⚡ Improve Engine Performance and Implementation
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- Use `pyproject.toml` for `bdist_wheel` configuration
- Improve `Engines` performance and implementation
Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## develop #578 +/- ##
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- Coverage 99.27% 94.85% -4.43%
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Files 71 75 +4
Lines 9161 9477 +316
Branches 1195 1238 +43
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- Hits 9095 8989 -106
- Misses 40 452 +412
- Partials 26 36 +10 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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- Refactor engines_abc.py
shaneahmed
changed the title
Engines Performance and ImplementationEngine Performance and Implementation
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# Conflicts: # tiatoolbox/utils/misc.py
# Conflicts: # tests/models/test_feature_extractor.py # tiatoolbox/models/models_abc.py
# Conflicts: # tiatoolbox/cli/common.py # tiatoolbox/cli/nucleus_instance_segment.py # tiatoolbox/cli/patch_predictor.py # tiatoolbox/models/engine/semantic_segmentor.py
* ⚡ Make WSIPatchDataset Pickleable to Support Windows Multithreading (#947) This PR makes the WSIPatchDataset class picklable by delaying the creation of the reader object until the first call to `__getitem__`. This enables the use of multiple loader workers on Windows without errors and provides significant performance improvements. - Delays reader object instantiation to the first `__getitem__` call instead of during initialization - Extracts reader creation logic into a separate `_get_reader` method - Stores image path and mode as instance variables for lazy initialization Speedup for the WSI prediction cell of the patch_prediction example notebook: 2min 48 sec with 0 loader workers -> 1min 13 sec with 4 workers. Note: this PR doesn't have any effect for Linux as the multi-threading already works fine there because Linux multithreading doesn't require things to be pickleable * 🔀 Merge branch develop into dev-engine-abc * 🐛 Fix reader_info read --------- Co-authored-by: Mark Eastwood <20169086+measty@users.noreply.github.com>
# Conflicts: # tiatoolbox/models/dataset/classification.py
# Conflicts: # tests/models/test_patch_predictor.py
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# Conflicts: # tests/models/test_feature_extractor.py # tests/models/test_multi_task_segmentor.py # tests/models/test_nucleus_instance_segmentor.py # tests/models/test_patch_predictor.py # tests/models/test_semantic_segmentation.py # tiatoolbox/models/architecture/__init__.py
## Summary of Changes ### Major Additions - **Dask Integration:** - Added `dask` as a dependency and integrated Dask arrays and lazy computation throughout the engine and patch predictor code. - Added Dask-based merging, chunking, and memory-aware processing for large images and WSIs. - **Zarr Output Support:** - Added support for saving model predictions and intermediate results directly to Zarr format. - New CLI options and internal logic for Zarr output, including memory thresholding and chunked writes. - **SemanticSegmentor Engine:** - Added a new `SemanticSegmentor` engine with Dask/Zarr support and new test coverage (`test_semantic_segmentor.py`). - Added CLI entrypoint for `semantic_segmentor` and removed the old `semantic_segment` CLI. - **Enhanced CLI and Config:** - Added CLI options for memory threshold, unified worker options, and improved mask handling. - Updated YAML configs and sample data for new models and test images. - **Utilities and Validation:** - Added utility functions for minimal dtype casting, patch/stride validation, and improved error handling (e.g., `DimensionMismatchError`). - Improved annotation store conversion for Dask arrays and Zarr-backed outputs. - **Changes to `kwarg`** - Add `memory-threshold` - Unified `num-loader-workers` and `num-postproc-workers` into `num-workers` - Removed `cache_mode` as cache mode is automatically handled. --- ### Major Removals/Refactors - **Removed Old CLI and Redundant Code:** - Deleted the old `semantic_segment.py` CLI and replaced it with `semantic_segmentor.py`. - Removed legacy cache mode and patch prediction Zarr store tests. - **Refactored Model and Dataset APIs:** - Unified and simplified model inference APIs to always return arrays (not dicts) for batch outputs. - Refactored dataset classes to enforce patch shape validation and remove legacy “mode” logic. - **Test Cleanup:** - Removed or updated tests that relied on old APIs or cache mode. - Refactored test assertions for new output types and Dask array handling. - **API Consistency:** - Standardized function and argument names across engines, CLI, and utility modules. - Updated docstrings and type hints for clarity and consistency. --- ### Notable File Changes - **New:** - `tiatoolbox/cli/semantic_segmentor.py` - `tests/engines/test_semantic_segmentor.py` - **Removed:** - `tiatoolbox/cli/semantic_segment.py` - Old cache mode and patch Zarr store tests - **Heavily Modified:** - `engine_abc.py`, `patch_predictor.py`, `semantic_segmentor.py` - CLI modules and test suites - Dataset and utility modules for Dask/Zarr compatibility --- ### Impact - Enables scalable, parallel, and memory-efficient inference and output saving for large images. - Simplifies downstream analysis by supporting Zarr as a native output format. - Lays the groundwork for further Dask-based optimizations in TIAToolbox. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
## 🚀Summary This PR introduces a new **[GrandQC Tissue Detection Model](https://github.com/cpath-ukk/grandqc/tree/main)** for digital pathology quality control and integrates **EfficientNet-based encoder architecture** into the TIAToolbox framework. --- ## ✨Key Changes - **New Model Architecture** - Added `grandqc.py` implementing a UNet++ decoder with EfficientNet encoder for tissue segmentation. - Includes preprocessing (JPEG compression + ImageNet normalization), postprocessing (argmin-based mask generation), and batch inference utilities. - **EfficientNet Encoder** - Added `timm_efficientnet.py` providing configurable EfficientNet encoders with dilation support and custom input channels. - **Pretrained Model Config** - Updated `pretrained_model.yaml` to register `grandqc_tissue_detection_mpp10` with associated IO configuration. - Corrected `IOSegmentorConfig` references and adjusted resolutions for SCCNN models. - **Testing** - Added comprehensive unit tests for: - `GrandQCModel` functionality, preprocessing/postprocessing, and decoder blocks. - EfficientNet encoder utilities and scaling logic. ## Impact - Enables high-resolution tissue detection for WSI quality control using state-of-the-art architectures. - Improves flexibility for segmentation tasks with EfficientNet encoders. - Enhances code quality and consistency through updated linting and formatting tools. ## Tasks - [x] Re-host GrandQC model weights on TIA Hugging Face - [x] Update `pretrained_model.yaml` - [x] Update `requirements.txt` - [x] Define GrandQC model architecture - [x] Add example usage - [x] Remove segmentation-models-pytorch dependency - [x] Wait for response from GrandQC authors - [x] Add tests - [x] Tidy up --------- Co-authored-by: Shan E Ahmed Raza <13048456+shaneahmed@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Enginesperformance and implementationmypyType Checks forcli/common.pyPatchPredictorEngine based onEngineABCreturn_probabilitiesoption to Paramsmerge_predictionsoption inPatchPredictorengine.post_process_cache_modewhich allows running the algorithm onWSIinfer_wsifor WSI inferencesave_wsi_outputas this is not required after post processing.merge_predictionsand fixes docstring in EngineABCRunParamscompile_modelis now moved to EngineABC init_calculate_scale_factorclass_dictdefinition._get_zarr_arrayis now a public functionget_zarr_arrayinmiscpatch_predictions_as_annotationsruns the loop onpatch_coordsinstead ofclass_probs