def testReadSavedModelInvalid(self):
saved_model_dir = os.path.join(test.get_temp_dir(),
"invalid_saved_model")
with self.assertRaisesRegexp(
IOError,
"SavedModel file does not exist at: %s" % saved_model_dir):
reader.read_saved_model(saved_model_dir)
def get_serving_meta_graph_def(savedmodel_dir):
"""Extract the SERVING MetaGraphDef from a SavedModel directory.
Args:
savedmodel_dir: the string path to the directory containing the .pb
and variables for a SavedModel. This is equivalent to the subdirectory
that is created under the directory specified by --export_dir when
running an Official Model.
Returns:
MetaGraphDef that should be used for tag_constants.SERVING mode.
Raises:
ValueError: if a MetaGraphDef matching tag_constants.SERVING is not found.
"""
# We only care about the serving graph def
tag_set = set([tf.saved_model.tag_constants.SERVING])
serving_graph_def = None
saved_model = reader.read_saved_model(savedmodel_dir)
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == tag_set:
serving_graph_def = meta_graph_def
if not serving_graph_def:
raise ValueError("No MetaGraphDef found for tag_constants.SERVING. "
"Please make sure the SavedModel includes a SERVING def.")
return serving_graph_def
def get_meta_graph_def(saved_model_dir, tag_set):
"""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.
"""
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set(tag_set.split(','))
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise RuntimeError('MetaGraphDef associated with tag-set ' + tag_set +
' could not be found in SavedModel')
def get_meta_graph_def(saved_model_dir, tag_set):
"""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.
"""
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set(tag_set.split(','))
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise RuntimeError('MetaGraphDef associated with tag-set ' + tag_set +
' could not be found in SavedModel')
def _get_meta_graph_def(saved_model_dir, tag_set):
"""Validate saved_model and extract MetaGraphDef.
Args:
saved_model_dir: saved_model path to convert.
tag_set: Set of tag(s) of the MetaGraphDef to load.
Returns:
The meta_graph_def used for tflite conversion.
Raises:
ValueError: No valid MetaGraphDef for given tag_set.
"""
saved_model = reader.read_saved_model(saved_model_dir)
tag_sets = []
result_meta_graph_def = None
for meta_graph_def in saved_model.meta_graphs:
meta_graph_tag_set = set(meta_graph_def.meta_info_def.tags)
tag_sets.append(meta_graph_tag_set)
if meta_graph_tag_set == tag_set:
result_meta_graph_def = meta_graph_def
logging.info("The given saved_model contains the following tags: %s",
tag_sets)
if result_meta_graph_def is not None:
return result_meta_graph_def
else:
raise ValueError(
"No valid MetaGraphDef for this tag_set '{}'. Possible "
"values are '{}'. ".format(tag_set, tag_sets))
def _read_sessions(self, model_dirs, tag):
"""Read graph and parameters.
Args:
model_dirs: Model dirs saved by Estimator
tag: Serving tag e.g. serve
"""
for model_dir in model_dirs:
saved_model = reader.read_saved_model(model_dir)
meta_graph = None
for meta_graph_def in saved_model.meta_graphs:
if tag in meta_graph_def.meta_info_def.tags:
meta_graph = meta_graph_def
break
if meta_graph is None:
raise ValueError("Cannot find saved_model with tag: " + tag)
self.signature_def_list.append(
get_signature_def_by_key(meta_graph, "probs"))
gpu_option = tf.GPUOptions(
allow_growth=True,
visible_device_list=self.config.train.visible_device_list)
session_config = tf.ConfigProto(gpu_options=gpu_option)
graph = tf.Graph()
self.graph_list.append(graph)
session = tf.Session(graph=graph, config=session_config)
self.sess_model_list.append(session)
with session.as_default():
with graph.as_default():
loader.load(session, [tag], model_dir)
graph.finalize()
def load_meta_graph(model_path, tags, graph, signature_def_key=None):
saved_model = reader.read_saved_model(model_path)
the_meta_graph = None
for meta_graph_def in saved_model.meta_graphs:
if sorted(meta_graph_def.meta_info_def.tags) == sorted(tags):
the_meta_graph = meta_graph_def
signature_def_key = signature_def_key or tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
try:
signature_def = signature_def_utils.get_signature_def_by_key(
the_meta_graph,
signature_def_key)
except ValueError as ex:
try:
formatted_key = 'default_input_alternative:{}'.format(
signature_def_key)
signature_def = signature_def_utils.get_signature_def_by_key(
the_meta_graph, formatted_key)
except ValueError:
raise ValueError(
'Got signature_def_key "{}". Available signatures are {}. '
'Original error:\n{}'.format(
signature_def_key, list(the_meta_graph.signature_def), ex)
)
input_names = {k: v.name for k, v in signature_def.inputs.items()}
output_names = {k: v.name for k, v in signature_def.outputs.items()}
feed_tensors = {k: graph.get_tensor_by_name(v)
for k, v in input_names.items()}
fetch_tensors = {k: graph.get_tensor_by_name(v)
for k, v in output_names.items()}
return feed_tensors, fetch_tensors
def get_serving_meta_graph_def(savedmodel_dir):
"""Extract the SERVING MetaGraphDef from a SavedModel directory.
