wsinfer.modellib.run_inference#
Run inference.
From the original paper (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369575/): > In the prediction (test) phase, no data augmentation was applied except for the > normalization of the color channels.
Functions#
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Run model inference on a directory of whole slide images and save results to CSV. |
Module Contents#
- wsinfer.modellib.run_inference.run_inference(wsi_dir: str | pathlib.Path, results_dir: str | pathlib.Path, model_info: wsinfer_zoo.client.HFModelTorchScript | wsinfer.modellib.models.LocalModelTorchScript, batch_size: int = 32, num_workers: int = 0, speedup: bool = False) tuple[list[str], list[str]][source]#
Run model inference on a directory of whole slide images and save results to CSV.
This assumes the patching has already been done and the results are stored in results_dir. An error will be raised otherwise.
Output CSV files are written to {results_dir}/model-outputs/.
- Parameters:
wsi_dir (str or Path) – Directory containing whole slide images. This directory can only contain whole slide images. Otherwise, an error will be raised during model inference.
results_dir (str or Path) – Directory containing results of patching.
model_info – Instance of Weights including the model object and information about how to apply the model to new data.
batch_size (int) – The batch size during the forward pass (default is 32).
num_workers (int) – Number of workers for data loading (default is 0, meaning use a single thread).
speedup (bool) – If True, JIT-compile the model. This has a startup cost but model inference should be faster (default False).
- Returns:
A tuple of two lists of strings. The first list contains the slide IDs for which
patching failed, and the second list contains the slide IDs for which model
inference failed.