Python saved_model_utils.get_meta_graph_def函数代码示例

  def _TestCreateInferenceGraph(self,
                                input_saved_model_dir=None,
                                output_saved_model_dir=None):
    """General method to test trt_convert.create_inference_graph()."""
    input_graph_def = None if input_saved_model_dir else self._GetGraphDef()
    output_graph_def = trt_convert.create_inference_graph(
        input_graph_def, ["output"],
        max_workspace_size_bytes=TrtConvertTest._TRT_MAX_WORKSPACE_SIZE_BYTES,
        input_saved_model_dir=input_saved_model_dir,
        output_saved_model_dir=output_saved_model_dir,
        session_config=self._GetConfigProto())
    graph_defs_to_verify = [output_graph_def]
    if output_saved_model_dir is not None:
      saved_model_graph_def = saved_model_utils.get_meta_graph_def(
          output_saved_model_dir, tag_constants.SERVING).graph_def
      self.assertTrue(isinstance(saved_model_graph_def, graph_pb2.GraphDef))
      graph_defs_to_verify.append(saved_model_graph_def)

    for graph_def in graph_defs_to_verify:
      node_name_to_op = {node.name: node.op for node in graph_def.node}
      self.assertEqual({
          "input": "Placeholder",
          "TRTEngineOp_0": "TRTEngineOp",
          "output": "Identity"
      }, node_name_to_op)

def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key):
  """Prints input and output TensorInfos.

  Prints the details of input and output TensorInfos for the SignatureDef mapped
  by the given signature_def_key.

  Args:
    saved_model_dir: Directory containing the SavedModel to inspect.
    tag_set: Group of tag(s) of the MetaGraphDef, in string format, separated by
        ','. For tag-set contains multiple tags, all tags must be passed in.
    signature_def_key: A SignatureDef key string.
  """
  meta_graph_def = saved_model_utils.get_meta_graph_def(saved_model_dir,
                                                        tag_set)
  inputs_tensor_info = _get_inputs_tensor_info_from_meta_graph_def(
      meta_graph_def, signature_def_key)
  outputs_tensor_info = _get_outputs_tensor_info_from_meta_graph_def(
      meta_graph_def, signature_def_key)

  print('The given SavedModel SignatureDef contains the following input(s):')
  for input_key, input_tensor in sorted(inputs_tensor_info.items()):
    print('inputs[\'%s\'] tensor_info:' % input_key)
    _print_tensor_info(input_tensor)

  print('The given SavedModel SignatureDef contains the following output(s):')
  for output_key, output_tensor in sorted(outputs_tensor_info.items()):
    print('outputs[\'%s\'] tensor_info:' % output_key)
    _print_tensor_info(output_tensor)

  print('Method name is: %s' %
        meta_graph_def.signature_def[signature_def_key].method_name)

def freeze_graph(input_graph,
                 input_saver,
                 input_binary,
                 input_checkpoint,
                 output_node_names,
                 restore_op_name,
                 filename_tensor_name,
                 output_graph,
                 clear_devices,
                 initializer_nodes,
                 variable_names_whitelist="",
                 variable_names_blacklist="",
                 input_meta_graph=None,
                 input_saved_model_dir=None,
                 saved_model_tags=tag_constants.SERVING):
  """Converts all variables in a graph and checkpoint into constants."""
  input_graph_def = None
  if input_saved_model_dir:
    input_graph_def = saved_model_utils.get_meta_graph_def(
        input_saved_model_dir, saved_model_tags).graph_def
  elif input_graph:
    input_graph_def = _parse_input_graph_proto(input_graph, input_binary)
  input_meta_graph_def = None
  if input_meta_graph:
    input_meta_graph_def = _parse_input_meta_graph_proto(
        input_meta_graph, input_binary)
  input_saver_def = None
  if input_saver:
    input_saver_def = _parse_input_saver_proto(input_saver, input_binary)
  freeze_graph_with_def_protos(
      input_graph_def, input_saver_def, input_checkpoint, output_node_names,
      restore_op_name, filename_tensor_name, output_graph, clear_devices,
      initializer_nodes, variable_names_whitelist, variable_names_blacklist,
      input_meta_graph_def, input_saved_model_dir, saved_model_tags.split(","))

