def testConvertSignaturesToSignatureDefs(self):
base_path = tf.test.test_src_dir_path(SESSION_BUNDLE_PATH)
meta_graph_filename = os.path.join(base_path,
constants.META_GRAPH_DEF_FILENAME)
metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(default_signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(len(default_signature_def.inputs), 1)
self.assertEqual(len(default_signature_def.outputs), 1)
self.assertProtoEquals(
default_signature_def.inputs[signature_constants.REGRESS_INPUTS],
meta_graph_pb2.TensorInfo(name="tf_example:0"))
self.assertProtoEquals(
default_signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
meta_graph_pb2.TensorInfo(name="Identity:0"))
self.assertEqual(named_signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(len(named_signature_def.inputs), 1)
self.assertEqual(len(named_signature_def.outputs), 1)
self.assertProtoEquals(named_signature_def.inputs["x"],
meta_graph_pb2.TensorInfo(name="x:0"))
self.assertProtoEquals(named_signature_def.outputs["y"],
meta_graph_pb2.TensorInfo(name="y:0"))
# Now try default signature only
collection_def = metagraph_def.collection_def
signatures_proto = manifest_pb2.Signatures()
signatures = collection_def[constants.SIGNATURES_KEY].any_list.value[0]
signatures.Unpack(signatures_proto)
named_only_signatures_proto = manifest_pb2.Signatures()
named_only_signatures_proto.CopyFrom(signatures_proto)
default_only_signatures_proto = manifest_pb2.Signatures()
default_only_signatures_proto.CopyFrom(signatures_proto)
default_only_signatures_proto.named_signatures.clear()
default_only_signatures_proto.ClearField("named_signatures")
metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
0].Pack(default_only_signatures_proto)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(default_signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(named_signature_def, None)
named_only_signatures_proto.ClearField("default_signature")
metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
0].Pack(named_only_signatures_proto)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(named_signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(default_signature_def, None)
def testBasic(self):
base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
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 _convert_signatures_to_signature_defs(metagraph_def):
"""Produce default and named upconverted SignatureDef objects from Signatures.
Args:
metagraph_def: object of type meta_graph_pb2.MetaGraphDef containing legacy
format Session Bundle signatures
Returns:
default_signature_def: object of type SignatureDef which contains an
upconverted version of default signatures in metagraph_def
named_signature_def: object of type SignatureDef which contains an
upconverted version of named signatures in metagraph_def
"""
collection_def = metagraph_def.collection_def
signatures_proto = manifest_pb2.Signatures()
signatures = collection_def[legacy_constants.SIGNATURES_KEY].any_list.value[0]
signatures.Unpack(signatures_proto)
default_signature_def = None
named_signature_def = None
if signatures_proto.HasField("default_signature"):
default_signature_def = _convert_default_signature_to_signature_def(
signatures_proto)
if len(signatures_proto.named_signatures) > 1:
named_signature_def = _convert_named_signatures_to_signature_def(
signatures_proto)
return default_signature_def, named_signature_def
def load_from_file():
sess, meta_graph_def = session_bundle.LoadSessionBundleFromPath("/tmp/fam/00000001")
with sess.as_default():
test_i, test_o = getdata(range(1), [3], process)
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
for s in range(len(test_i)):
gd = 0
z = 0
allv = test_i[s]
if len(allv) > 0:
for v in allv:
if np.argmax(sess.run ([output_name],{input_name: [ v ]})[0]) == np.argmax(test_o[s][z]):
gd += 1
z = z + 1
print ("SNR",SNR[s],"ACC",gd/z)
def testConvertNamedSignatureToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
generic_signature = manifest_pb2.GenericSignature()
generic_signature.map["input_key"].CopyFrom(
manifest_pb2.TensorBinding(tensor_name="input"))
signatures_proto.named_signatures[
signature_constants.PREDICT_INPUTS].generic_signature.CopyFrom(
generic_signature)
generic_signature = manifest_pb2.GenericSignature()
generic_signature.map["output_key"].CopyFrom(
manifest_pb2.TensorBinding(tensor_name="output"))
signatures_proto.named_signatures[
signature_constants.PREDICT_OUTPUTS].generic_signature.CopyFrom(
generic_signature)
signature_def = bundle_shim._convert_named_signatures_to_signature_def(
signatures_proto)
self.assertEqual(signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(len(signature_def.inputs), 1)
self.assertEqual(len(signature_def.outputs), 1)
self.assertProtoEquals(signature_def.inputs["input_key"],
meta_graph_pb2.TensorInfo(name="input"))
self.assertProtoEquals(signature_def.outputs["output_key"],
meta_graph_pb2.TensorInfo(name="output"))
def testConvertDefaultSignatureRegressionToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
regression_signature = manifest_pb2.RegressionSignature()
regression_signature.input.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.REGRESS_INPUTS))
regression_signature.output.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.REGRESS_OUTPUTS))
signatures_proto.default_signature.regression_signature.CopyFrom(
regression_signature)
signature_def = bundle_shim._convert_default_signature_to_signature_def(
signatures_proto)
