def from_tensorflow_frozen_model(frozen_file,
output_nodes=[],
preprocessor=None,
**kwargs):
"""
Converts a TensorFlow frozen graph to a UFF model.
Args:
frozen_file (str): The path to the frozen TensorFlow graph to convert.
output_nodes (list(str)): The names of the outputs of the graph. If not provided, graphsurgeon is used to automatically deduce output nodes.
output_filename (str): The UFF file to write.
preprocessor (str): The path to a preprocessing script that will be executed before the converter. This script should define a ``preprocess`` function which accepts a graphsurgeon DynamicGraph and modifies it in place.
write_preprocessed (bool): If set to True, the converter will write out the preprocessed graph as well as a TensorBoard visualization. Must be used in conjunction with output_filename.
text (bool): If set to True, the converter will also write out a human readable UFF file. Must be used in conjunction with output_filename.
quiet (bool): If set to True, suppresses informational messages. Errors may still be printed.
list_nodes (bool): If set to True, the converter displays a list of all nodes present in the graph.
debug_mode (bool): If set to True, the converter prints verbose debug messages.
return_graph_info (bool): If set to True, this function returns the graph input and output nodes in addition to the serialized UFF graph.
Returns:
serialized UFF MetaGraph (str)
OR, if return_graph_info is set to True,
serialized UFF MetaGraph (str), graph inputs (list(tensorflow.NodeDef)), graph outputs (list(tensorflow.NodeDef))
"""
graphdef = GraphDef()
with tf.gfile.GFile(frozen_file, "rb") as frozen_pb:
graphdef.ParseFromString(frozen_pb.read())
return from_tensorflow(graphdef, output_nodes, preprocessor, **kwargs)
def __post_init__(self, model_path: Path):
self.graph = tf.Graph()
with self.graph.as_default():
od_graph_def = GraphDef()
with GFile(str(model_path / "frozen_inference_graph.pb"),
"rb") as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name="")
def __init__(self, graph: tf.GraphDef = None):
"""
Wrap a tf.GraphDef protocol buffer in a Graph object.
Args:
graph: a tf.Graph or tf.GraphDef protobuf that represents a
TensorFlow operator graph. If set to None, generate an empty
tf.GraphDef
"""
if graph is None:
graph_def = tf.GraphDef()
elif isinstance(graph, tf.GraphDef):
graph_def = graph
elif isinstance(graph, tf.Graph):
graph_def = graph.as_graph_def()
else:
raise TypeError(
"Graph is of type {}. Expected a tf.Graph or GraphDef "
"proto".format(type(graph)))
self._graph_def = graph_def
self._next_id = 1
output_map = _decode_graph(graph_def)
self._immutable_nodes = [
node.ImmutableNode(self, self._get_next_id(), n,
output_map[n.name]) for n in graph_def.node
]
self._deleted_nodes = set()
self._added_nodes = {}
self._version = 0
self._collections = {}
def load_frozen_graph(self, model_path):
'''
Function to load the frozen protobuf file from the disk and parse it
to retrieve the unserialized graph_def
Arguments -
model_path : A string having the path of the tensorflow model(.pb).
Returns -
detection_graph : The unserialized graph_def that holds the network architecture.
'''
detection_graph = Graph()
with detection_graph.as_default():
od_graph_def = GraphDef()
with gfile.GFile(model_path, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
import_graph_def(od_graph_def, name='')
return detection_graph
def optimize_frozen_graph(logdir: str, frozen_graph: str):
with FastGFile(path_join(logdir, frozen_graph),
mode='rb') as frozen_graph_file:
frozen_graph_def = GraphDef()
frozen_graph_def.ParseFromString(frozen_graph_file.read())
optimized_frozen_graph_def = optimize_for_inference(
frozen_graph_def, ["Reshape"], ["softmax"], [])
optimized_frozen_graph_as_bytes = optimized_frozen_graph_def.SerializeToString(
)
optimized_frozen_graph = frozen_graph.replace("_frozen",
"_frozen_optimized")
optimized_frozen_graph_path = path_join(logdir, optimized_frozen_graph)
with open(optimized_frozen_graph_path,
mode='wb') as optimized_frozen_graph_file:
optimized_frozen_graph_file.write(optimized_frozen_graph_as_bytes)
return optimized_frozen_graph_path
def Model_from_Kinetica(self, Model_ID):
from tensorflow import GraphDef, Graph, import_graph_def
h_db = self.h_db
response = h_db.get_records(
table_name='TFmodel',
encoding="binary",
options={'expression': "model_id=\"" + Model_ID + "\""})
records = gpudb.GPUdbRecord.decode_binary_data(
response["type_schema"], response["records_binary"])
record = records[0]
record["model_binary"]
graph_def = GraphDef()
graph_def.ParseFromString(record["model_binary"])
graph = Graph()
with graph.as_default():
# The name var will prefix every op/nodes in your graph
# Since we load everything in a new graph, this is not needed
import_graph_def(graph_def)
return graph
def __init__(
self,
g: tf.GraphDef = None,
collections: Iterable[tf.MetaGraphDef.CollectionDefEntry] = None):
"""
Wrap a tf.GraphDef protocol buffer in a Graph object.
Args:
g: a tf.Graph or tf.GraphDef protobuf that represents a
TensorFlow graph. If set to None, generate an empty
tf.GraphDef
collections: Optional iterable of tf.MetaGraphDef.CollectionDefEntry
objects containing information about collections in the graph.
Note that this constructor will pull collection info out of `g` if
it is a `tf.Graph` and `collections` is `None`.
