python类GraphDef()的实例源码

def load_model(model):
    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
    #  or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print('Model filename: %s' % model_exp)
        with gfile.FastGFile(model_exp,'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, name='')
    else:
        print('Model directory: %s' % model_exp)
        meta_file, ckpt_file = get_model_filenames(model_exp)

        print('Metagraph file: %s' % meta_file)
        print('Checkpoint file: %s' % ckpt_file)

        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))

def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Session() as sess:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def, 
            input_map=None, 
            return_elements=None, 
            name="prefix", 
            op_dict=None, 
            producer_op_list=None
        )

    return graph

# make the raw data acceptable for the model

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def, 
            input_map=None, 
            return_elements=None, 
            name="prefix", 
            op_dict=None, 
            producer_op_list=None
        )

    return graph

# make the raw data acceptable for the model

def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Session() as sess:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor

def load_graph(frozen_graph_filename):
    """load the protobuf file from the disk and parse it to retrieve the unserialized graph_def"""

    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name="prefix",
            op_dict=None,
            producer_op_list=None
        )
    return graph

def create_model_graph(model_info):
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Args:
    model_info: Dictionary containing information about the model architecture.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Graph().as_default() as graph:
    model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
    with gfile.FastGFile(model_path, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
          graph_def,
          name='',
          return_elements=[
              model_info['bottleneck_tensor_name'],
              model_info['resized_input_tensor_name'],
          ]))
  return graph, bottleneck_tensor, resized_input_tensor

def Graph(self):
    """Return the graph definition, if there is one.

    If the graph is stored directly, return that.  If no graph is stored
    directly but a metagraph is stored containing a graph, return that.

    Raises:
      ValueError: If there is no graph for this run.

    Returns:
      The `graph_def` proto.
    """
    graph = tf.GraphDef()
    if self._graph is not None:
      graph.ParseFromString(self._graph)
      return graph
    raise ValueError('There is no graph in this EventAccumulator')

def Graph(self):
    """Return the graph definition, if there is one.

    If the graph is stored directly, return that.  If no graph is stored
    directly but a metagraph is stored containing a graph, return that.

    Raises:
      ValueError: If there is no graph for this run.

    Returns:
      The `graph_def` proto.
    """
    graph = tf.GraphDef()
    if self._graph is not None:
      graph.ParseFromString(self._graph)
      return graph
    raise ValueError('There is no graph in this EventAccumulator')

def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Session() as sess:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor

def load_graph(frozen_graph_filename):
    # We parse the graph_def file
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

        # We load the graph_def in the default graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name="prefix",
            op_dict=None,
            producer_op_list=None
        )
    return graph

def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Session() as sess:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor

def __init__(self, alpha=0.9, graph_path='', checkpoint_path='', metagraph_path=''):
        if graph_path:
            assert os.path.isfile(graph_path)
        else:
            assert os.path.isfile(checkpoint_path) and os.path.isfile(metagraph_path)
        self.graph = tf.Graph()
        with self.graph.as_default():
            if graph_path:
                # load a graph with weights frozen as constants
                graph_def = tf.GraphDef()
                with open(graph_path, "rb") as f:
                    graph_def.ParseFromString(f.read())
                    _ = tf.import_graph_def(graph_def, name="")
                self.session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
            else:
                # load a meta-graph and initialize variables form checkpoint
                saver = tf.train.import_meta_graph(metagraph_path)
                self.session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
                saver.restore(self.session, checkpoint_path)
        self.model_input = self.session.graph.get_tensor_by_name("input_placeholder:0")
        self.model_output = self.session.graph.get_tensor_by_name("output_steer:0")
        self.last_steering_angle = 0  # None
        self.alpha = alpha

def extract_fc7_features(image_path, model_path):
    vgg_file = open(model_path)
    vgg16raw = vgg_file.read()
    vgg_file.close()

    graph_def = tf.GraphDef()
    graph_def.ParseFromString(vgg16raw)
    images = tf.placeholder("float32", [None, 224, 224, 3])
    tf.import_graph_def(graph_def, input_map={ "images": images })
    graph = tf.get_default_graph()

