Add batch transform decorator

[ternaus]

Summary by Sourcery

Add a batch transform decorator to facilitate batch transformations for spatial and channel data, along with utility functions for reshaping and restoring data. Update project version and CI configuration, and introduce tests for the new functionality.

New Features:

• Introduce a batch transform decorator to handle batch transformations for spatial and channel data.

Enhancements:

• Add utility functions for reshaping and restoring 3D and batch data for spatial and channel transformations.

Build:

• Update project version from 0.0.21 to 0.0.22 in pyproject.toml.

CI:

• Upgrade Ruff linter version from v0.7.2 to v0.8.3 in pre-commit configuration.

Tests:

• Add comprehensive tests for the new batch transform decorator, including tests for spatial and channel reshaping and restoration.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR introduces a new batch transform decorator that enables efficient handling of batch transformations for image processing operations. The decorator supports different types of transformations (spatial, channel, and full) and handles various input tensor shapes including 2D and 3D data with optional batch and depth dimensions.

Class diagram for batch transform decorator

classDiagram
    class BatchTransformType {
        <<enumeration>>
        spatial
        channel
        full
    }

    class ShapeType {
        <<enumeration>>
        DHW
        DHWC
        NHW
        NHWC
        NDHW
        NDHWC
    }

    class Decorator {
        +batch_transform(transform_type: BatchTransformType, has_batch_dim: bool, has_depth_dim: bool, keep_depth_dim: bool): Callable[[F], F]
    }

    class ReshapeFunctions {
        +reshape_for_spatial(data: np.ndarray, has_batch_dim: bool, has_depth_dim: bool, keep_depth_dim: bool): tuple[np.ndarray, tuple[int, ...]]
        +reshape_for_channel(data: np.ndarray, has_batch_dim: bool, has_depth_dim: bool, keep_depth_dim: bool): tuple[np.ndarray, tuple[int, ...]]
    }

    class RestoreFunctions {
        +restore_from_spatial(data: np.ndarray, original_shape: tuple[int, ...], has_batch_dim: bool, has_depth_dim: bool, keep_depth_dim: bool): np.ndarray
        +restore_from_channel(data: np.ndarray, original_shape: tuple[int, ...], has_batch_dim: bool, has_depth_dim: bool, keep_depth_dim: bool): np.ndarray
    }

    Decorator --> BatchTransformType
    Decorator --> ReshapeFunctions
    Decorator --> RestoreFunctions
    ReshapeFunctions --> ShapeType
    RestoreFunctions --> ShapeType

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File-Level Changes

Change	Details	Files
Implemented a new batch transform decorator with support for different transformation types	Added BatchTransformType enum with 'spatial', 'channel', and 'full' options Created ShapeType enum to handle various tensor shapes (DHW, DHWC, NHW, NHWC, NDHW, NDHWC) Implemented batch_transform decorator with configurable parameters for batch and depth dimensions	albucore/decorators.py
Added reshape and restore functions for different transformation types	Implemented reshape functions for spatial transformations (reshape_3d, reshape_batch, reshape_batch_3d) Added reshape functions for channel transformations (reshape_3d_channel, reshape_batch_channel, reshape_batch_3d_channel) Created corresponding restore functions to revert the transformations Added support for keeping depth dimension during transformations	albucore/decorators.py
Added comprehensive test suite for the batch transform functionality	Created tests for spatial and channel transformations with various input shapes Added tests for 3D volume handling and dimension preservation Implemented roundtrip testing to ensure data integrity Added edge case tests for empty arrays and non-contiguous inputs	tests/test_batch_decorator.py

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[codecov-commenter]

Codecov Report

Attention: Patch coverage is 90.96386% with 30 lines in your changes missing coverage. Please review.

Please upload report for BASE (main@ef808a5). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
albucore/decorators.py	86.11%	30 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main      #46   +/-   ##
=======================================
  Coverage        ?   62.21%           
=======================================
  Files           ?       21           
  Lines           ?     2554           
  Branches        ?        0           
=======================================
  Hits            ?     1589           
  Misses          ?      965           
  Partials        ?        0           

☔ View full report in Codecov by Sentry.
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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: 1 issue found
• 🟢 Security: all looks good
• 🟡 Testing: 1 issue found
• 🟡 Complexity: 1 issue found
• 🟢 Documentation: all looks good

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