Source code for tensorpack.predict.config

# -*- coding: utf-8 -*-
# File:

import six
from ..compat import tfv1 as tf

from ..train.model_desc import ModelDescBase
from ..tfutils.sessinit import JustCurrentSession, SessionInit
from ..tfutils.sesscreate import NewSessionCreator
from ..tfutils.tower import TowerFunc
from ..utils import logger

__all__ = ['PredictConfig']

[docs]class PredictConfig(object):
[docs] 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__) if model is not None: assert_type(model, ModelDescBase, 'model') assert input_signature is None and tower_func is None self.input_signature = model.get_input_signature() self.tower_func = TowerFunc(model.build_graph, self.input_signature) else: if isinstance(tower_func, TowerFunc): input_signature = tower_func.input_signature assert input_signature is not None and tower_func is not None self.input_signature = input_signature self.tower_func = TowerFunc(tower_func, input_signature) if session_init is None: session_init = JustCurrentSession() self.session_init = session_init assert_type(self.session_init, SessionInit, 'session_init') if session_creator is None: self.session_creator = NewSessionCreator() else: self.session_creator = session_creator # inputs & outputs self.input_names = input_names if self.input_names is None: self.input_names = [ for k in self.input_signature] assert output_names is not None, "Argument 'output_names' is not provided!" self.output_names = output_names assert_type(self.output_names, list, 'output_names') assert_type(self.input_names, list, 'input_names') if len(self.input_names) == 0: logger.warn('PredictConfig receives empty "input_names".') for v in self.input_names: assert_type(v, six.string_types, 'Each item in input_names') assert len(self.output_names), "Argument 'output_names' cannot be empty!" self.return_input = bool(return_input) self.create_graph = bool(create_graph)
def _maybe_create_graph(self): if self.create_graph: return tf.Graph() return tf.get_default_graph()