Python tensorflow.import_graph_def() Examples

The following are 30 code examples of tensorflow.import_graph_def(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source File: util.py    From ARU-Net with GNU General Public License v2.0 8 votes vote down vote up
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="",
            op_dict=None,
            producer_op_list=None
        )
    return graph 
Example #2
Source File: object_detection_multithreading.py    From object_detector_app with MIT License 7 votes vote down vote up
def worker(input_q, output_q):
    # Load a (frozen) Tensorflow model into memory.
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

        sess = tf.Session(graph=detection_graph)

    fps = FPS().start()
    while True:
        fps.update()
        frame = input_q.get()
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        output_q.put(detect_objects(frame_rgb, sess, detection_graph))

    fps.stop()
    sess.close() 
Example #3
Source File: utils.py    From speech_separation with MIT License 7 votes vote down vote up
def load_graph(graph_path,tensorboard=False,**kwargs):
    '''
    :param graph_filename: the path of the pb file
    :return: tensorflow graph
    '''
    with gfile.FastGFile(graph_path,'rb') as f:
        graph_def = graph_pb2.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def,name="")

    if tensorboard:
        writer = tf.summary.FileWriter("log/")
        writer.add_graph(graph)

    return graph 
Example #4
Source File: retrain.py    From tensorflow-image-detection with MIT License 6 votes vote down vote up
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 
Example #5
Source File: facenet.py    From TNT with GNU General Public License v3.0 6 votes vote down vote up
def load_model(model, input_map=None):
    # 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, input_map=input_map, 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), input_map=input_map)
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file)) 
Example #6
Source File: facenet.py    From TNT with GNU General Public License v3.0 6 votes vote down vote up
def load_model(model, input_map=None):
    # 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, input_map=input_map, 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), input_map=input_map)
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file)) 
Example #7
Source File: facenet.py    From TNT with GNU General Public License v3.0 6 votes vote down vote up
def load_model(model, input_map=None):
    # 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, input_map=input_map, 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), input_map=input_map)
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file)) 
Example #8
Source File: generate_detections.py    From deep_sort with GNU General Public License v3.0 6 votes vote down vote up
def __init__(self, checkpoint_filename, input_name="images",
                 output_name="features"):
        self.session = tf.Session()
        with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(file_handle.read())
        tf.import_graph_def(graph_def, name="net")
        self.input_var = tf.get_default_graph().get_tensor_by_name(
            "net/%s:0" % input_name)
        self.output_var = tf.get_default_graph().get_tensor_by_name(
            "net/%s:0" % output_name)

        assert len(self.output_var.get_shape()) == 2
        assert len(self.input_var.get_shape()) == 4
        self.feature_dim = self.output_var.get_shape().as_list()[-1]
        self.image_shape = self.input_var.get_shape().as_list()[1:] 
Example #9
Source File: build.py    From Automatic-Identification-and-Counting-of-Blood-Cells with GNU General Public License v3.0 6 votes vote down vote up
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() 
Example #10
Source File: detect_face.py    From face-detection-ssd-mobilenet with Apache License 2.0 6 votes vote down vote up
def __init__(self, PATH_TO_CKPT):
        """Tensorflow detector
        """

        self.detection_graph = tf.Graph()
        with self.detection_graph.as_default():
            od_graph_def = tf.GraphDef()
            with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)
                tf.import_graph_def(od_graph_def, name='')


        with self.detection_graph.as_default():
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            with tf.Session(graph=self.detection_graph, config=config) as self.sess:

                self.windowNotSet = True 
Example #11
Source File: convertmodel.py    From DmsMsgRcg with Apache License 2.0 6 votes vote down vote up
def __init__(self, config, graph, model_scope, model_dir, model_file):
        self.config = config

        frozen_model = os.path.join(model_dir, model_file)
        with tf.gfile.GFile(frozen_model, "rb") as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())