"""
if g is None:
graph_def = tf.GraphDef()
elif isinstance(g, tf.GraphDef):
graph_def = g
elif isinstance(g, tf.Graph):
graph_def = g.as_graph_def()
if collections is None:
collections = _make_collection_defs(g)
else:
raise TypeError(
"Graph is of type {}. Expected a tf.Graph or GraphDef "
"proto".format(type(g)))
self._version = 0 # Must happen first; other init code needs self._version
self._frozen = False
self._graph_def = graph_def
self._next_id = 1
output_map = _decode_graph(graph_def)
self._node_name_to_node = {} # Dict[str, node.Node]; key is node name
self._node_to_frame_names = None
self._frame_name_to_nodes = None
self._head_name_to_coloc_group = None # Dict[str, FrozenList[str]]
self._variable_name_to_variable = {} # Dict[str, Variable]
# Load nodes in three passes because the g may contain cycles.
for node_def in graph_def.node:
self.add_node_from_node_def(node_def, set_inputs=False)
for node_def in graph_def.node:
self[node_def.name].set_outputs_from_pairs(
output_map[node_def.name])
for node_def in graph_def.node:
self[node_def.name].set_inputs_from_strings(
node_def.input, set_control_inputs=True)
self._collections = {}
if collections is not None:
for c in collections:
self.add_collection_from_collection_def(c)
def strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def."""
strip_def = GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.MergeFrom(n0)
if n.op == 'Const':
tensor = n.attr['value'].tensor
size = len(tensor.tensor_content)
if size > max_const_size:
tensor.tensor_content = "<stripped %d bytes>" % size
return strip_def
def main(args):
with GFile(args.frozen_model_path, "rb") as f:
graph_def = GraphDef()
graph_def.ParseFromString(f.read())
with Session() as sess:
# Then, we import the graph_def into a new Graph and returns it
with Graph().as_default() as graph:
import_graph_def(graph_def, name='')
signature = predict_signature_def(
inputs={'image_batch': graph.get_tensor_by_name('image_batch:0'),
'phase_train': graph.get_tensor_by_name('phase_train:0')},
outputs={'embeddings': graph.get_tensor_by_name('embeddings:0')}
)
builder = saved_model_builder.SavedModelBuilder(args.output_model_dir)
builder.add_meta_graph_and_variables(
sess=sess,
tags=[tag_constants.SERVING],
signature_def_map={'serving_default': signature}
)
builder.save()
def __init__(self, name, batch_size=64):
self._graph = tf.Graph()
with self._graph.as_default():
if isinstance(name, STRING_TYPES) and '/' not in name:
model = load_model(name)
elif not os.path.exists(name):
raise Exception(
"Argument is neither a valid module name nor a path to an existing file/folder: {}"
.format(name))
else:
if not os.path.isdir(name):
with open(name, 'rb') as f:
model = f.read()
else:
model = {}
with open(os.path.join(name, 'meta.json')) as f:
model['meta'] = json.load(f)
with open(os.path.join(name, 'model.pb'), 'rb') as f:
model['model'] = f.read()
with codecs.open(os.path.join(name, 'vocab.txt'),
encoding='utf-8') as f:
model['vocab'] = f.read()
if isinstance(model, dict):
graph_def = GraphDef.FromString(model['model'])
else:
graph_def = GraphDef.FromString(model)
tf.import_graph_def(graph_def, name='')
self._sess = Session(graph=self._graph)
if not isinstance(model, dict):
# Older model format (for ELMo-based models)
self._chars = self._graph.get_tensor_by_name('chars:0')
self._charts = self._graph.get_tensor_by_name('charts:0')
self._label_vocab = LABEL_VOCAB
self._language_code = 'en'
self._provides_tags = False
self._make_feed_dict = self._make_feed_dict_elmo
else:
# Newer model format (for BERT-based models)
meta = model['meta']
# Label vocab is made immutable because it is potentially exposed to
# users through the spacy plugin
self._label_vocab = tuple(
[tuple(label) for label in meta['label_vocab']])
self._language_code = meta['language_code']
self._provides_tags = meta['provides_tags']
self._input_ids = self._graph.get_tensor_by_name('input_ids:0')
self._word_end_mask = self._graph.get_tensor_by_name(
'word_end_mask:0')
self._charts = self._graph.get_tensor_by_name('charts:0')
if self._provides_tags:
self._tag_vocab = meta['tag_vocab']
self._tags = self._graph.get_tensor_by_name('tags:0')
self._bert_tokenizer = BertTokenizer(
model['vocab'], do_lower_case=meta['bert_do_lower_case'])
self._make_feed_dict = self._make_feed_dict_bert
self.batch_size = batch_size
help='Compiler input node names')
parser.add_argument('--c_output_nodes',
default=None,
help='Compiler output node names')
args = dict2attr(parser.parse_args())
if not args.pre_process:
raise ValueError(
'please provide --pre_process input. Valid option: resnet50, inception_v1, inception_v3, inception_v4, squeezenet'
)
if args.quantize:
from tensorflow import GraphDef
from tensorflow.contrib import decent_q
input_graph_def = GraphDef()
with open(args.model, "rb") as f:
input_graph_def.ParseFromString(f.read())
if os.path.isdir(args.output_dir):
print('Cleaning model artifacts in {}'.format(
os.path.abspath(args.output_dir)))
filesToClean = [
os.path.join(os.path.abspath(args.output_dir), f)
for f in os.listdir(args.output_dir)
]
for f in filesToClean:
if os.path.isfile(f):
os.remove(f)
elif os.path.isdir(f):
rmtree(f)
