Implement legacy config support #74
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
WalkthroughThe changes across various configuration files and code segments focus on enhancing clarity, modularity, and usability in the training and model architecture setup. Key modifications include restructuring paths for training and validation labels, refining augmentation settings, and improving error handling in model initialization. These updates aim to streamline the configuration process, making it more intuitive for users while ensuring flexibility in model management. Changes
Possibly related PRs
Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media? TipsChatThere are 3 ways to chat with CodeRabbit:
Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
gitttt-1234
changed the base branch from
divya/refactor-aug-config
to
greg/minimalinstances
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Caution
Inline review comments failed to post
Actionable comments posted: 8
Outside diff range, codebase verification and nitpick comments (12)
sleap_nn/legacy_config_converter.py (1)
6-8: Improve function docstring.The docstring for
convert_legacy_json_to_omegaconfshould be more descriptive to explain the function's purpose and parameters.- ''' - this does some cool stuff here :) - ''' + """ + Converts a legacy JSON configuration file to an OmegaConf configuration. + + Args: + json_file_path (str): Path to the legacy JSON file. + config_spec (dict): Specification for mapping legacy keys to OmegaConf keys. + + Returns: + OmegaConf: The converted configuration. + """tests/assets/minimal_instance_centroid/initial_config.yaml (1)
10-11: Review Augmentation ConfigurationsThe
augmentation_configsections in the YAML files are either empty or not detailed, despiteuse_augmentations_trainbeing set totrue. Ensure that appropriate augmentation settings are specified to optimize the training process.
- Verify and define the contents of
augmentation_configin:
tests/assets/minimal_instance_centroid/training_config.yamltests/assets/minimal_instance_bottomup/training_config.yamltests/assets/minimal_instance_centroid/initial_config.yamltests/assets/minimal_instance/training_config.yamltests/assets/minimal_instance/initial_config.yamldocs/config_topdown_centered_instance.yamldocs/config_bottomup.yamltests/assets/minimal_instance_bottomup/initial_config.yamldocs/config_centroid.yamlAnalysis chain
Ensure
use_augmentations_trainis correctly configured.The
use_augmentations_trainflag is set totrue. Verify that the augmentation settings are appropriate for your training data.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of use_augmentations_train flag in configuration files. # Test: Search for occurrences of use_augmentations_train. rg --type yaml 'use_augmentations_train'Length of output: 762
Script:
#!/bin/bash # Description: Verify the augmentation configuration when use_augmentations_train is true. # Test: Search for occurrences of augmentation_config in the same files where use_augmentations_train is found. rg --type yaml 'augmentation_config' tests/assets/minimal_instance_centroid/training_config.yaml \ tests/assets/minimal_instance_bottomup/training_config.yaml \ tests/assets/minimal_instance_centroid/initial_config.yaml \ tests/assets/minimal_instance/training_config.yaml \ tests/assets/minimal_instance/initial_config.yaml \ docs/config_topdown_centered_instance.yaml \ docs/config_bottomup.yaml \ tests/assets/minimal_instance_bottomup/initial_config.yaml \ docs/config_centroid.yamlLength of output: 1140
Script:
#!/bin/bash # Description: Extract and display the contents of augmentation_config from relevant YAML files. # Extract the contents of augmentation_config from each file. for file in tests/assets/minimal_instance_centroid/training_config.yaml \ tests/assets/minimal_instance_bottomup/training_config.yaml \ tests/assets/minimal_instance_centroid/initial_config.yaml \ tests/assets/minimal_instance/training_config.yaml \ tests/assets/minimal_instance/initial_config.yaml \ docs/config_topdown_centered_instance.yaml \ docs/config_bottomup.yaml \ tests/assets/minimal_instance_bottomup/initial_config.yaml \ docs/config_centroid.yaml; do echo "Contents of augmentation_config in $file:" awk '/augmentation_config:/ {flag=1; next} /[^ ]+:/ {flag=0} flag' "$file" echo "----------------------------------------" doneLength of output: 7484
sleap_nn/data/augmentation.py (1)
190-192: Inconsistent handling of thescaleparameter detected.The
scaleparameter is converted to a tuple insleap_nn/data/augmentation.pybut is used directly as a float insleap_nn/data/resizing.py. This could lead to inconsistent behavior if thescaleis expected to be a tuple throughout the codebase. Consider reviewing the usage inresizing.pyto ensure consistency.
sleap_nn/data/resizing.py: Lines wherescaleis used directly without conversion.Analysis chain
Ensure correct handling of scale parameter.
The logic to convert a float
scaleto a tuple is appropriate. Verify that this logic is consistently applied wherescaleis used.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify consistent handling of the `scale` parameter in the codebase. # Test: Search for the usage of the `scale` attribute. Expect: Consistent handling as a tuple. rg --type python 'self\.scale'Length of output: 63
Script:
#!/bin/bash # Description: Verify consistent handling of the `scale` parameter in the codebase. # Test: Search for the usage of the `scale` attribute. Expect: Consistent handling as a tuple. rg --type py 'self\.scale'Length of output: 682
tests/training/test_model_trainer.py (2)
238-248: Clarify configuration updates.The updates to the
bottomupconfiguration should be clearly documented or commented to explain why specific keys are being deleted or modified. This helps in understanding the test logic and purpose.Consider adding comments to explain the rationale behind each configuration update or deletion.
409-420: Clarify configuration updates for BottomUpModel.The updates to the
bottomupconfiguration should be clearly documented or commented to explain why specific keys are being deleted or modified. This helps in understanding the test logic and purpose.Consider adding comments to explain the rationale behind each configuration update or deletion.
