Python v1.GraphDef方法代码示例

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def _load_frozen_graph(self, frozen_graph_path):
    frozen_graph = tf.GraphDef()
    with open(frozen_graph_path, 'rb') as f:
      frozen_graph.ParseFromString(f.read())

    self.graph = tf.Graph()
    with self.graph.as_default():
      self.output_node = tf.import_graph_def(
          frozen_graph, return_elements=[
              'probabilities:0',
          ])
    self.session = tf.InteractiveSession(graph=self.graph)

    tf_probabilities = self.graph.get_tensor_by_name('import/probabilities:0')
    self._output_nodes = [tf_probabilities]
    self.sliding_window = None
    self.frames_since_last_inference = self.config.inference_rate
    self.last_annotations = [] 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def load(self, saved_model_dir_or_frozen_graph: Text):
    """Load the model using saved model or a frozen graph."""
    if not self.sess:
      self.sess = self._build_session()
    self.signitures = {
        'image_files': 'image_files:0',
        'image_arrays': 'image_arrays:0',
        'prediction': 'detections:0',
    }

    # Load saved model if it is a folder.
    if tf.io.gfile.isdir(saved_model_dir_or_frozen_graph):
      return tf.saved_model.load(self.sess, ['serve'],
                                 saved_model_dir_or_frozen_graph)

    # Load a frozen graph.
    graph_def = tf.GraphDef()
    with tf.gfile.GFile(saved_model_dir_or_frozen_graph, 'rb') as f:
      graph_def.ParseFromString(f.read())
    return tf.import_graph_def(graph_def, name='') 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def __init__(self, weight_path):
    helpers.ensure_lpips_weights_exist(weight_path)

    def wrap_frozen_graph(graph_def, inputs, outputs):
      def _imports_graph_def():
        tf.graph_util.import_graph_def(graph_def, name="")
      wrapped_import = tf.wrap_function(_imports_graph_def, [])
      import_graph = wrapped_import.graph
      return wrapped_import.prune(
          tf.nest.map_structure(import_graph.as_graph_element, inputs),
          tf.nest.map_structure(import_graph.as_graph_element, outputs))

    # Pack LPIPS network into a tf function
    graph_def = tf.GraphDef()
    with open(weight_path, "rb") as f:
      graph_def.ParseFromString(f.read())
    self._lpips_func = tf.function(
        wrap_frozen_graph(
            graph_def, inputs=("0:0", "1:0"), outputs="Reshape_10:0")) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
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 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
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, mode='rb') 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 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def test_export_tflite_graph_with_postprocess_op_and_additional_tensors(self):
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.eval_config.use_moving_averages = False
    pipeline_config.model.ssd.post_processing.score_converter = (
        post_processing_pb2.PostProcessing.SIGMOID)
    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
    pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
    pipeline_config.model.ssd.num_classes = 2
    tflite_graph_file = self._export_graph_with_postprocessing_op(
        pipeline_config, additional_output_tensors=['UnattachedTensor'])
    self.assertTrue(os.path.exists(tflite_graph_file))
    graph = tf.Graph()
    with graph.as_default():
      graph_def = tf.GraphDef()
      with tf.gfile.Open(tflite_graph_file, mode='rb') as f:
        graph_def.ParseFromString(f.read())
      all_op_names = [node.name for node in graph_def.node]
      self.assertIn('TFLite_Detection_PostProcess', all_op_names)
      self.assertIn('UnattachedTensor', all_op_names) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def _load_frozen_graph(self, frozen_graph_path):
    trt_graph = tf.GraphDef()
    with open(frozen_graph_path, 'rb') as f:
      trt_graph.ParseFromString(f.read())

    self._is_lstm = self._check_lstm(trt_graph)
    if self._is_lstm:
      print('Loading an LSTM model.')

    self.graph = tf.Graph()
    with self.graph.as_default():
      self.output_node = tf.import_graph_def(
          trt_graph,
          return_elements=[
              'detection_boxes:0', 'detection_classes:0', 'detection_scores:0',
              'num_detections:0'
          ] + (['raw_outputs/lstm_c:0', 'raw_outputs/lstm_h:0']
               if self._is_lstm else []))
    self.session = tf.InteractiveSession(graph=self.graph)

    tf_scores = self.graph.get_tensor_by_name('import/detection_scores:0')
    tf_boxes = self.graph.get_tensor_by_name('import/detection_boxes:0')
    tf_classes = self.graph.get_tensor_by_name('import/detection_classes:0')
    tf_num_detections = self.graph.get_tensor_by_name('import/num_detections:0')
    if self._is_lstm:
      tf_lstm_c = self.graph.get_tensor_by_name('import/raw_outputs/lstm_c:0')
      tf_lstm_h = self.graph.get_tensor_by_name('import/raw_outputs/lstm_h:0')

