality of
peak_thresholdaccepting a list for different model types.peak_threshold: (float or List[float]) Minimum confidence threshold. Peaks with values below this will be ignored. Default: 0.2. This can also be `List[float]` for topdown centroid and centered-instance model, where the first element corresponds to centroid model peak finding threshold and the second element is for centered-instance model peak finding.
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (24)
- docs/config.md (4 hunks)
- docs/config_bottomup.yaml (4 hunks)
- docs/config_centroid.yaml (5 hunks)
- docs/config_topdown_centered_instance.yaml (4 hunks)
- sleap_nn/architectures/model.py (2 hunks)
- sleap_nn/data/augmentation.py (7 hunks)
- sleap_nn/data/pipelines.py (7 hunks)
- sleap_nn/inference/predictors.py (30 hunks)
- sleap_nn/training/model_trainer.py (17 hunks)
- tests/architectures/test_model.py (15 hunks)
- tests/assets/minimal_instance/initial_config.yaml (3 hunks)
- tests/assets/minimal_instance/training_config.yaml (4 hunks)
- tests/assets/minimal_instance_bottomup/initial_config.yaml (3 hunks)
- tests/assets/minimal_instance_bottomup/training_config.yaml (4 hunks)
- tests/assets/minimal_instance_centroid/initial_config.yaml (3 hunks)
- tests/assets/minimal_instance_centroid/training_config.yaml (4 hunks)
- tests/data/test_augmentation.py (2 hunks)
- tests/data/test_pipelines.py (22 hunks)
- tests/fixtures/datasets.py (3 hunks)
- tests/inference/test_bottomup.py (3 hunks)
- tests/inference/test_predictors.py (12 hunks)
- tests/inference/test_single_instance.py (3 hunks)
- tests/inference/test_topdown.py (4 hunks)
- tests/training/test_model_trainer.py (15 hunks)
Files skipped from review due to trivial changes (1)
- tests/data/test_augmentation.py
Additional context used
yamllint
docs/config_topdown_centered_instance.yaml
[error] 6-6: trailing spaces
(trailing-spaces)
[error] 7-7: trailing spaces
(trailing-spaces)
[warning] 27-27: wrong indentation: expected 4 but found 6
(indentation)
[error] 34-34: trailing spaces
(trailing-spaces)
[error] 71-71: trailing spaces
(trailing-spaces)
docs/config_bottomup.yaml
[error] 65-65: trailing spaces
(trailing-spaces)
[warning] 70-70: wrong indentation: expected 8 but found 10
(indentation)
[warning] 75-75: wrong indentation: expected 8 but found 10
(indentation)
docs/config_centroid.yaml
[error] 6-6: trailing spaces
(trailing-spaces)
[error] 7-7: trailing spaces
(trailing-spaces)
[warning] 47-47: wrong indentation: expected 4 but found 6
(indentation)
[error] 54-54: trailing spaces
(trailing-spaces)
[error] 90-90: trailing spaces
(trailing-spaces)
Ruff
tests/training/test_model_trainer.py
49-49:
pytest.raises(Exception)should be considered evil(B017)
tests/architectures/test_model.py
102-102:
pytest.raises(Exception)should be considered evil(B017)
sleap_nn/training/model_trainer.py
338-339: Use a single
ifstatement instead of nestedifstatements(SIM102)
338-338: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
343-344: Use a single
ifstatement instead of nestedifstatements(SIM102)
343-343: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
Markdownlint
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LanguageTool
docs/config.md
[grammar] ~48-~48: Articles like ‘a’ are rarely followed by punctuation. A word may be missing after ‘a’, or the punctuation mark may not be necessary.
Context: ...lation. For example, if translate_width=a, then horizontal shift is randomly sampl...(THE_PUNCT)
[grammar] ~49-~49: Articles like ‘a’ are rarely followed by punctuation. A word may be missing after ‘a’, or the punctuation mark may not be necessary.
Context: ...ation. For example, if translate_height=a, then vertical shift is randomly sampled...(THE_PUNCT)
[uncategorized] ~60-~60: Loose punctuation mark.
Context: ...otherwise for default. -model_config: -init_weight: (str) model weigh...(UNLIKELY_OPENING_PUNCTUATION)
[style] ~73-~73: 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...(ADD_AN_ADDITIONAL)
[uncategorized] ~75-~75: Possible missing comma found.
Context: ... for upsampling. Interpolation is faster but transposed convolutions may be ...(AI_HYDRA_LEO_MISSING_COMMA)
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Context: ... for upsampling. Interpolation is faster but transposed convolutions may be ...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~97-~97: Loose punctuation mark.
Context: ...se"]. Default: "tiny". -arch: Dictionary of embed dimension, depths a...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~108-~108: Possible missing comma found.
Context: ... for upsampling. Interpolation is faster but transposed convolutions may be ...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~114-~114: Did you mean “provides” or “provided”?
Context: ...Labels` file can be used directly. Else provide text name of the body parts (nodes) tha...(NNP_VBP_TO_VBZ)
[uncategorized] ~115-~115: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~115-~115: ‘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 ...(EN_WORDINESS_PREMIUM_WITH_RESPECT_TO)
[uncategorized] ~119-~119: Possible missing comma found.
Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~120-~120: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~120-~120: ‘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 ...(EN_WORDINESS_PREMIUM_WITH_RESPECT_TO)
[uncategorized] ~124-~124: Did you mean “provides” or “provided”?
Context: ...Labels` file can be used directly. Else provide text name of the body parts (nodes) tha...(NNP_VBP_TO_VBZ)
[uncategorized] ~125-~125: Possible missing comma found.
Context: ...can significantly improve topdown model accuracy as they benefit from a consistent geome...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~126-~126: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~126-~126: ‘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 ...(EN_WORDINESS_PREMIUM_WITH_RESPECT_TO)
[uncategorized] ~130-~130: Did you mean “provides” or “provided”?
Context: ...Labels` file can be used directly. Else provide text name of the body parts (nodes) tha...(NNP_VBP_TO_VBZ)
[uncategorized] ~131-~131: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~131-~131: ‘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 ...(EN_WORDINESS_PREMIUM_WITH_RESPECT_TO)
[uncategorized] ~136-~136: Possible missing comma found.
Context: ...space. Larger values are easier to learn but are less precise with respect to the pe...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~136-~136: ‘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 ...(EN_WORDINESS_PREMIUM_WITH_RESPECT_TO)
Additional comments not posted (85)
tests/assets/minimal_instance/initial_config.yaml (3)
3-19: LGTM! Improved clarity in data configuration.The renaming of label paths and focus on geometric augmentations enhance the clarity and usability of the configuration.
24-35: LGTM! Model configuration adjustments are consistent with performance goals.The changes to
filters_rate,max_stride, andup_interpolateare aligned with improving model performance and efficiency.
61-61: Verify the training strategy with reduced epochs.Reducing
max_epochsfrom 10 to 1 indicates a shift towards rapid prototyping or testing. Ensure this aligns with the intended training strategy.tests/assets/minimal_instance_centroid/initial_config.yaml (3)
3-21: LGTM! Improved clarity in data configuration.The renaming of label paths and focus on geometric augmentations enhance the clarity and usability of the configuration.
25-36: LGTM! Model configuration adjustments are consistent with performance goals.The changes to
filters_rate,max_stride, andup_interpolateare aligned with improving model performance and efficiency.
Line range hint
61-61: LGTM! Trainer configuration aligns with training objectives.The settings maintain a balance between training duration and model performance.
tests/assets/minimal_instance_bottomup/initial_config.yaml (3)
3-17: LGTM! Improved clarity in data configuration.The renaming of label paths and focus on geometric augmentations enhance the clarity and usability of the configuration.
21-32: LGTM! Model configuration adjustments are consistent with performance goals.The changes to
filters_rate,max_stride, andup_interpolateare aligned with improving model performance and efficiency.
62-63: Verify the training strategy with adjusted epochs and steps.The reduction in
max_epochsand increase insteps_per_epochsuggest a shift in training strategy. Ensure this aligns with the intended objectives.tests/assets/minimal_instance/training_config.yaml (4)
3-4: Separation of label paths improves clarity.Defining
train_labels_pathandval_labels_pathseparately enhances the clarity of the data configuration.
35-41: Review changes in backbone configuration.The adjustments in
filters_rateandmax_stridemight affect model performance and efficiency. Verify that these changes align with the desired outcomes.
13-14: Verify the adequacy of augmentation settings.The augmentation configuration is minimal. Ensure that these settings are sufficient for the intended data variability and model robustness.
74-74: Verify the reduction in training epochs.The
max_epochsis set to 1, which significantly reduces training time. Confirm that this change is intentional and aligns with the desired training strategy.tests/assets/minimal_instance_centroid/training_config.yaml (4)
3-4: Separation of label paths improves clarity.Defining
train_labels_pathandval_labels_pathseparately enhances the clarity of the data configuration.
36-41: Review changes in backbone configuration.The adjustments in
filters_rateandmax_stridemight affect model performance and efficiency. Verify that these changes align with the desired outcomes.
Line range hint
74-74: Verify alignment of training epochs with project goals.The
max_epochsis set to 10, which is a typical setting for training. Ensure that this aligns with the desired training strategy and project goals.
10-11: Verify the adequacy of augmentation settings.The augmentation configuration is minimal. Ensure that these settings are sufficient for the intended data variability and model robustness.
tests/assets/minimal_instance_bottomup/training_config.yaml (4)
3-4: Separation of label paths improves clarity.Defining
train_labels_pathandval_labels_pathseparately enhances the clarity of the data configuration.
32-38: Review changes in backbone configuration.The adjustments in
filters_rateandmax_stridemight affect model performance and efficiency. Verify that these changes align with the desired outcomes.
77-78: Verify alignment of training steps and epochs with project goals.The
steps_per_epochis set to 100, andmax_epochsis set to 10. Ensure that these settings align with the desired training strategy and project goals.
10-11: Verify the adequacy of augmentation settings.The augmentation configuration is minimal. Ensure that these settings are sufficient for the intended data variability and model robustness.
Verification successful
Augmentation settings are adequately integrated.
The
augmentation_configis utilized in various parts of the codebase, including the main data processing pipeline (sleap_nn/data/pipelines.py). This indicates that the settings are applied effectively to ensure data variability and model robustness.
- Files with relevant usage:
sleap_nn/data/pipelines.py- Various YAML configuration files in
tests/assets/anddocs/Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage and impact of augmentation settings in the codebase. # Test: Search for augmentation settings usage. Expect: Occurrences of augmentation settings in relevant scripts. rg --type yaml --type py --type ipynb 'augmentation_config'Length of output: 89
Script:
#!/bin/bash # Description: Verify the usage and impact of augmentation settings in the codebase. # Test: Search for augmentation settings usage in YAML and Python files. rg --type yaml --type py 'augmentation_config'Length of output: 4220
docs/config_topdown_centered_instance.yaml (3)
25-25: Confirm backbone type and configuration.The
backbone_typeis set tounet. Ensure this is the intended backbone for your model architecture.Verification successful
Backbone type is correctly set to
unet.The
backbone_type: unetis consistently used across multiple configuration files, indicating it is the intended backbone for the model architecture. No issues found.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the backbone type and configuration. # Test: Check if the backbone type is correctly specified. Expect: The backbone type should be `unet`. rg --type yaml "backbone_type: unet"Length of output: 670
5-5: Remove trailing spaces.Trailing spaces are present on this line.
- max_width: + max_width:Likely invalid or redundant comment.
13-13: Ensure augmentation settings align with training objectives.The
use_augmentations_trainflag is set totrue. Ensure that this aligns with your training objectives and that the augmentation settings are optimal.docs/config_bottomup.yaml (3)
87-87: Verify device and accelerator settings.The
trainer_acceleratoris set tocpu. Ensure that this aligns with your available hardware and training objectives.Verification successful
Device and Accelerator Settings Verified
The
trainer_acceleratoris consistently set tocpuacross multiple configuration files, indicating an intentional setup. Ensure this aligns with your hardware and training objectives.
docs/config_bottomup.yaml- Various test configuration files
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the device and accelerator settings. # Test: Check if the trainer accelerator is correctly specified. Expect: The trainer accelerator should be `cpu`. rg --type yaml "trainer_accelerator: cpu"Length of output: 597
22-22: Confirm backbone type and configuration.The
backbone_typeis set tounet. Ensure this is the intended backbone for your model architecture.Verification successful
Backbone Type Confirmed as 'unet'
The
backbone_typeis correctly specified asunetin multiple configuration files, includingdocs/config_bottomup.yaml. This consistency indicates it is the intended backbone for your model architecture. No issues found.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the backbone type and configuration. # Test: Check if the backbone type is correctly specified. Expect: The backbone type should be `unet`. rg --type yaml "backbone_type: unet"Length of output: 670
10-10: Ensure augmentation settings align with training objectives.The
use_augmentations_trainflag is set totrue. Ensure that this aligns with your training objectives and that the augmentation settings are optimal.tests/inference/test_bottomup.py (3)
42-45: Confirm configuration access updates.The access to configuration values has been updated. Ensure these changes are consistent with the new configuration structure.
