def testBasic(self):
base_path = tf.test.test_src_dir_path(
"contrib/session_bundle/example/half_plus_two/00000123")
tf.reset_default_graph()
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
base_path, target="", config=tf.ConfigProto(device_count={"CPU": 2}))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
self._checkRegressionSignature(signatures, sess)
self._checkNamedSigantures(signatures, sess)
def testVarCheckpointV2(self):
base_path = test.test_src_dir_path(
"contrib/session_bundle/testdata/half_plus_two_ckpt_v2/00000123")
ops.reset_default_graph()
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
base_path,
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2}))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
self._checkRegressionSignature(signatures, sess)
self._checkNamedSignatures(signatures, sess)
def testBasic(self):
base_path = tf.test.test_src_dir_path(
"contrib/session_bundle/example/half_plus_two/00000123")
tf.reset_default_graph()
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
base_path, target="", config=tf.ConfigProto(device_count={"CPU": 2}))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
input_name = default_signature.regression_signature.input.tensor_name
output_name = default_signature.regression_signature.output.tensor_name
y = sess.run([output_name], {input_name: np.array([[0], [1], [2], [3]])})
# The operation is y = 0.5 * x + 2
self.assertEqual(y[0][0], 2)
self.assertEqual(y[0][1], 2.5)
self.assertEqual(y[0][2], 3)
self.assertEqual(y[0][3], 3.5)
def testBadPath(self):
base_path = tf.test.test_src_dir_path("/no/such/a/dir")
tf.reset_default_graph()
with self.assertRaises(RuntimeError) as cm:
_, _ = session_bundle.load_session_bundle_from_path(
base_path, target="local",
config=tf.ConfigProto(device_count={"CPU": 2}))
self.assertTrue("Expected meta graph file missing" in str(cm.exception))
def testBadPath(self):
base_path = tf.test.test_src_dir_path("/no/such/a/dir")
tf.reset_default_graph()
with self.assertRaises(RuntimeError) as cm:
_, _ = session_bundle.load_session_bundle_from_path(
base_path, target="local",
config=tf.ConfigProto(device_count={"CPU": 2}))
self.assertTrue("Expected meta graph file missing" in str(cm.exception))
def extract_embeddings():
"""Extract embedding vectors from the specified model and paths."""
session, _ = session_bundle.load_session_bundle_from_path(FLAGS.model_path)
all_paths = FLAGS.input_path.split(',')
with tf.gfile.Open(FLAGS.output_path, 'w') as out:
for dataset, pattern in enumerate(all_paths, start=FLAGS.offset):
paths = tf.gfile.Glob(pattern)
for path in paths:
make_request(dataset, path, out, session)
def _load_saved_model_from_session_bundle_path(export_dir, target, config):
"""Load legacy TF Exporter/SessionBundle checkpoint.
Args:
export_dir: the directory that contains files exported by exporter.
target: The execution engine to connect to. See target in
tf.compat.v1.Session()
config: A ConfigProto proto with configuration options. See config in
tf.compat.v1.Session()
Returns:
session: a tensorflow session created from the variable files.
metagraph_def: The `MetaGraphDef` protocol buffer loaded in the provided
session. This can be used to further extract signature-defs,
collection-defs, etc.
This model is up-converted to SavedModel format. Specifically, metagraph_def
SignatureDef field is populated with Signatures converted from legacy
signatures contained within CollectionDef
Raises:
RuntimeError: If metagraph already contains signature_def and cannot be
up-converted.
"""
meta_graph_filename = os.path.join(
export_dir, legacy_constants.META_GRAPH_DEF_FILENAME)
metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
if metagraph_def.signature_def:
raise RuntimeError("Legacy graph contains signature def, unable to "
"up-convert.")