Args:
savedmodel_dir: the string path to the directory containing the .pb
and variables for a SavedModel. This is equivalent to the subdirectory
that is created under the directory specified by --export_dir when
running an Official Model.
Returns:
MetaGraphDef that should be used for tag_constants.SERVING mode.
Raises:
ValueError: if a MetaGraphDef matching tag_constants.SERVING is not found.
"""
# We only care about the serving graph def
tag_set = set([tf.saved_model.tag_constants.SERVING])
serving_graph_def = None
saved_model = reader.read_saved_model(savedmodel_dir)
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == tag_set:
serving_graph_def = meta_graph_def
if not serving_graph_def:
raise ValueError(
"No MetaGraphDef found for tag_constants.SERVING. "
"Please make sure the SavedModel includes a SERVING def.")
return serving_graph_def
def _get_meta_graph_def(saved_model_dir, tag_set):
"""Validate saved_model and extract MetaGraphDef.
Args:
saved_model_dir: saved_model path to convert.
tag_set: Set of tag(s) of the MetaGraphDef to load.
Returns:
The meta_graph_def used for tflite conversion.
Raises:
ValueError: No valid MetaGraphDef for given tag_set.
"""
saved_model = reader.read_saved_model(saved_model_dir)
tag_sets = []
result_meta_graph_def = None
for meta_graph_def in saved_model.meta_graphs:
meta_graph_tag_set = set(meta_graph_def.meta_info_def.tags)
tag_sets.append(meta_graph_tag_set)
if meta_graph_tag_set == tag_set:
result_meta_graph_def = meta_graph_def
logging.info("The given saved_model contains the following tags: %s",
tag_sets)
if result_meta_graph_def is not None:
return result_meta_graph_def
else:
raise ValueError("No valid MetaGraphDef for this tag_set '{}'. Possible "
"values are '{}'. ".format(tag_set, tag_sets))
def RunModel(saved_model_dir, signature_def_key, tag, text, ngrams_list=None):
saved_model = reader.read_saved_model(saved_model_dir)
meta_graph = None
for meta_graph_def in saved_model.meta_graphs:
if tag in meta_graph_def.meta_info_def.tags:
meta_graph = meta_graph_def
break
if meta_graph_def is None:
raise ValueError("Cannot find saved_model with tag" + tag)
signature_def = signature_def_utils.get_signature_def_by_key(
meta_graph, signature_def_key)
text = text_utils.TokenizeText(text)
ngrams = None
if ngrams_list is not None:
ngrams_list = text_utils.ParseNgramsOpts(ngrams_list)
ngrams = text_utils.GenerateNgrams(text, ngrams_list)
example = inputs.BuildTextExample(text, ngrams=ngrams)
example = example.SerializeToString()
inputs_feed_dict = {
signature_def.inputs["inputs"].name: [example],
}
if signature_def_key == "proba":
output_key = "scores"
elif signature_def_key == "embedding":
output_key = "outputs"
else:
raise ValueError("Unrecognised signature_def %s" % (signature_def_key))
output_tensor = signature_def.outputs[output_key].name
with tf.Session() as sess:
loader.load(sess, [tag], saved_model_dir)
outputs = sess.run(output_tensor, feed_dict=inputs_feed_dict)
return outputs
def _get_meta_graph_def(saved_model_dir, tag_set):
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set(tag_set.split(','))
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise RuntimeError('MetaGraphDef associated with tag-set ' + tag_set +
' could not be found in SavedModel')
def main(args):
bf = BaseFilter()
tokenizer = Tokenizer()
data_test = get_data("data/test-10.csv")
data_test = preprocessing(bf, tokenizer, data_test)
label = [v.strip() for v in tf.gfile.Open(FLAGS.label_file).readlines()]
tag = FLAGS.tag
output_key = "scores"
saved_model = reader.read_saved_model(FLAGS.saved_model)
meta_graph = None
for meta_graph_def in saved_model.meta_graphs:
if FLAGS.tag in meta_graph_def.meta_info_def.tags:
meta_graph = meta_graph_def
break
if meta_graph is None:
raise ValueError("Cannot find saved_model with tag" + FLAGS.tag)
# print(meta_graph)
signature_def = meta_graph.signature_def[FLAGS.signature_def]
print(signature_def.inputs["inputs"].name)
output_tensor = signature_def.outputs[output_key].name
with tf.Session() as sess:
loader.load(sess, [tag], FLAGS.saved_model)
example = []
for data in data_test:
text = [tf.compat.as_bytes(x) for x in data.get("tokens")]
record = tf.train.Example()
record.features.feature["text"].bytes_list.value.extend(text)
example.append(record.SerializeToString())
inputs_feed_dict = {
signature_def.inputs["inputs"].name:
[record.SerializeToString()],
}
outputs = sess.run(output_tensor, feed_dict=inputs_feed_dict)
index = np.argmax(outputs)
print(" ".join(data.get("tokens")))
print("benar" if label[index] == data.get("label") else "salah",
"predict: ", label[index], "harusnya:", data.get("label"))
print()
def get_meta_graph_def(saved_model_dir, tag_set):
"""
Utility function to read a meta_graph_def from disk.