  def _TestTrtGraphConverter(self,
                             input_saved_model_dir=None,
                             output_saved_model_dir=None,
                             need_calibration=False,
                             is_dynamic_op=False):
    """General method to test trt_convert.TrtGraphConverter()."""
    output_graph_def = self._ConvertGraph(
        input_saved_model_dir=input_saved_model_dir,
        output_saved_model_dir=output_saved_model_dir,
        need_calibration=need_calibration,
        is_dynamic_op=is_dynamic_op,
        use_function_backup=need_calibration)
    graph_defs_to_verify = [output_graph_def]

    if output_saved_model_dir:
      if context.executing_eagerly():
        root = load.load(output_saved_model_dir)
        saved_model_graph_def = root.signatures[
            signature_constants
            .DEFAULT_SERVING_SIGNATURE_DEF_KEY].graph.as_graph_def()
      else:
        saved_model_graph_def = saved_model_utils.get_meta_graph_def(
            output_saved_model_dir, tag_constants.SERVING).graph_def
      self.assertTrue(isinstance(saved_model_graph_def, graph_pb2.GraphDef))
      graph_defs_to_verify.append(saved_model_graph_def)

    for graph_def in graph_defs_to_verify:
      node_name_to_op = {node.name: node.op for node in graph_def.node}
      if context.executing_eagerly():
        # In V2 the actual graph could be inside a function.
        for func in graph_def.library.function:
          node_name_to_op.update({node.name: node.op for node in func.node_def})
        self.assertIn("TRTEngineOp_0", node_name_to_op)
        self.assertEqual("TRTEngineOp", node_name_to_op["TRTEngineOp_0"])
      else:
        self.assertEqual({
            "input": "Placeholder",
            "TRTEngineOp_0": "TRTEngineOp",
            "output": "Identity"
        }, node_name_to_op)

      if need_calibration:
        trt_engine_nodes = [
            node for node in graph_def.node if node.op == "TRTEngineOp"
        ]
        self.assertNotEmpty(trt_engine_nodes)
        for node in trt_engine_nodes:
          self.assertTrue(len(node.attr["calibration_data"].s))
        # Run the calibrated graph.
        # TODO(laigd): consider having some input where the answer is different.
        with ops.Graph().as_default():
          importer.import_graph_def(graph_def, name="")
          with self.session(config=self._GetConfigProto()) as sess:
            for test_data in range(10):
              self.assertEqual((test_data + 1.0)**2,
                               sess.run(
                                   "output:0",
                                   feed_dict={"input:0": [[[test_data]]]}))

def scan(args):
  """Function triggered by scan command.

  Args:
    args: A namespace parsed from command line.
  """
  if args.tag_set:
    scan_meta_graph_def(
        saved_model_utils.get_meta_graph_def(args.dir, args.tag_set))
  else:
    saved_model = reader.read_saved_model(args.dir)
    for meta_graph_def in saved_model.meta_graphs:
      scan_meta_graph_def(meta_graph_def)

def get_signature_def_map(saved_model_dir, tag_set):
  """Gets SignatureDef map from a MetaGraphDef in a SavedModel.

  Returns the SignatureDef map for the given tag-set in the SavedModel
  directory.

  Args:
    saved_model_dir: Directory containing the SavedModel to inspect or execute.
    tag_set: Group of tag(s) of the MetaGraphDef with the SignatureDef map, in
        string format, separated by ','. For tag-set contains multiple tags, all
        tags must be passed in.

  Returns:
    A SignatureDef map that maps from string keys to SignatureDefs.
  """
  meta_graph = saved_model_utils.get_meta_graph_def(saved_model_dir, tag_set)
  return meta_graph.signature_def

  def _TestTrtGraphConverter(self,
                             input_saved_model_dir=None,
                             output_saved_model_dir=None,
                             need_calibration=False,
                             is_dynamic_op=False):
    """General method to test trt_convert.TrtGraphConverter()."""
    output_graph_def = self._ConvertGraph(
        input_saved_model_dir=input_saved_model_dir,
        output_saved_model_dir=output_saved_model_dir,
        need_calibration=need_calibration,
        is_dynamic_op=is_dynamic_op,
        use_function_backup=need_calibration)
    graph_defs_to_verify = [output_graph_def]

    if output_saved_model_dir:
      saved_model_graph_def = saved_model_utils.get_meta_graph_def(
          output_saved_model_dir, tag_constants.SERVING).graph_def
      self.assertIsInstance(saved_model_graph_def, graph_pb2.GraphDef)
      graph_defs_to_verify.append(saved_model_graph_def)

