📝 Update 05-patch-prediction notebook for the New API

[gozdeg]

Summary

This PR updates the patch‑prediction example to align with the new PatchPredictor engine and fixes a long‑standing issue in EngineABC related to model‑attribute retrieval when using DataParallel.

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What’s Changed

🔧 Example Notebook Updates

• Updated examples/05-patch-prediction.ipynb to use the new PatchPredictor engine API.
• Added a new “Visualize in TIAViz” section, allowing readers to directly inspect prediction results inside TIAViz for a smoother, more interactive workflow.

🐛 EngineABC Bug Fix

• Fixed a bug in EngineABC where model attributes were incorrectly retrieved from a DataParallel wrapper.
• Introduced _get_model_attr() to safely unwrap the underlying model when needed.
• This resolves multi‑GPU crashes caused by attributes living on the wrapped module instead of the actual model.

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Why This Matters

• Ensures the patch‑prediction example stays up‑to‑date with the latest engine design.
• Improves multi‑GPU stability and prevents confusing attribute‑access errors.
• Enhances the user experience by integrating TIAViz visualization directly into the example workflow.

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Testing

• Verified that the updated notebook runs end‑to‑end with the new engine.
• Confirmed that multi‑GPU training and inference no longer crash when accessing model attributes.

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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 95.30%. Comparing base (d40dc64) to head (e600960).

Additional details and impacted files

@@                   Coverage Diff                   @@
##           dev-define-engines-abc     #977   +/-   ##
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  Coverage                   95.30%   95.30%           
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  Files                          79       79           
  Lines                        9945     9955   +10     
  Branches                     1280     1280           
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+ Hits                         9478     9488   +10     
  Misses                        431      431           
  Partials                       36       36           

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[shaneahmed]

Suggested change

	with Path.open(path, encoding="utf-8", errors="ignore") as f:
	notebook = json.loads(path.read_text())

[shaneahmed]

Suggested change

notebook = json.load(f)

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shaneahmed commented on 2025-12-19T11:40:55Z
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zarr_output = zarr.open("path/to/zarr", mode="r")

predictions = {}

for key in zarr_output:

predictions[key]=da.from_zarr(zarr_output[key})

_ = predictor.save_predictions(predictions, output_type="annotation_store")

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shaneahmed commented on 2025-12-19T11:40:55Z
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Line #2.    tiatoolbox visualize --slides ./tmp/slides --overlays ./tmp/wsi_predictions_annotationstore/

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shaneahmed commented on 2025-12-19T11:45:19Z
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Add class_dict for visualization