def import_metagraph(model):
"""Imports a trained model metagraph into the current graph."""
string = load_cached(model + ".metagraph")
metagraph = tf.MetaGraphDef()
metagraph.ParseFromString(string)
tf.train.import_meta_graph(metagraph)
return metagraph.signature_def
def load_tf_graph_def(graph_file_name: str = "", is_binary: bool = True, checkpoint: str = "",
model_dir: str = "", saved_model_tags: list = [], meta_graph_file: str = "",
user_output_node_names_list: list = []):
# As a provisional solution, use a native TF methods to load a model protobuf
graph_def = tf_v1.GraphDef()
if isinstance(graph_file_name, str) and (re.match('.*\.(ckpt|meta)$', graph_file_name)):
print('[ WARNING ] The value for the --input_model command line parameter ends with ".ckpt" or ".meta" '
'extension.\n'
'It means that the model is not frozen.\n'
'To load non frozen model to Model Optimizer run:'
'\n\n1. For "*.ckpt" file:'
'\n- if inference graph is in binary format'
'\npython3 mo_tf.py --input_model "path/to/inference_graph.pb" --input_checkpoint "path/to/*.ckpt"'
'\n- if inference graph is in text format'
'\npython3 mo_tf.py --input_model "path/to/inference_graph.pbtxt" --input_model_is_text '
'--input_checkpoint "path/to/*.ckpt"'
'\n\n2. For "*.meta" file:'
'\npython3 mo_tf.py --input_meta_graph "path/to/*.meta"')
variables_values = {}
try:
if graph_file_name and not meta_graph_file and not checkpoint:
# frozen graph
return read_file_to_graph_def(graph_def, graph_file_name, is_binary), variables_values, 'tf'
if graph_file_name and not meta_graph_file and checkpoint:
# inference graph and checkpoint
graph_def = read_file_to_graph_def(graph_def, graph_file_name, is_binary)
outputs = get_output_node_names_list(graph_def, user_output_node_names_list)
if os.path.isfile(checkpoint):
graph_def = freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs)
elif os.path.isdir(checkpoint):
graph_def, variables_values = freeze_checkpoints(graph_def=graph_def, checkpoint_dir=checkpoint,
output_node_names=outputs)
# we are sure that checkpoint is existing file or directory due to cli_parser configuration
return graph_def, variables_values, 'tf'
if not graph_file_name and meta_graph_file:
meta_graph_file = deducing_metagraph_path(meta_graph_file)
input_meta_graph_def = read_file_to_graph_def(tf_v1.MetaGraphDef(), meta_graph_file, is_binary)
# pylint: disable=no-member
with tf_v1.Session() as sess:
restorer = tf_v1.train.import_meta_graph(input_meta_graph_def)
restorer.restore(sess, re.sub('\.meta$', '', meta_graph_file))
outputs = get_output_node_names_list(input_meta_graph_def.graph_def, user_output_node_names_list)
graph_def = tf_v1.graph_util.convert_variables_to_constants(sess, input_meta_graph_def.graph_def,
outputs)
return graph_def, variables_values, 'tf'
if model_dir:
# saved model directory
try:
env_setup = get_environment_setup("tf")
# enable eager execution temporarily while TensorFlow 2 model is being loaded
tf_v1.enable_eager_execution()
# code to extract GraphDef for TF 2.0 SavedModel format
# tf.saved_model.load function throws TypeError for TF 1.x SavedModel format in case TF 1.x installed
imported = tf.saved_model.load(model_dir, saved_model_tags) # pylint: disable=E1120
# to get a signature by key throws KeyError for TF 1.x SavedModel format in case TF 2.x installed
concrete_func = imported.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
# the aggressive inlining parameter needs to freeze a table of embeddings for Keras Embedding operation
# and a model with Embedding operation cannot properly converted to IR without this function parameter
if "tensorflow" in env_setup and env_setup["tensorflow"] >= LooseVersion("2.2.0"):
frozen_func = convert_variables_to_constants_v2(concrete_func,
lower_control_flow=False,
aggressive_inlining=True) # pylint: disable=E1123
else:
frozen_func = convert_variables_to_constants_v2(concrete_func,
lower_control_flow=False) # pylint: disable=E1123
graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
# disable eager execution since next steps are executed with a graph in non-eager mode
tf_v1.disable_eager_execution()
return graph_def, variables_values, 'tf2'
except (TypeError, KeyError):
# disable eager execution since TensorFlow 1 model is handled
tf_v1.disable_eager_execution()
# code to extract GraphDef for TF 1.0 SavedModel format
tags = saved_model_tags if saved_model_tags is not None else [tf_v1.saved_model.tag_constants.SERVING]
with tf_v1.Session() as sess:
meta_graph_def = tf_v1.saved_model.loader.load(sess, tags, model_dir)
outputs = get_output_node_names_list(meta_graph_def.graph_def, user_output_node_names_list)
graph_def = tf_v1.graph_util.convert_variables_to_constants(sess, meta_graph_def.graph_def, outputs)
return graph_def, variables_values, 'tf'
except Exception as e:
raise FrameworkError('SavedModel format load failure: {}', e) from e
except Exception as e:
raise FrameworkError('Cannot load input model: {}', e) from e
raise Error("Unknown configuration of input model parameters")
def load_tf_graph_def(graph_file_name: str = "",
is_binary: bool = True,
checkpoint: str = "",
model_dir: str = "",
saved_model_tags: list = [],