    for graph_def in graph_defs_to_verify:
      node_name_to_op = {node.name: node.op for node in graph_def.node}
      self.assertEqual(
          {
              "input": "Placeholder",
              "TRTEngineOp_0": "TRTEngineOp",
              "output": "Identity"
          }, node_name_to_op)

      if need_calibration:
        trt_engine_nodes = [
            node for node in graph_def.node if node.op == "TRTEngineOp"
        ]
        self.assertNotEmpty(trt_engine_nodes)
        for node in trt_engine_nodes:
          self.assertTrue(len(node.attr["calibration_data"].s))
        # Run the calibrated graph.
        # TODO(laigd): consider having some input where the answer is different.
        with ops.Graph().as_default():
          importer.import_graph_def(graph_def, name="")
          with self.session(config=self._GetConfigProto()) as sess:
            for test_data in range(10):
              self.assertEqual(
                  (test_data + 1.0)**2,
                  sess.run("output:0", feed_dict={"input:0": [[[test_data]]]}))

def get_meta_graph_def(saved_model_dir, tag_set):
  """DEPRECATED: Use saved_model_utils.get_meta_graph_def instead.

  Gets MetaGraphDef from SavedModel. Returns the MetaGraphDef for the given
  tag-set and SavedModel directory.

  Args:
    saved_model_dir: Directory containing the SavedModel to inspect or execute.
    tag_set: Group of tag(s) of the MetaGraphDef to load, in string format,
        separated by ','. For tag-set contains multiple tags, all tags must be
        passed in.

  Raises:
    RuntimeError: An error when the given tag-set does not exist in the
        SavedModel.

  Returns:
    A MetaGraphDef corresponding to the tag-set.
  """
  return saved_model_utils.get_meta_graph_def(saved_model_dir, tag_set)

def run_saved_model_with_feed_dict(saved_model_dir, tag_set, signature_def_key,
                                   input_tensor_key_feed_dict, outdir,
                                   overwrite_flag, tf_debug=False):
  """Runs SavedModel and fetch all outputs.

  Runs the input dictionary through the MetaGraphDef within a SavedModel
  specified by the given tag_set and SignatureDef. Also save the outputs to file
  if outdir is not None.

  Args:
    saved_model_dir: Directory containing the SavedModel to execute.
    tag_set: Group of tag(s) of the MetaGraphDef with the SignatureDef map, in
        string format, separated by ','. For tag-set contains multiple tags, all
        tags must be passed in.
    signature_def_key: A SignatureDef key string.
    input_tensor_key_feed_dict: A dictionary maps input keys to numpy ndarrays.
    outdir: A directory to save the outputs to. If the directory doesn't exist,
        it will be created.
    overwrite_flag: A boolean flag to allow overwrite output file if file with
        the same name exists.
    tf_debug: A boolean flag to use TensorFlow Debugger (TFDBG) to observe the
        intermediate Tensor values and runtime GraphDefs while running the
        SavedModel.

  Raises:
    ValueError: When any of the input tensor keys is not valid.
    RuntimeError: An error when output file already exists and overwrite is not
    enabled.
  """
  # Get a list of output tensor names.
  meta_graph_def = saved_model_utils.get_meta_graph_def(saved_model_dir,
                                                        tag_set)

  # Re-create feed_dict based on input tensor name instead of key as session.run
  # uses tensor name.
  inputs_tensor_info = _get_inputs_tensor_info_from_meta_graph_def(
      meta_graph_def, signature_def_key)

  # Check if input tensor keys are valid.
  for input_key_name in input_tensor_key_feed_dict.keys():
    if input_key_name not in inputs_tensor_info.keys():
      raise ValueError(
          '"%s" is not a valid input key. Please choose from %s, or use '
          '--show option.' %
          (input_key_name, '"' + '", "'.join(inputs_tensor_info.keys()) + '"'))

  inputs_feed_dict = {
      inputs_tensor_info[key].name: tensor
      for key, tensor in input_tensor_key_feed_dict.items()
  }
  # Get outputs
  outputs_tensor_info = _get_outputs_tensor_info_from_meta_graph_def(
      meta_graph_def, signature_def_key)
  # Sort to preserve order because we need to go from value to key later.
  output_tensor_keys_sorted = sorted(outputs_tensor_info.keys())
  output_tensor_names_sorted = [
      outputs_tensor_info[tensor_key].name
      for tensor_key in output_tensor_keys_sorted
  ]

