First commit

[ternaus]

Summary by Sourcery

Introduce a benchmarking suite for image augmentation libraries, supporting Albumentations, imgaug, torchvision, Kornia, and Augly. Implement a BenchmarkRunner class to manage the benchmarking process, including adaptive warmup and variance stability checks. Add scripts for running benchmarks and generating comparison outputs. Set up CI for code formatting and type checking. Update README with detailed documentation.

New Features:

• Introduce a comprehensive benchmarking suite for comparing the performance of popular image augmentation libraries, including Albumentations, imgaug, torchvision, Kornia, and Augly.

Enhancements:

• Implement a BenchmarkRunner class to manage the benchmarking process, including loading images, running transforms, and collecting results.
• Add support for adaptive warmup and variance stability checks to ensure reliable benchmarking results.
• Include detailed performance metrics and system information in the benchmarking output.

Build:

• Add a script to run benchmarks for a single library and generate output in JSON format.
• Add a script to run benchmarks for all supported libraries and generate a comparison CSV file.

CI:

• Set up a CI workflow to check code formatting with ruff and type checking with mypy.

Documentation:

• Update README.md with detailed documentation on the benchmarking suite, including setup instructions, usage, and methodology.

Tests:

• Add implementations for various image augmentation libraries to support benchmarking, including Albumentations, imgaug, torchvision, Kornia, and Augly.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request establishes a comprehensive benchmarking suite for comparing the performance of popular image augmentation libraries. The implementation includes a modular architecture for running benchmarks, collecting metrics, and generating comparison reports. The suite supports multiple libraries (Albumentations, imgaug, torchvision, Kornia, and Augly) and features adaptive warmup, statistical analysis, and thread control settings.

No diagrams generated as the changes look simple and do not need a visual representation.

File-Level Changes

Change	Details	Files
Implemented the core benchmarking infrastructure	Created a BenchmarkRunner class to manage benchmark execution Added support for adaptive warmup to ensure stable measurements Implemented thread control settings for consistent single-threaded performance Added system information collection and environment verification	benchmark/runner.py benchmark/utils.py
Added library-specific transform implementations	Created transform implementations for Albumentations Created transform implementations for imgaug Created transform implementations for torchvision Created transform implementations for Kornia Created transform implementations for Augly Defined standardized transform specifications	benchmark/transforms/albumentations_impl.py benchmark/transforms/imgaug_impl.py benchmark/transforms/torchvision_impl.py benchmark/transforms/kornia_impl.py benchmark/transforms/augly_impl.py benchmark/transforms/specs.py
Implemented results processing and comparison functionality	Added JSON result file generation for each library Created comparison table generation with statistical analysis Added support for markdown report generation	benchmark/compare_results.py
Added shell scripts for running benchmarks	Created script for running single library benchmark Created script for running all libraries and generating comparison Added virtual environment management for each library	run_single.sh run_all.sh
Added project configuration and CI setup	Added pre-commit configuration for code quality checks Set up GitHub Actions workflow for CI Added GitHub funding configuration	.pre-commit-config.yaml .github/workflows/ci.yml .github/FUNDING.yml

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[sourcery-ai]

Hey @ternaus - I've reviewed your changes - here's some feedback:

Overall Comments:

• Consider adding a LICENSE file to clarify the terms under which this benchmarking suite can be used and distributed.

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 1 issue found
• 🟡 Documentation: 3 issues found

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