meta_graph_file: str = "",
user_output_node_names_list: list = []):
# As a provisional solution, use a native TF methods to load a model protobuf
graph_def = tf_v1.GraphDef()
if isinstance(graph_file_name, str) and (re.match('.*\.(ckpt|meta)$',
graph_file_name)):
print(
'[ WARNING ] The value for the --input_model command line parameter ends with ".ckpt" or ".meta" '
'extension.\n'
'It means that the model is not frozen.\n'
'To load non frozen model to Model Optimizer run:'
'\n\n1. For "*.ckpt" file:'
'\n- if inference graph is in binary format'
'\npython3 mo_tf.py --input_model "path/to/inference_graph.pb" --input_checkpoint "path/to/*.ckpt"'
'\n- if inference graph is in text format'
'\npython3 mo_tf.py --input_model "path/to/inference_graph.pbtxt" --input_model_is_text '
'--input_checkpoint "path/to/*.ckpt"'
'\n\n2. For "*.meta" file:'
'\npython3 mo_tf.py --input_meta_graph "path/to/*.meta"')
variables_values = {}
try:
if graph_file_name and not meta_graph_file and not checkpoint:
# frozen graph
return read_file_to_graph_def(graph_def, graph_file_name,
is_binary), variables_values
if graph_file_name and not meta_graph_file and checkpoint:
# inference graph and checkpoint
graph_def = read_file_to_graph_def(graph_def, graph_file_name,
is_binary)
outputs = get_output_node_names_list(graph_def,
user_output_node_names_list)
if os.path.isfile(checkpoint):
graph_def = freeze_checkpoint(graph_def=graph_def,
checkpoint=checkpoint,
output_node_names=outputs)
elif os.path.isdir(checkpoint):
graph_def, variables_values = freeze_checkpoints(
graph_def=graph_def,
checkpoint_dir=checkpoint,
output_node_names=outputs)
# we are sure that checkpoint is existing file or directory due to cli_parser configuration
return graph_def, variables_values
if not graph_file_name and meta_graph_file:
meta_graph_file = deducing_metagraph_path(meta_graph_file)
input_meta_graph_def = read_file_to_graph_def(
tf_v1.MetaGraphDef(), meta_graph_file, is_binary)
# pylint: disable=no-member
with tf_v1.Session() as sess:
restorer = tf_v1.train.import_meta_graph(input_meta_graph_def)
restorer.restore(sess, re.sub('\.meta$', '', meta_graph_file))
outputs = get_output_node_names_list(
input_meta_graph_def.graph_def,
user_output_node_names_list)
graph_def = tf_v1.graph_util.convert_variables_to_constants(
sess, input_meta_graph_def.graph_def, outputs)
return graph_def, variables_values
if model_dir:
# saved model directory
tags = saved_model_tags if saved_model_tags is not None else [
tf_v1.saved_model.tag_constants.SERVING
]
with tf_v1.Session() as sess:
meta_graph_def = tf_v1.saved_model.loader.load(
sess, tags, model_dir)
outputs = get_output_node_names_list(
meta_graph_def.graph_def, user_output_node_names_list)
graph_def = tf_v1.graph_util.convert_variables_to_constants(
sess, meta_graph_def.graph_def, outputs)
return graph_def, variables_values
except Exception as e:
raise FrameworkError('Cannot load input model: {}', e) from e
raise Error("Unknown configuration of input model parameters")
def load_tf_graph_def(graph_file_name: str = "",
is_binary: bool = True,
checkpoint: str = "",
model_dir: str = "",
saved_model_tags: list = [],
meta_graph_file: str = "",
user_output_node_names_list: list = []):
# As a provisional solution, use a native TF methods to load a model protobuf
graph_def = tf_v1.GraphDef()
if isinstance(graph_file_name, str) and (re.match(r'.*\.(ckpt|meta)$',
graph_file_name)):
print(
'[ WARNING ] The value for the --input_model command line parameter ends with ".ckpt" or ".meta" '
'extension.\n'
'It means that the model is not frozen.\n'
'To load non frozen model to Model Optimizer run:'
'\n\n1. For "*.ckpt" file:'
'\n- if inference graph is in binary format'
'\npython3 mo_tf.py --input_model "path/to/inference_graph.pb" --input_checkpoint "path/to/*.ckpt"'
'\n- if inference graph is in text format'
'\npython3 mo_tf.py --input_model "path/to/inference_graph.pbtxt" --input_model_is_text '
'--input_checkpoint "path/to/*.ckpt"'
'\n\n2. For "*.meta" file:'
'\npython3 mo_tf.py --input_meta_graph "path/to/*.meta"')
variables_values = {}
try:
if graph_file_name and not meta_graph_file and not checkpoint:
# frozen graph
return read_file_to_graph_def(
graph_def, graph_file_name,
is_binary), variables_values, 'tf', None
if graph_file_name and not meta_graph_file and checkpoint:
# inference graph and checkpoint
graph_def = read_file_to_graph_def(graph_def, graph_file_name,
is_binary)
outputs = get_output_node_names_list(graph_def,
user_output_node_names_list)
if os.path.isfile(checkpoint):
graph_def = freeze_checkpoint(graph_def=graph_def,
checkpoint=checkpoint,
output_node_names=outputs)
elif os.path.isdir(checkpoint):
graph_def, variables_values = freeze_checkpoints(
graph_def=graph_def,
checkpoint_dir=checkpoint,
output_node_names=outputs)
# we are sure that checkpoint is existing file or directory due to cli_parser configuration
return graph_def, variables_values, 'tf', None
if not graph_file_name and meta_graph_file:
meta_graph_file = deducing_metagraph_path(meta_graph_file)
input_meta_graph_def = read_file_to_graph_def(
tf_v1.MetaGraphDef(), meta_graph_file, is_binary)