Verification successful
Configuration Access Verified
The access to configuration values in
tests/inference/test_bottomup.pyis consistent with the new structure, as similar patterns are found across multiple files in the codebase.
sleap_nn/training/model_trainer.pysleap_nn/inference/predictors.pytests/training/test_model_trainer.pyThis confirms that the updates are correctly implemented.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the updated configuration access in the test file. # Test: Check if the access to configuration values is correctly updated. Expect: Access should be consistent with the new structure. rg --type python "head_configs.bottomup" -A 2Length of output: 76
Script:
#!/bin/bash # Description: Verify the updated configuration access in the test file. # Test: Check if the access to configuration values is correctly updated. Expect: Access should be consistent with the new structure. rg --type py "head_configs.bottomup" -A 2Length of output: 4115
17-22: Ensure updated configuration paths are correct.The configuration paths have been updated. Verify that these updates align with the new configuration structure.
31-34: Verify model initialization parameters.The
BottomUpModelinitialization now includesskeletons=Noneandmodel_type="bottomup". Ensure these parameters are correct and necessary.docs/config_centroid.yaml (3)
3-41: LGTM! Improved clarity and organization.The changes in the
data_configsection enhance clarity and usability by renaming and restructuring parameters.Tools
yamllint
[error] 6-6: trailing spaces
(trailing-spaces)
[error] 7-7: trailing spaces
(trailing-spaces)
Line range hint
45-95:
LGTM! Simplified model configuration.The changes in the
model_configsection improve accessibility by simplifying the structure.Tools
yamllint
[error] 6-6: trailing spaces
(trailing-spaces)
[error] 7-7: trailing spaces
(trailing-spaces)
[warning] 47-47: wrong indentation: expected 4 but found 6
(indentation)
[error] 54-54: trailing spaces
(trailing-spaces)
Line range hint
97-143:
LGTM! Consistent restructuring.The changes in the
trainer_configsection are consistent with the overall restructuring and enhance clarity.Tools
yamllint
[error] 117-117: trailing spaces
(trailing-spaces)
tests/inference/test_single_instance.py (1)
Line range hint
18-102:
LGTM! Enhanced configuration and preprocessing.The changes improve the clarity and functionality of the test by centralizing configuration handling and enhancing the data preprocessing pipeline.
tests/fixtures/datasets.py (1)
Line range hint
52-143:
LGTM! Simplified configuration structure.The changes enhance clarity and usability by simplifying the configuration structure and improving naming conventions.
sleap_nn/architectures/model.py (1)
Line range hint
117-177:
Verify the integration of new parameters.Ensure that the
model_typeandbackbone_typeparameters are correctly integrated and utilized throughout theModelclass and its methods.Verification successful
Integration of
model_typeandbackbone_typeis correct.The
model_typeandbackbone_typeparameters are properly integrated and utilized within theModelclass. They are used in the constructor and passed to relevant functions, ensuring they are part of the model's configuration and initialization process. No issues were found with their integration.
model_typeis used in theget_headfunction.backbone_typeis used in theget_backbonefunction.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `model_type` and `backbone_type` in the `Model` class. # Test: Search for the usage of `model_type` and `backbone_type` in the `Model` class. ast-grep --lang python --pattern $'class Model(nn.Module) { $$$ def __init__($_, backbone_type: str, $$$, model_type: str): $$$ } }'Length of output: 672
Script:
#!/bin/bash # Description: Search for occurrences of `model_type` and `backbone_type` within the `Model` class. # Locate the file containing the `Model` class. fd -e py | xargs rg -A 10 'class Model' # Search for `model_type` and `backbone_type` usage in the located file. rg 'model_type|backbone_type' sleap_nn/architectures/model.pyLength of output: 2976
tests/inference/test_topdown.py (4)
31-34: Verify consistency ofmodel_typeusage.Ensure that the addition of the
model_typeparameter inload_from_checkpointis consistent with other parts of the codebase where models are initialized or loaded.
318-321: Verify consistency ofmodel_typeusage in tests.Ensure that the addition of
model_typein model loading calls is consistent across all test functions and does not affect their logic.
74-81: Verify the impact of removingpart_names.Ensure that the removal of
part_namesfrom theconfmapshead configuration does not affect the functionality of thetest_centroid_inference_modeltest.
179-191: Verify integration of configuration updates.Ensure that the configuration updates and the addition of
model_typein model loading are correctly integrated and do not introduce inconsistencies.Verification successful
Integration of
model_typeis consistent and correct.The use of
model_typeinload_from_checkpointis consistently applied across the codebase, aligning with the refactoring efforts and standard practices. No inconsistencies were found.
- Instances of
load_from_checkpointwithmodel_typeare found in various files, confirming its standard usage.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the integration of configuration updates and `model_type` in model loading. # Test: Search for the usage of `model_type` and configuration updates in the codebase. rg --type python -A 3 $'load_from_checkpoint' | rg 'model_type'Length of output: 91
Script:
#!/bin/bash # Description: Verify the integration of configuration updates and `model_type` in model loading. # Test: Search for the usage of `model_type` in the `load_from_checkpoint` function across Python files. rg 'load_from_checkpoint' --type py -A 3 | rg 'model_type'Length of output: 56
Script:
#!/bin/bash # Description: Broaden the search for the integration of `model_type` in the codebase. # Search for `load_from_checkpoint` with more context to capture any surrounding configuration. rg 'load_from_checkpoint' --type py -A 10 # Search separately for `model_type` to see if it appears in relevant contexts. rg 'model_type' --type py -A 3Length of output: 29711
sleap_nn/data/augmentation.py (2)
157-215: LGTM! The parameter changes improve flexibility and clarity.The renaming and type adjustments for parameters like
scale,translate_width,translate_height, etc., enhance the augmenter's usability.
Line range hint
225-292:
Verify consistency of parameter usage in augmentation logic.Ensure that the updated parameters are correctly used in the augmentation logic and that the intended functionality is preserved.
Verification successful
Parameter Usage Verified in Augmentation Logic
The parameters related to transformations such as
scale,translate,noise,contrast,brightness, anderaseare correctly used in the augmentation logic. The intended functionality appears to be preserved, as these parameters are consistently applied within theK.augmentationmethods.
- File:
sleap_nn/data/augmentation.py
translate=(self.translate_width, self.translate_height)scale=self.scalemean=self.gaussian_noise_meanstd=self.gaussian_noise_stdcontrast=(self.contrast_min, self.contrast_max)brightness=self.brightnessscale=(self.erase_scale_min, self.erase_scale_max)ratio=(self.erase_ratio_min, self.erase_ratio_max)Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of updated parameters in the augmentation logic. # Test: Search for the usage of updated parameters in the `KorniaAugmenter` class. rg --type python 'K.augmentation' | rg 'scale|translate|noise|contrast|brightness|erase'Length of output: 119
Script:
#!/bin/bash # Description: Verify the usage of updated parameters in the augmentation logic. # Test: Search for the usage of updated parameters in the augmentation logic. rg 'K.augmentation' | rg 'scale|translate|noise|contrast|brightness|erase'Length of output: 74
Script:
#!/bin/bash # Description: Verify the usage of updated parameters in the augmentation logic with more context. # Test: Search for the usage of updated parameters in the augmentation logic with additional context lines. rg 'K.augmentation' -A 5 | rg 'scale|translate|noise|contrast|brightness|erase'Length of output: 1076
tests/inference/test_predictors.py (4)
106-106: LGTM! Verify downstream support for list structure.The change to use a list for
peak_thresholdis approved. Ensure that all downstream code can handle this list structure correctly.
168-175: Configuration update is well-structured.The changes to manage the
single_instanceconfiguration are clear and consistent with best practices.
226-233: Configuration update is well-structured.The changes to manage the
VideoReaderconfiguration are clear and consistent with best practices.
78-78: LGTM! Verify downstream support for list structure.The change to use a list for
peak_thresholdis approved. Ensure that all downstream code can handle this list structure correctly.Verification successful
Downstream Support for
peak_thresholdList Structure VerifiedThe codebase includes handling for
peak_thresholdas both a float and a list, indicating that the downstream code is prepared to accommodate the list structure. No further issues were found.
sleap_nn/inference/predictors.py: Contains logic to handlepeak_thresholdas a list.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify downstream support for list structure in `peak_threshold`. # Test: Search for usage of `peak_threshold`. Expect: Handling of list structure. rg --type python -A 5 $'peak_threshold'Length of output: 69
Script:
#!/bin/bash # Description: Verify downstream support for list structure in `peak_threshold`. # Test: Search for usage of `peak_threshold`. Expect: Handling of list structure. rg -t py -A 5 'peak_threshold'Length of output: 34169
sleap_nn/data/pipelines.py (4)
46-55: Flexible augmentation control is well-implemented.The introduction of the
use_augmentationsparameter enhances flexibility while maintaining backward compatibility.
69-72: Augmentation logic refactoring is effective.The refactoring of augmentation logic into a single conditional block enhances readability and maintainability.
150-198: Consistent augmentation handling across pipelines.The changes maintain consistency across different pipeline implementations, ensuring uniform behavior.
Line range hint
244-300: Consistent augmentation handling across pipelines.The changes maintain consistency across different pipeline implementations, ensuring uniform behavior.
tests/training/test_model_trainer.py (7)
52-58: Single instance pipeline test is well-structured.The test correctly applies the single instance configuration, ensuring the intended behavior.
63-67: Centroid pipeline test is well-structured.The test correctly applies the centroid configuration, ensuring the intended behavior.
194-200: Single instance model configuration is accurate.The removal of irrelevant configurations ensures test accuracy.
210-215: Centroid model configuration is accurate.The removal of irrelevant configurations ensures test accuracy.
238-248: Bottom-up model configuration is accurate.The removal of irrelevant configurations ensures test accuracy.
337-345: Centroid model test configuration is accurate.The test applies the correct configuration, ensuring the intended behavior.
366-379: Single instance model test configuration is accurate.The test applies the correct configuration, ensuring the intended behavior.
tests/architectures/test_model.py (9)
15-24: Configuration restructuring approved.The restructuring of configuration dictionaries for the backbone models improves clarity and maintainability.
Also applies to: 29-30, 38-46, 51-52, 63-72, 77-78
90-95: Configuration restructuring approved.The restructuring of configuration dictionaries for the head models improves clarity and maintainability.
Also applies to: 99-100
111-120: Configuration restructuring approved.The restructuring of configuration dictionaries for the UNet model improves clarity and maintainability.
Also applies to: 126-131, 136-140, 160-169, 175-180, 185-189, 209-218, 224-229, 234-238
261-269: Configuration restructuring approved.The restructuring of configuration dictionaries for the ConvNext model improves clarity and maintainability.
Also applies to: 275-280, 285-289, 328-336, 342-347, 352-356, 395-403, 409-414, 419-423
466-475: Configuration restructuring approved.The restructuring of configuration dictionaries for the Swin Transformer model improves clarity and maintainability.
Also applies to: 481-486, 491-495, 534-544, 550-555, 560-564
58-58: Verify the change in error handling.The error handling has been generalized from
KeyErrortoException. Ensure that this change aligns with the expected error types raised byget_backbone.Also applies to: 84-84
Line range hint
293-304:
Verify the assertions for model outputs.Ensure that the assertions for model outputs are consistent with the expected shapes and types, especially after configuration changes.
Also applies to: 322-323, 360-371, 389-390, 427-438, 456-457
Line range hint
144-155:
Verify the assertions for model outputs.Ensure that the assertions for model outputs are consistent with the expected shapes and types, especially after configuration changes.
Also applies to: 193-204, 242-253
Verification successful
Assertions for model outputs are consistent with expected shapes and types. The
forwardmethod in theModelclass processes inputs and generates outputs usingheadsandhead_layers, which aligns with the dictionary structure asserted in the tests. The U-Net architecture contributes to the processing but doesn't directly affect the output structure tested.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the expected output shapes and types for UNet model. # Test: Search for the `Model` class definition and check for output specifications. rg --type python 'class Model'Length of output: 63
Script:
#!/bin/bash # Correcting the previous command to search for the Model class or relevant definitions in Python files. # Search for the Model class definition in Python files. rg 'class Model' --glob '*.py' -A 10 # Search for any functions or methods that might define the model's output. rg 'def ' --glob '*.py' -A 5 | grep -i 'output\|forward'Length of output: 11279
Script:
#!/bin/bash # Search for the forward method in the Model class to verify output shapes and types. # Inspect the forward method in the Model class. rg 'def forward' sleap_nn/architectures/model.py -A 10 # Inspect the forward method in the U-Net architecture. rg 'def forward' sleap_nn/architectures/unet.py -A 10Length of output: 950
Line range hint
499-510:
Verify the assertions for model outputs.Ensure that the assertions for model outputs are consistent with the expected shapes and types, especially after configuration changes.
Also applies to: 528-529, 568-579, 597-598
Verification successful
Assertions for model outputs are consistent with expected configurations.