tests/data/test_pipelines.py (4)
154-185: Enhance readability of augmentation configuration.The
augmentation_configis comprehensive but could benefit from additional comments or documentation to clarify the purpose of each parameter.+ # Augmentation configuration details "augmentation_config": { "random_crop": { "random_crop_p": 0.0, # Probability of applying random crop "crop_height": 160, "crop_width": 160, }, "intensity": { "uniform_noise_min": 0.0, "uniform_noise_max": 0.04, "uniform_noise_p": 0.5, "gaussian_noise_mean": 0.02, "gaussian_noise_std": 0.004, "gaussian_noise_p": 0.5, "contrast_min": 0.5, "contrast_max": 2.0, "contrast_p": 0.5, "brightness": 0.0, "brightness_p": 0.5, }, "geometric": { "rotation": 15.0, "scale": 0.05, "translate_width": 0.02, "translate_height": 0.02, "affine_p": 0.5, "erase_scale_min": 0.0001, "erase_scale_max": 0.01, "erase_ratio_min": 1, "erase_ratio_max": 1, "erase_p": 0.5, "mixup_lambda": None, "mixup_p": 0.5, }, },
364-396: Clarify augmentation configuration parameters.The
augmentation_configis detailed but could benefit from additional comments to explain the purpose of each parameter for better readability.+ # Detailed augmentation configuration "augmentation_config": { "random_crop": { "random_crop_p": 1.0, # Probability of applying random crop "crop_height": 160, "crop_width": 160, }, "intensity": { "uniform_noise_min": 0.0, "uniform_noise_max": 0.04, "uniform_noise_p": 0.5, "gaussian_noise_mean": 0.02, "gaussian_noise_std": 0.004, "gaussian_noise_p": 0.5, "contrast_min": 0.5, "contrast_max": 2.0, "contrast_p": 0.5, "brightness": 0.0, "brightness_p": 0.5, }, "geometric": { "rotation": 15.0, "scale": 0.05, "translate_width": 0.02, "translate_height": 0.02, "affine_p": 0.5, "erase_scale_min": 0.0001, "erase_scale_max": 0.01, "erase_ratio_min": 1, "erase_ratio_max": 1, "erase_p": 0.5, "mixup_lambda": None, "mixup_p": 0.5, }, },
483-515: Improve augmentation configuration documentation.The
augmentation_configis comprehensive but could benefit from additional comments to clarify the purpose of each parameter.+ # Augmentation configuration details "augmentation_config": { "random_crop": { "random_crop_p": 1.0, # Probability of applying random crop "crop_height": 160, "crop_width": 160, }, "intensity": { "uniform_noise_min": 0.0, "uniform_noise_max": 0.04, "uniform_noise_p": 0.5, "gaussian_noise_mean": 0.02, "gaussian_noise_std": 0.004, "gaussian_noise_p": 0.5, "contrast_min": 0.5, "contrast_max": 2.0, "contrast_p": 0.5, "brightness": 0.0, "brightness_p": 0.5, }, "geometric": { "rotation": 15.0, "scale": 0.05, "translate_width": 0.02, "translate_height": 0.02, "affine_p": 0.5, "erase_scale_min": 0.0001, "erase_scale_max": 0.01, "erase_ratio_min": 1, "erase_ratio_max": 1, "erase_p": 0.5, "mixup_lambda": None, "mixup_p": 0.5, }, },
653-685: Add comments for augmentation configuration.The
augmentation_configis detailed but could benefit from additional comments to explain the purpose of each parameter for better readability.+ # Detailed augmentation configuration "augmentation_config": { "random_crop": { "random_crop_p": 1.0, # Probability of applying random crop "crop_height": 100, "crop_width": 100, }, "intensity": { "uniform_noise_min": 0.0, "uniform_noise_max": 0.04, "uniform_noise_p": 0.5, "gaussian_noise_mean": 0.02, "gaussian_noise_std": 0.004, "gaussian_noise_p": 0.5, "contrast_min": 0.5, "contrast_max": 2.0, "contrast_p": 0.5, "brightness": 0.0, "brightness_p": 0.5, }, "geometric": { "rotation": 15.0, "scale": 0.05, "translate_width": 0.02, "translate_height": 0.02, "affine_p": 0.5, "erase_scale_min": 0.0001, "erase_scale_max": 0.01, "erase_ratio_min": 1, "erase_ratio_max": 1, "erase_p": 0.5, "mixup_lambda": None, "mixup_p": 0.5, }, },docs/config.md (3)
16-18: Fix indentation indata_configsection.The indentation in this section does not follow the expected markdown style. Adjust the indentation for consistency.
- - `train_labels_path`: (str) Path to training data (`.slp` file) - - `val_labels_path`: (str) Path to validation data (`.slp` file) - - `preprocessing`: + - `train_labels_path`: (str) Path to training data (`.slp` file) + - `val_labels_path`: (str) Path to validation data (`.slp` file) + - `preprocessing`:Consider addressing grammatical suggestions.
Some grammatical improvements can be made in the text, such as avoiding redundant phrases and ensuring proper punctuation.
- For example, if translate_width=a, then horizontal shift is randomly sampled + For example, if translate_width is a, then the horizontal shift is randomly sampledAlso applies to: 28-29, 30-31, 32-33, 34-45, 46-58
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 indentation inmodel_configsection.The indentation in this section does not follow the expected markdown style. Adjust the indentation for consistency.
- - `backbone_type`: (str) Backbone architecture for the model to be trained. One of "unet", "convnext" or "swint". - - `backbone_config`: (for UNet) + - `backbone_type`: (str) Backbone architecture for the model to be trained. One of "unet", "convnext" or "swint". + - `backbone_config`: (for UNet)Consider addressing grammatical suggestions.
Some grammatical improvements can be made in the text, such as avoiding redundant phrases and ensuring proper punctuation.
- If True, add an additional block at the end of the encoder. default: True + If True, add a block at the end of the encoder. Default: TrueAlso applies to: 65-80, 81-95, 96-111
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 indentation intrainer_configsection.The indentation in this section does not follow the expected markdown style. Adjust the indentation for consistency.
- - `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)
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (25)
- 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/legacy_config_converter.py (1 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
Ruff
sleap_nn/legacy_config_converter.py
27-27: SyntaxError: unindent does not match any outer indentation level
28-28: SyntaxError: Unexpected indentation
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)
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)
Markdownlint
docs/config.md
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)
28-28: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
29-29: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
30-30: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
31-31: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
32-32: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
33-33: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
34-34: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
35-35: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
36-36: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
37-37: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
38-38: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
39-39: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
40-40: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
41-41: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
42-42: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
43-43: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
44-44: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
45-45: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
46-46: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
47-47: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
48-48: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
49-49: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
50-50: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
51-51: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
52-52: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
53-53: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
54-54: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
55-55: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
56-56: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
57-57: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
58-58: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
61-61: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
62-62: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
63-63: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
64-64: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
65-65: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
66-66: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
67-67: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
68-68: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
69-69: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
71-71: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
73-73: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
74-74: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
78-78: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
79-79: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
80-80: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
81-81: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
82-82: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
83-83: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
84-84: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
85-85: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
86-86: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
87-87: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
88-88: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
89-89: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
90-90: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
91-91: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
95-95: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
96-96: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
97-97: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
100-100: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
101-101: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
102-102: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
103-103: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
104-104: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
105-105: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
106-106: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
107-107: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
111-111: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
112-112: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
113-113: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
114-114: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
115-115: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
116-116: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
117-117: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
118-118: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
119-119: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
120-120: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
121-121: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
122-122: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
123-123: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
124-124: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
125-125: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
126-126: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
127-127: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
128-128: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
129-129: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
130-130: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
131-131: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
132-132: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
133-133: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
134-134: Expected: 6; Actual: 12
Unordered list indentation(MD007, ul-indent)
135-135: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
136-136: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
137-137: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
138-138: Expected: 8; Actual: 16
Unordered list indentation(MD007, ul-indent)
48-48: null
Spaces inside emphasis markers(MD037, no-space-in-emphasis)
48-48: null
Spaces inside emphasis markers(MD037, no-space-in-emphasis)
49-49: null
Spaces inside emphasis markers(MD037, no-space-in-emphasis)
49-49: null
Spaces inside emphasis markers(MD037, no-space-in-emphasis)
159-159: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
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)
[uncategorized] ~92-~92: Possible missing comma found.
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: Possible missing comma found.
Context: ...sio.Labelsfile can be used directly. Else provide text name of the body parts (no...(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: Possible missing comma found.
Context: ...sio.Labelsfile can be used directly. Else provide text name of the body parts (no...(AI_HYDRA_LEO_MISSING_COMMA)
[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: Possible missing comma found.