# Add SignatureDef to metagraph.
default_signature_def, named_signature_def = (
_convert_signatures_to_signature_defs(metagraph_def))
if default_signature_def:
metagraph_def.signature_def[
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].CopyFrom(
default_signature_def)
if named_signature_def:
signature_def_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
if default_signature_def:
signature_def_key += "_from_named"
metagraph_def.signature_def[signature_def_key].CopyFrom(
named_signature_def)
# We cannot just output session we loaded with older metagraph_def and
# up-converted metagraph definition because Session has an internal object of
# type Graph which is populated from meta_graph_def. If we do not create
# session with our new meta_graph_def, then Graph will be out of sync with
# meta_graph_def.
sess, metagraph_def = session_bundle.load_session_bundle_from_path(
export_dir, target, config, meta_graph_def=metagraph_def)
return sess, metagraph_def
def _load_saved_model_from_session_bundle_path(export_dir, target, config):
"""Load legacy TF Exporter/SessionBundle checkpoint.
Args:
export_dir: the directory that contains files exported by exporter.
target: The execution engine to connect to. See target in
tf.compat.v1.Session()
config: A ConfigProto proto with configuration options. See config in
tf.compat.v1.Session()
Returns:
session: a tensorflow session created from the variable files.
metagraph_def: The `MetaGraphDef` protocol buffer loaded in the provided
session. This can be used to further extract signature-defs,
collection-defs, etc.
This model is up-converted to SavedModel format. Specifically, metagraph_def
SignatureDef field is populated with Signatures converted from legacy
signatures contained within CollectionDef
Raises:
RuntimeError: If metagraph already contains signature_def and cannot be
up-converted.
"""
meta_graph_filename = os.path.join(export_dir,
legacy_constants.META_GRAPH_DEF_FILENAME)
metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
if metagraph_def.signature_def:
raise RuntimeError("Legacy graph contains signature def, unable to "
"up-convert.")
# Add SignatureDef to metagraph.
default_signature_def, named_signature_def = (
_convert_signatures_to_signature_defs(metagraph_def))
if default_signature_def:
metagraph_def.signature_def[
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].CopyFrom(
default_signature_def)
if named_signature_def:
signature_def_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
if default_signature_def:
signature_def_key += "_from_named"
metagraph_def.signature_def[signature_def_key].CopyFrom(named_signature_def)
# We cannot just output session we loaded with older metagraph_def and
# up-converted metagraph definition because Session has an internal object of
# type Graph which is populated from meta_graph_def. If we do not create
# session with our new meta_graph_def, then Graph will be out of sync with
# meta_graph_def.
sess, metagraph_def = session_bundle.load_session_bundle_from_path(
export_dir, target, config, meta_graph_def=metagraph_def)
return sess, metagraph_def
def load_graph(self, output_graph_path):
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
output_graph_path)
with sess.as_default():
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[
constants.SIGNATURES_KEY].any_list.value
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
input_name = default_signature.classification_signature.input.tensor_name
output_name = default_signature.classification_signature.scores.tensor_name
classes = default_signature.classification_signature.classes.tensor_name
classes = sess.run(sess.graph.get_tensor_by_name(classes))
return (sess, input_name, output_name,classes)
def load_graph(self, output_graph_path):
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
output_graph_path)
with sess.as_default():
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[
constants.SIGNATURES_KEY].any_list.value
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
input_name = default_signature.classification_signature.input.tensor_name