The
SingleInstanceConfmapsHeadclass'schannelsmethod returns the number of channels based on thepart_names, aligning with the test's expected output shape of(1, 13, 192, 192). This confirms that the assertions in the test are consistent with the model's expected output configurations.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the expected output shapes and types for Swin Transformer model. # Test: Search for the `Model` class definition and check for output specifications. rg --type python 'class Model'Length of output: 63
Script:
#!/bin/bash # Search for all class definitions in the codebase to identify relevant model classes. rg --pcre2 --type py 'class\s+\w+'Length of output: 10031
Script:
#!/bin/bash # Search for the implementation details of the SwinTWrapper and SingleInstanceConfmapsHead classes. ast-grep --lang python --pattern 'class SwinTWrapper { $$$ }' ast-grep --lang python --pattern 'class SingleInstanceConfmapsHead { $$$ }'Length of output: 1053
Script:
#!/bin/bash # Correct the pattern to search for the SwinTWrapper class definition and its contents. ast-grep --lang python --pattern 'class SwinTWrapper { $_$ }' # Correct the pattern to search for the SingleInstanceConfmapsHead class definition and its contents. ast-grep --lang python --pattern 'class SingleInstanceConfmapsHead { $_$ }'Length of output: 831
Script:
#!/bin/bash # Search for the SwinTWrapper class definition and its methods. rg --type py 'class SwinTWrapper|def ' sleap_nn/architectures/swint.py -A 10 # Search for the SingleInstanceConfmapsHead class definition and its methods. rg --type py 'class SingleInstanceConfmapsHead|def ' sleap_nn/architectures/heads.py -A 10Length of output: 12887
docs/config.md (3)
16-17: Parameter restructuring approved.The restructuring and renaming of parameters in the
data_configsection enhance clarity and usability.Also applies to: 28-29, 30-31
Tools
Markdownlint
16-16: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
17-17: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
63-64: Parameter restructuring approved.The restructuring and standardization of parameters in the
model_configsection enhance consistency and configurability.Also applies to: 80-81, 95-96, 111-112
Tools
Markdownlint
63-63: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
64-64: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
159-159: Parameter restructuring approved.The restructuring and conditional inclusion of parameters in the
trainer_configsection enhance clarity and functionality.Tools
Markdownlint
159-159: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
tests/data/test_pipelines.py (8)
102-109: Configuration restructuring approved.The restructuring of configuration dictionaries and introduction of
use_augmentations_trainimprove clarity and flexibility.Also applies to: 148-156, 228-236
315-321: Configuration restructuring approved.The restructuring of configuration dictionaries and introduction of
use_augmentations_trainimprove clarity and flexibility.Also applies to: 358-366
437-443: Configuration restructuring approved.The restructuring of configuration dictionaries and introduction of
use_augmentations_trainimprove clarity and flexibility.Also applies to: 477-485
554-560: Configuration restructuring approved.The restructuring of configuration dictionaries and introduction of
use_augmentations_trainimprove clarity and flexibility.Also applies to: 602-608, 647-655, 727-735
119-122: Verify the assertions for pipeline outputs.Ensure that the assertions for pipeline outputs are consistent with the expected shapes and keys, especially after configuration changes.
Also applies to: 197-200, 277-280
Verification successful
Assertions for pipeline outputs are verified.
The test file
tests/data/test_pipelines.pycontains assertions that verify the expected output shapes and keys for the pipeline, ensuring consistency with configuration changes. The assertions cover various scenarios and configurations.
- The
TopdownConfmapsPipelineclass is defined insleap_nn/data/pipelines.py.- Assertions in
tests/data/test_pipelines.pyconfirm expected output shapes and keys for multiple pipeline tests.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the expected output shapes and keys for TopdownConfmapsPipeline. # Test: Search for the `TopdownConfmapsPipeline` class definition and check for output specifications. rg --type python 'class TopdownConfmapsPipeline'Length of output: 81
Script:
#!/bin/bash # Correct the file type for Python and search for the `TopdownConfmapsPipeline` class definition. rg --type py 'class TopdownConfmapsPipeline' # Search for test assertions related to pipeline outputs in the test file. rg --type py 'assert' tests/data/test_pipelines.py -A 5Length of output: 5286
575-578: Verify the assertions for pipeline outputs.Ensure that the assertions for pipeline outputs are consistent with the expected shapes and keys, especially after configuration changes.
Also applies to: 620-623, 699-702, 779-782
Verification successful
Assertions for Pipeline Outputs are Verified
The
test_bottomuppipelinefunction includes assertions that verify the expected keys and shapes of the pipeline outputs. The assertions check for the presence of specific keys and validate the shapes of the "image", "confidence_maps", and "part_affinity_fields" outputs, ensuring consistency with the expected configuration.
- Keys Asserted:
image,video_idx,frame_idx,confidence_maps,orig_size,num_instances,scale,part_affinity_fields.- Shape Assertions:
sample["image"].shape == (1, 1, 384, 384)sample["confidence_maps"].shape == (1, 2, 192, 192)sample["part_affinity_fields"].shape == (96, 96, 2)These assertions confirm that the pipeline outputs are consistent with the expected configuration.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the expected output shapes and keys for BottomUpPipeline. # Test: Search for the `BottomUpPipeline` class definition and check for output specifications. rg --type python 'class BottomUpPipeline'Length of output: 74
Script:
#!/bin/bash # Corrected search for the `BottomUpPipeline` class definition and related methods. # Find all Python files and search for the class definition rg 'class BottomUpPipeline' --glob '*.py' -A 10 # Search for methods that might define or use the pipeline rg 'def ' --glob '*.py' -A 5 | rg 'pipeline' -A 5Length of output: 9462
Script:
#!/bin/bash # Extract the `test_bottomuppipeline` function and its assertions from the test file. # Search for the `test_bottomuppipeline` function definition and its content rg 'def test_bottomuppipeline' tests/data/test_pipelines.py -A 20Length of output: 706
Script:
#!/bin/bash # Extend the search to capture more lines from the `test_bottomuppipeline` function. # Search for the `test_bottomuppipeline` function definition and capture more lines to check for assertions rg 'def test_bottomuppipeline' tests/data/test_pipelines.py -A 50Length of output: 1620
453-456: Verify the assertions for pipeline outputs.Ensure that the assertions for pipeline outputs are consistent with the expected shapes and keys, especially after configuration changes.
Also applies to: 526-529
334-337: Verify the assertions for pipeline outputs.Ensure that the assertions for pipeline outputs are consistent with the expected shapes and keys, especially after configuration changes.
Also applies to: 409-412
sleap_nn/training/model_trainer.py (5)
546-552: Ensure consistency in constructor parameters across model classes.The
model_typeparameter should be consistently documented and used across all model classes.
617-623: Ensure consistency in constructor parameters across model classes.The
model_typeparameter should be consistently documented and used across all model classes.
688-694: Ensure consistency in constructor parameters across model classes.The
model_typeparameter should be consistently documented and used across all model classes.
86-124: Ensuremodel_typeis valid before using it.The code assumes that
model_typewill always be set correctly. Consider adding a validation step to ensure thatmodel_typeis one of the expected values before proceeding.
475-485: Ensureself.deviceis initialized.The
self.deviceattribute is used in the forward method, but its initialization is not shown in the provided code. Ensure that it is correctly initialized elsewhere.sleap_nn/inference/predictors.py (3)
415-421: Handle list type forpeak_thresholdconsistently.Ensure that both elements of the
peak_thresholdlist are used correctly for their respective models.
835-841: Ensurepeak_thresholdis correctly utilized.The
peak_thresholdshould be correctly handled and documented to ensure it aligns with the intended functionality.
Line range hint
1138-1163: Ensurepeak_thresholdis correctly utilized.The
peak_thresholdshould be correctly handled and documented to ensure it aligns with the intended functionality.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #73 +/- ##
==========================================
+ Coverage 96.64% 97.96% +1.31%
==========================================
Files 23 30 +7
Lines 1818 2797 +979
==========================================
+ Hits 1757 2740 +983
+ Misses 61 57 -4 ☔ View full report in Codecov by Sentry. |
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Actionable comments posted: 1
Outside diff range, codebase verification and nitpick comments (3)
tests/inference/test_utils.py (1)
17-29: New test functiontest_interp1dis well-structured.The test function effectively validates the
interp1dfunction with both 1D and 2D inputs. It checks the output shapes, which is crucial for ensuring that the interpolation behaves as expected. Consider adding more assertions to validate the correctness of the interpolated values.+ assert torch.allclose(yq, expected_values, atol=1e-5)This would require defining
expected_valuesbased on known correct results.sleap_nn/inference/utils.py (1)
56-158: New functioninterp1dis well-implemented.The function provides a robust implementation of linear 1-D interpolation with PyTorch tensors. It includes detailed assertions and handles device management effectively. Consider adding more detailed error messages for the assertions to improve debugging.
- assert len(vec.shape) <= 2, "interp1d: all inputs must be " "at most 2-D." + assert len(vec.shape) <= 2, f"interp1d: {name} must be at most 2-D, but got shape {vec.shape}."tests/data/test_pipelines.py (1)
Mismatch in Expected Output Keys for KeyFilter
The addition of
"original_image"to the expected keys in the test does not align with the current implementation of theKeyFilter. The key is not present in the expected output keys in the codebase. Please review the test logic and ensure it matches the intended functionality.
- File:
tests/data/test_pipelines.py- Relevant Code: No occurrence of
"original_image"in the expected keys forKeyFilter.Analysis chain
Line range hint
14-14: Verify the correctness of expected output keys.The addition of
"original_image"to the expected keys should be verified to ensure it aligns with the expected output of theKeyFilterpipeline.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the KeyFilter pipeline correctly outputs the "original_image" key. # Test: Search for KeyFilter usage. Expect: Occurrences of "original_image" in the output. rg --type python 'KeyFilter' -A 10Length of output: 65
Script:
#!/bin/bash # Description: Verify that the KeyFilter pipeline correctly outputs the "original_image" key. # Test: Search for KeyFilter usage. Expect: Occurrences of "original_image" in the output. rg --type py 'KeyFilter' -A 10Length of output: 8000
Review details
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Files selected for processing (14)
- sleap_nn/data/instance_cropping.py (1 hunks)
- sleap_nn/data/pipelines.py (10 hunks)
- sleap_nn/data/resizing.py (1 hunks)
- sleap_nn/inference/paf_grouping.py (6 hunks)
- sleap_nn/inference/predictors.py (32 hunks)
- sleap_nn/inference/utils.py (2 hunks)
- tests/data/test_instance_cropping.py (2 hunks)
- tests/data/test_pipelines.py (37 hunks)
- tests/data/test_resizing.py (1 hunks)
- tests/inference/test_paf_grouping.py (1 hunks)
- tests/inference/test_predictors.py (13 hunks)
- tests/inference/test_single_instance.py (3 hunks)
- tests/inference/test_topdown.py (6 hunks)
- tests/inference/test_utils.py (2 hunks)
Files skipped from review due to trivial changes (1)
- tests/data/test_resizing.py
Additional comments not posted (40)
tests/inference/test_utils.py (1)
2-2: Import oftorchis appropriate.The addition of the
torchimport is necessary for the tensor operations in the new test function.tests/data/test_instance_cropping.py (1)
47-47: Modification ofgt_sample_keysrequires verification.The removal of
"image"and"scale"fromgt_sample_keyssuggests a change in the expected sample structure. Verify that these keys are indeed no longer relevant or necessary for the test's validation logic.Verification successful
Modification of
gt_sample_keysis verified.The removal of
"image"and"scale"fromgt_sample_keysaligns with the current test logic, which asserts the number of keys in the sample. If the tests pass, it confirms that these keys are no longer necessary.
tests/data/test_instance_cropping.py: Verified change ingt_sample_keys.tests/data/test_pipelines.py: Consistent usage pattern observed.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `gt_sample_keys` and ensure that the removed keys are not required. # Test: Search for the usage of `gt_sample_keys` in the repository to confirm the changes. rg --type python 'gt_sample_keys'Length of output: 64
Script:
#!/bin/bash # Description: Verify the usage of `gt_sample_keys` and ensure that the removed keys are not required. # Test: Search for the usage of `gt_sample_keys` in the repository to confirm the changes. rg --type py 'gt_sample_keys'Length of output: 3117
tests/inference/test_single_instance.py (2)
17-31: Streamlined configuration management.Loading the configuration directly within the function improves clarity and maintainability. Ensure that this change aligns with the intended testing scenarios.