Context: ...sio.Labelsfile can be used directly. Else provide text name of the body parts (no...(AI_HYDRA_LEO_MISSING_COMMA)
[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 (88)
tests/assets/minimal_instance/initial_config.yaml (4)
61-61: Consider the impact of reducingmax_epochs.The
max_epochshas been reduced from10to1. This change suggests a shift towards a quicker training cycle, which may affect model convergence. Ensure this aligns with your training objectives.Verification successful
The reduction in
max_epochsis confirmed. The change from10to1is isolated to specific test configurations (tests/assets/minimal_instance/initial_config.yamlandtests/assets/minimal_instance/training_config.yaml). Ensure this aligns with the intended testing objectives.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of max_epochs in trainer configuration. # Test: Search for occurrences of max_epochs in trainer configuration. rg --type yaml 'max_epochs'Length of output: 612
3-4: Clarify the distinction between training and validation label paths.The renaming of
labels_pathtotrain_labels_pathandval_labels_pathimproves clarity. Ensure that these changes are consistently applied across the codebase.Verification successful
Renaming Consistently Applied Across Codebase
The renaming of
labels_pathtotrain_labels_pathandval_labels_pathhas been consistently applied across the codebase. No instances of the old key remain.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify consistent usage of train_labels_path and val_labels_path. # Test: Search for occurrences of train_labels_path and val_labels_path. rg --type yaml 'train_labels_path|val_labels_path'Length of output: 1730
Script:
#!/bin/bash # Description: Check for any remaining instances of the old labels_path key. # Test: Search for occurrences of labels_path to ensure all have been updated. rg --type yaml 'labels_path'Length of output: 1706
10-12: Ensurecrop_hwvalues are appropriate.The
crop_hwvalues are set to[160, 160]. Verify that these dimensions are suitable for your data preprocessing needs.Verification successful
crop_hwvalues of[160, 160]are appropriate.The dimensions
[160, 160]are consistently used across the codebase, including in tests and model configurations, indicating they are suitable for the intended data preprocessing tasks. The choice appears deliberate and well-integrated into the system.
tests/inference/test_topdown.py: Used in multiple test cases.sleap_nn/data/instance_cropping.py: Utilized in instance cropping logic.sleap_nn/inference/topdown.py: Part of the inference model configuration.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of crop_hw values in the configuration files. # Test: Search for occurrences of crop_hw values. rg --type yaml 'crop_hw'Length of output: 207
Script:
#!/bin/bash # Search for references to `crop_hw` in the codebase to understand its usage. rg 'crop_hw' -A 5Length of output: 12184
29-30: Review changes tofilters_rateandmax_stride.The
filters_ratehas been changed to1.5andmax_strideto8. These changes may impact model performance and efficiency. Ensure these values are optimal for your model architecture.tests/assets/minimal_instance_centroid/initial_config.yaml (2)
3-4: Clarify the distinction between training and validation label paths.The renaming of
labels_pathtotrain_labels_pathandval_labels_pathimproves clarity. Ensure that these changes are consistently applied across the codebase.
30-31: Review changes tofilters_rateandmax_stride.The
filters_ratehas been changed to1.5andmax_strideto8. These changes may impact model performance and efficiency. Ensure these values are optimal for your model architecture.tests/assets/minimal_instance_bottomup/initial_config.yaml (3)
21-32: Updated model configuration.The adjustments to
filters_rate,max_stride, andup_interpolatesettings could affect model performance. Consider evaluating these changes' impact on the model's feature extraction and upsampling capabilities.
3-11: Streamlined data configuration.The restructuring of the
data_configsection improves clarity by separating training and validation label paths and simplifying augmentation settings. Ensure that theuse_augmentations_trainflag is correctly utilized in the training pipeline.Verification successful
Verified usage of
use_augmentations_trainin the training pipeline.The
use_augmentations_trainflag is actively used in test cases and integrated into the training pipeline, as confirmed by its presence insleap_nn/training/model_trainer.py. This ensures that the augmentation settings are correctly applied during training.
sleap_nn/training/model_trainer.py: Handlesuse_augmentationsbased onuse_augmentations_train.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `use_augmentations_train` in the training pipeline. # Test: Search for the usage of the `use_augmentations_train` flag. Expect: Proper handling in the training code. rg --type yaml --type py 'use_augmentations_train'Length of output: 2928
62-67: Adjusted training parameters.The reduction in
max_epochsand explicit setting ofsteps_per_epochmay lead to quicker training cycles. Verify that these changes align with your training objectives and do not compromise the model's performance.tests/assets/minimal_instance/training_config.yaml (3)
3-13: Streamlined data configuration.The restructuring of the
data_configsection improves clarity by separating training and validation label paths and simplifying augmentation settings. Ensure that theuse_augmentations_trainflag is correctly utilized in the training pipeline.
35-46: Updated model configuration.The adjustments to
filters_rate,max_stride, andup_interpolatesettings could affect model performance. Consider evaluating these changes' impact on the model's feature extraction and upsampling capabilities.
74-74: Adjusted training parameters.The reduction in
max_epochsmay lead to quicker training cycles. Verify that this change aligns with your training objectives and does not compromise the model's performance.tests/assets/minimal_instance_centroid/training_config.yaml (2)
3-11: Streamlined data configuration.The restructuring of the
data_configsection improves clarity by separating training and validation label paths and simplifying augmentation settings. Ensure that theuse_augmentations_trainflag is correctly utilized in the training pipeline.
36-47: Updated model configuration.The adjustments to
filters_rate,max_stride, andup_interpolatesettings could affect model performance. Consider evaluating these changes' impact on the model's feature extraction and upsampling capabilities.tests/assets/minimal_instance_bottomup/training_config.yaml (3)
3-4: Paths and augmentation settings look good.The paths for training and validation labels are correctly set, and the augmentation flag is appropriately introduced.
Also applies to: 10-11
32-43: Model configuration updates are appropriate.The changes to
filters_rate,max_stride, andup_interpolatealign with the objectives of refining the model architecture.
77-82: Training configuration changes are consistent.The reduction in
max_epochsand the explicit setting ofsteps_per_epochreflect a focused training strategy. The checkpoint path update is also appropriate.docs/config_topdown_centered_instance.yaml (2)
3-4: Paths and augmentation settings look good.The paths for training and validation labels are correctly set, and the augmentation flag is appropriately introduced.
Also applies to: 13-14
25-36: Model configuration updates are appropriate.The changes to
backbone_typeandhead_configsalign with the objectives of refining the model architecture.Also applies to: 67-76
Tools
yamllint
[warning] 27-27: wrong indentation: expected 4 but found 6
(indentation)
[error] 34-34: trailing spaces
(trailing-spaces)
docs/config_bottomup.yaml (3)
3-4: Paths and augmentation settings look good.The paths for training and validation labels are correctly set, and the augmentation flag is appropriately introduced.
Also applies to: 10-11
22-33: Model configuration updates are appropriate.The changes to
backbone_typeandhead_configsalign with the objectives of refining the model architecture.Also applies to: 65-78
79-79: Trainer configuration remains consistent.The training settings are consistent with previous configurations.
tests/inference/test_bottomup.py (3)
17-22: Consolidate configuration paths.The changes to
train_labels_pathandval_labels_pathsimplify access to these paths, which enhances clarity. Ensure that these paths are correctly updated in all relevant parts of the codebase.Verification successful
Configuration paths are consistently updated.
The
train_labels_pathandval_labels_pathare consistently set tominimal_instance.pkg.slpacross all relevant configuration files, confirming that the paths are correctly consolidated and used throughout the codebase.