output_name = default_signature.classification_signature.scores.tensor_name
classes = default_signature.classification_signature.classes.tensor_name
classes = sess.run(sess.graph.get_tensor_by_name(classes))
return (sess, input_name, output_name, classes)
def testBasic(self):
base_path = tf.test.test_src_dir_path(
"contrib/session_bundle/example/half_plus_two/00000123")
tf.reset_default_graph()
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
base_path,
target="",
config=tf.ConfigProto(device_count={"CPU": 2}))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[
constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
input_name = default_signature.regression_signature.input.tensor_name
output_name = default_signature.regression_signature.output.tensor_name
y = sess.run([output_name],
{input_name: np.array([[0], [1], [2], [3]])})
# The operation is y = 0.5 * x + 2
self.assertEqual(y[0][0], 2)
self.assertEqual(y[0][1], 2.5)
self.assertEqual(y[0][2], 3)
self.assertEqual(y[0][3], 3.5)
def testGraphWithoutVarsLoadsCorrectly(self):
session, _ = session_bundle.load_session_bundle_from_path(
self.base_path)
got = session.run(["y:0"], {"x:0": 5.0})[0]
self.assertEquals(got, 5.0 * 3.0 - 7.0)
self.assertEquals(ops.get_collection("meta"), [b"this is meta"])
# weights_regularizer=slim.l2_regularizer(0.0005)):
# net = tf.reshape(inputs,[-1,FLAGS.im_size ,FLAGS.im_size,3])
# net = slim.conv2d(net, 32, [3,3], scope='conv1')
# net = slim.max_pool2d(net, [4,4], scope = 'conv1')
# net = slim.conv2d(net,128,[3,3], scope = 'conv2')
# net = slim.max_pool2d(net,[4,4], scope = 'pool2')
# net = slim.flatten(net)
# net = slim.fully_connected(net,64, scope = 'fc')
# net = slim.fully_connected(net, len(labelmap), activation_fn = None, scope = 'output')
# return net
if __name__ == '__main__':
#args = parse_args()
# Create a session for running Ops on the Graph.
session, _ = session_bundle.load_session_bundle_from_path(
FLAGS.model_dir + '/' + '19000/')
print session
# get the mappings between aliases and tensor names
# for both inputs and outputs
input_alias_map = json.loads(session.graph.get_collection('inputs')[0])
output_alias_map = json.loads(session.graph.get_collection('outputs')[0])
aliases, tensor_names = zip(*output_alias_map.items())
np.random.seed(45) #shuffle the same way each time for consistency
#examples = read_test_list(args.test_dir)
examples = read_im_list('tf_test.csv')
np.random.shuffle(examples)
start_time = time.time()
y_true = []
y_pred = []
count = 0
def testGraphWithoutVarsLoadsCorrectly(self):
session, _ = session_bundle.load_session_bundle_from_path(self.base_path)
got = session.run(["y:0"], {"x:0": 5.0})[0]
self.assertEquals(got, 5.0 * 3.0 - 7.0)
self.assertEquals(tf.get_collection("meta"), [b"this is meta"])
# weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
# weights_regularizer=slim.l2_regularizer(0.0005)):
# net = tf.reshape(inputs,[-1,FLAGS.im_size ,FLAGS.im_size,3])
# net = slim.conv2d(net, 32, [3,3], scope='conv1')
# net = slim.max_pool2d(net, [4,4], scope = 'conv1')
# net = slim.conv2d(net,128,[3,3], scope = 'conv2')
# net = slim.max_pool2d(net,[4,4], scope = 'pool2')
# net = slim.flatten(net)
# net = slim.fully_connected(net,64, scope = 'fc')
# net = slim.fully_connected(net, len(labelmap), activation_fn = None, scope = 'output')
# return net
if __name__ == '__main__':
#args = parse_args()
# Create a session for running Ops on the Graph.
session, _ = session_bundle.load_session_bundle_from_path(FLAGS.model_dir +
'/' + '12000/')
print session
# get the mappings between aliases and tensor names
# for both inputs and outputs
input_alias_map = json.loads(session.graph.get_collection('inputs')[0])
output_alias_map = json.loads(session.graph.get_collection('outputs')[0])
aliases, tensor_names = zip(*output_alias_map.items())
np.random.seed(45) #shuffle the same way each time for consistency
#examples = read_test_list(args.test_dir)
examples = read_im_list('tf_test.csv')
np.random.shuffle(examples)
start_time = time.time()
y_true = []
y_pred = []
count = 0