    sess = tf.Session()
    image_array = load_image_array(image_path)
    image_feed = np.ndarray((1,224,224,3))
    image_feed[0:,:,:] = image_array
    feed_dict  = { images : image_feed }
    fc7_tensor = graph.get_tensor_by_name("import/Relu_1:0")
    fc7_features = sess.run(fc7_tensor, feed_dict = feed_dict)
    sess.close()
    return fc7_features

def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Graph().as_default() as graph:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor

def load_model(frozen_graph_filename):
    # First we need to load the protobuf file from the disk and parse it to retrieve the 
    # Unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def, 
            input_map=None, 
            return_elements=None, 
            name="prefix", 
            op_dict=None, 
            producer_op_list=None
        )
    return graph

def build_from_pb(self):
        with tf.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())

        tf.import_graph_def(
            graph_def,
            name=""
        )
        with open(self.FLAGS.metaLoad, 'r') as fp:
            self.meta = json.load(fp)
        self.framework = create_framework(self.meta, self.FLAGS)

        # Placeholders
        self.inp = tf.get_default_graph().get_tensor_by_name('input:0')
        self.feed = dict() # other placeholders
        self.out = tf.get_default_graph().get_tensor_by_name('output:0')

        self.setup_meta_ops()

def loadpb(filename, model_name='dcgan'):
        """Loads pretrained graph from ProtoBuf file

        Arguments:
            filename - path to ProtoBuf graph definition
            model_name - prefix to assign to loaded graph node names

        Returns:
            graph, graph_def - as per Tensorflow definitions
        """
        with tf.gfile.GFile(filename, 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())

        with tf.Graph().as_default() as graph:
            tf.import_graph_def(graph_def,
                                input_map=None,
                                return_elements=None,
                                op_dict=None,
                                producer_op_list=None,
                                name=model_name)

        return graph, graph_def

def load_model(model):
    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
    #  or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print('Model filename: %s' % model_exp)
        with gfile.FastGFile(model_exp,'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, name='')
    else:
        print('Model directory: %s' % model_exp)
        meta_file, ckpt_file = get_model_filenames(model_exp)

        print('Metagraph file: %s' % meta_file)
        print('Checkpoint file: %s' % ckpt_file)

        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))

def load_metadata(model_dir):
  """Loads RunMetadata, Graph and OpLog from files
  """
  # Import RunMetadata
  run_meta_path = os.path.join(model_dir, "metadata/run_meta")
  run_meta = tf.RunMetadata()
  if gfile.Exists(run_meta_path):
    with gfile.GFile(run_meta_path, "rb") as file:
      run_meta.MergeFromString(file.read())
    print("Loaded RunMetadata from {}".format(run_meta_path))
  else:
    print("RunMetadata does not exist a {}. Skipping.".format(run_meta_path))

  # Import Graph
  graph_def_path = os.path.join(model_dir, "graph.pbtxt")
  graph = tf.Graph()
  if gfile.Exists(graph_def_path):
    with graph.as_default():
      _register_function_ops(CUSTOM_OP_FUNCTIONS)
      graph_def = tf.GraphDef()
      with gfile.GFile(graph_def_path, "rb") as file:
        text_format.Parse(file.read(), graph_def)
      tf.import_graph_def(graph_def, name="")
      print("Loaded Graph from {}".format(graph_def_path))
  else:
    print("Graph does not exist a {}. Skipping.".format(graph_def_path))

  # Import OpLog
  op_log_path = os.path.join(model_dir, "metadata/tfprof_log")
  op_log = tfprof_log_pb2.OpLog()
  if gfile.Exists(op_log_path):
    with gfile.GFile(op_log_path, "rb") as file:
      op_log.MergeFromString(file.read())
      print("Loaded OpLog from {}".format(op_log_path))
  else:
    print("OpLog does not exist a {}. Skipping.".format(op_log_path))

  return run_meta, graph, op_log