  with session.Session(graph=ops_lib.Graph()) as sess:
    loader.load(sess, tag_set.split(','), saved_model_dir)

    if tf_debug:
      sess = local_cli_wrapper.LocalCLIDebugWrapperSession(sess)

    outputs = sess.run(output_tensor_names_sorted, feed_dict=inputs_feed_dict)

    for i, output in enumerate(outputs):
      output_tensor_key = output_tensor_keys_sorted[i]
      print('Result for output key %s:\n%s' % (output_tensor_key, output))

      # Only save if outdir is specified.
      if outdir:
        # Create directory if outdir does not exist
        if not os.path.isdir(outdir):
          os.makedirs(outdir)
        output_full_path = os.path.join(outdir, output_tensor_key + '.npy')

        # If overwrite not enabled and file already exist, error out
        if not overwrite_flag and os.path.exists(output_full_path):
          raise RuntimeError(
              'Output file %s already exists. Add \"--overwrite\" to overwrite'
              ' the existing output files.' % output_full_path)

        np.save(output_full_path, output)
        print('Output %s is saved to %s' % (output_tensor_key,
                                            output_full_path))

def freeze_graph(input_graph,
                 input_saver,
                 input_binary,
                 input_checkpoint,
                 output_node_names,
                 restore_op_name,
                 filename_tensor_name,
                 output_graph,
                 clear_devices,
                 initializer_nodes,
                 variable_names_whitelist="",
                 variable_names_blacklist="",
                 input_meta_graph=None,
                 input_saved_model_dir=None,
                 saved_model_tags=tag_constants.SERVING,
                 checkpoint_version=saver_pb2.SaverDef.V2):
  """Converts all variables in a graph and checkpoint into constants.

  Args:
    input_graph: A `GraphDef` file to load.
    input_saver: A TensorFlow Saver file.
    input_binary: A Bool. True means input_graph is .pb, False indicates .pbtxt.
    input_checkpoint: The prefix of a V1 or V2 checkpoint, with V2 taking
      priority.  Typically the result of `Saver.save()` or that of
      `tf.train.latest_checkpoint()`, regardless of sharded/non-sharded or
      V1/V2.
    output_node_names: The name(s) of the output nodes, comma separated.
    restore_op_name: Unused.
    filename_tensor_name: Unused.
    output_graph: String where to write the frozen `GraphDef`.
    clear_devices: A Bool whether to remove device specifications.
    initializer_nodes: Comma separated list of initializer nodes to run before
                       freezing.
    variable_names_whitelist: The set of variable names to convert (optional, by
                              default, all variables are converted),
    variable_names_blacklist: The set of variable names to omit converting
                              to constants (optional).
    input_meta_graph: A `MetaGraphDef` file to load (optional).
    input_saved_model_dir: Path to the dir with TensorFlow 'SavedModel' file and
                           variables (optional).
    saved_model_tags: Group of comma separated tag(s) of the MetaGraphDef to
                      load, in string format.
    checkpoint_version: Tensorflow variable file format (saver_pb2.SaverDef.V1
                        or saver_pb2.SaverDef.V2).
  Returns:
    String that is the location of frozen GraphDef.
  """
  input_graph_def = None
  if input_saved_model_dir:
    input_graph_def = saved_model_utils.get_meta_graph_def(
        input_saved_model_dir, saved_model_tags).graph_def
  elif input_graph:
    input_graph_def = _parse_input_graph_proto(input_graph, input_binary)
  input_meta_graph_def = None
  if input_meta_graph:
    input_meta_graph_def = _parse_input_meta_graph_proto(
        input_meta_graph, input_binary)
  input_saver_def = None
  if input_saver:
    input_saver_def = _parse_input_saver_proto(input_saver, input_binary)
  freeze_graph_with_def_protos(
      input_graph_def,
      input_saver_def,
      input_checkpoint,
      output_node_names,
      restore_op_name,
      filename_tensor_name,
      output_graph,
      clear_devices,
      initializer_nodes,
      variable_names_whitelist,
      variable_names_blacklist,
      input_meta_graph_def,
      input_saved_model_dir,
      saved_model_tags.replace(" ", "").split(","),
      checkpoint_version=checkpoint_version)