Python tensorflow.GraphDef() Examples

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 

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() 

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) 

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() 

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:] 

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 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 _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 

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 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) 

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 __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:] 

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 

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. 

def _load_saved_model_as_frozen_graph(
        saved_model_dir: str
) -> Tuple[Dict[str, tf.Tensor], Dict[str, tf.Tensor], tf.GraphDef]:
    tag = tf.saved_model.tag_constants.SERVING
    signature_def_tag = (
        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
    graph = tf.Graph()
    with tf.Session(graph=graph) as sess:
        meta_graph_def = tf.saved_model.loader.load(
            sess, [tag],
            export_dir=saved_model_dir, clear_devices=True
        )
        inputs = meta_graph_def.signature_def[signature_def_tag].inputs
        outputs = meta_graph_def.signature_def[signature_def_tag].outputs
        output_names = [
            tf_utils.remove_tag_from_variable_name(each_node.name)
            for each_node in outputs.values()]
        frozen_graph_def = tf.graph_util.convert_variables_to_constants(
            sess, graph.as_graph_def(), output_names)
    input_tensors = {
        k: graph.get_tensor_by_name(v.name) for k, v in inputs.items()}
    output_tensors = {
        k: graph.get_tensor_by_name(v.name) for k, v in outputs.items()}
    return input_tensors, output_tensors, frozen_graph_def 

def import_graph(saved_path, endpoints, image_value_range, scope='import'):
    t_input = tf.placeholder(np.float32, [None, None, None, 3])
    graph = tf.Graph()
    assert graph.unique_name(scope, False) == scope, (
        'Scope "%s" already exists. Provide explicit scope names when '
        'importing multiple instances of the model.') % scope

    graph_def = tf.GraphDef.FromString(
        tf.io.gfile.GFile(saved_path, 'rb').read())

    with tf.name_scope(scope) as sc:
      t_input, t_prep_input = PublicImageModelWrapper.create_input(
          t_input, image_value_range)

      graph_inputs = {}
      graph_inputs[endpoints['input']] = t_prep_input
      myendpoints = tf.import_graph_def(
          graph_def, graph_inputs, list(endpoints.values()), name=sc)
      myendpoints = dict(list(zip(list(endpoints.keys()), myendpoints)))
      myendpoints['input'] = t_input
    return myendpoints 

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)) 

def load_model(self):
        """
        Loads the detection model

        Args:

        Returns:

        """

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

        label_map = label_map_util.load_labelmap(self._path_to_labels)
        categories = label_map_util.convert_label_map_to_categories(\
            label_map, max_num_classes=self._num_classes, use_display_name=True)
        self.category_index = label_map_util.create_category_index(categories) 

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 

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)) 

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)) 

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)) 

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 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 build_graph_from_proto(self, graph_def_file, saver_def_file,
                             checkpoint_path):
    """Builds the inference graph from serialized GraphDef and SaverDef protos.

    Args:
      graph_def_file: File containing a serialized GraphDef proto.
      saver_def_file: File containing a serialized SaverDef proto.
      checkpoint_path: Checkpoint file or a directory containing a checkpoint
        file.

    Returns:
      restore_fn: A function such that restore_fn(sess) loads model variables
        from the checkpoint file.
    """
    # Load the Graph.
    tf.logging.info("Loading GraphDef from file: %s", graph_def_file)
    graph_def = tf.GraphDef()
    with tf.gfile.FastGFile(graph_def_file, "rb") as f:
      graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name="")

    # Load the Saver.
    tf.logging.info("Loading SaverDef from file: %s", saver_def_file)
    saver_def = tf.train.SaverDef()
    with tf.gfile.FastGFile(saver_def_file, "rb") as f:
      saver_def.ParseFromString(f.read())
    saver = tf.train.Saver(saver_def=saver_def)

    return self._create_restore_fn(checkpoint_path, saver) 

def run_inference_on_image(image):
  """Runs inference on an image.

  Args:
    image: Image file name.

  Returns:
    Nothing
  """
  if not tf.gfile.Exists(image):
    tf.logging.fatal('File does not exist %s', image)
  image_data = tf.gfile.FastGFile(image, 'rb').read()

  # Creates graph from saved GraphDef.
  create_graph()

  with tf.Session() as sess:
    # Some useful tensors:
    # 'softmax:0': A tensor containing the normalized prediction across
    #   1000 labels.
    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
    #   float description of the image.
    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
    #   encoding of the image.
    # Runs the softmax tensor by feeding the image_data as input to the graph.
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
    predictions = sess.run(softmax_tensor,
                           {'DecodeJpeg/contents:0': image_data})
    predictions = np.squeeze(predictions)

    # Creates node ID --> English string lookup.
    node_lookup = NodeLookup()

    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
    for node_id in top_k:
      human_string = node_lookup.id_to_string(node_id)
      score = predictions[node_id]
      print('%s (score = %.5f)' % (human_string, score)) 

def _load_inference_graph(self, inference_graph_path):
    od_graph = tf.Graph()
    with od_graph.as_default():
      od_graph_def = tf.GraphDef()
      with tf.gfile.GFile(inference_graph_path) as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')
    return od_graph 

def write_frozen_graph(frozen_graph_path, frozen_graph_def):
  """Writes frozen graph to disk.

  Args:
    frozen_graph_path: Path to write inference graph.
    frozen_graph_def: tf.GraphDef holding frozen graph.
  """
  with gfile.GFile(frozen_graph_path, 'wb') as f:
    f.write(frozen_graph_def.SerializeToString())
  logging.info('%d ops in the final graph.', len(frozen_graph_def.node)) 

def write_frozen_graph(frozen_graph_path, frozen_graph_def):
  """Writes frozen graph to disk.

  Args:
    frozen_graph_path: Path to write inference graph.
    frozen_graph_def: tf.GraphDef holding frozen graph.
  """
  with gfile.GFile(frozen_graph_path, 'wb') as f:
    f.write(frozen_graph_def.SerializeToString())
  logging.info('%d ops in the final graph.', len(frozen_graph_def.node)) 

def __init__(self, graph, model_scope, model_dir, model_file, k=1):
        """
        Args:
            graph: The model graph.
            model_scope: The variable_scope used when this model was trained.
            model_dir: The full path to the folder in which the result file locates.
            model_file: The file that saves the training results.  
            k: Optional. Number of elements to be predicted.
        Returns:
            values and indices. Refer to tf.nn.top_k for details.
        """
        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())

        tf.import_graph_def(graph_def, input_map=None, return_elements=None, name="")

        # 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)

        # Retrieve the Ops we 'remembered'
        logits = graph.get_tensor_by_name("{}/readout/logits:0".format(model_scope))
        self.images_placeholder = graph.get_tensor_by_name("{}/images_placeholder:0".format(model_scope))
        self.keep_prob_placeholder = graph.get_tensor_by_name("{}/keep_prob_placeholder:0".format(model_scope))

        # Add an Op that chooses the top k predictions. Apply softmax so that
        # we can have the probabilities (percentage) in the output.
        self.eval_op = tf.nn.top_k(tf.nn.softmax(logits), k=k)