        # This model_scope adds a prefix to all the nodes in the graph
        tf.import_graph_def(graph_def, input_map=None, return_elements=None,
                            name="{}/".format(model_scope))

        # Uncomment the two lines below to look for the names of all the operations in the graph
        # for op in graph.get_operations():
        #    print(op.name)

        # Using the lines commented above to look for the tensor name of the input node
        # Or you can figure it out in your original model, if you explicitly named it.
        self.input_tensor = graph.get_tensor_by_name("{}/input_1:0".format(model_scope))
        self.output_tensor = graph.get_tensor_by_name("{}/s1_output0:0".format(model_scope)) 
Example #12
Source File: model.py    From tensorprob with MIT License 6 votes vote down vote up
def _rewrite_graph(self, transform):
        input_map = {k.name: v for k, v in transform.items()}

        # Modify the input dictionary to replace variables which have been
        # superseded with the use of combinators
        for k, v in self._silently_replace.items():
            input_map[k.name] = self._observed[v]

        with self.session.graph.as_default():
            try:
                tf.import_graph_def(
                        self._model_graph.as_graph_def(),
                        input_map=input_map,
                        name='added',
                )
            except ValueError:
                # Ignore errors that ocour when the input_map tries to
                # rewrite a variable that isn't present in the graph
                pass 
Example #13
Source File: object_detection_app.py    From object_detector_app with MIT License 6 votes vote down vote up
def worker(input_q, output_q):
    # Load a (frozen) Tensorflow model into memory.
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

        sess = tf.Session(graph=detection_graph)

    fps = FPS().start()
    while True:
        fps.update()
        frame = input_q.get()
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        output_q.put(detect_objects(frame_rgb, sess, detection_graph))

    fps.stop()
    sess.close() 
Example #14
Source File: retrain.py    From diabetic-retinopathy-screening with GNU General Public License v3.0 6 votes vote down vote up
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 
Example #15
Source File: model_ops.py    From speech_separation with MIT License 6 votes vote down vote up
def load_graph(graph_path,tensorboard=False,**kwargs):
    '''
    :param graph_filename: the path of the pb file
    :return: tensorflow graph
    '''
    with gfile.FastGFile(graph_path,'rb') as f:
        graph_def = graph_pb2.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def,name="")

    if tensorboard:
        writer = tf.summary.FileWriter("log/")
        writer.add_graph(graph)

    return graph 
Example #16
Source File: input.py    From spark-deep-learning with Apache License 2.0 6 votes vote down vote up
def fromGraphDef(cls, graph_def, feed_names, fetch_names):
        """
        Construct a TFInputGraph from a tf.GraphDef object.

        :param graph_def: :py:class:`tf.GraphDef`, a serializable object containing the topology and
                           computation units of the TensorFlow graph.
        :param feed_names: list, names of the input tensors.
        :param fetch_names: list, names of the output tensors.
        """
        assert isinstance(graph_def, tf.GraphDef), \
            ('expect tf.GraphDef type but got', type(graph_def))

        graph = tf.Graph()
        with tf.Session(graph=graph) as sess:
            tf.import_graph_def(graph_def, name='')
            return _build_with_feeds_fetches(sess=sess, graph=graph, feed_names=feed_names,
                                             fetch_names=fetch_names) 
Example #17
Source File: detector_utils.py    From Emojinator with MIT License 6 votes vote down vote up
def load_inference_graph():
    # load frozen tensorflow model into memory
    print("> ====== loading HAND frozen graph into memory")
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
        sess = tf.Session(graph=detection_graph)
    print(">  ====== Hand Inference graph loaded.")
    return detection_graph, sess