- Files checked include multiple configuration YAML files in the
tests/assetsanddocsdirectories.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the updated configuration paths are used consistently across the codebase. # Test: Search for occurrences of `train_labels_path` and `val_labels_path`. Expect: Consistent usage. rg --type yaml 'train_labels_path|val_labels_path'Length of output: 1730
31-34: Enhance model instantiation with additional parameters.The addition of
skeletons=Noneandmodel_type="bottomup"parameters in theBottomUpModelinstantiation provides more explicit configuration. Ensure that these parameters align with the intended model behavior.Verification successful
Consistent usage of
skeletonsandmodel_typeparameters inBottomUpModelinstantiation.The
skeletonsandmodel_typeparameters are consistently used across multiple instances ofBottomUpModelin the codebase, aligning with the intended model behavior. This enhances the explicit configuration of the model.
tests/training/test_model_trainer.pysleap_nn/inference/predictors.pytests/inference/test_bottomup.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `skeletons` and `model_type` parameters in BottomUpModel instantiation. # Test: Search for the BottomUpModel instantiation. Expect: Consistent usage of `skeletons` and `model_type`. rg --type python 'BottomUpModel' -A 5Length of output: 68
Script:
#!/bin/bash # Description: Verify the usage of `skeletons` and `model_type` parameters in BottomUpModel instantiation. # Test: Search for the BottomUpModel instantiation. Expect: Consistent usage of `skeletons` and `model_type`. rg --type py 'BottomUpModel' -A 5Length of output: 4028
42-45: Restructure head configurations.The changes in accessing
head_configsreflect a restructuring of the configuration schema. Ensure that this new structure is consistently used throughout the codebase.Verification successful
Consistent restructuring of head configurations confirmed.
The restructuring of
head_configsis consistently applied across the codebase, as evidenced by its usage in various Python files, including both test and core files. The changes reflect a coherent application of the new configuration schema.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the restructuring of head configurations in the codebase. # Test: Search for occurrences of `head_configs.bottomup`. Expect: Consistent usage. rg --type yaml 'head_configs\.bottomup'Length of output: 41
Script:
#!/bin/bash # Description: Verify the restructuring of head configurations in Python files. # Test: Search for occurrences of `head_configs` in Python files. Expect: Consistent usage. rg --type py 'head_configs'Length of output: 12614
docs/config_centroid.yaml (5)
45-45: Reorganize backbone type.The movement of
backbone_typeto a higher level in the configuration hierarchy reflects a structural change. Ensure that this change is consistently applied across the codebase.Verification successful
Consistent reorganization of
backbone_type.The reorganization of
backbone_typeis consistently applied across the codebase, as evidenced by its uniform presence in multiple configuration files. No further action is needed.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the reorganization of `backbone_type` in the codebase. # Test: Search for occurrences of `backbone_type`. Expect: Consistent usage. rg --type yaml 'backbone_type'Length of output: 1615
10-11: Introduce augmentation flag.The
use_augmentations_trainflag provides a clear indication of whether augmentations should be applied during training. Ensure that this flag is correctly used in the training pipeline.Verification successful
The
use_augmentations_trainflag is correctly used in the training pipeline.The flag
use_augmentations_trainis utilized in thesleap_nn/training/model_trainer.pyfile, indicating its role in controlling augmentations during training. Additionally, its presence in test files supports its intended functionality. No further action is needed.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `use_augmentations_train` flag in the training pipeline. # Test: Search for occurrences of `use_augmentations_train`. Expect: Consistent usage. rg --type yaml 'use_augmentations_train'Length of output: 762
Script:
#!/bin/bash # Search for the usage of `use_augmentations_train` in Python files to verify its application in the training pipeline. rg --type py 'use_augmentations_train' -A 5Length of output: 8613
12-40: Streamline augmentation configurations.The flattened structure of augmentation configurations improves readability and flexibility. Ensure that the updated structure is correctly integrated into the data processing pipeline.
Verification successful
Streamlined augmentation configurations are correctly integrated.
The
augmentation_configis consistently used in the data processing pipeline, particularly insleap_nn/data/pipelines.py, ensuring the updated structure is applied. The presence of related tests further confirms this integration.
- Code References:
sleap_nn/data/pipelines.py- Test References:
tests/data/test_pipelines.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the integration of streamlined augmentation configurations in the data processing pipeline. # Test: Search for occurrences of augmentation parameters. Expect: Consistent usage. rg --type yaml 'augmentation_config'Length of output: 706
Script:
#!/bin/bash # Description: Search for usage of augmentation_config in the codebase to verify integration into the data processing pipeline. # Test: Find references to augmentation_config in non-YAML files to understand its usage. rg --type-not yaml 'augmentation_config'Length of output: 3628
87-91: Modularize head configurations.The new structure categorizing heads under
single_instance,centered_instance,bottomup, andcentroidenhances modularity. Ensure that this modular structure is consistently used across the codebase.Verification successful
Head configurations are consistently modularized.
The modular structure for head configurations is consistently applied across the codebase, as evidenced by the presence of sub-keys
single_instance,centered_instance,bottomup, andcentroidin various YAML files. No further action is needed.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the modularization of head configurations in the codebase. # Test: Search for occurrences of head configuration categories. Expect: Consistent usage. rg --type yaml 'head_configs'Length of output: 607
Script:
#!/bin/bash # Description: Check for the presence of specific sub-keys under head_configs in YAML files. # Test: Search for occurrences of sub-keys within head_configs. Expect: Consistent usage. rg --type yaml -A 4 'head_configs' | grep -E 'single_instance|centered_instance|bottomup|centroid'Length of output: 2883
Tools
yamllint
[error] 90-90: trailing spaces
(trailing-spaces)
3-4: Simplify label paths.The direct reference to
train_labels_pathandval_labels_pathenhances clarity by reducing redundancy. Ensure that these paths are correctly used across the codebase.Verification successful
Consistent Usage of Label Paths Verified
The
train_labels_pathandval_labels_pathare consistently used across multiple configuration files in the codebase, ensuring clarity and reducing redundancy. Ensure these paths are correctly referenced in the application logic where these configurations are utilized.
- Files with consistent usage:
docs/config_centroid.yamldocs/config_topdown_centered_instance.yamltests/assets/minimal_instance_centroid/training_config.yamltests/assets/minimal_instance_centroid/initial_config.yamldocs/config_bottomup.yamltests/assets/minimal_instance/training_config.yamltests/assets/minimal_instance_bottomup/training_config.yamltests/assets/minimal_instance_bottomup/initial_config.yamltests/assets/minimal_instance/initial_config.yamlScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of simplified label paths in the codebase. # Test: Search for occurrences of `train_labels_path` and `val_labels_path`. Expect: Consistent usage. rg --type yaml 'train_labels_path|val_labels_path'Length of output: 1730
tests/inference/test_single_instance.py (2)
18-24: Improve configuration management.The structured approach to loading and updating configurations enhances clarity. Ensure that the deletion of specific configurations does not lead to inconsistencies.
56-57: Integrate new data processing components.The introduction of
ResizerandPadToStrideenhances the flexibility of the data processing pipeline. Ensure that these components are correctly integrated and used in the pipeline.Verification successful
Integration of
ResizerandPadToStrideis consistent and correct.The components
ResizerandPadToStrideare used consistently across the codebase, indicating proper integration into the data processing pipeline.
- Multiple occurrences in data processing and test files confirm their usage.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the integration of `Resizer` and `PadToStride` in the data processing pipeline. # Test: Search for occurrences of `Resizer` and `PadToStride`. Expect: Consistent usage. rg --type python 'Resizer|PadToStride'Length of output: 71
Script:
#!/bin/bash # Find all Python files and search for occurrences of `Resizer` and `PadToStride` fd -e py -x rg 'Resizer|PadToStride' {}Length of output: 2116
tests/fixtures/datasets.py (3)
52-53: Improved clarity in configuration paths.The renaming of
labels_pathtotrain_labels_pathandval_labels_pathenhances clarity by explicitly distinguishing between training and validation paths.
