Method __init__

tensorpack/train/config.py:59–160

Args: dataflow (DataFlow): data (InputSource): model (ModelDesc): callbacks (list[Callback]): a list of :class:`Callback` to use during training. extra_callbacks (list[Callback]): This argument is only used to prov

(self,
                 dataflow=None, data=None,
                 model=None,
                 callbacks=None, extra_callbacks=None, monitors=None,
                 session_creator=None, session_config=None, session_init=None,
                 starting_epoch=1, steps_per_epoch=None, max_epoch=99999)

    """

    def __init__(self,
                 dataflow=None, data=None,
                 model=None,
                 callbacks=None, extra_callbacks=None, monitors=None,
                 session_creator=None, session_config=None, session_init=None,
                 starting_epoch=1, steps_per_epoch=None, max_epoch=99999):
        """
        Args:
            dataflow (DataFlow):
            data (InputSource):
            model (ModelDesc):

            callbacks (list[Callback]): a list of :class:`Callback` to use during training.
            extra_callbacks (list[Callback]): This argument
                is only used to provide the defaults in addition to ``callbacks``.
                The list of callbacks that will be used in the end is simply ``callbacks + extra_callbacks``.

                It is usually left as None, and the default value for this argument is :func:`DEFAULT_CALLBACKS()`.
                You can override it when you don't like any of the default callbacks.
                For example, if you'd like to let the progress bar print tensors, you can use

                .. code-block:: none

                    extra_callbacks=[ProgressBar(names=['name']),
                                     MovingAverageSummary(),
                                     MergeAllSummaries(),
                                     RunUpdateOps()]

            monitors (list[MonitorBase]): Defaults to :func:`DEFAULT_MONITORS()`.

            session_creator (tf.train.SessionCreator): Defaults to :class:`sesscreate.NewSessionCreator()`
                with the config returned by :func:`tfutils.get_default_sess_config()`.
            session_config (tf.ConfigProto): when session_creator is None, use this to create the session.
            session_init (SessionInit): how to initialize variables of a session. Defaults to do nothing.

            starting_epoch (int): The index of the first epoch.
            steps_per_epoch (int): the number of steps (defined by :meth:`Trainer.run_step`) to run in each epoch.
                Defaults to the input data size. You may want to divide it by the #GPUs in multi-GPU training.

                Number of steps per epoch only affects the schedule of callbacks.
                It does not affect the sequence of input data seen by the model.
            max_epoch (int): maximum number of epoch to run training.
        """

        # TODO type checker decorator
        def assert_type(v, tp, name):
            assert isinstance(v, tp), \
                "{} has to be type '{}', but an object of type '{}' found.".format(
                    name, tp.__name__, v.__class__.__name__)

        # process data & model
        assert data is None or dataflow is None, "dataflow and data cannot be both presented in TrainConfig!"
        if dataflow is not None:
            assert_type(dataflow, DataFlow, 'dataflow')
        if data is not None:
            assert_type(data, InputSource, 'data')
        self.dataflow = dataflow
        self.data = data