# draw the detected bounding boxes on the images
# You can modify this to also draw a label. 
Example #18
Source File: generate_detections.py    From multi-object-tracking with GNU General Public License v3.0 6 votes vote down vote up
def __init__(self, checkpoint_filename, input_name="images",
                 output_name="features"):
        self.session = tf.Session()
        with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(file_handle.read())
        tf.import_graph_def(graph_def, name="net")
        self.input_var = tf.get_default_graph().get_tensor_by_name(
            "net/%s:0" % input_name)
        self.output_var = tf.get_default_graph().get_tensor_by_name(
            "net/%s:0" % output_name)

        assert len(self.output_var.get_shape()) == 2
        assert len(self.input_var.get_shape()) == 4
        self.feature_dim = self.output_var.get_shape().as_list()[-1]
        self.image_shape = self.input_var.get_shape().as_list()[1:] 
Example #19
Source File: test_exportPb.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up
def load_graph(frozen_graph_file):

    # we parse the graph_def file
    with tf.gfile.GFile(frozen_graph_file, '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="",
                            op_dict=None,
                            producer_op_list=None)
    return graph 
Example #20
Source File: inference.py    From CycleGAN-TensorFlow with MIT License 6 votes vote down vote up
def inference():
  graph = tf.Graph()

  with graph.as_default():
    with tf.gfile.FastGFile(FLAGS.input, 'rb') as f:
      image_data = f.read()
      input_image = tf.image.decode_jpeg(image_data, channels=3)
      input_image = tf.image.resize_images(input_image, size=(FLAGS.image_size, FLAGS.image_size))
      input_image = utils.convert2float(input_image)
      input_image.set_shape([FLAGS.image_size, FLAGS.image_size, 3])

    with tf.gfile.FastGFile(FLAGS.model, 'rb') as model_file:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(model_file.read())
    [output_image] = tf.import_graph_def(graph_def,
                          input_map={'input_image': input_image},
                          return_elements=['output_image:0'],
                          name='output')

  with tf.Session(graph=graph) as sess:
    generated = output_image.eval()
    with open(FLAGS.output, 'wb') as f:
      f.write(generated) 
Example #21
Source File: export_tflite_ssd_graph_lib_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up
def _import_graph_and_run_inference(self, tflite_graph_file, num_channels=3):
    """Imports a tflite graph, runs single inference and returns outputs."""
    graph = tf.Graph()
    with graph.as_default():
      graph_def = tf.GraphDef()
      with tf.gfile.Open(tflite_graph_file) as f:
        graph_def.ParseFromString(f.read())
      tf.import_graph_def(graph_def, name='')
      input_tensor = graph.get_tensor_by_name('normalized_input_image_tensor:0')
      box_encodings = graph.get_tensor_by_name('raw_outputs/box_encodings:0')
      class_predictions = graph.get_tensor_by_name(
          'raw_outputs/class_predictions:0')
      with self.test_session(graph) as sess:
        [box_encodings_np, class_predictions_np] = sess.run(
            [box_encodings, class_predictions],
            feed_dict={input_tensor: np.random.rand(1, 10, 10, num_channels)})
    return box_encodings_np, class_predictions_np 
Example #22
Source File: predict.py    From Custom-vision-service-iot-edge-raspberry-pi with MIT License 6 votes vote down vote up
def initialize():
    print('Loading model...',end=''),
    with open(filename, 'rb') as f:
        graph_def.ParseFromString(f.read())
        tf.import_graph_def(graph_def, name='')

    # Retrieving 'network_input_size' from shape of 'input_node'
    with tf.compat.v1.Session() as sess:
        input_tensor_shape = sess.graph.get_tensor_by_name(input_node).shape.as_list()
        
    assert len(input_tensor_shape) == 4
    assert input_tensor_shape[1] == input_tensor_shape[2]

    global network_input_size
    network_input_size = input_tensor_shape[1]
   
    print('Success!')
    print('Loading labels...', end='')
    with open(labels_filename, 'rt') as lf:
        global labels
        labels = [l.strip() for l in lf.readlines()]
    print(len(labels), 'found. Success!') 
Example #23
Source File: build.py    From Traffic-Signs-and-Object-Detection with GNU General Public License v3.0 6 votes vote down vote up
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() 
Example #24
Source File: predict-amd64.py    From Custom-vision-service-iot-edge-raspberry-pi with MIT License 6 votes vote down vote up
def initialize():
    print('Loading model...',end=''),
    with open(filename, 'rb') as f:
        graph_def.ParseFromString(f.read())
        tf.import_graph_def(graph_def, name='')