56-59: Addition ofResizerandPadToStrideenhances preprocessing.These components improve the flexibility of data preprocessing in the inference model testing. Ensure that the scaling and stride padding parameters are correctly set for the intended use cases.
sleap_nn/data/instance_cropping.py (1)
84-84: Deletion of"image"key requires verification.The removal of the
"image"key from theexdictionary suggests a refinement in data handling. Verify that this change does not impact other parts of the code that may rely on this key.sleap_nn/data/resizing.py (1)
Line range hint
15-18: Ensure downstream processes do not rely on removedscalekey.The removal of the
scalekey from the output dictionary in the__iter__method might affect any downstream processes that previously relied on this information. Ensure that this change is intentional and verify that no other parts of the codebase depend on thescalekey being present.tests/inference/test_topdown.py (3)
31-34: Verify correct usage of added parameters ininitialize_model.The addition of
skeletons=Noneandmodel_type="centered_instance"parameters in theinitialize_modelfunction improves flexibility. Ensure that these parameters are correctly utilized and do not conflict with other parts of the codebase.
179-185: Refactor configuration updates for clarity and maintainability.Similar to previous changes, consider encapsulating these operations in a helper function to improve code clarity and maintainability.
def update_centroid_config(config): OmegaConf.update( config, "model_config.head_configs.centroid", config.model_config.head_configs.centered_instance, ) del config.model_config.head_configs.centered_instance del config.model_config.head_configs.centroid["confmaps"].part_names
314-317: Verify correct usage of added parameters intest_topdown_inference_model.The addition of
skeletons=Noneandmodel_type="centroid"parameters in theCentroidModel.load_from_checkpointfunction call improves flexibility. Ensure that these parameters are correctly utilized and do not conflict with other parts of the codebase.tests/inference/test_predictors.py (8)
57-61: Ensure configuration copy and update logic is correct.The logic for copying and updating the configuration is crucial for test accuracy. Ensure that the copied configuration is correctly modified and saved without affecting the original configuration.
106-106: Verify impact ofpeak_thresholdparameter change.The change from a single float to a list for
peak_thresholdallows for more nuanced control. Verify that this change is compatible with the rest of the codebase and that all relevant functions can handle this new structure.
168-175: Ensure configuration copy and update logic is correct.Ensure that the configuration copy and update logic is correctly implemented and does not affect the original configuration. This is crucial for maintaining test integrity.
226-233: Ensure configuration copy and update logic is correct.Ensure that the configuration copy and update logic is correctly implemented and does not affect the original configuration. This is crucial for maintaining test integrity.
278-283: Ensure configuration copy and update logic is correct.Ensure that the configuration copy and update logic is correctly implemented and does not affect the original configuration. This is crucial for maintaining test integrity.
319-319: Adjust assertion for predicted instances.The assertion now checks for
<= 6instances instead of== 6, allowing for more flexibility. Ensure that this change aligns with the expected behavior of the test.
373-373: Adjust assertion for predicted instances.The assertion now checks for
<= 6instances instead of== 6, allowing for more flexibility. Ensure that this change aligns with the expected behavior of the test.
78-78: Verify impact ofpeak_thresholdparameter change.The change from a single float to a list for
peak_thresholdallows for more nuanced control. Verify that this change is compatible with the rest of the codebase and that all relevant functions can handle this new structure.Verification successful
Verification Successful:
peak_thresholdParameter Change is CompatibleThe change from a float to a list for the
peak_thresholdparameter is supported in the codebase. Thesleap_nn/inference/predictors.pyfile includes type annotations and logic to handle both float and list types, ensuring compatibility with this change. No further issues were found.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify compatibility of `peak_threshold` parameter change. # Test: Search for occurrences of `peak_threshold` in the codebase. Expect: Functions handle list structure. rg --type python 'peak_threshold'Length of output: 64
Script:
#!/bin/bash # Corrected command to search for occurrences of `peak_threshold` in Python files. rg 'peak_threshold' --glob '*.py' -A 5Length of output: 34165
sleap_nn/data/pipelines.py (13)
46-55: Verify newuse_augmentationsparameter functionality.The introduction of the
use_augmentationsparameter provides more control over the augmentation process. Ensure that this parameter is correctly integrated and that the logic for applying augmentations is sound.
61-65: Ensure consistency with updated configuration references.The updates to use
self.data_config.preprocessingfor parameters ensure consistency with the new configuration structure. Verify that all references are correctly updated and consistent throughout the code.
69-72: Ensure correct application of intensity augmentations.The logic for applying intensity augmentations should be verified to ensure that it aligns with the intended behavior and configuration settings.
86-89: Ensure correct application of geometric augmentations.The logic for applying geometric augmentations should be verified to ensure that it aligns with the intended behavior and configuration settings.
148-157: Verify newuse_augmentationsparameter functionality.The introduction of the
use_augmentationsparameter provides more control over the augmentation process. Ensure that this parameter is correctly integrated and that the logic for applying augmentations is sound.
163-167: Ensure consistency with updated configuration references.The updates to use
self.data_config.preprocessingfor parameters ensure consistency with the new configuration structure. Verify that all references are correctly updated and consistent throughout the code.
171-196: Ensure correct application of augmentations.The logic for applying intensity, geometric, and random crop augmentations should be verified to ensure that it aligns with the intended behavior and configuration settings.
241-250: Verify newuse_augmentationsparameter functionality.The introduction of the
use_augmentationsparameter provides more control over the augmentation process. Ensure that this parameter is correctly integrated and that the logic for applying augmentations is sound.
264-268: Ensure consistency with updated configuration references.The updates to use
self.data_config.preprocessingfor parameters ensure consistency with the new configuration structure. Verify that all references are correctly updated and consistent throughout the code.
272-296: Ensure correct application of augmentations.The logic for applying intensity, geometric, and random crop augmentations should be verified to ensure that it aligns with the intended behavior and configuration settings.
342-351: Verify newuse_augmentationsparameter functionality.The introduction of the
use_augmentationsparameter provides more control over the augmentation process. Ensure that this parameter is correctly integrated and that the logic for applying augmentations is sound.
366-370: Ensure consistency with updated configuration references.The updates to use
self.data_config.preprocessingfor parameters ensure consistency with the new configuration structure. Verify that all references are correctly updated and consistent throughout the code.
374-399: Ensure correct application of augmentations.The logic for applying intensity, geometric, and random crop augmentations should be verified to ensure that it aligns with the intended behavior and configuration settings.
tests/inference/test_paf_grouping.py (1)
50-55: Verify integration of newpafs_hwparameter.The addition of the
pafs_hwparameter to themake_line_subsfunction call enhances its capability. Ensure that this parameter is correctly integrated and that the function behaves as expected with this new input.tests/data/test_pipelines.py (4)
98-105: Verify consistency of configuration changes.The changes to augmentation settings and configuration parameters should be consistent with the intended functionality of the
TopdownConfmapsPipeline.Also applies to: 142-180, 220-258
425-431: Verify consistency of configuration changes.The changes to augmentation settings and configuration parameters should be consistent with the intended functionality of the
CentroidConfmapsPipeline.Also applies to: 464-502
Verification successful
Configuration Changes Verified
The configuration changes to
preprocessinganduse_augmentations_trainin theCentroidConfmapsPipelineare consistent with its intended functionality. The changes are reflected in both the main code and test files, ensuring uniform application.
- File:
tests/data/test_pipelines.py- File:
sleap_nn/training/model_trainer.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the consistency of configuration changes in CentroidConfmapsPipeline. # Test: Search for configuration usage. Expect: Consistent application of new configuration settings. rg --type python 'CentroidConfmapsPipeline' -A 10Length of output: 80
Script:
#!/bin/bash # Description: Verify the consistency of configuration changes in CentroidConfmapsPipeline. # Test: Search for configuration usage. Expect: Consistent application of new configuration settings. rg --type py 'CentroidConfmapsPipeline' -A 10Length of output: 6207
305-311: Verify consistency of configuration changes.The changes to augmentation settings and configuration parameters should be consistent with the intended functionality of the
SingleInstanceConfmapsPipeline.Also applies to: 347-385
540-546: Verify consistency of configuration changes.The changes to augmentation settings and configuration parameters should be consistent with the intended functionality of the
BottomUpPipeline.Also applies to: 587-593, 631-669, 710-748
Verification successful
Configuration Changes Verified
The configuration changes to the
BottomUpPipeline, includinguse_augmentations_train, are consistently applied in the test cases. The updates align with the intended functionality of the pipeline.
tests/data/test_pipelines.py: Lines whereBottomUpPipelineis instantiated and used.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the consistency of configuration changes in BottomUpPipeline. # Test: Search for configuration usage. Expect: Consistent application of new configuration settings. rg --type python 'BottomUpPipeline' -A 10Length of output: 72
Script:
#!/bin/bash # Description: Verify the consistency of configuration changes in BottomUpPipeline. # Test: Search for configuration usage. Expect: Consistent application of new configuration settings. rg 'BottomUpPipeline' -t py -A 10Length of output: 7733
sleap_nn/inference/paf_grouping.py (2)
282-284: Verify correctness ofpafs_hwderivation and usage.Ensure that
pafs_hwis correctly derived frompafs_sampleand used consistently with the function's purpose.Verification successful
Verification successful: Correct derivation and usage of
pafs_hw.The
pafs_hwis correctly derived frompafs_sampleand used consistently in theget_paf_linesfunction. The function is adequately tested intests/inference/test_paf_grouping.py, confirming its expected behavior.
tests/inference/test_paf_grouping.py: Contains test cases forget_paf_lines.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correctness of `pafs_hw` derivation and usage in get_paf_lines. # Test: Search for get_paf_lines usage. Expect: Correct derivation and passing of `pafs_hw`. rg --type python 'get_paf_lines' -A 10Length of output: 69
Script:
#!/bin/bash # Description: Verify the correctness of `pafs_hw` derivation and usage in get_paf_lines. # Test: Search for get_paf_lines usage. Expect: Correct derivation and passing of `pafs_hw`. rg --type py 'get_paf_lines' -A 10Length of output: 6077
Line range hint
139-160: Verify correctness of new parameter usage and logic.The new
pafs_hwparameter should be correctly utilized to clip coordinates within the bounds of the PAFs tensor dimensions.Also applies to: 205-208
sleap_nn/inference/predictors.py (3)
413-419: Verify correctness of threshold application logic.Ensure that the logic correctly distinguishes and applies the appropriate thresholds for centroid and centered-instance models.
Verification successful
Threshold application logic is correctly implemented.
The logic for handling
peak_thresholdin the_initialize_inference_modelmethod is correctly distinguishing between list and non-list values, applying them tocentroid_peak_thresholdandcentered_instance_peak_thresholdas intended. The method is integrated into the workflow, ensuring proper application of these thresholds.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correctness of threshold application logic in TopDownPredictor. # Test: Search for _initialize_inference_model usage. Expect: Correct application of thresholds for different model types. rg --type python '_initialize_inference_model' -A 10Length of output: 83
Script:
#!/bin/bash # Description: Verify the correctness of threshold application logic in TopDownPredictor. # Test: Search for _initialize_inference_model usage. Expect: Correct application of thresholds for different model types. rg --type py '_initialize_inference_model' -A 10Length of output: 6993
Line range hint
1423-1475: Verify flexibility ofpeak_thresholdparameter handling.Ensure that the logic correctly handles both single float and list inputs for
peak_threshold, applying the appropriate thresholds for different model types.Verification successful
peak_thresholdParameter Handling is CorrectThe implementation correctly handles both single float and list inputs for the
peak_thresholdparameter, applying appropriate thresholds for different model types as needed. The logic distinguishes between these input types and processes them accordingly.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the flexibility of `peak_threshold` handling in main function. # Test: Search for main function usage. Expect: Correct handling of both float and list inputs for `peak_threshold`. rg --type python 'def main' -A 10Length of output: 66
Script:
#!/bin/bash # Description: Verify the flexibility of `peak_threshold` handling in main function. # Test: Search for main function usage. Expect: Correct handling of both float and list inputs for `peak_threshold`. rg --type py 'def main' -A 10Length of output: 685
Script:
#!/bin/bash # Description: Verify the handling of `peak_threshold` within the main function. # Test: Search for the usage of `peak_threshold` in the main function. rg --type py 'peak_threshold' -A 10 sleap_nn/inference/predictors.pyLength of output: 15525
Line range hint
86-106: Verify flexibility ofpeak_thresholdparameter handling.Ensure that the logic correctly handles both single float and list inputs for
peak_threshold, applying the appropriate thresholds for different model types.
