vak.predict.predict_.predict

vak.predict.predict_.predict(model_name: str, model_config: dict, dataset_path: str | Path, checkpoint_path: str | Path, labelmap_path: str | Path, num_workers: int = 2, transform_params: dict | None = None, dataset_params: dict | None = None, timebins_key: str = 't', spect_scaler_path: str | Path | None = None, device: str | None = None, annot_csv_filename: str | None = None, output_dir: str | Path | None = None, min_segment_dur: float | None = None, majority_vote: bool = False, save_net_outputs: bool = False)

Make predictions on a dataset with a trained model.

Parameters:

• model_name (str) – Model name, must be one of vak.models.registry.MODEL_NAMES.

• model_config (dict) –

  Model configuration in a dict,

  as loaded from a .toml file, and used by the model method from_config.

  dataset_pathstr

  Path to dataset, e.g., a csv file generated by running vak prep.

  checkpoint_pathstr

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

  labelmap_pathstr

  path to ‘labelmap.json’ file.

  window_sizeint

  size of windows taken from spectrograms, in number of time bins, shown to neural networks

  num_workersint

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

• transform_params (dict, optional) – Parameters for data transform. Passed as keyword arguments. Optional, default is None.

• dataset_params (dict, optional) –

  Parameters for dataset.

  Passed as keyword arguments. Optional, default is None.

  timebins_keystr

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

  devicestr

  Device on which to work with model + data. Defaults to ‘cuda’ if torch.cuda.is_available is True.

  spect_scaler_pathstr

  path to a saved SpectScaler object used to normalize spectrograms. If spectrograms were normalized and this is not provided, will give incorrect results.

  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.

  min_segment_durfloat

  minimum duration of segment, in seconds. If specified, then any segment with a duration less than min_segment_dur is removed from lbl_tb. Default is None, in which case no segments are removed.

  majority_votebool

  if True, transform segments containing multiple labels into segments with a single label by taking a “majority vote”, i.e. assign all time bins in the segment the most frequently occurring label in the segment. This transform can only be applied if the labelmap contains an ‘unlabeled’ label, because unlabeled segments makes it possible to identify the labeled segments. Default is False.

• save_net_outputs (bool) – if True, save ‘raw’ outputs of neural networks before they are converted to annotations. Default is False. Typically the output will be “logits” to which a softmax transform might be applied. For each item in the dataset–each row in the dataset_path .csv– the output will be saved in a separate file in output_dir, with the extension {MODEL_NAME}.output.npz. E.g., if the input is a spectrogram with spect_path filename gy6or6_032312_081416.npz, and the network is TweetyNet, then the net output file will be gy6or6_032312_081416.tweetynet.output.npz.