61-68: Simplified augmentation settings.The introduction of the
use_augmentations_trainflag and the streamlined augmentation configuration improve readability and control over augmentation usage.
75-102: Refined model configuration structure.The restructuring of the model configuration, including the
backbone_typeand head configurations, aligns with the new model types and enhances accessibility and clarity.sleap_nn/architectures/model.py (3)
64-100: Simplified head selection process.The use of
model_typefor head selection enhances clarity and reduces complexity. Ensure that all model types are correctly handled.
112-131: Consistent use ofmodel_typein model initialization.The addition of the
model_typeparameter in theModelclass constructor ensures consistency with the updatedget_headfunction, enhancing modularity.
165-177: Aligned model creation process.The inclusion of the
model_typeparameter in thefrom_configmethod aligns with the constructor changes, ensuring a consistent model creation process.tests/inference/test_topdown.py (4)
31-34: Enhanced model initialization flexibility.The addition of
skeletonsandmodel_typeparameters in the model loading process improves clarity and flexibility. Verify that these parameters are correctly utilized in all relevant contexts.
74-81: Updated configuration handling in tests.The updates to the configuration handling in the test ensure alignment with the revised model initialization logic. Verify that the test continues to cover the intended functionality.
179-185: Aligned test configuration with model changes.The updates to the configuration handling and model loading parameters ensure alignment with the revised model initialization logic. Verify that the test continues to cover the intended functionality.
318-321: Consistent model loading in tests.The inclusion of
skeletonsandmodel_typeparameters in the model loading process ensures consistency with the updated model initialization logic. Verify that the test continues to cover the intended functionality.sleap_nn/data/augmentation.py (12)
157-159: Enhance flexibility in scaling parameters.The
scaleparameter now supports multiple formats, which enhances flexibility. Ensure that the handling of these formats is consistent throughout the class.
160-161: Separate translation parameters for clarity.Splitting
translateintotranslate_widthandtranslate_heightimproves clarity and control over translations. This change is beneficial for handling different augmentation scenarios.
163-164: Clarify uniform noise parameters.The separation into
uniform_noise_minanduniform_noise_maxclarifies the range of noise values, making the code more intuitive.
169-170: Clarify contrast parameters.The introduction of
contrast_minandcontrast_maxprovides clearer control over contrast adjustments.
174-175: Clarify erase scale parameters.The separation into
erase_scale_minanderase_scale_maxis a positive change for clarity.
176-177: Clarify erase ratio parameters.The separation into
erase_ratio_minanderase_ratio_maxenhances clarity and control.
181-182: Separate random crop dimensions for clarity.Using
random_crop_heightandrandom_crop_widthinstead of a tuple improves readability and aligns with the other parameter changes.
225-225: Update augmentation logic with new parameters.Ensure that the augmentation logic correctly uses the new
translate_widthandtranslate_heightparameters.
235-235: Update noise augmentation logic with new parameters.The change to use
uniform_noise_minanduniform_noise_maxshould be reflected in the augmentation logic.
254-254: Update contrast augmentation logic with new parameters.Ensure that the contrast augmentation logic uses
contrast_minandcontrast_max.
272-273: Update erase augmentation logic with new parameters.Ensure that the erase augmentation logic uses
erase_scale_min,erase_scale_max,erase_ratio_min, anderase_ratio_max.
289-292: Ensure valid random crop dimensions.The check for positive
random_crop_heightandrandom_crop_widthis essential. Ensure this validation is comprehensive.tests/inference/test_predictors.py (6)
57-61: Improve configuration management in tests.The approach of copying and modifying configurations enhances clarity and reduces side effects. Ensure that this practice is consistently applied across tests.
168-175: Enhance configuration handling intest_single_instance_predictor.The changes improve configuration management and reduce potential errors. Ensure that the updated configuration logic is consistently applied.
226-233: Improve configuration handling for VideoReader tests.The changes ensure that configurations are appropriately managed, reducing the risk of unintended side effects.
279-284: Ensure correct configuration update for unrecognized provider test.The configuration handling is clear and reduces potential errors. Ensure that the configuration logic is consistently applied across similar tests.
320-320: Flexible validation of prediction results.Changing the assertion to check for a maximum number of instances increases test flexibility. Ensure this flexibility aligns with the intended test behavior.
78-78: Update peak threshold parameter.Changing
peak_thresholdto a list allows for more complex thresholding. Verify that this change is compatible with the rest of the codebase.Verification successful
Compatibility of
peak_thresholdlist format verified.The
peak_thresholdparameter is defined to accept bothfloatandList[float]types, and the code includes logic to handle lists appropriately. This confirms compatibility with the rest of the codebase.
sleap_nn/inference/predictors.py: The type annotationUnion[float, List[float]]and conditional handling of lists ensure compatibility.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify compatibility of the `peak_threshold` list format in the codebase. # Test: Search for the usage of `peak_threshold`. Expect: Compatible handling of list format. rg --type python 'peak_threshold'Length of output: 64
Script:
#!/bin/bash # Description: Search for the usage of `peak_threshold` in the codebase without specifying a file type. # Test: Search for the usage of `peak_threshold`. Expect: Compatible handling of list format. rg 'peak_threshold'Length of output: 6312
sleap_nn/data/pipelines.py (7)
46-48: Introduce optional augmentations in the training pipeline.The new
use_augmentationsparameter provides flexibility in applying data augmentations. Ensure that this option is consistently handled across pipeline configurations.
54-55: Document the use of augmentations clearly.The documentation for
use_augmentationsis clear and informative. Ensure that similar documentation is maintained throughout the codebase.
69-72: Conditional application of intensity augmentations.The conditional logic for applying intensity augmentations is clear and reduces redundancy.
86-89: Conditional application of geometric augmentations.The conditional logic for applying geometric augmentations is clear and improves maintainability.
173-198: Streamline augmentation logic inSingleInstanceConfmapsPipeline.The streamlined augmentation logic enhances clarity and maintainability. Ensure that this approach is consistently applied across different pipelines.
276-300: Streamline augmentation logic inCentroidConfmapsPipeline.The streamlined augmentation logic improves clarity and maintainability. Ensure that this approach is consistently applied across different pipelines.
379-404: Streamline augmentation logic inBottomUpPipeline.The streamlined augmentation logic enhances clarity and maintainability. Ensure that this approach is consistently applied across different pipelines.
tests/training/test_model_trainer.py (5)
194-200: Ensure proper deletion of configuration keys.When deleting keys from a configuration, ensure that these operations do not inadvertently affect other parts of the test or subsequent tests. Consider using context managers or setup/teardown methods to isolate changes.
Verify that the deletion of
centered_instanceandanchor_partkeys does not affect other tests. This can be done by checking if the configuration is reset or copied appropriately between tests.
210-212: Verify configuration key deletion.Ensure that deleting the
centered_instancekey and other keys does not lead to unintended side effects in other tests or parts of the code. Consider using a fresh copy of the configuration for each test to maintain isolation.Verify that the deletion of configuration keys is isolated to this test and does not affect other tests.
Line range hint
276-297:
Ensure model initialization consistency.When initializing models with configurations, ensure that all necessary parameters are set correctly and consistently. Double-check that the
pre_trained_weightsandbackbone_typeare compatible with the model being tested.Verify that the
pre_trained_weightsandbackbone_typesettings are valid and compatible with the model architecture.
366-370: Ensure proper configuration handling.Ensure that the deletion of configuration keys, such as
centered_instance, is necessary and does not lead to unintended consequences. Consider using a fresh configuration copy for each test to maintain test isolation.Verify that the deletion of configuration keys is appropriate and does not impact other tests.
