vak.predict.frame_classification.predict_with_frame_classification_model

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

Make predictions on a dataset with a trained model.

model_namestr

Model name, must be one of vak.models.registry.MODEL_NAMES.

model_configdict

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.

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.

spect_keystr

key for accessing spectrogram in files. Default is ‘s’.

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_outputsbool

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.