vak.config.predict.PredictConfig#

class vak.config.predict.PredictConfig(checkpoint_path, labelmap_path, model, batch_size, dataset_path=None, spect_scaler_path=None, num_workers=2, device='cpu', annot_csv_filename=None, output_dir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/vak/checkouts/latest/doc'), min_segment_dur=None, majority_vote=True, save_net_outputs=False, transform_params=None, dataset_params=None)[source]#

Bases: object

class that represents [PREDICT] section of config.toml file

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.

modelstr

Model name, e.g., model = "TweetyNet"

batch_sizeint: number of samples per batch presented to models during training.
num_workersint: Number of processes to use for parallel loading of data. Argument to torch.DataLoader. Default is 2.
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 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.

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.

__init__(checkpoint_path, labelmap_path, model, batch_size, dataset_path=None, spect_scaler_path=None, num_workers=2, device='cpu', annot_csv_filename=None, output_dir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/vak/checkouts/latest/doc'), min_segment_dur=None, majority_vote=True, save_net_outputs=False, transform_params=None, dataset_params=None) → None#: Method generated by attrs for class PredictConfig.

Methods

__init__(checkpoint_path, labelmap_path, ...)

Method generated by attrs for class PredictConfig.