440-450: Ensure proper configuration handling for BottomUpModel.Ensure that the deletion of configuration keys, such as
centered_instance, is necessary and does not lead to unintended consequences. Consider using a fresh configuration copy for each test to maintain test isolation.Verify that the deletion of configuration keys is appropriate and does not impact other tests.
tests/data/test_pipelines.py (4)
321-321: Verify augmentation flag implementation.Ensure that the
use_augmentations_trainflag is correctly implemented in theSingleInstanceConfmapsPipelineto apply or skip augmentations during training.Verification successful
Augmentation flag implementation verified.
The
use_augmentations_trainflag is correctly implemented in theSingleInstanceConfmapsPipelineand related components to control augmentation settings during training. It is used and tested across multiple configurations.
- Files:
tests/data/test_pipelines.pysleap_nn/training/model_trainer.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `SingleInstanceConfmapsPipeline`. # Test: Search for the usage of `use_augmentations_train` in the pipeline implementation. rg --type python $'use_augmentations_train'Length of output: 73
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `SingleInstanceConfmapsPipeline`. # Test: Search for the usage of `use_augmentations_train` in Python files. rg --type py 'use_augmentations_train'Length of output: 2194
560-560: Validate augmentation flag implementation.Ensure the
use_augmentations_trainflag is correctly used in theBottomUpPipelineto apply or skip augmentations during training.Verification successful
Augmentation flag correctly implemented in pipeline configuration.
The
use_augmentations_trainflag is used within themodel_trainer.pyfile, indicating that it is correctly integrated into the pipeline's configuration to control augmentation behavior during training.
- File:
sleap_nn/training/model_trainer.py
use_augmentations=self.config.data_config.use_augmentations_trainThis confirms the intended functionality of the flag in the pipeline.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `BottomUpPipeline`. # Test: Search for the usage of `use_augmentations_train` in the pipeline implementation. rg --type python $'use_augmentations_train'Length of output: 73
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `BottomUpPipeline`. # Test: Search for the usage of `use_augmentations_train` in Python files. rg --type py $'use_augmentations_train'Length of output: 2194
108-109: Verify augmentation configuration usage.Ensure that the
use_augmentations_trainflag is correctly utilized in the pipeline to apply or skip augmentations during training.Verification successful
Augmentation configuration is correctly utilized.
The
use_augmentations_trainflag is used in themodel_trainer.pyto control whether augmentations are applied during training, as intended. It is also tested with different configurations intest_pipelines.py.
- Files of interest:
sleap_nn/training/model_trainer.py: Controls augmentation withuse_augmentations_train.tests/data/test_pipelines.py: Tests various configurations of the flag.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `TopdownConfmapsPipeline`. # Test: Search for the usage of `use_augmentations_train` in the pipeline implementation. rg --type python $'use_augmentations_train'Length of output: 73
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in the codebase. # Test: Search for the usage of `use_augmentations_train` across all files. rg 'use_augmentations_train'Length of output: 3044
443-443: Check augmentation flag usage.Ensure the
use_augmentations_trainflag is correctly utilized in theCentroidConfmapsPipelineto apply or skip augmentations during training.Verification successful
Flag
use_augmentations_trainis correctly utilized.The
use_augmentations_trainflag is appropriately integrated and tested across the codebase. It is used in configurations and tests, ensuring its functionality is verified in various scenarios.
- Files:
tests/data/test_pipelines.py: Multiple test cases use this flag.sleap_nn/training/model_trainer.py: Utilizes the flag in training configurations.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `CentroidConfmapsPipeline`. # Test: Search for the usage of `use_augmentations_train` in the pipeline implementation. rg --type python $'use_augmentations_train'Length of output: 73
Script:
#!/bin/bash # Description: Verify usage of `use_augmentations_train` in `CentroidConfmapsPipeline`. # Test: Search for the usage of `use_augmentations_train` in the pipeline implementation. rg 'use_augmentations_train'Length of output: 3044
sleap_nn/training/model_trainer.py (4)
70-70: Centralized model type initialization.The introduction of
model_typecentralizes the model type selection process, enhancing maintainability and clarity in configuring data pipelines and model instantiation.
170-176: Flexible model initialization.Using
model_typeto select the appropriate model class enhances flexibility and ensures correct model initialization based on the specified type.
308-311: Model type parameterization.The addition of
model_typeas a parameter in theTrainingModelconstructor aligns with the centralized model type selection approach, ensuring correct model configuration from the outset.
92-124: Dynamic data pipeline selection.The use of
model_typeto dynamically select the appropriate data pipeline simplifies the logic and enhances maintainability. Ensure thatmodel_typeis correctly set before this method is called.Verification successful
model_typeis set correctly before usage.The
model_typeis assigned values in multiple places in the code, ensuring it is correctly set before its usage in the_create_data_loadersmethod. This confirms the dynamic data pipeline selection logic is sound.
self.model_typeis initially set toNoneand later assigned values in the code.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify correct setting of `model_type` before `_create_data_loaders` is called. # Test: Search for all instances where `model_type` is set or modified. rg --type python $'self.model_type'Length of output: 65
Script:
#!/bin/bash # Description: Verify correct setting of `model_type` before `_create_data_loaders` is called. # Test: Search for all instances where `model_type` is set or modified. rg --type py $'self.model_type'Length of output: 1421
sleap_nn/inference/predictors.py (4)
415-421: Conditional peak threshold setting.The method conditionally sets peak thresholds based on whether
peak_thresholdis a list or a single float, ensuring correct application for different model components.
472-474: Custom preprocessing configuration.Returning
preprocess_configif it is notNoneallows for overriding the default data configuration with a custom preprocessing configuration, enhancing flexibility.
Line range hint
1426-1478:
Flexible peak threshold in main function.The
peak_thresholdparameter now accepts aUnion[float, List[float]], consistent with updates to thePredictorclass, allowing for more flexible thresholding options.
Line range hint
86-106:
Flexible peak threshold handling.The
peak_thresholdparameter now accepts aUnion[float, List[float]], allowing for more flexible thresholding options. Verify that the logic correctly handles both types.Verification successful
Flexible peak threshold handling verified.
The
peak_thresholdparameter is correctly handled as bothfloatandList[float]in the codebase. The logic includes checks for list types and assigns values appropriately, and this is supported by various test cases that use different configurations.
sleap_nn/inference/predictors.py: Checks for list type and handles accordingly.- Test files:
tests/inference/test_predictors.py,tests/inference/test_topdown.py, etc., demonstrate usage with both floats and lists.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify handling of `peak_threshold` as both `float` and `List[float]`. # Test: Search for all instances where `peak_threshold` is used to ensure correct handling. rg --type python $'peak_threshold'Length of output: 64
Script:
#!/bin/bash # Description: Verify handling of `peak_threshold` as both `float` and `List[float]`. # Test: Search for all instances where `peak_threshold` is used to ensure correct handling. rg 'peak_threshold'Length of output: 6312
tests/architectures/test_model.py (4)
Line range hint
110-155:
Comprehensive test coverage for UNet model.The test function effectively covers various configurations and ensures consistent model output. The changes are well-structured and maintain readability.
Also applies to: 159-204, 208-253
Tools
Ruff
102-102:
pytest.raises(Exception)should be considered evil(B017)
Line range hint
260-304:
Comprehensive test coverage for ConvNext model.The test function effectively covers various configurations and ensures consistent model output. The changes are well-structured and maintain readability.