    self._output_nodes = [tf_scores, tf_boxes, tf_classes, tf_num_detections
                         ] + ([tf_lstm_c, tf_lstm_h] if self._is_lstm else [])

    if self._is_lstm:
      self.lstm_c = np.ones((1, 8, 8, 320))
      self.lstm_h = np.ones((1, 8, 8, 320)) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def main():
  parser = argparse.ArgumentParser()
  parser.add_argument('--model', help='.pb model path')
  parser.add_argument(
      '--downgrade',
      help='Downgrades the model for use with Tensorflow 1.14 '
      '(There maybe some quality degradation.)',
      action='store_true')
  args = parser.parse_args()

  filename, extension = os.path.splitext(args.model)
  output_file_path = '{}_trt{}'.format(filename, extension)

  frozen_graph = tf.GraphDef()
  with open(args.model, 'rb') as f:
    frozen_graph.ParseFromString(f.read())

  if args.downgrade:
    downgrade_equal_op(frozen_graph)
    downgrade_nmv5_op(frozen_graph)

  is_lstm = check_lstm(frozen_graph)
  if is_lstm:
    print('Converting LSTM model.')

  trt_graph = trt.create_inference_graph(
      input_graph_def=frozen_graph,
      outputs=[
          'detection_boxes', 'detection_classes', 'detection_scores',
          'num_detections'
      ] + ([
          'raw_outputs/lstm_c', 'raw_outputs/lstm_h', 'raw_inputs/init_lstm_c',
          'raw_inputs/init_lstm_h'
      ] if is_lstm else []),
      max_batch_size=1,
      max_workspace_size_bytes=1 << 25,
      precision_mode='FP16',
      minimum_segment_size=50)

  with open(output_file_path, 'wb') as f:
    f.write(trt_graph.SerializeToString()) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def create_graph():
  """Creates a graph from saved GraphDef file and returns a saver."""
  # Creates graph from saved graph_def.pb.
  with tf.gfile.FastGFile(os.path.join(
      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='') 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
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)) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def load_frozen_model(pb_path, prefix='', print_nodes=False):
    """Load frozen model (.pb file) for testing.
    After restoring the model, operators can be accessed by
    graph.get_tensor_by_name('<prefix>/<op_name>')
    Args:
        pb_path: the path of frozen model.
        prefix: prefix added to the operator name.
        print_nodes: whether to print node names.
    Returns:
        graph: tensorflow graph definition.
    """
    if os.path.exists(pb_path):
        #with tf.gfile.GFile(pb_path, "rb") as f:
        with tf.io.gfile.GFile(pb_path, "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,
                name=prefix
            )
            if print_nodes:
                for op in graph.get_operations():
                    print(op.name)
            return graph
    else:
        print('Model file does not exist', pb_path)
        exit(-1) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def load_frozen_model(pb_path, prefix='', print_nodes=False):
    """Load frozen model (.pb file) for testing.
    After restoring the model, operators can be accessed by
    graph.get_tensor_by_name('<prefix>/<op_name>')
    Args:
        pb_path: the path of frozen model.
        prefix: prefix added to the operator name.
        print_nodes: whether to print node names.
    Returns:
        graph: tensorflow graph definition.
    """
    if os.path.exists(pb_path):
        with tf.io.gfile.GFile(pb_path, "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,
                name=prefix
            )
            if print_nodes:
                for op in graph.get_operations():
                    print(op.name)
            return graph
    else:
        print('Model file does not exist', pb_path)
        exit(-1) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def __init__(self, graph_pb_path=None, graph_def=None):
        if graph_pb_path is not None:
            with tf.compat.v1.gfile.GFile(graph_pb_path, 'rb') as f:
                self.graph = tf.compat.v1.GraphDef()
                self.graph.ParseFromString(f.read())
        else:
            self.graph = graph_def
        self.summray_dict = {} 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def graph(self, graph):
        if graph is not None:
            if isinstance(graph, GraphDef):
                self.graph_def = graph
            else:
                raise ValueError("graph({}) should be type of GraphDef.".format(type(graph))) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import GraphDef [as 别名]
def _load_graph_def(pb_file):
    if isinstance(pb_file, tf.GraphDef):
      return pb_file, "tf_graph_{}".format(random_str(6))
    assert isinstance(pb_file, six.string_types)
    graph_name, ext = os.path.splitext(os.path.basename(pb_file))
    graph_def = tf.GraphDef()
    if ext == ".pb":
      with open(pb_file, "rb") as fid:
        graph_def.ParseFromString(fid.read())
    elif ext == ".pbtxt":
      with open(pb_file, "r") as fid:
        text_format.Parse(fid.read(), graph_def)
    else:
      raise ValueError("unknown file format: %s" % pb_file)
    return graph_def, graph_name