# Since version 2.2 TF can fail with internal error while loading graph from .meta file.
# It happens because some operation may has an _output_shapes attribute inconsistent with the GraphDef
# calculated value. To avoid this problem we must delete `_output_shapes` attributes from operations
for node in input_meta_graph_def.graph_def.node:
if '_output_shapes' in node.attr:
del node.attr['_output_shapes']
# pylint: disable=no-member
with tf_v1.Session() as sess:
restorer = tf_v1.train.import_meta_graph(input_meta_graph_def)
restorer.restore(sess, re.sub(r'\.meta$', '', meta_graph_file))
outputs = get_output_node_names_list(
input_meta_graph_def.graph_def,
user_output_node_names_list)
graph_def = tf_v1.graph_util.convert_variables_to_constants(
sess, input_meta_graph_def.graph_def, outputs)
return graph_def, variables_values, 'tf', None
if model_dir:
# saved model directory
try:
env_setup = get_environment_setup("tf")
# enable eager execution temporarily while TensorFlow 2 model is being loaded
tf_v1.enable_eager_execution()
try:
# Code to extract Keras model.
# tf.keras.models.load_model function throws TypeError,KeyError or IndexError
# for TF 1.x SavedModel format in case TF 1.x installed
imported = tf.keras.models.load_model(model_dir,
compile=False)
except:
imported = tf.saved_model.load(model_dir, saved_model_tags) # pylint: disable=E1120
# to get a signature by key throws KeyError for TF 1.x SavedModel format in case TF 2.x installed
concrete_func = imported.signatures[
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
# the aggressive inlining parameter needs to freeze a table of embeddings for Keras Embedding operation
# and a model with Embedding operation cannot properly converted to IR without this function parameter
if "tensorflow" in env_setup and env_setup[
"tensorflow"] >= LooseVersion("2.2.0"):
frozen_func = convert_variables_to_constants_v2(
concrete_func,
lower_control_flow=False,
aggressive_inlining=True) # pylint: disable=E1123
else:
frozen_func = convert_variables_to_constants_v2(
concrete_func, lower_control_flow=False) # pylint: disable=E1123
graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
# disable eager execution since next steps are executed with a graph in non-eager mode
tf_v1.disable_eager_execution()
input_names = []
if hasattr(imported, 'inputs'):
# Extract tensor names order from Keras model
input_names = [tensor.name for tensor in imported.inputs]
# After model freezing output tensor names are changing and recieve "Func/PartitionedCall" prefix,
# so output_names from saved_model cannot be used. Here tensor names from frozen graph are used,
# as TF adds indexed Identity nodes during freezing to each output, so this indexing is used for
# order alignment.
output_names = [tensor.name for tensor in frozen_func.outputs]
inputs_outputs_order = (input_names, output_names)
return graph_def, variables_values, 'tf2', inputs_outputs_order
except:
# disable eager execution since TensorFlow 1 model is handled
tf_v1.disable_eager_execution()
# code to extract GraphDef for TF 1.0 SavedModel format
tags = saved_model_tags if saved_model_tags is not None else [
tf_v1.saved_model.tag_constants.SERVING
]
with tf_v1.Session() as sess:
meta_graph_def = tf_v1.saved_model.loader.load(
sess, tags, model_dir)
outputs = get_output_node_names_list(
meta_graph_def.graph_def, user_output_node_names_list)
graph_def = tf_v1.graph_util.convert_variables_to_constants(
sess, meta_graph_def.graph_def, outputs)
return graph_def, variables_values, 'tf', None
except Exception as e:
raise FrameworkError('Cannot load input model: {}', e) from e
raise Error("Unknown configuration of input model parameters")