Also applies to: 307-323, 328-371, 374-390, 395-438, 441-457
Line range hint
465-510:
Comprehensive test coverage for SwinT model.The test function effectively covers various configurations and ensures consistent model output. The changes are well-structured and maintain readability.
Also applies to: 513-529, 534-579, 582-598
58-58: Verify the exception type inpytest.raises.The use of
KeyErrorinpytest.raisesmay not align with the current implementation ofget_backbone. Ensure that the function indeed raises aKeyErrorfor invalid inputs.Also applies to: 84-84
Comments failed to post (8)
tests/training/test_model_trainer.py
43-49: Avoid using broad exception handling.
The use of
pytest.raises(Exception)is discouraged because it can catch unintended exceptions, making it harder to diagnose issues. Consider specifying a more precise exception type.- with pytest.raises(Exception): + with pytest.raises(SomeSpecificException):Replace
SomeSpecificExceptionwith the specific exception you expect.Committable suggestion was skipped due to low confidence.
Tools
Ruff
49-49:
pytest.raises(Exception)should be considered evil(B017)
sleap_nn/legacy_config_converter.py
20-24: Consider moving
get_new_key_from_oldoutside of the main function.The nested function
get_new_key_from_oldcould be moved outside ofconvert_legacy_json_to_omegaconffor better readability and to allow potential reuse.def get_new_key_from_old(old_key): mapping = { # Add key mappings here } return mapping.get(old_key, old_key) # defaults to old_key if no mapping def convert_legacy_json_to_omegaconf(json_file_path, config_spec): # Function implementation
2-2: Fix import statement for OmegaConf.
The import statement for
omegaconfshould be corrected tofrom omegaconf import OmegaConfto ensure proper usage.- from omegaconf import omegaconf + from omegaconf import OmegaConfCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.from omegaconf import OmegaConf
27-28: Fix indentation errors.
There are indentation errors in the code that need to be corrected to ensure proper execution.
- config = OmegaConf.create(mapped_dict) - return config + config = OmegaConf.create(mapped_dict) + return configCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.config = OmegaConf.create(mapped_dict) return configTools
Ruff
27-27: SyntaxError: unindent does not match any outer indentation level
28-28: SyntaxError: Unexpected indentation
docs/config_topdown_centered_instance.yaml
78-78: Consider addressing the TODO comment.
The TODO comment suggests revisiting device and trainer accelerator settings.
Would you like assistance in configuring the device and trainer accelerator settings?
sleap_nn/training/model_trainer.py
338-339: Simplify condition and remove
.keys().Combine the nested
ifstatements and remove the.keys()call for improved readability and performance.- if "part_names" in head_config[key].keys(): - if head_config[key]["part_names"] is None: + if "part_names" in head_config[key] and head_config[key]["part_names"] is None:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.if "part_names" in head_config[key] and head_config[key]["part_names"] is None:Tools
Ruff
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: Simplify condition and remove
.keys().Combine the nested
ifstatements and remove the.keys()call for improved readability and performance.- if "edges" in head_config[key].keys(): - if head_config[key]["edges"] is None: + if "edges" in head_config[key] and head_config[key]["edges"] is None:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.if "edges" in head_config[key] and head_config[key]["edges"] is None:Tools
Ruff
343-344: Use a single
ifstatement instead of nestedifstatements(SIM102)
343-343: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
tests/architectures/test_model.py
102-103: Avoid using general exceptions in tests.
Using
Exceptioninpytest.raisesis discouraged. Consider using a more specific exception type to improve clarity and maintainability.- with pytest.raises(Exception): + with pytest.raises(ValueError): # Replace with the actual exception type raised by `get_head`.Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.with pytest.raises(ValueError): # Replace with the actual exception type raised by `get_head`. _ = get_head("invalid_input", base_unet_head_config)Tools
Ruff
102-102:
pytest.raises(Exception)should be considered evil(B017)
There was a problem hiding this comment.
Actionable comments posted: 5
Outside diff range and nitpick comments (6)
sleap_nn/config/trainer_config.py (2)
4-7: Improve docstring: Add attributes or remove empty section.The docstring provides a brief description of the class purpose, which is good. However, the "Attributes:" section is empty. Consider one of the following options:
- If attributes are planned for this class, add them along with their descriptions in the docstring.
- If no attributes are currently planned, remove the empty "Attributes:" section to avoid confusion.
Example of option 1:
"""Configurations related to trainer. Attributes: batch_size (int): The number of samples per batch. learning_rate (float): The learning rate for the optimizer. """
1-7: Consider adding configuration attributes and methods.The
TrainerConfigclass provides a good starting point for encapsulating trainer-related configurations. However, it currently lacks any specific attributes or methods. As you continue to develop this class, consider the following:
- Add relevant configuration attributes that will be used by the trainer.
- Implement any necessary methods for validating or processing the configuration.
- If applicable, add type hints to the attributes for better code clarity and IDE support.
- Update the docstring with descriptions for each added attribute.
Example:
import attr from typing import Optional @attr.s(auto_attribs=True) class TrainerConfig: """Configurations related to trainer. Attributes: batch_size (int): The number of samples per batch. learning_rate (float): The learning rate for the optimizer. max_epochs (int): The maximum number of training epochs. device (Optional[str]): The device to use for training (e.g., 'cpu', 'cuda'). Defaults to None. """ batch_size: int learning_rate: float max_epochs: int device: Optional[str] = None def validate(self) -> None: """Validate the configuration values.""" if self.batch_size <= 0: raise ValueError("batch_size must be positive") if self.learning_rate <= 0: raise ValueError("learning_rate must be positive") if self.max_epochs <= 0: raise ValueError("max_epochs must be positive")This structure will provide a more comprehensive and useful configuration class for the trainer.
Tools
Ruff
2-2: Undefined name
attr(F821)
sleap_nn/config/data_config.py (2)
4-10: Enhance the class docstringWhile the docstring provides a good overview, it could be improved for clarity and completeness.
Consider updating the docstring to follow a standard format (e.g., Google-style) and provide more details about each attribute. Here's a suggested improvement:
class DataConfig: """Configuration for data handling in training or testing machine learning models. Attributes: labels (LabelConfig): Configuration options related to user labels for training or testing. preprocessing (PreprocessingConfig): Configuration options related to data preprocessing techniques. instance_cropping (InstanceCroppingConfig): Configuration options for instance cropping in centroid and topdown models. """Replace
LabelConfig,PreprocessingConfig, andInstanceCroppingConfigwith the actual types of these attributes if they are defined elsewhere in your codebase.
1-10: Overall structure and purposeThis file introduces a new
DataConfigclass that appears to be part of a configuration system for machine learning models. The use ofattr.swithauto_attribs=Truesuggests a focus on clean, maintainable code. The class is designed to hold configuration options for labels, preprocessing, and instance cropping, which are likely crucial components in your ML pipeline.To complete this class:
- Ensure that the
__init__method (not shown here) properly defines thelabels,preprocessing, andinstance_croppingattributes with appropriate type annotations.- Consider adding validation logic for these attributes if there are specific constraints or expected values.
- If this class is part of a larger configuration system, make sure it integrates well with other configuration classes and follows the same design patterns.