From https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186
"""
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set(tag_set.split(','))
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise RuntimeError("MetaGraphDef associated with tag-set {0} could not be found in SavedModel".format(tag_set))
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 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 testReadSavedModelValid(self):
saved_model_dir = os.path.join(test.get_temp_dir(), "valid_saved_model")
builder = saved_model_builder.SavedModelBuilder(saved_model_dir)
with self.test_session(graph=ops.Graph()) as sess:
self._init_and_validate_variable(sess, "v", 42)
builder.add_meta_graph_and_variables(sess, [tag_constants.TRAINING])
builder.save()
actual_saved_model_pb = reader.read_saved_model(saved_model_dir)
self.assertEqual(len(actual_saved_model_pb.meta_graphs), 1)
self.assertEqual(
len(actual_saved_model_pb.meta_graphs[0].meta_info_def.tags), 1)
self.assertEqual(actual_saved_model_pb.meta_graphs[0].meta_info_def.tags[0],
tag_constants.TRAINING)
def testReadSavedModelValid(self):
saved_model_dir = os.path.join(test.get_temp_dir(), "valid_saved_model")
builder = saved_model_builder.SavedModelBuilder(saved_model_dir)
with self.session(graph=ops.Graph()) as sess:
self._init_and_validate_variable(sess, "v", 42)
builder.add_meta_graph_and_variables(sess, [tag_constants.TRAINING])
builder.save()
actual_saved_model_pb = reader.read_saved_model(saved_model_dir)
self.assertEqual(len(actual_saved_model_pb.meta_graphs), 1)
self.assertEqual(
len(actual_saved_model_pb.meta_graphs[0].meta_info_def.tags), 1)
self.assertEqual(actual_saved_model_pb.meta_graphs[0].meta_info_def.tags[0],
tag_constants.TRAINING)
def get_meta_graph_def(saved_model_dir, tags):
"""Gets `MetaGraphDef` from a directory containing a `SavedModel`.
Returns the `MetaGraphDef` for the given tag-set and SavedModel directory.
Args:
saved_model_dir: Directory containing the SavedModel.
tags: Comma separated list of tags used to identify the correct
`MetaGraphDef`.
Raises:
ValueError: An error when the given tags cannot be found.
Returns:
A `MetaGraphDef` corresponding to the given tags.
"""
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set([tag.strip() for tag in tags.split(',')])
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise ValueError('Could not find MetaGraphDef with tags {}'.format(tags))
def get_meta_graph_def(saved_model_dir, tag_set):
"""Utility function to read a meta_graph_def from disk.
From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
Args:
:saved_model_dir: path to saved_model.
:tag_set: list of string tags identifying the TensorFlow graph within the saved_model.
Returns:
A TensorFlow meta_graph_def, or raises an Exception otherwise.
"""
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set(tag_set.split(','))
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise RuntimeError("MetaGraphDef associated with tag-set {0} could not be found in SavedModel".format(tag_set))
def RunModel(saved_model_dir, signature_def_key, feature_text, feature_map):
saved_model = reader.read_saved_model(saved_model_dir)
meta_graph = None
for meta_graph_def in saved_model.meta_graphs:
if meta_graph_def.meta_info_def.tags == _TAG:
meta_graph = meta_graph_def
signature_def = signature_def_utils.get_signature_def_by_key(
meta_graph, signature_def_key)
features = pi.get_feature_list(feature_map, feature_text.split(" "))
inputs_feed_dict = {signature_def.inputs["inputs"].name: features}
if signature_def_key == "proba":
output_key = "scores"
elif signature_def_key == "embedding":
output_key = "outputs"
else:
raise ValueError("Unrecognised signature_def %s" % (signature_def_key))
output_tensor = signature_def.outputs[output_key].name
with tf.Session() as sess:
loader.load(sess, [_TAG], saved_model_dir)
outputs = sess.run(output_tensor, feed_dict=inputs_feed_dict)
return outputs
def get_meta_graph_def(saved_model_dir, tags):
"""Gets `MetaGraphDef` from a directory containing a `SavedModel`.
Returns the `MetaGraphDef` for the given tag-set and SavedModel directory.
Args:
saved_model_dir: Directory containing the SavedModel.
tags: Comma separated list of tags used to identify the correct
`MetaGraphDef`.
Raises:
ValueError: An error when the given tags cannot be found.
Returns:
A `MetaGraphDef` corresponding to the given tags.
"""
saved_model = reader.read_saved_model(saved_model_dir)
set_of_tags = set([tag.strip() for tag in tags.split(',')])
for meta_graph_def in saved_model.meta_graphs:
if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
return meta_graph_def
raise ValueError('Could not find MetaGraphDef with tags {}'.format(tags))
def testReadSavedModelInvalid(self):
saved_model_dir = os.path.join(test.get_temp_dir(), "invalid_saved_model")
with self.assertRaisesRegexp(
IOError, "SavedModel file does not exist at: %s" % saved_model_dir):
reader.read_saved_model(saved_model_dir)