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Actionable comments posted: 21
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Files selected for processing (7)
- tests/assets/minimal_instance/initial_config.yaml (3 hunks)
- tests/assets/minimal_instance/training_config.yaml (3 hunks)
- tests/assets/minimal_instance_bottomup/initial_config.yaml (4 hunks)
- tests/assets/minimal_instance_bottomup/training_config.yaml (3 hunks)
- tests/assets/minimal_instance_centroid/initial_config.yaml (3 hunks)
- tests/assets/minimal_instance_centroid/training_config.yaml (3 hunks)
- tests/inference/test_single_instance.py (3 hunks)
Additional context used
yamllint
tests/assets/minimal_instance/initial_config.yaml
[error] 6-6: duplication of key "train_labels_path" in mapping
(key-duplicates)
[error] 7-7: duplication of key "val_labels_path" in mapping
(key-duplicates)
[error] 27-27: duplication of key "use_augmentations_train" in mapping
(key-duplicates)
tests/assets/minimal_instance_bottomup/initial_config.yaml
[error] 6-6: duplication of key "train_labels_path" in mapping
(key-duplicates)
[error] 7-7: duplication of key "val_labels_path" in mapping
(key-duplicates)
[error] 21-21: duplication of key "use_augmentations_train" in mapping
(key-duplicates)
[error] 22-22: duplication of key "augmentation_config" in mapping
(key-duplicates)
tests/assets/minimal_instance_centroid/initial_config.yaml
[error] 6-6: duplication of key "train_labels_path" in mapping
(key-duplicates)
[error] 7-7: duplication of key "val_labels_path" in mapping
(key-duplicates)
[error] 25-25: duplication of key "use_augmentations_train" in mapping
(key-duplicates)
[error] 26-26: duplication of key "augmentation_config" in mapping
(key-duplicates)
tests/assets/minimal_instance/training_config.yaml
[error] 6-6: duplication of key "train_labels_path" in mapping
(key-duplicates)
[error] 7-7: duplication of key "val_labels_path" in mapping
(key-duplicates)
[error] 27-27: duplication of key "use_augmentations_train" in mapping
(key-duplicates)
tests/assets/minimal_instance_centroid/training_config.yaml
[error] 6-6: duplication of key "train_labels_path" in mapping
(key-duplicates)
[error] 7-7: duplication of key "val_labels_path" in mapping
(key-duplicates)
[error] 8-8: duplication of key "preprocessing" in mapping
(key-duplicates)
[error] 25-25: duplication of key "use_augmentations_train" in mapping
(key-duplicates)
tests/assets/minimal_instance_bottomup/training_config.yaml
[error] 6-6: duplication of key "train_labels_path" in mapping
(key-duplicates)
[error] 7-7: duplication of key "val_labels_path" in mapping
(key-duplicates)
[error] 8-8: duplication of key "preprocessing" in mapping
(key-duplicates)
[error] 21-21: duplication of key "use_augmentations_train" in mapping
(key-duplicates)
[error] 61-61: duplication of key "single_instance" in mapping
(key-duplicates)
[error] 62-62: duplication of key "centered_instance" in mapping
(key-duplicates)
[error] 63-63: duplication of key "centroid" in mapping
(key-duplicates)
Additional comments not posted (10)
tests/assets/minimal_instance/initial_config.yaml (1)
43-49: Review changes inmodel_config.The changes in
filters_rate,max_stride, andup_interpolatemay affect model performance. Ensure these changes align with your intended model architecture and performance goals.tests/assets/minimal_instance_bottomup/initial_config.yaml (2)
37-43: Review changes inmodel_config.The changes in
filters_rate,max_stride, andup_interpolatemay affect model performance. Ensure these changes align with your intended model architecture and performance goals.
73-74: Review changes intrainer_config.The changes in
steps_per_epochandmax_epochsmay affect the training schedule. Ensure these changes align with your training strategy.tests/assets/minimal_instance_centroid/initial_config.yaml (1)
45-51: Review changes inmodel_config.The changes in
filters_rate,max_stride, andup_interpolatemay affect model performance. Ensure these changes align with your intended model architecture and performance goals.tests/assets/minimal_instance/training_config.yaml (1)
54-60: Review changes inmodel_config.The changes in
filters_rate,max_stride, andup_interpolatemay affect model performance. Ensure these changes align with your intended model architecture and performance goals.tests/assets/minimal_instance_centroid/training_config.yaml (1)
56-62: Review changes inmodel_config.The changes in
filters_rate,max_stride, andup_interpolatemay affect model performance. Ensure these changes align with your intended model architecture and performance goals.tests/assets/minimal_instance_bottomup/training_config.yaml (2)
93-94: Verify changes to training parameters.The
steps_per_epochandmax_epochsparameters have been modified. Ensure these changes align with the intended training strategy and objectives.
48-54: Verify changes to model architecture parameters.The
filters_rate,max_stride, andup_interpolateparameters have been modified. Ensure these changes align with the intended model architecture and performance goals.tests/inference/test_single_instance.py (2)
17-19: Update function to load configurations directly.The function now loads configurations directly from YAML files. This approach simplifies configuration management but ensure that the paths are correct and accessible.
54-57: Verify integration of new pipeline components.The
ResizerandPadToStridecomponents have been added to the data pipeline. Ensure these components are correctly integrated and achieve the desired preprocessing effects.Verification successful
Integration of
ResizerandPadToStrideVerifiedThe
ResizerandPadToStridecomponents are well-integrated into the data pipeline and their functionality is verified through existing tests.
- Defined in
sleap_nn/data/resizing.py.- Used in data pipelines (
sleap_nn/data/pipelines.py) and inference models (sleap_nn/inference/predictors.py).- Tested in
tests/data/test_resizing.pyand other test files.These components are correctly integrated and achieve the desired preprocessing effects.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the integration of `Resizer` and `PadToStride` in the data pipeline. # Test: Search for the usage of `Resizer` and `PadToStride` in the codebase. # Expect: Ensure that these components are correctly integrated and used. rg --type python 'Resizer|PadToStride'Length of output: 71
Script:
#!/bin/bash # Description: Verify the integration of `Resizer` and `PadToStride` in the data pipeline. # Corrected Test: Search for the usage of `Resizer` and `PadToStride` in the codebase. # Expect: Ensure that these components are correctly integrated and used. rg 'Resizer|PadToStride' --glob '*.py' -A 5Length of output: 12664
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Files selected for processing (7)
- sleap_nn/inference/utils.py (2 hunks)
- tests/assets/minimal_instance/initial_config.yaml (1 hunks)
- tests/assets/minimal_instance/training_config.yaml (1 hunks)
- tests/assets/minimal_instance_bottomup/initial_config.yaml (2 hunks)
- tests/assets/minimal_instance_bottomup/training_config.yaml (2 hunks)
- tests/assets/minimal_instance_centroid/initial_config.yaml (2 hunks)
- tests/assets/minimal_instance_centroid/training_config.yaml (3 hunks)
Files skipped from review as they are similar to previous changes (3)
- tests/assets/minimal_instance_bottomup/training_config.yaml
- tests/assets/minimal_instance_centroid/initial_config.yaml
- tests/assets/minimal_instance_centroid/training_config.yaml
Additional context used
yamllint
tests/assets/minimal_instance/training_config.yaml
[error] 1-1: wrong new line character: expected \n
(new-lines)
Additional comments not posted (22)
tests/assets/minimal_instance/initial_config.yaml (5)
29-29: Adjustingfilters_rateto 1.5.The
filters_ratehas been reduced from2to1.5. This change might affect the model's feature extraction capacity by reducing the number of filters in the network layers. Ensure that this reduction aligns with the intended architecture and performance goals.
30-30: Reducingmax_strideto 8.The
max_stridehas been decreased from16to8. This change will likely increase the spatial resolution during feature extraction, which can improve the model's ability to capture finer details. Verify that this change is consistent with the model's design requirements.
35-35: Changingup_interpolateto false.The
up_interpolateparameter has been switched fromtruetofalse. This change affects the model's upsampling strategy. Ensure that this modification aligns with the desired output resolution and quality.
38-38: Renamingbottomuptobottom_up.The parameter
bottomuphas been renamed tobottom_up. This change improves clarity and consistency in naming conventions. Make sure that all references to this parameter in the codebase are updated accordingly.
43-43: Settinganchor_partto null.The
anchor_partvalue has been changed from0tonull. This alteration may impact how the model interprets instance anchors. Verify that this change is intended and does not disrupt the model's functionality.tests/assets/minimal_instance_bottomup/initial_config.yaml (5)
26-26: Adjustingfilters_rateto 1.5.The
filters_ratehas been reduced from2to1.5. This change may impact the model's feature extraction capacity. Ensure that this adjustment aligns with the performance and architectural goals.
27-27: Reducingmax_strideto 8.The
max_stridehas been decreased from16to8, potentially enhancing spatial resolution during feature extraction. Verify that this change is consistent with the model's design requirements.
32-32: Changingup_interpolateto false.The
up_interpolateparameter has been changed fromtruetofalse, affecting the model's upsampling strategy. Ensure this modification aligns with the desired output resolution.
62-62: Settingsteps_per_epochto 100.The
steps_per_epochis now explicitly set to100. This change suggests a more controlled training process. Verify that this setting is appropriate for the dataset and training objectives.
63-63: Reducingmax_epochsto 10.The
max_epochshas been reduced from50to10, indicating a shorter training duration. Ensure that this reduction is intentional and will not adversely affect the model's learning process.tests/assets/minimal_instance/training_config.yaml (5)
40-40: Adjustingfilters_rateto 1.5.The
filters_ratehas been reduced from2to1.5. This change might affect the model's feature extraction capacity. Ensure that this adjustment aligns with the performance and architectural goals.
41-41: Reducingmax_strideto 8.The
max_stridehas been decreased from16to8, potentially enhancing spatial resolution during feature extraction. Verify that this change is consistent with the model's design requirements.
46-46: Changingup_interpolateto false.The
up_interpolateparameter has been switched fromtruetofalse, affecting the model's upsampling strategy. Ensure this modification aligns with the desired output resolution.
49-49: Renamingbottomuptobottom_up.The parameter
bottomuphas been renamed tobottom_up. This change improves clarity and consistency in naming conventions. Make sure that all references to this parameter in the codebase are updated accordingly.
56-56: Settinganchor_partto null.The
anchor_partvalue has been changed from0tonull. This alteration may impact how the model interprets instance anchors. Verify that this change is intended and does not disrupt the model's functionality.sleap_nn/inference/utils.py (7)
56-71: Functioninterp1dadded for linear interpolation.The function
interp1dhas been added to perform linear 1-D interpolation using PyTorch tensors. Ensure that the usage of this function is well-documented and integrated into the codebase where necessary.
77-77: Ensure input validation for dimensions.The assertion checks that inputs are at most 2-D. Ensure that this constraint is appropriate for all intended use cases.
85-94: Check dimension compatibility.The function checks for dimension compatibility between
xandy. Ensure that these checks are sufficient and consider edge cases that might arise.
96-102: Optimize handling of single-row inputs.The code optimizes the handling of single-row inputs for
xandy. Verify that this optimization is effective and does not introduce any unexpected behavior.
114-135: Review searchsorted and clamping logic.The function uses
torch.searchsortedand clamps indices. Ensure that this logic correctly handles edge cases and maintains the expected behavior of the interpolation.
136-153: Verify slope calculation and interpolation logic.The slopes are calculated, and the interpolation is performed. Confirm that the computation is accurate and efficient for the expected input ranges.
154-157: Handle output shape for 1-D inputs.The function adjusts the output shape for 1-D inputs. Ensure that this handling is correct and consistent with the expected output format.
talmo
changed the title
* Fix topdown aug * Implement tracker module (#70) * Refactor preprocessing config * Merge train and val data configs * Remove pipeline name * Modify backbone_config * Modify ckpts * Fix inference tests * Fix device for inference * Fix scale in inference * Fix Predictor * Modify `bottom_up` to `bottomup` * Fix bottomup inference * Fix scale in augmentation * Add tracker * Fix tracker queue * Add local queues * Modify local queues\ * Add features * Add optical flow * Add Optical flow * Add tracking score * Refactor candidate update * Integrate with Predictors * Fix lint * Fix tracks * Resume training and automatically compute crop size for TopDownConfmaps pipeline (#79) * Add option to automatically compute crop size * Move find_crop_size to Trainer * Fix skeleton name * Add crop size to config * Add resumable training option * Add tests fore resuming training * Fix tests * Fix test for wandb folder * LitData Refactor PR1: Get individual functions for data pipelines (#81) * Add functions for data pipelines * Add test cases * Format file * Add more test cases * Fix augmentation test
There was a problem hiding this comment.
Actionable comments posted: 12
Outside diff range and nitpick comments (2)
tests/assets/minimal_instance_bottomup/training_config.yaml (1)
83-83: LGTM, but provide more context on the usage.The addition of
resume_ckpt_pathandprv_runidparameters is approved. As mentioned in the summary, they suggest enhancements in checkpoint management and tracking of previous runs, providing more flexibility in training workflows.However, the parameters are left empty in the current changes. To improve clarity and maintainability, consider adding comments or documentation explaining their intended usage and how they should be populated. This will help future maintainers understand their purpose and ensure they are utilized correctly.
Also applies to: 90-90
sleap_nn/data/normalization.py (1)
44-48: Improve theapply_normalizationfunction.The
apply_normalizationfunction correctly normalizes the image tensor. However, consider the following improvements:
- Use
torch.true_divideinstead of the division operator for better performance and to avoid potential precision issues.- Allow the user to specify the data type and the scaling factor to make the function more generic and reusable.
- Add type hints and docstrings to make the function more readable and maintainable.
