Class PredictConfig

tensorpack/predict/config.py:17–118

class PredictConfig(object):
    def __init__(self,
                 model=None,
                 tower_func=None,
                 input_signature=None,

                 input_names=None,
                 output_names=None,

                 session_creator=None,
                 session_init=None,
                 return_input=False,
                 create_graph=True,
                 ):
        """
        Users need to provide enough arguments to create a tower function,
        which will be used to construct the graph.
        This can be provided in the following ways:

        1. `model`: a :class:`ModelDesc` instance. It will contain a tower function by itself.
        2. `tower_func`: a :class:`tfutils.TowerFunc` instance.
            Provide a tower function instance directly.
        3. `tower_func`: a symbolic function and `input_signature`: the signature of the function.
            Provide both a function and its signature.

        Example:

        .. code-block:: python

            config = PredictConfig(model=my_model,
                                   inputs_names=['image'],
                                   output_names=['linear/output', 'prediction'])

        Args:
            model (ModelDescBase): to be used to construct a tower function.
            tower_func: a callable which takes input tensors (by positional args) and construct a tower.
                or a :class:`tfutils.TowerFunc` instance.
            input_signature ([tf.TensorSpec]): if tower_func is a plain function (instead of a TowerFunc),
                this describes the list of inputs it takes.

            input_names (list): a list of input tensor names. Defaults to match input_signature.
                The name can be either the name of a tensor, or the name of one input of the tower.
            output_names (list): a list of names of the output tensors to predict, the
                tensors can be any tensor in the graph that's computable from the tensors correponding to `input_names`.

            session_creator (tf.train.SessionCreator): how to create the
                session. Defaults to :class:`NewSessionCreator()`.
            session_init (SessionInit): how to initialize variables of the session.
                Defaults to do nothing.

            return_input (bool): same as in :attr:`PredictorBase.return_input`.
            create_graph (bool): create a new graph, or use the default graph
                when predictor is first initialized.
        """
        def assert_type(v, tp, name):
            assert isinstance(v, tp), \
                "Argument '{}' has to be type '{}', but an object of type '{}' found.".format(
                    name, tp.__name__, v.__class__.__name__)