# Validate regression signature correctly copied over.
self.assertEqual(signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(len(signature_def.inputs), 1)
self.assertEqual(len(signature_def.outputs), 1)
self.assertProtoEquals(
signature_def.inputs[signature_constants.REGRESS_INPUTS],
meta_graph_pb2.TensorInfo(name=signature_constants.REGRESS_INPUTS))
self.assertProtoEquals(
signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
meta_graph_pb2.TensorInfo(
name=signature_constants.REGRESS_OUTPUTS))
def testConvertDefaultSignatureBadTypeToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
generic_signature = manifest_pb2.GenericSignature()
signatures_proto.default_signature.generic_signature.CopyFrom(
generic_signature)
with self.assertRaises(RuntimeError) as cm:
_ = bundle_shim._convert_default_signature_to_signature_def(
signatures_proto)
def testConvertDefaultSignatureGenericToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
generic_signature = manifest_pb2.GenericSignature()
signatures_proto.default_signature.generic_signature.CopyFrom(
generic_signature)
signature_def = bundle_shim._convert_default_signature_to_signature_def(
signatures_proto)
self.assertEquals(signature_def, None)
def testConvertNamedSignatureNonGenericToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
regression_signature = manifest_pb2.RegressionSignature()
signatures_proto.named_signatures[
signature_constants.PREDICT_INPUTS].regression_signature.CopyFrom(
regression_signature)
with self.assertRaises(RuntimeError):
_ = bundle_shim._convert_named_signatures_to_signature_def(
signatures_proto)
signatures_proto = manifest_pb2.Signatures()
classification_signature = manifest_pb2.ClassificationSignature()
signatures_proto.named_signatures[
signature_constants.PREDICT_INPUTS].classification_signature.CopyFrom(
classification_signature)
with self.assertRaises(RuntimeError):
_ = bundle_shim._convert_named_signatures_to_signature_def(
signatures_proto)
def read_serving_signatures(meta_file):
tf.reset_default_graph()
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph(meta_file)
graph = tf.get_default_graph()
pb = graph.get_collection("serving_signatures")[0]
signatures = manifest_pb2.Signatures()
pb.Unpack(signatures)
return signatures
def _get_default_signature(self, export_meta_filename):
"""Gets the default signature from the export.meta file."""
with tf.Session():
save = tf.train.import_meta_graph(export_meta_filename)
meta_graph_def = save.export_meta_graph()
collection_def = meta_graph_def.collection_def
signatures_any = collection_def['serving_signatures'].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
return default_signature
def load_graph(output_graph_path):
#sess, meta_graph_def = session_bundle.load_session_bundle_from_path(output_graph_path)
sess, meta_graph_def = bundle_shim.load_session_bundle_or_saved_model_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 testConvertDefaultSignatureClassificationToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
classification_signature = manifest_pb2.ClassificationSignature()
classification_signature.input.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_INPUTS))
classification_signature.classes.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_OUTPUT_CLASSES))
classification_signature.scores.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_OUTPUT_SCORES))
signatures_proto.default_signature.classification_signature.CopyFrom(
classification_signature)
signatures_proto.default_signature.classification_signature.CopyFrom(
classification_signature)
signature_def = bundle_shim._convert_default_signature_to_signature_def(
signatures_proto)
