vak.predict.parametric_umap.predict_with_parametric_umap_model

vak.predict.parametric_umap.predict_with_parametric_umap_model(model_config: dict, dataset_config: dict, trainer_config: dict, checkpoint_path, num_workers=2, transform_params: dict | None = None, output_dir=None)

Make predictions on a dataset with a trained vak.models.ParametricUMAPModel.

model_configdict

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().

trainer_config: dict

Configuration for lightning.pytorch.Trainer. Can be obtained by calling vak.config.TrainerConfig.asdict().

checkpoint_pathstr

path to directory with checkpoint files saved by Torch, to reload model

num_workersint

Number of processes to use for parallel loading of data. Argument to torch.DataLoader. Default is 2.

timebins_keystr

key for accessing vector of time bins in files. Default is ‘t’.

annot_csv_filenamestr

name of .csv file containing predicted annotations. Default is None, in which case the name of the dataset .csv is used, with ‘.annot.csv’ appended to it.

output_dirstr, Path

path to location where .csv containing predicted annotation should be saved. Defaults to current working directory.