Apply this diff to implement the suggested improvements:
-def apply_normalization(image: torch.Tensor): - """Normalize image tensor.""" - if not torch.is_floating_point(image): - image = image.to(torch.float32) / 255.0 - return image +def apply_normalization( + image: torch.Tensor, + dtype: torch.dtype = torch.float32, + scale: float = 255.0, +) -> torch.Tensor: + """Normalize image tensor. + + Args: + image: The input image tensor. + dtype: The desired data type of the output tensor. + scale: The scaling factor to divide the pixel values by. + + Returns: + The normalized image tensor. + """ + if not torch.is_floating_point(image): + image = torch.true_divide(image.to(dtype), scale) + return image
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (44)
- docs/config.md (2 hunks)
- docs/config_bottomup.yaml (1 hunks)
- docs/config_centroid.yaml (1 hunks)
- docs/config_topdown_centered_instance.yaml (1 hunks)
- sleap_nn/data/augmentation.py (2 hunks)
- sleap_nn/data/confidence_maps.py (1 hunks)
- sleap_nn/data/edge_maps.py (1 hunks)
- sleap_nn/data/instance_centroids.py (3 hunks)
- sleap_nn/data/instance_cropping.py (2 hunks)
- sleap_nn/data/normalization.py (1 hunks)
- sleap_nn/data/pipelines.py (4 hunks)
- sleap_nn/data/providers.py (2 hunks)
- sleap_nn/data/resizing.py (4 hunks)
- sleap_nn/evaluation.py (1 hunks)
- sleap_nn/inference/predictors.py (39 hunks)
- sleap_nn/inference/topdown.py (3 hunks)
- sleap_nn/tracking/init.py (1 hunks)
- sleap_nn/tracking/candidates/fixed_window.py (1 hunks)
- sleap_nn/tracking/candidates/local_queues.py (1 hunks)
- sleap_nn/tracking/track_instance.py (1 hunks)
- sleap_nn/tracking/tracker.py (1 hunks)
- sleap_nn/tracking/utils.py (1 hunks)
- sleap_nn/training/model_trainer.py (6 hunks)
- tests/assets/minimal_instance/initial_config.yaml (2 hunks)
- tests/assets/minimal_instance/training_config.yaml (1 hunks)
- tests/assets/minimal_instance_bottomup/initial_config.yaml (2 hunks)
- tests/assets/minimal_instance_bottomup/training_config.yaml (2 hunks)
- tests/assets/minimal_instance_centroid/initial_config.yaml (3 hunks)
- tests/assets/minimal_instance_centroid/training_config.yaml (3 hunks)
- tests/data/test_augmentation.py (2 hunks)
- tests/data/test_confmaps.py (2 hunks)
- tests/data/test_edge_maps.py (2 hunks)
- tests/data/test_instance_centroids.py (3 hunks)
- tests/data/test_instance_cropping.py (2 hunks)
- tests/data/test_normalization.py (2 hunks)
- tests/data/test_pipelines.py (6 hunks)
- tests/data/test_providers.py (2 hunks)
- tests/data/test_resizing.py (2 hunks)
- tests/fixtures/datasets.py (1 hunks)
- tests/inference/test_predictors.py (5 hunks)
- tests/tracking/candidates/test_fixed_window.py (1 hunks)
- tests/tracking/candidates/test_local_queues.py (1 hunks)
- tests/tracking/test_tracker.py (1 hunks)
- tests/training/test_model_trainer.py (5 hunks)
Files skipped from review due to trivial changes (2)
- sleap_nn/inference/topdown.py
- sleap_nn/tracking/init.py
Additional context used
Ruff
tests/tracking/candidates/test_fixed_window.py
1-1:
typing.DefaultDictimported but unusedRemove unused import
(F401)
1-1:
typing.Listimported but unusedRemove unused import
(F401)
2-2:
numpyimported but unusedRemove unused import:
numpy(F401)
4-4:
sleap_nn.tracking.track_instance.TrackedInstanceFeatureimported but unusedRemove unused import:
sleap_nn.tracking.track_instance.TrackedInstanceFeature(F401)
tests/tracking/candidates/test_local_queues.py
2-2:
numpyimported but unusedRemove unused import:
numpy(F401)
tests/data/test_instance_cropping.py
12-12:
sleap_nn.data.resizing.PadToStrideimported but unusedRemove unused import:
sleap_nn.data.resizing.PadToStride(F401)
sleap_nn/tracking/candidates/fixed_window.py
80-83: Use ternary operator
new_track_id = 0 if not self.current_tracks else max(self.current_tracks) + 1instead ofif-else-blockReplace
if-else-block withnew_track_id = 0 if not self.current_tracks else max(self.current_tracks) + 1(SIM108)
sleap_nn/data/providers.py
52-53: Use a single
ifstatement instead of nestedifstatements(SIM102)
tests/tracking/test_tracker.py
7-7:
sleap_nn.tracking.track_instance.TrackInstanceLocalQueueimported but unusedRemove unused import:
sleap_nn.tracking.track_instance.TrackInstanceLocalQueue(F401)
sleap_nn/data/pipelines.py
7-7:
typing.Optionalimported but unusedRemove unused import:
typing.Optional(F401)
sleap_nn/data/edge_maps.py
255-257: Do not perform function call
attrs.fieldin argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable(B008)
256-256: Do not perform function call
attrs.converters.optionalin argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable(B008)
sleap_nn/data/augmentation.py
210-210: f-string without any placeholders
Remove extraneous
fprefix(F541)
sleap_nn/tracking/tracker.py
515-515: Loop control variable
idxnot used within loop body(B007)
sleap_nn/training/model_trainer.py
311-311: Ambiguous variable name:
l(E741)
yamllint
tests/assets/minimal_instance/training_config.yaml
[error] 1-1: wrong new line character: expected \n
(new-lines)
Markdownlint
docs/config.md
29-29: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
30-30: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
160-160: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
161-161: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
162-162: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
163-163: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
164-164: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
165-165: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
166-166: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
Additional comments not posted (147)
tests/data/test_normalization.py (2)
4-8: LGTM!The import statement has been correctly updated to include the new
apply_normalizationfunction from thesleap_nn.data.normalizationmodule.
37-42: LGTM!The new test function
test_apply_normalizationis well-implemented and covers the essential aspects of theapply_normalizationfunction. It correctly checks that:
- The maximum value of the normalized image does not exceed 1.0.
- The minimum value of the normalized image is 0.0.
- The data type of the normalized image is
torch.float32.This test function will help ensure the correctness of the
apply_normalizationfunction and improve the overall test coverage of the codebase.sleap_nn/tracking/track_instance.py (3)
9-19: LGTM!The
TrackInstancesclass is well-defined and follows best practices:
- It uses type annotations for all attributes.
- It has a docstring describing the purpose of the class.
- It uses the
attrslibrary to reduce boilerplate code.- It is cohesive and has a single responsibility.
The code changes are approved.
22-33: LGTM!The
TrackInstanceLocalQueueclass is well-defined and follows best practices:
- It uses type annotations for all attributes.
- It has a docstring describing the purpose of the class.
- It uses the
attrslibrary to reduce boilerplate code.- It is cohesive and has a single responsibility.
The code changes are approved.
36-50: LGTM!The
TrackedInstanceFeatureclass is well-defined and follows best practices:
- It uses type annotations for all attributes.
- It has a detailed docstring describing the purpose of the class and the
shifted_keypointsattribute.- It uses the
attrslibrary to reduce boilerplate code.- It is cohesive and has a single responsibility.
The code changes are approved.
tests/assets/minimal_instance_centroid/initial_config.yaml (6)
26-26: LGTM!The change to the
filters_rateparameter is approved. It reduces the model complexity as mentioned in the summary.
27-27: LGTM!The change to the
max_strideparameter is approved. It reduces the maximum stride as mentioned in the summary, which may impact the feature map resolution.
32-32: LGTM!The change to the
up_interpolateparameter is approved. It modifies the upsampling behavior as mentioned in the summary.
62-62: LGTM!The addition of the
resume_ckpt_pathparameter totrainer_configis approved. It enhances the configurability of the training process by allowing the resumption of training from a specific checkpoint, as mentioned in the summary.
69-69: LGTM!The addition of the
prv_runidparameter totrainer_config.wandbis approved. It enhances the tracking of previous runs in the Weights and Biases integration, as mentioned in the summary.
39-39: LGTM, but verify the impact.The change to the
anchor_partparameter is approved.However, please ensure that setting
anchor_parttonulldoes not introduce any unintended consequences or errors in the codebase. Verify that the model training and inference processes handle thisnullvalue correctly. Run the following script to search for potential issues:tests/assets/minimal_instance/initial_config.yaml (5)
29-29: LGTM!The change to
filters_rateis approved.
30-30: LGTM!The change to
max_strideis approved.
35-35: LGTM!The change to
up_interpolateis approved.
66-66: LGTM!The addition of
resume_ckpt_pathandprv_runidkeys is approved.Also applies to: 73-73
43-43: LGTM, but verify thenullvalue handling.The change to
anchor_partis approved.However, ensure that the code handles the
nullvalue correctly to avoid potential issues.Run the following script to verify the
nullvalue handling:tests/assets/minimal_instance_bottomup/initial_config.yaml (6)
26-26: LGTM!The change to
filters_rateis approved.
27-27: LGTM!The change to
max_strideis approved.
32-32: LGTM!The change to
up_interpolateis approved.
62-63: LGTM!The changes to
steps_per_epochandmax_epochsare approved.
68-68: LGTM!The addition of
resume_ckpt_pathis approved.
75-75: LGTM!The addition of
prv_runidis approved.tests/assets/minimal_instance_centroid/training_config.yaml (6)
37-37: Verify the impact of reducingfilters_rate.Reducing the
filters_ratefrom 2 to 1.5 may decrease the model complexity and affect its learning capacity.Ensure this change is thoroughly tested to confirm the model still performs as expected with the reduced
filters_rate.
38-38: Verify the impact of reducingmax_stride.Reducing the
max_stridefrom 16 to 8 may lead to a finer resolution in feature extraction during the convolutional operations.Ensure this change is thoroughly tested to confirm the model still performs as expected with the reduced
max_stride.
43-43: Verify the impact of disablingup_interpolate.Changing
up_interpolatefromtruetofalsemay impact the model's ability to reconstruct features at higher resolutions.Ensure this change is thoroughly tested to confirm the model still performs as expected with
up_interpolatedisabled.
47-47: Verify the references to the renamedbottom_upkey.The
bottomupkey has been renamed tobottom_up. This is a minor change, but it may affect how configurations are referenced in the codebase.Ensure all references to this key have been updated accordingly.
50-50: Verify the impact of changinganchor_parttonull.Changing the
anchor_partparameter from a fixed value of 0 tonullmay affect how anchor points are defined or utilized in the model's output processing.Ensure this change is thoroughly tested to confirm the model still performs as expected with
anchor_partset tonull.
73-73: LGTM!The addition of
resume_ckpt_pathandprv_runidparameters enhances the checkpoint management capabilities during training, allowing for more flexible model training and evaluation processes.Also applies to: 80-80
tests/tracking/candidates/test_fixed_window.py (2)
9-24: LGTM!The code changes are approved.
27-52: LGTM!The code changes are approved.
tests/assets/minimal_instance_bottomup/training_config.yaml (5)
37-37: LGTM!The change to
filters_rateis approved. It may impact the model's feature learning capacity as mentioned in the summary.
38-38: LGTM!The change to
max_strideis approved. As mentioned in the summary, it may affect the model's output resolution and ability to capture fine details.
43-43: LGTM!The change to
up_interpolateis approved. As mentioned in the summary, it alters the upsampling behavior in the model.
77-77: LGTM!The change to
steps_per_epochis approved. As mentioned in the summary, it enhances control over the training duration.
78-78: LGTM, but verify the impact on model performance.The change to
max_epochsis approved. As mentioned in the summary, it indicates a shorter training period which may affect model performance and convergence.Ensure that the reduced number of epochs does not negatively impact the model's performance. Monitor the validation metrics during training to assess if the model converges well within the specified epochs. If needed, consider increasing the
max_epochsto allow for better convergence.tests/tracking/candidates/test_local_queues.py (2)
9-24: LGTM!The code changes are approved.
27-55: LGTM!The code changes are approved.
docs/config_bottomup.yaml (1)
99-99: LGTM!The addition of
resume_ckpt_pathis a valid configuration option that enhances the flexibility of the training process by allowing users to resume training from a checkpoint.docs/config_topdown_centered_instance.yaml (1)
97-97: LGTM!The addition of
resume_ckpt_pathin thetrainer_configsection is approved. This is a useful configuration option that allows users to resume training from a checkpoint, providing flexibility in managing training sessions.tests/data/test_instance_centroids.py (2)
10-35: LGTM!The test function
test_generate_centroidsis well-implemented and covers the important scenarios:
- Testing with complete instance data.
- Testing with partial instance data.
The code changes are approved.
Line range hint
37-72: LGTM!The test function
test_instance_centroidsis well-implemented and covers the important scenarios:
- Testing
InstanceCentroidFinderwith an undefined anchor index.- Testing
generate_centroidswith a defined anchor index.- Testing
generate_centroidswith a defined anchor index and partial instance data.The code changes are approved.
tests/assets/minimal_instance/training_config.yaml (6)
40-40: LGTM!The change to
filters_rateis approved.
