Trains the network with the labels in the training dataset. Parameters ---------- config : string Full path of the config.yaml file as a string. shuffle: int, optional, default=1 Integer value specifying the shuffle index to select for training. trainingsetinde
(
config: str | Path,
shuffle: int = 1,
trainingsetindex: int = 0,
max_snapshots_to_keep: int | None = None,
display_iters: int | None = None,
save_iters: int | None = None,
max_iters: int | None = None,
epochs: int | None = None,
save_epochs: int | None = None,
allow_growth: bool = True,
gputouse: str | None = None,
autotune: bool = False,
keepdeconvweights: bool = True,
modelprefix: str = "",
superanimal_name: str = "",
superanimal_transfer_learning: bool = False,
engine: Engine | None = None,
device: str | None = None,
snapshot_path: str | Path | None = None,
detector_path: str | Path | None = None,
batch_size: int | None = None,
detector_batch_size: int | None = None,
detector_epochs: int | None = None,
detector_save_epochs: int | None = None,
pose_threshold: float | None = 0.1,
pytorch_cfg_updates: dict | None = None,
)
| 64 | @renamed_parameter(old="saveiters", new="save_iters", since="3.0.0") |
| 65 | @renamed_parameter(old="displayiters", new="display_iters", since="3.0.0") |
| 66 | def train_network( |
| 67 | config: str | Path, |
| 68 | shuffle: int = 1, |
| 69 | trainingsetindex: int = 0, |
| 70 | max_snapshots_to_keep: int | None = None, |
| 71 | display_iters: int | None = None, |
| 72 | save_iters: int | None = None, |
| 73 | max_iters: int | None = None, |
| 74 | epochs: int | None = None, |
| 75 | save_epochs: int | None = None, |
| 76 | allow_growth: bool = True, |
| 77 | gputouse: str | None = None, |
| 78 | autotune: bool = False, |
| 79 | keepdeconvweights: bool = True, |
| 80 | modelprefix: str = "", |
| 81 | superanimal_name: str = "", |
| 82 | superanimal_transfer_learning: bool = False, |
| 83 | engine: Engine | None = None, |
| 84 | device: str | None = None, |
| 85 | snapshot_path: str | Path | None = None, |
| 86 | detector_path: str | Path | None = None, |
| 87 | batch_size: int | None = None, |
| 88 | detector_batch_size: int | None = None, |
| 89 | detector_epochs: int | None = None, |
| 90 | detector_save_epochs: int | None = None, |
| 91 | pose_threshold: float | None = 0.1, |
| 92 | pytorch_cfg_updates: dict | None = None, |
| 93 | ): |
| 94 | """Trains the network with the labels in the training dataset. |
| 95 | |
| 96 | Parameters |
| 97 | ---------- |
| 98 | config : string |
| 99 | Full path of the config.yaml file as a string. |
| 100 | |
| 101 | shuffle: int, optional, default=1 |
| 102 | Integer value specifying the shuffle index to select for training. |
| 103 | |
| 104 | trainingsetindex: int, optional, default=0 |
| 105 | Integer specifying which TrainingsetFraction to use. |
| 106 | Note that TrainingFraction is a list in config.yaml. |
| 107 | |
| 108 | max_snapshots_to_keep: int or None |
| 109 | Sets how many snapshots are kept, i.e. states of the trained network. Every |
| 110 | saving iteration many times a snapshot is stored, however only the last |
| 111 | ``max_snapshots_to_keep`` many are kept! If you change this to None, then all |
| 112 | are kept. |
| 113 | See: https://github.com/DeepLabCut/DeepLabCut/issues/8#issuecomment-387404835 |
| 114 | |
| 115 | display_iters: optional, default=None |
| 116 | This variable is actually set in ``pose_config.yaml``. However, you can |
| 117 | overwrite it with this hack. Don't use this regularly, just if you are too lazy |
| 118 | to dig out the ``pose_config.yaml`` file for the corresponding project. If |
| 119 | ``None``, the value from there is used, otherwise it is overwritten! |
| 120 | |
| 121 | save_iters: optional, default=None |
| 122 | Only for the TensorFlow engine (for the PyTorch engine see the ``torch_kwargs``: |
| 123 | you can use ``save_epochs``). |
nothing calls this directly
no test coverage detected