# Validate classification signature correctly copied over.
self.assertEqual(signature_def.method_name,
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(len(signature_def.inputs), 1)
self.assertEqual(len(signature_def.outputs), 2)
self.assertProtoEquals(
signature_def.inputs[signature_constants.CLASSIFY_INPUTS],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_INPUTS))
self.assertProtoEquals(
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_OUTPUT_SCORES))
self.assertProtoEquals(
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_OUTPUT_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 doBasicsOneExportPath(self,
export_path,
clear_devices=False,
global_step=GLOBAL_STEP,
sharded=True):
# Build a graph with 2 parameter nodes on different devices.
tf.reset_default_graph()
with tf.Session(target="",
config=config_pb2.ConfigProto(
device_count={"CPU": 2})) as sess:
# v2 is an unsaved variable derived from v0 and v1. It is used to
# exercise the ability to run an init op when restoring a graph.
with sess.graph.device("/cpu:0"):
v0 = tf.Variable(10, name="v0")
with sess.graph.device("/cpu:1"):
v1 = tf.Variable(20, name="v1")
v2 = tf.Variable(1, name="v2", trainable=False, collections=[])
assign_v2 = tf.assign(v2, tf.add(v0, v1))
init_op = tf.group(assign_v2, name="init_op")
tf.add_to_collection("v", v0)
tf.add_to_collection("v", v1)
tf.add_to_collection("v", v2)
global_step_tensor = tf.Variable(global_step, name="global_step")
named_tensor_bindings = {
"logical_input_A": v0,
"logical_input_B": v1
}
signatures = {
"foo":
exporter.regression_signature(input_tensor=v0,
output_tensor=v1),
"generic":
exporter.generic_signature(named_tensor_bindings)
}
asset_filepath_orig = os.path.join(tf.test.get_temp_dir(),
"hello42.txt")
asset_file = tf.constant(asset_filepath_orig, name="filename42")
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, asset_file)
with gfile.FastGFile(asset_filepath_orig, "w") as f:
f.write("your data here")
assets_collection = tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS)
ignored_asset = os.path.join(tf.test.get_temp_dir(), "ignored.txt")
with gfile.FastGFile(ignored_asset, "w") as f:
f.write("additional data here")
tf.initialize_all_variables().run()
# Run an export.
save = tf.train.Saver({
"v0": v0,
"v1": v1
},
restore_sequentially=True,
sharded=sharded)
export = exporter.Exporter(save)
export.init(
sess.graph.as_graph_def(),
init_op=init_op,
clear_devices=clear_devices,
default_graph_signature=exporter.classification_signature(
input_tensor=v0),
named_graph_signatures=signatures,
assets_collection=assets_collection)
export.export(export_path,
global_step_tensor,
sess,
exports_to_keep=gc.largest_export_versions(2))
# Restore graph.
compare_def = tf.get_default_graph().as_graph_def()
tf.reset_default_graph()
with tf.Session(target="",
config=config_pb2.ConfigProto(
device_count={"CPU": 2})) as sess:
save = tf.train.import_meta_graph(
os.path.join(export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.META_GRAPH_DEF_FILENAME))
self.assertIsNotNone(save)
meta_graph_def = save.export_meta_graph()
collection_def = meta_graph_def.collection_def
# Validate custom graph_def.
graph_def_any = collection_def[constants.GRAPH_KEY].any_list.value
self.assertEquals(len(graph_def_any), 1)
graph_def = tf.GraphDef()
graph_def_any[0].Unpack(graph_def)
if clear_devices:
for node in compare_def.node:
node.device = ""
self.assertProtoEquals(compare_def, graph_def)
# Validate init_op.
init_ops = collection_def[constants.INIT_OP_KEY].node_list.value
self.assertEquals(len(init_ops), 1)
self.assertEquals(init_ops[0], "init_op")