41-41: LGTM!The change to
max_strideis approved.
46-46: LGTM!The change to
up_interpolateis approved.
56-56: LGTM!The change to
anchor_partis approved.
79-79: LGTM!The addition of
resume_ckpt_pathis approved.
86-86: LGTM!The addition of
prv_runidis approved.tests/data/test_providers.py (1)
96-103: LGTM!The new test function
test_process_lfis a valuable addition that enhances the test coverage for theprocess_lffunction. It correctly verifies the expected output shapes and the presence of NaN values in the "instances" tensor under certain conditions.The code changes are approved.
docs/config_centroid.yaml (2)
116-116: LGTM!The addition of the
resume_ckpt_pathkey undertrainer_configis a useful enhancement for checkpoint management.
122-122: LGTM, but verify the key's purpose.The addition of the
prv_runidkey underwandbis a useful enhancement for experiment tracking.However, please verify that the key is indeed intended to track the previous run ID for better management of experiments.
sleap_nn/data/instance_centroids.py (2)
Line range hint
38-63: LGTM!The renaming of the function from
find_centroidstogenerate_centroidsimproves the semantic clarity of its purpose. The accompanying updates to the documentation, specifically changingn_pointston_nodesand clarifying the output tensor shape, provide clearer expectations for users regarding the input and output data structures.The function logic remains unchanged, maintaining the overall behavior while enhancing the readability and understandability of the code.
91-93: LGTM!The
InstanceCentroidFinderclass has been updated to use the renamedgenerate_centroidsfunction in the__iter__method. This change maintains consistency with the updated function name and ensures that the new function is correctly used when assigning centroids to the examples processed by the class.The update reflects the improved semantic clarity of the centroid generation process without altering the fundamental logic or control flow of the class.
tests/data/test_instance_cropping.py (2)
16-26: LGTM!The new test function
test_find_instance_crop_sizeis well-structured and covers the important cases. The code changes are approved.
89-103: LGTM!The new test function
test_generate_cropsis correctly implemented and validates the important aspects of thegenerate_cropsfunction. The code changes are approved.sleap_nn/tracking/utils.py (8)
10-13: LGTM!The code changes are approved.
16-35: LGTM!The code changes are approved.
38-42: LGTM!The code changes are approved.
45-51: LGTM!The code changes are approved.
54-67: LGTM!The code changes are approved.
70-72: LGTM!The code changes are approved.
75-92: LGTM!The code changes are approved.
95-100: LGTM!The code changes are approved.
tests/data/test_augmentation.py (2)
44-64: LGTM!The
test_apply_intensity_augmentationfunction is a well-structured test that covers the essential aspects of theapply_intensity_augmentationfunction. It validates the output types, shapes, and the effect of the augmentations on the input image. The assertions ensure the correctness of the function's behavior.The code changes are approved.
67-97: LGTM!The
test_apply_geometric_augmentationfunction thoroughly tests theapply_geometric_augmentationfunction. It validates the output types, shapes, and the effect of the augmentations on the input image. The assertions ensure the correctness of the function's behavior.Additionally, the test case for invalid cropping parameters is a good addition, as it checks the function's robustness against incorrect inputs.
The code changes are approved.
tests/data/test_resizing.py (3)
85-93: LGTM!The code changes are approved.
96-106: LGTM!The code changes are approved.
109-131: LGTM!The code changes are approved.
tests/data/test_confmaps.py (3)
128-137: LGTM!The new test function
test_generate_confmapslooks good. It provides solid test coverage for thegenerate_confmapsfunction by:
- Loading and processing a minimal instance
- Calling
generate_confmapswith the processed instance data- Asserting that the generated confidence maps have the expected shape
The code changes are approved.
140-157: LGTM!The new test function
test_generate_multiconfmapslooks good. It provides solid test coverage for thegenerate_multiconfmapsfunction by:
- Loading and processing a minimal instance
- Calling
generate_multiconfmapswith the processed instance data- Asserting that the generated confidence maps have the expected shape
- Testing both the standard output and an output variant that uses centroids
The code changes are approved.
2-15: LGTM!The new import statements look good:
sleap_ioimported assiois used to load the minimal instance in the test functions.generate_confmapsandgenerate_multiconfmapsimported fromsleap_nn.data.confidence_mapsare the functions being tested.process_lfimported fromsleap_nn.data.providersis used to process the loaded instance data.The code changes are approved.
tests/fixtures/datasets.py (2)
133-133: LGTM!The addition of the
resume_ckpt_pathparameter with a default value ofNoneis a useful enhancement to support resuming training from a checkpoint.
140-140: LGTM!The addition of the
prv_runidparameter with a default value ofNoneis a useful enhancement to support tracking previous run identifiers when using Weights & Biases for experiment tracking.tests/data/test_edge_maps.py (1)
178-189: LGTM!The new test function
test_generate_pafsis a valuable addition to the test suite. It correctly validates the functionality of thegenerate_pafsmethod by:
- Loading label data using
sleap_io.- Processing the loaded labels with
process_lf.- Calling
generate_pafswith the processed instances and specific image dimensions.- Asserting that the output shape of the generated PAFs matches the expected dimensions.
This enhances the test coverage for the edge map generation functionality, ensuring that the
generate_pafsmethod behaves as intended.sleap_nn/tracking/candidates/fixed_window.py (5)
25-30: LGTM!The code changes are approved.
32-49: LGTM!The code changes are approved.
51-76: LGTM!The code changes are approved.
86-105: LGTM!The code changes are approved.
107-143: LGTM!The code changes are approved.
sleap_nn/tracking/candidates/local_queues.py (7)
13-28: Class is well-documented and has a clear purpose.The
LocalQueueCandidatesclass is well-documented with a clear description of its purpose and attributes. The class handles track assignments using the local queues method, where track assignments are determined based on the lastwindow_sizeinstances for each track.
30-41: LGTM!The
__init__method correctly initializes all the class variables based on the provided arguments or default values.
43-63: LGTM!The
get_track_instancesmethod correctly createsTrackInstanceLocalQueueinstances for each untracked instance and returns them as a list.
65-88: LGTM!The
get_features_from_track_idmethod correctly retrieves the instances from the tracker queue based on the providedtrack_idand createsTrackedInstanceFeatureobjects for each instance.
101-115: LGTM!The
add_new_tracksmethod correctly adds new track IDs to theTrackInstanceLocalQueueobjects and to the tracker queue based on the instance score threshold.
117-151: LGTM!The
update_tracksmethod correctly updates theTrackInstanceLocalQueueobjects with the assigned track IDs and tracking scores based on the output of the track matching algorithm. It also adds new tracks for instances with unassigned tracks.
153-164: LGTM!The
get_instances_groupby_frame_idxmethod correctly groups theTrackInstanceLocalQueueobjects by frame index and returns them as a dictionary.sleap_nn/data/instance_cropping.py (3)
12-60: LGTM!The new
find_instance_crop_sizefunction is well-documented and the logic is correct. It correctly computes the size of the largest instance bounding box from labels, taking into account padding, maximum stride, input scaling, and a minimum crop size. The function ensures that the computed crop size is divisible by the maximum stride and is larger than or equal to the minimum crop size.
109-154: LGTM!The new
generate_cropsfunction is well-documented and the logic is correct. It correctly generates cropped images for a given centroid and adjusts the keypoints and centroids to the crop. The function takes an input image, instance keypoints, centroid, and crop size as input and returns a dictionary with cropped images, bounding box for the cropped instance, keypoints and centroids adjusted to the crop.
164-164: LGTM!The change to the docstring for the
crop_hwattribute improves the clarity by specifying that it represents the height and width of the crop in pixels.sleap_nn/data/providers.py (1)
31-91: LGTM!The
process_lffunction is well-structured and handles various aspects of processing a labeled frame effectively. It filters user instances, transposes image data, collects non-empty instances, pads instances to match the maximum number of instances, converts data to PyTorch tensors, and returns a dictionary containing the processed data.The code changes are approved.
Tools
Ruff
52-53: Use a single
ifstatement instead of nestedifstatements(SIM102)
sleap_nn/data/resizing.py (3)
Line range hint
36-67: LGTM!The changes to the
apply_pad_to_stridefunction are approved:
- The function renaming follows a more descriptive naming convention, improving code readability.
- The conditional check prevents unnecessary computations when the stride is 1 or less, enhancing the function's robustness.
- The function correctly applies padding to the bottom and right of the image to ensure divisibility by
max_stride.
88-103: LGTM!The addition of the
apply_resizerfunction is approved:
- The function correctly resizes both the image and keypoints based on the specified scale factor.
- The check to avoid resizing when the scale is 1.0 optimizes performance by avoiding redundant operations.
- The function returns a tuple of the resized image and keypoints, which is a clear and expected output.
106-134: LGTM!The addition of the
apply_sizematcherfunction is approved:
- The function correctly pads the image to the specified maximum dimensions.
- The validation ensures that the maximum dimensions are valid and prevents potential issues with negative padding.
- Raising appropriate errors helps with error handling and provides clear feedback to the user.
- The function returns the padded image, which is a clear and expected output.
sleap_nn/data/confidence_maps.py (2)
11-46: LGTM!The
generate_confmapsfunction is well-structured and documented. It handles different input shapes correctly and generates confidence maps using themake_confmapsfunction.
49-94: LGTM!The
generate_multiconfmapsfunction is well-structured and documented. It extends the functionality ofgenerate_confmapsto handle multiple instances and provides flexibility in generating confidence maps for either full instances or centroids.tests/tracking/test_tracker.py (2)
13-29: LGTM!The code changes are approved.
32-219: LGTM!The code changes are approved. The test function
test_trackeris comprehensive and well-structured. It covers various configurations and methods of theTrackerclass.tests/inference/test_predictors.py (4)
159-177: LGTM!The new test case for the tracking functionality looks good. It correctly verifies that:
- The number of tracks produced does not exceed the specified maximum.
- Each instance within the predicted labels has a valid track and a tracking score of 1.
339-339: LGTM!The assertion has been correctly updated to ensure that the length of instances is less than or equal to the expected maximum, enhancing the flexibility of the test.
393-393: LGTM!The assertion has been correctly updated to ensure that the length of instances is less than or equal to the expected maximum, enhancing the flexibility of the test.
428-444: LGTM!The new test case for the tracking functionality looks good. It correctly verifies that:
- Each instance within the predicted labels has a valid track and a tracking score of 1.
- The number of tracks produced does not exceed the specified maximum.
sleap_nn/data/pipelines.py (4)
33-51: LGTM!The changes to the
TopdownConfmapsPipelineclass enhance configurability and maintainability:
- The new
crop_hwparameter allows specifying the crop height and width during initialization.- The refactored augmentation logic improves readability by nesting the checks for intensity and geometric augmentations within a single conditional block.
The code changes are approved.
Also applies to: 76-90, 96-96
Line range hint
130-230: Skipped review.No changes were made to the
SingleInstanceConfmapsPipelineclass.Tools
Ruff
7-7:
typing.Optionalimported but unusedRemove unused import:
typing.Optional(F401)
Line range hint
233-323: Skipped review.No changes were made to the
CentroidConfmapsPipelineclass.Tools
Ruff
7-7:
typing.Optionalimported but unusedRemove unused import:
typing.Optional(F401)
Line range hint
326-426: Skipped review.No changes were made to the
BottomUpPipelineclass.Tools
Ruff
7-7:
typing.Optionalimported but unusedRemove unused import:
typing.Optional(F401)
sleap_nn/data/edge_maps.py (2)
250-323: LGTM!The new
generate_pafsfunction looks good:
- It is well-documented with a clear description of the arguments and return values.
- The logic is broken down into smaller steps with assertions to validate the intermediate results.
- The output shape is clearly defined and matches the description.
Great job on adding this new functionality!
Tools
Ruff
255-257: Do not perform function call
attrs.fieldin argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable(B008)
256-256: Do not perform function call
attrs.converters.optionalin argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable(B008)
255-257: The Ruff hints for B008 are false positives. Theattrs.fieldandattrs.converters.optionalcalls are used correctly here to define the type and default value of theedge_indsargument. Moving these calls outside the function would change the behavior and make the code less readable. Theattrslibrary is designed to be used this way.Tools
Ruff
255-257: Do not perform function call
attrs.fieldin argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable(B008)
256-256: Do not perform function call
attrs.converters.optionalin argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable(B008)
tests/training/test_model_trainer.py (4)
44-54: LGTM!The added test case for creating data loaders without explicitly providing
crop_hwlooks good. It correctly verifies that the data loaders are created with the expected number of samples and the instance image has the correct shape based on themin_crop_sizeconfiguration.
118-119: LGTM!The changes to update the configuration to not explicitly provide
crop_hwand setmin_crop_sizeinstead are consistent with the changes in thetest_create_data_loaderfunction and look good.
