vak.eval.parametric_umap.eval_parametric_umap_model¶

vak.eval.parametric_umap.eval_parametric_umap_model(model_config: dict, dataset_config: dict, checkpoint_path: str | Path, output_dir: str | Path, batch_size: int, num_workers: int, trainer_config: dict) → None[source]¶

Evaluate a trained model.

Parameters:

model_config (dict) – Model configuration in a dict. Can be obtained by calling vak.config.ModelConfig.asdict().
dataset_config (dict) – Dataset configuration in a dict. Can be obtained by calling vak.config.DatasetConfig.asdict().
checkpoint_path (str, pathlib.Path) – Path to directory with checkpoint files saved by Torch, to reload model
output_dir (str, pathlib.Path) – Path to location where .csv files with evaluation metrics should be saved.
batch_size (int) – Number of samples per batch fed into model.
trainer_config (dict) – Configuration for lightning.pytorch.Trainer. Can be obtained by calling vak.config.TrainerConfig.asdict().
num_workers (int) – Number of processes to use for parallel loading of data. Argument to torch.DataLoader. Default is 2.
split (str) – Split of dataset on which model should be evaluated. One of {‘train’, ‘val’, ‘test’}. Default is ‘test’.