# Validate signatures.
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
self.assertEqual(
default_signature.classification_signature.input.tensor_name,
"v0:0")
bindings = signatures.named_signatures[
"generic"].generic_signature.map
self.assertEquals(bindings["logical_input_A"].tensor_name, "v0:0")
self.assertEquals(bindings["logical_input_B"].tensor_name, "v1:0")
read_foo_signature = (
signatures.named_signatures["foo"].regression_signature)
self.assertEquals(read_foo_signature.input.tensor_name, "v0:0")
self.assertEquals(read_foo_signature.output.tensor_name, "v1:0")
# Validate the assets.
assets_any = collection_def[constants.ASSETS_KEY].any_list.value
self.assertEquals(len(assets_any), 1)
asset = manifest_pb2.AssetFile()
assets_any[0].Unpack(asset)
assets_path = os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.ASSETS_DIRECTORY, "hello42.txt")
asset_contents = gfile.GFile(assets_path).read()
self.assertEqual(asset_contents, "your data here")
self.assertEquals("hello42.txt", asset.filename)
self.assertEquals("filename42:0", asset.tensor_binding.tensor_name)
ignored_asset_path = os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.ASSETS_DIRECTORY, "ignored.txt")
self.assertFalse(gfile.Exists(ignored_asset_path))
# Validate graph restoration.
if sharded:
save.restore(
sess,
os.path.join(
export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.VARIABLES_FILENAME_PATTERN))
else:
save.restore(
sess,
os.path.join(
export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.VARIABLES_FILENAME))
self.assertEqual(10, tf.get_collection("v")[0].eval())
self.assertEqual(20, tf.get_collection("v")[1].eval())
tf.get_collection(constants.INIT_OP_KEY)[0].run()
self.assertEqual(30, tf.get_collection("v")[2].eval())
def init(self,
graph_def=None,
init_op=None,
clear_devices=False,
default_graph_signature=None,
named_graph_signatures=None,
assets_collection=None,
assets_callback=gfile_copy_callback):
"""Initialization.
Args:
graph_def: A GraphDef message of the graph to be used in inference.
GraphDef of default graph is used when None.
init_op: Op to be used in initialization.
clear_devices: If device info of the graph should be cleared upon export.
default_graph_signature: Default signature of the graph.
named_graph_signatures: Map of named input/output signatures of the graph.
assets_collection: A collection of constant asset filepath tensors. If set
the assets will be exported into the asset directory.
assets_callback: callback with two argument called during export with the
list of files to copy and the asset path.
Raises:
RuntimeError: if init is called more than once.
TypeError: if init_op is not an Operation or None.
ValueError: if asset file path tensors are not non-empty constant string
scalar tensors.
"""
# Avoid Dangerous default value []
if named_graph_signatures is None:
named_graph_signatures = {}
assets = []
if assets_collection:
for asset_tensor in assets_collection:
asset_filepath = self._file_path_value(asset_tensor)
if not asset_filepath:
raise ValueError("invalid asset filepath tensor %s" % asset_tensor)
basename = os.path.basename(asset_filepath)
assets.append((basename, asset_tensor))
self._assets_to_copy[asset_filepath] = basename
if self._has_init:
raise RuntimeError("init should be called only once")
self._has_init = True
if graph_def or clear_devices:
copy = graph_pb2.GraphDef()
if graph_def:
copy.CopyFrom(graph_def)
else:
copy.CopyFrom(ops.get_default_graph().as_graph_def())
if clear_devices:
for node in copy.node:
node.device = ""
graph_any_buf = Any()
graph_any_buf.Pack(copy)
ops.add_to_collection(constants.GRAPH_KEY, graph_any_buf)
if init_op:
if not isinstance(init_op, ops.Operation):
raise TypeError("init_op needs to be an Operation: %s" % init_op)
ops.add_to_collection(constants.INIT_OP_KEY, init_op)
signatures_proto = manifest_pb2.Signatures()
if default_graph_signature:
signatures_proto.default_signature.CopyFrom(default_graph_signature)
for signature_name, signature in six.iteritems(named_graph_signatures):
signatures_proto.named_signatures[signature_name].CopyFrom(signature)
signatures_any_buf = Any()
signatures_any_buf.Pack(signatures_proto)
ops.add_to_collection(constants.SIGNATURES_KEY, signatures_any_buf)
for filename, tensor in assets:
asset = manifest_pb2.AssetFile()
asset.filename = filename
asset.tensor_binding.tensor_name = tensor.name
asset_any_buf = Any()
asset_any_buf.Pack(asset)
ops.add_to_collection(constants.ASSETS_KEY, asset_any_buf)
self._assets_callback = assets_callback