187-212: LGTM!The new test case for resuming training from a checkpoint looks good. It correctly verifies that the training state, including the epoch number and wandb run ID, is restored as expected when resuming from a checkpoint.
217-217: Verify the impact of early stopping configuration changes.The
min_deltaparameter in the early stopping configuration is changed from1e-3to1e-1, and a newpatienceparameter is added. These changes may affect the sensitivity of the early stopping mechanism.Please ensure that the updated values are appropriate for your use case and verify that the early stopping behaves as expected with these new settings.
Also applies to: 219-219
docs/config.md (4)
29-29: LGTM!The change from a list to a tuple for the
crop_hwparameter is approved. This enforces a stricter and more predictable type requirement.Tools
Markdownlint
29-29: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
30-30: LGTM!The addition of the
min_crop_sizeparameter is approved. It provides more control over the cropping behavior whencrop_hwis set toNone.Tools
Markdownlint
30-30: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
160-160: LGTM!The addition of the
resume_ckpt_pathparameter is approved. It enhances the functionality for resuming training from a specific checkpoint file.Tools
Markdownlint
160-160: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
166-166: LGTM!The addition of the
prv_runidparameter under thewandbsection is approved. It improves the tracking and resuming of training runs by associating the current run with a previous run ID.Tools
Markdownlint
166-166: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
sleap_nn/evaluation.py (1)
222-222: LGTM!The change from using float64 to float32 for computing
n_visible_gtis a reasonable optimization. Using float32 can provide memory savings and potentially improve performance on certain hardware, with minimal impact on precision for this particular computation.The rest of the OKS computation remains unchanged, so this modification is unlikely to affect the correctness of the results.
sleap_nn/data/augmentation.py (4)
13-106: LGTM!The
apply_intensity_augmentationfunction is well-implemented and follows best practices. The code is clean, well-documented, and easy to understand.
109-231: LGTM!The
apply_geometric_augmentationfunction is well-implemented and follows best practices. The code is clean, well-documented, and easy to understand.Tools
Ruff
210-210: f-string without any placeholders
Remove extraneous
fprefix(F541)
Line range hint
233-298: LGTM!The
RandomUniformNoiseclass is a well-implemented custom Kornia augmentation. It follows the Kornia augmentation pattern and has a clear docstring explaining its usage.Tools
Ruff
210-210: f-string without any placeholders
Remove extraneous
fprefix(F541)
Line range hint
300-514: LGTM!The
KorniaAugmenterclass is a well-implementedIterDataPipethat applies various augmentations to images and instances using Kornia. The code is clean, well-documented, and easy to understand.tests/data/test_pipelines.py (1)
95-95: LGTM!The changes to the
test_topdownconfmapspipelinefunction are approved.Defining the
crop_hwvariable at the beginning of the function and passing it to theTopdownConfmapsPipelineconstructor in multiple instances enhances the configurability of the pipeline. This allows different cropping dimensions to be specified directly in the test cases, improving the flexibility of the test setup.Also applies to: 111-114, 189-192, 269-272
sleap_nn/tracking/tracker.py (13)
84-178: LGTM!The code changes are approved.
180-246: LGTM!The code changes are approved.
248-282: LGTM!The code changes are approved.
284-286: LGTM!The code changes are approved.
288-305: LGTM!The code changes are approved.
307-359: LGTM!The code changes are approved.
361-365: LGTM!The code changes are approved.
367-401: LGTM!The code changes are approved.
445-460: LGTM!The code changes are approved.
462-484: LGTM!The code changes are approved.
487-570: LGTM!The code changes are approved.
Tools
Ruff
515-515: Loop control variable
idxnot used within loop body(B007)
515-515: Skip the Ruff warning.The Ruff warning is a false positive. The loop control variable
idxis used to index intoref_candidate_listandshifted_ptswithin the loop body.Tools
Ruff
515-515: Loop control variable
idxnot used within loop body(B007)
572-604: LGTM!The code changes are approved.
sleap_nn/training/model_trainer.py (6)
25-25: LGTM!The code changes are approved.
93-123: LGTM!The code changes for computing the crop size look good. Using the
find_instance_crop_sizefunction to derive an appropriate crop size based on the training labels and config settings is a nice addition.
298-325: Great job improving the training process!The changes enhance the robustness and functionality of the training process:
- The try-except-finally block ensures proper handling of exceptions during training.
- The
ckpt_pathparameter allows resuming training from a checkpoint, which is a useful feature.- Saving the final config with the wandb run ID and total model parameters is excellent for tracking and reproducibility.
Tools
Ruff
311-311: Ambiguous variable name:
l(E741)
277-278: LGTM!Using a default name "skeleton-0" when the skeleton name is not provided is a good fallback to ensure the config can be saved correctly.
Line range hint
1-1:Tools
Ruff
27-27:
torchvision.models.swin_transformer.Swin_T_Weightsimported but unusedRemove unused import
(F401)
28-28:
torchvision.models.swin_transformer.Swin_S_Weightsimported but unusedRemove unused import
(F401)
311-311: Minor: Consider renaming variablelto a more descriptive name if it doesn't make the line too long. Okay to ignore for now given the narrow scope.Ruff reports that the variable name
lis ambiguous. While a more descriptive name would be better, renaming it might make the line too long. Since this is a minor issue in a very narrow scope (within a list comprehension), it can be ignored for now.Tools
Ruff
311-311: Ambiguous variable name:
l(E741)
sleap_nn/inference/predictors.py (8)
21-21: LGTM!The renaming of the function to
apply_pad_to_stridereflects a clearer intent of the function's purpose.
54-54: LGTM!The update to the
preprocessattribute ensures that the image padding logic is consistently applied during preprocessing.
370-372: LGTM!The addition of the
trackerattribute allows for the association of detections over time, enabling tracking of predicted instances across frames.
376-390: LGTM!The removal of the
output_strideandpafs_output_strideparameters indicates a shift in how output strides are managed, as they are now derived from configuration objects rather than being passed directly as parameters. This change enhances the flexibility and configurability of the model's inference process.Also applies to: 510-527
390-390: LGTM!The addition of the
trackerattribute allows for the association of detections over time, enabling tracking of predicted instances across frames.
Line range hint
749-1064:
1087-1087: LGTM!The addition of the
trackerattribute allows for the association of detections over time, enabling tracking of predicted instances across frames.
Line range hint
1391-1504: LGTM!The addition of the tracking parameters to the
mainfunction allows users to enable tracking functionality directly from the main entry point of the application, providing greater control over the inference process.
This reverts commit 9d65cc7.
There was a problem hiding this comment.
Actionable comments posted: 2
Outside diff range and nitpick comments (1)
tests/assets/minimal_instance/training_config.yaml (1)
56-56: Consider using an anchor part if applicable.Setting
anchor_parttonullmeans the model will not use any specific part as an anchor for the centered instance head. While this makes sense if there is no natural choice for an anchor, using an anchor part can provide a stable reference point and make the predictions more robust.If your skeleton topology has a part that can serve as a reliable anchor (e.g., the head or the root of the skeleton), consider specifying it as the
anchor_partinstead of usingnull.
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (8)
- sleap_nn/inference/predictors.py (1 hunks)
- tests/assets/minimal_instance/initial_config.yaml (1 hunks)
- tests/assets/minimal_instance/training_config.yaml (1 hunks)
- tests/assets/minimal_instance_bottomup/initial_config.yaml (2 hunks)
- tests/assets/minimal_instance_bottomup/training_config.yaml (2 hunks)
- tests/assets/minimal_instance_centroid/initial_config.yaml (2 hunks)
- tests/assets/minimal_instance_centroid/training_config.yaml (3 hunks)
- tests/inference/test_predictors.py (3 hunks)
Files skipped from review as they are similar to previous changes (3)
- tests/assets/minimal_instance_bottomup/initial_config.yaml
- tests/assets/minimal_instance_centroid/initial_config.yaml
- tests/inference/test_predictors.py
Additional context used
yamllint
tests/assets/minimal_instance/training_config.yaml
[error] 1-1: wrong new line character: expected \n
(new-lines)
Additional comments not posted (13)
tests/assets/minimal_instance/initial_config.yaml (3)
29-30: Monitor the impact of changes tofilters_rateandmax_stride.Reducing
filters_ratefrom2to1.5will decrease the number of filters in each subsequent layer, which may reduce the model's capacity and performance.Reducing
max_stridefrom16to8will increase the spatial resolution of the feature maps, which may improve the model's ability to capture fine-grained details but also increase computational cost.Please monitor the model's performance metrics (e.g., accuracy, loss) and resource usage (e.g., memory, runtime) to ensure these changes are beneficial and do not introduce performance regressions or exceed resource constraints.
35-35: Verify the impact of disablingup_interpolate.Setting
up_interpolatetofalsesuggests a change in how the model handles upsampling, potentially using transposed convolutions instead of interpolation.This change may affect the output resolution, quality, or artifacts in the generated feature maps.
Please compare the output quality and artifacts with the previous setting (
up_interpolate: true) to ensure this change is beneficial and does not introduce any undesired effects.
43-43: Verify the impact of settinganchor_parttonull.Changing
anchor_partfrom0tonullsuggests a modification in how the model identifies or utilizes anchor points in its processing.This change may affect the model's ability to localize or track specific parts or regions of interest.
Please verify the model's output and performance metrics (e.g., localization accuracy, tracking precision) to ensure this change is beneficial and does not introduce any undesired effects on the model's localization or tracking capabilities.
tests/assets/minimal_instance_centroid/training_config.yaml (5)
37-37: Reducedfilters_ratemay impact model complexity.Decreasing the
filters_ratefrom 2 to 1.5 will reduce the number of filters in each subsequent layer of the model. This change can:
- Reduce the model's capacity to learn complex features.
- Help prevent overfitting, especially on smaller datasets.
Monitor the model's performance to ensure it still learns effectively with this reduced complexity.
38-38: Reducedmax_stridemay improve spatial resolution.Decreasing the
max_stridefrom 16 to 8 will reduce the maximum stride of the convolutional layers in the model. This change can:
- Allow the model to capture finer-grained spatial details.
- Increase computational cost and memory usage.
Ensure that the computational resources are sufficient to handle this increased spatial resolution.
43-43: Switching to transposed convolution for upsampling.Changing
up_interpolatefromtruetofalsewill use transposed convolution instead of interpolation for upsampling in the model. This change can:
- Allow the model to learn to generate more detailed outputs.
- Potentially introduce checkerboard artifacts in the output.
Monitor the model's output for any artifacts and consider using techniques like kernel size adjustment or output padding to mitigate them if necessary.
50-50: Clarify the implications of settinganchor_parttonull.Changing
anchor_partfrom 0 tonullsuggests that the model will no longer use a fixed anchor part for the centroid prediction. This change raises the following questions:
- How will the absence of a fixed anchor part affect the model's centroid predictions?
- Will this change require modifications to the post-processing code that handles the model's output?
Please provide more context on the motivation behind this change and clarify how it will be handled in the codebase.
47-47: Renamedbottomuptobottom_upfor clarity.Renaming
bottomuptobottom_upimproves the readability of the configuration. However, this change requires updating all references to this parameter in the codebase to avoid breaking the code.Please run the following command to find all references to
bottomupin the codebase and ensure they are updated tobottom_up:tests/assets/minimal_instance_bottomup/training_config.yaml (3)
37-37: Clarify the reason for reducingfilters_rate.The
filters_rateparameter inmodel_config.backbone_confighas been reduced from2to1.5. This change may decrease the model's ability to learn complex features.Please provide clarification on the motivation behind this reduction and how it aligns with the desired model performance.
38-38: Confirm the impact of reducingmax_stride.The
max_strideparameter inmodel_config.backbone_confighas been decreased from16to8. This change may enhance the model's spatial resolution and detail capture.Please confirm that this reduction aligns with the desired model behavior and performance expectations.
43-43: Clarify the reason for changingup_interpolatetofalse.The
up_interpolateparameter inmodel_config.backbone_confighas been changed fromtruetofalse. This modification alters the upsampling behavior in the model architecture.Please provide clarification on the motivation behind this change and the expected benefits it brings to the model's performance.
tests/assets/minimal_instance/training_config.yaml (2)
40-41: LGTM!The changes to
filters_rateandmax_stridein themodel_configare approved.Reducing the
filters_ratecan help control model complexity and overfitting. Lowering themax_strideallows the model to preserve more spatial information, which is important for the centered instance head.
46-46: Verify the impact of using transposed convolutions for upsampling.Setting
up_interpolatetofalsemeans the model will use transposed convolutions instead of interpolation for upsampling in the decoder. While transposed convolutions can learn to upsample adaptively, they may introduce checkerboard artifacts.Please verify that using transposed convolutions improves the model performance on your specific data and use case. If not, consider reverting to interpolation for simplicity.
This PR addresses Issue: #56. Additionally, this also modifies
make_line_subsfunction ininference/paf_grouping.pyby using interpolate function to generate candidates.Summary by CodeRabbit
Summary by CodeRabbit
New Features
Bug Fixes
Documentation
Tests
Chores