Added more transforms to benchmark

[ternaus]

Summary by Sourcery

Add new transforms to the benchmark suite, including Clahe, CoarseDropout, Blur, Brightness, and Contrast. Improve the formatting of transform specifications and enhance the benchmark runner for better error handling and logging. Update documentation with new performance results.

New Features:

• Add support for new transforms: Clahe, CoarseDropout, Blur, Brightness, and Contrast in the benchmark suite.

Enhancements:

• Improve the formatting and readability of the transform specifications in the codebase.
• Enhance the benchmark runner to handle new transforms and improve error handling and logging.

Documentation:

• Update README.md to include new performance comparison results and additional transforms.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request adds more image transforms to the benchmark suite and includes code formatting improvements. The changes focus on expanding the test coverage by implementing additional transforms in the imgaug and torchvision libraries, while also improving code quality through consistent formatting and style.

Class diagram for TransformSpec modifications

classDiagram
    class TransformSpec {
        +String name
        +dict[str, Any] params
        +String __str__()
    }
    note for TransformSpec "Base class that defines exact parameters for each transform"
    class TransformSpecList {
        +TransformSpec[] TRANSFORM_SPECS
    }
    note for TransformSpecList "List of all transform specifications based on the original implementations"

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Class diagram for TorchvisionImpl modifications

classDiagram
    class TorchvisionImpl {
        +static Transform HorizontalFlip(dict[str, Any] params)
        +static Transform VerticalFlip(dict[str, Any] params)
        +static Transform Rotate(dict[str, Any] params)
        +static Transform Affine(dict[str, Any] params)
        +static Transform Equalize(dict[str, Any] params)
        +static Transform RandomCrop80(dict[str, Any] params)
        +static Transform RandomResizedCrop(dict[str, Any] params)
        +static Transform Resize(dict[str, Any] params)
        +static Transform ColorJitter(dict[str, Any] params)
        +static Transform RandomPerspective(dict[str, Any] params)
        +static Transform GaussianBlur(dict[str, Any] params)
        +static Transform Posterize(dict[str, Any] params)
        +static Transform Elastic(dict[str, Any] params)
        +static Transform Normalize(dict[str, Any] params)
        +static Transform Brightness(dict[str, Any] params)
        +static Transform Contrast(dict[str, Any] params)
        +static Transform JpegCompression(dict[str, Any] params)
    }
    note for TorchvisionImpl "Torchvision implementations of transforms"

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File-Level Changes

Change	Details	Files
Added new transform implementations to imgaug and torchvision libraries	Added Clahe transform implementation to imgaug Added CoarseDropout transform implementation to imgaug Added Blur transform implementation to imgaug Added Brightness and Contrast transforms to torchvision Updated benchmark results to include new transforms	benchmark/transforms/imgaug_impl.py benchmark/transforms/torchvision_impl.py output/imgaug_results.json output/torchvision_results.json
Applied code formatting and style improvements across the codebase	Standardized import ordering Fixed line length issues Added trailing commas to function parameters Improved type hints and docstrings Applied consistent indentation	benchmark/runner.py benchmark/transforms/specs.py benchmark/transforms/albumentations_impl.py benchmark/transforms/kornia_impl.py benchmark/transforms/augly_impl.py benchmark/utils.py
Enhanced results presentation and documentation	Updated README.md with new benchmark results Added comparison.md for detailed results Modified run_all.sh to generate markdown output Updated performance comparison table with new transforms	README.md output/comparison.md run_all.sh

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[sourcery-ai]

Hey @ternaus - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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