    # Retrieving 'network_input_size' from shape of 'input_node'
    with tf.compat.v1.Session() as sess:
        input_tensor_shape = sess.graph.get_tensor_by_name(input_node).shape.as_list()
        
    assert len(input_tensor_shape) == 4
    assert input_tensor_shape[1] == input_tensor_shape[2]

    global network_input_size
    network_input_size = input_tensor_shape[1]
   
    print('Success!')
    print('Loading labels...', end='')
    with open(labels_filename, 'rt') as lf:
        global labels
        labels = [l.strip() for l in lf.readlines()]
    print(len(labels), 'found. Success!') 
Example #25
Source File: retrain.py    From powerai-transfer-learning with Apache License 2.0 6 votes vote down vote up
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 
Example #26
Source File: test_import.py    From spark-deep-learning with Apache License 2.0 6 votes vote down vote up
def _check_output(gin, tf_input, expected):
    """
    Takes a TFInputGraph object (assumed to have the input and outputs of the given
    names above) and compares the outcome against some expected outcome.
    """
    graph = tf.Graph()
    graph_def = gin.graph_def
    with tf.Session(graph=graph) as sess:
        tf.import_graph_def(graph_def, name="")
        tgt_feed = tfx.get_tensor(_tensor_input_name, graph)
        tgt_fetch = tfx.get_tensor(_tensor_output_name, graph)
        # Run on the testing target
        tgt_out = sess.run(tgt_fetch, feed_dict={tgt_feed: tf_input})
        # Working on integers, the calculation should be exact
        assert np.all(tgt_out == expected), (tgt_out, expected)


# TODO: we could factorize with _check_output, but this is not worth the time doing it. 
Example #27
Source File: test_import.py    From spark-deep-learning with Apache License 2.0 6 votes vote down vote up
def _check_output_2(gin, tf_input1, tf_input2, expected):
    """
    Takes a TFInputGraph object (assumed to have the input and outputs of the given
    names above) and compares the outcome against some expected outcome.
    """
    graph = tf.Graph()
    graph_def = gin.graph_def
    with tf.Session(graph=graph) as sess:
        tf.import_graph_def(graph_def, name="")
        tgt_feed1 = tfx.get_tensor(_tensor_input_name, graph)
        tgt_feed2 = tfx.get_tensor(_tensor_input_name_2, graph)
        tgt_fetch = tfx.get_tensor(_tensor_output_name, graph)
        # Run on the testing target
        tgt_out = sess.run(tgt_fetch, feed_dict={tgt_feed1: tf_input1, tgt_feed2: tf_input2})
        # Working on integers, the calculation should be exact
        assert np.all(tgt_out == expected), (tgt_out, expected) 
Example #28
Source File: build.py    From Traffic_sign_detection_YOLO with MIT License 6 votes vote down vote up
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() 
Example #29
Source File: label_image.py    From powerai-transfer-learning with Apache License 2.0 5 votes vote down vote up
def load_graph(filename):
  """Unpersists graph from file as default graph."""
  with tf.gfile.FastGFile(filename, 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='') 
Example #30
Source File: tf_tensor.py    From spark-deep-learning with Apache License 2.0 5 votes vote down vote up
def _get_placeholder_types(self, user_graph_def):
        """ Returns a list of placeholder type enums for the input nodes """
        user_graph = tf.Graph()
        with user_graph.as_default():
            # Load user-specified graph into memory, then get the data type of each input node
            tf.import_graph_def(user_graph_def, name="")
            res = []
            for _, tensor_name in self.getInputMapping():
                placeholder_type = tfx.get_tensor(tensor_name, user_graph).dtype.as_datatype_enum
                res.append(placeholder_type)
        return res