engine_abc¶

Abstract Base Class for TIAToolbox Deep Learning Engines.

This module defines the EngineABC class, which serves as a base for implementing deep learning inference workflows in TIAToolbox. It supports both patch-based and whole slide image (WSI) processing, and provides a unified interface for model initialization, data loading, inference, post-processing, and output saving.

Classes:

  • EngineABC: Abstract base class for deep learning engines.

  • EngineABCRunParams: TypedDict for runtime configuration parameters.

Functions:

  • prepare_engines_save_dir: Utility to create or validate output directories.

Features:

  • Supports patch and WSI modes.

  • Handles caching and memory-efficient inference using Dask.

  • Integrates with TIAToolbox models and IO configurations.

  • Outputs predictions in multiple formats including dict, zarr, and AnnotationStore.

Intended Usage:

Subclass EngineABC to implement specific inference logic by overriding abstract methods such as preprocessing, postprocessing, and model-specific behavior.

Example

>>> class MyEngine(EngineABC):
>>>     def __init__(self, model, weights, verbose):
>>>         super().__init__(model=model, weights=weights, verbose=verbose)
>>> # Implement base class functions and then call.
>>> engine = MyEngine(model="resnet18-kather100k")
>>> output = engine.run(images, patch_mode=True)

Functions

prepare_engines_save_dir

Create or validate the save directory for engine outputs.

Classes

EngineABC

Abstract base class for TIAToolbox deep learning engines to run CNN models.

EngineABCRunParams

Parameters for configuring the EngineABC.run() method.