Tools
Ruff
2-2: Undefined name
attr(F821)
sleap_nn/config/model_config.py (1)
4-10: LGTM: Well-structured docstring with minor suggestion.The docstring for the
ModelConfigclass is well-written and follows the Google style guide format. It provides clear descriptions of the class purpose and its attributes.For consistency, consider ending all attribute descriptions with periods:
"""Configurations related to model architecture. Attributes: backbone: Configurations related to the main network architecture. heads: Configurations related to the output heads. base_checkpoint: Path to model folder for loading a checkpoint. Should contain the .h5 file. """sleap_nn/config/training_job.py (1)
1-25: Enhance docstring readability with minor formatting improvements.The file-level docstring provides excellent context and design principles. To further improve its readability, consider the following suggestions:
- Use consistent indentation for the numbered list (lines 9-11 and 13-16).
- Add a blank line before "Conveniently, this format..." (line 23) to separate it from the previous paragraph.
Here's a suggested diff for the improvements:
"""Serializable configuration classes for specifying all training job parameters. These configuration classes are intended to specify all the parameters required to run a training job or perform inference from a serialized one. They are explicitly not intended to implement any of the underlying functionality that they parametrize. This serves two purposes: - 1. Parameter specification through simple attributes. These can be read/edited by a - human, as well as easily be serialized/deserialized to/from simple dictionaries - and JSON. +1. Parameter specification through simple attributes. These can be read/edited by a + human, as well as easily be serialized/deserialized to/from simple dictionaries + and JSON. - 2. Decoupling from the implementation. This makes it easier to design functional - modules with attributes/parameters that contain objects that may not be easily - serializable or may implement additional logic that relies on runtime - information or other parameters. +2. Decoupling from the implementation. This makes it easier to design functional + modules with attributes/parameters that contain objects that may not be easily + serializable or may implement additional logic that relies on runtime + information or other parameters. In general, classes that implement the actual functionality related to these configuration classes should provide a classmethod for instantiation from the configuration class instances. This makes it easier to implement other logic not related to the high level parameters at creation time. + Conveniently, this format also provides a single location where all user-facing parameters are aggregated and documented for end users (as opposed to developers). """
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (4)
- sleap_nn/config/data_config.py (1 hunks)
- sleap_nn/config/model_config.py (1 hunks)
- sleap_nn/config/trainer_config.py (1 hunks)
- sleap_nn/config/training_job.py (1 hunks)
Additional context used
Ruff
sleap_nn/config/data_config.py
2-2: Undefined name
attr(F821)
sleap_nn/config/model_config.py
2-2: Undefined name
attr(F821)
sleap_nn/config/trainer_config.py
2-2: Undefined name
attr(F821)
sleap_nn/config/training_job.py
27-27: Undefined name
attr(F821)
Additional comments not posted (4)
sleap_nn/config/trainer_config.py (1)
2-3: LGTM: Class definition and decorator usage.The use of
@attr.s(auto_attribs=True)is a good practice for creating data classes with reduced boilerplate code. The class nameTrainerConfigfollows the PascalCase convention, which is correct for Python classes.Tools
Ruff
2-2: Undefined name
attr(F821)
sleap_nn/config/data_config.py (1)
2-3: Class definition and decorator usage look goodThe use of
@attr.s(auto_attribs=True)is a good practice for creating data classes with reduced boilerplate. The class nameDataConfigfollows the correct PascalCase convention for Python classes.Tools
Ruff
2-2: Undefined name
attr(F821)
sleap_nn/config/model_config.py (1)
2-3: LGTM: Class definition and decorator usage.The
ModelConfigclass definition and the use of the@attr.s(auto_attribs=True)decorator are well-implemented. This approach reduces boilerplate code and follows good practices for creating data classes in Python.Tools
Ruff
2-2: Undefined name
attr(F821)
sleap_nn/config/training_job.py (1)
1-40: LGTM with suggested improvements.Overall, the introduction of the
TrainingJobConfigclass is well-structured and serves its purpose effectively. The file provides a clear and organized approach to defining training job configurations. The suggested improvements, including docstring formatting, type hinting, and import addition, will further enhance its usability and maintainability.Great job on implementing this configuration class! Once the suggested changes are applied, this will be a solid foundation for managing training job parameters.
Tools
Ruff
27-27: Undefined name
attr(F821)
| @@ -0,0 +1,7 @@ | |||
|
|
|||
There was a problem hiding this comment.
Add missing import for attr module.
The attr module is used in the decorator but not imported. To resolve the undefined name error and improve code clarity, add the following import at the beginning of the file:
import attr| @@ -0,0 +1,10 @@ | |||
|
|
|||
There was a problem hiding this comment.
Add missing import for attr module
The attr module is used in the decorator but not imported. This will cause a NameError when the code is executed.
Please add the following import statement at the beginning of the file:
import attr| @@ -0,0 +1,10 @@ | |||
|
|
|||
There was a problem hiding this comment.
Add missing import for attr module.
The attr module is used in the decorator but not imported. To resolve the undefined name error and improve code clarity, please add the following import statement at the beginning of the file:
import attr| @attr.s(auto_attribs=True) | ||
| class TrainingJobConfig: | ||
| """Configuration of a training job. | ||
|
|
||
| Attributes: | ||
| data: Configuration options related to the training data. | ||
| model: Configuration options related to the model architecture. | ||
| optimization: Configuration options related to the training. | ||
| outputs: Configuration options related to outputs during training. | ||
| name: Optional name for this configuration profile. | ||
| description: Optional description of the configuration. | ||
| sleap_version: Version of SLEAP that generated this configuration. | ||
| filename: Path to this config file if it was loaded from disk. | ||
| """ |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance class definition with type hints and expand attribute descriptions.
The TrainingJobConfig class is well-structured, but we can improve it further:
- Add type hints to the class attributes for better clarity and IDE support.
- Provide more detailed descriptions for some attributes, especially
data,model,optimization, andoutputs.
Here's a suggested improvement for the class definition:
from typing import Optional
@attr.s(auto_attribs=True)
class TrainingJobConfig:
"""Configuration of a training job.
Attributes:
data: Configuration options related to the training data, including dataset paths, preprocessing, and augmentation settings.
model: Configuration options related to the model architecture, such as network type, layers, and activation functions.
optimization: Configuration options related to the training process, including optimizer settings, learning rate, and batch size.
outputs: Configuration options related to outputs during training, such as logging frequency, checkpoint saving, and evaluation metrics.
name: Optional name for this configuration profile.
description: Optional description of the configuration.
sleap_version: Version of SLEAP that generated this configuration.
filename: Path to this config file if it was loaded from disk.
"""
data: dict
model: dict
optimization: dict
outputs: dict
name: Optional[str] = None
description: Optional[str] = None
sleap_version: Optional[str] = None
filename: Optional[str] = NoneThis improvement adds type hints and provides more detailed descriptions for the main configuration attributes.
Tools
Ruff
27-27: Undefined name
attr(F821)
| parameters are aggregated and documented for end users (as opposed to developers). | ||
| """ | ||
|
|
||
| @attr.s(auto_attribs=True) |
There was a problem hiding this comment.
Add missing import for attr package.
The static analysis tool Ruff has flagged an undefined name attr on line 27. This is likely because the attr package is used for the class decorator but not imported in the visible part of the file.
To resolve this issue, add the following import statement at the beginning of the file:
import attrThis will ensure that the attr package is properly imported and available for use in the class decorator.
Tools
Ruff
27-27: Undefined name
attr(F821)
created an outline for the new converter from legacy config to omega
Summary by CodeRabbit
New Features
use_augmentations_trainflag for optional data augmentations during training.DataConfig,ModelConfig,TrainerConfig, andTrainingJobConfig.Bug Fixes
Documentation
Refactor
KorniaAugmenterclass for clarity and flexibility.Tests