def _no_trainable_variable_attribute(self, trainable):
"""A SavedModel where the VariableDef has no 'trainable' (it's false)."""
class _MissingFieldsVariable(resource_variable_ops.ResourceVariable):
def to_proto(self, export_scope=None):
full_proto = super(_MissingFieldsVariable,
self).to_proto(export_scope)
return variable_pb2.VariableDef(
variable_name=full_proto.variable_name,
initial_value_name=full_proto.initial_value_name,
initializer_name=full_proto.snapshot_name,
save_slice_info_def=full_proto.save_slice_info_def,
is_resource=full_proto.is_resource)
export_graph = ops.Graph()
with export_graph.as_default():
v = _MissingFieldsVariable(3., trainable=trainable)
with session_lib.Session() as session:
session.run([v.initializer])
path = os.path.join(self.get_temp_dir(), "saved_model",
str(ops.uid()))
b = builder_impl.SavedModelBuilder(path)
b.add_meta_graph_and_variables(session,
tags=[tag_constants.SERVING],
signature_def_map={})
b.save()
return path
def _v1_multi_input_saved_model(self):
export_graph = ops.Graph()
with export_graph.as_default():
input1 = array_ops.placeholder(shape=[None],
dtype=dtypes.float32,
name="input1")
input2 = array_ops.placeholder(shape=[None],
dtype=dtypes.float32,
name="input2")
v = resource_variable_ops.ResourceVariable(21.)
output = array_ops.identity(input1 * v + input2, name="output")
with session_lib.Session() as session:
session.run(v.initializer)
path = os.path.join(self.get_temp_dir(), "saved_model",
str(ops.uid()))
builder = builder_impl.SavedModelBuilder(path)
builder.add_meta_graph_and_variables(
session,
tags=[tag_constants.SERVING],
signature_def_map={
"serving_default":
signature_def_utils.build_signature_def(
{
"input1": utils_impl.build_tensor_info(input1),
"input2": utils_impl.build_tensor_info(input2)
},
{"output": utils_impl.build_tensor_info(output)})
})
builder.save()
return path
def _v1_multi_metagraph_saved_model(self):
export_graph = ops.Graph()
with export_graph.as_default():
start = array_ops.placeholder(
shape=[None], dtype=dtypes.float32, name="start")
v = resource_variable_ops.ResourceVariable(21.)
first_output = array_ops.identity(start * v, name="first_output")
second_output = array_ops.identity(v, name="second_output")
with session_lib.Session() as session:
session.run(v.initializer)
path = os.path.join(self.get_temp_dir(), "saved_model", str(ops.uid()))
builder = builder_impl.SavedModelBuilder(path)
builder.add_meta_graph_and_variables(
session, tags=["first"],
signature_def_map={
"first_key": signature_def_utils.build_signature_def(
{"first_start": utils_impl.build_tensor_info(start)},
{"first_output": utils_impl.build_tensor_info(
first_output)})})
builder.add_meta_graph(
tags=["second"],
signature_def_map={
"second_key": signature_def_utils.build_signature_def(
{"second_start": utils_impl.build_tensor_info(start)},
{"second_output": utils_impl.build_tensor_info(
second_output)})})
builder.save()
return path
def _no_signatures_model(self):
export_graph = ops.Graph()
with export_graph.as_default():
inp = array_ops.placeholder(name="x", shape=[], dtype=dtypes.float32)
array_ops.identity(inp + 1., name="out")
with session_lib.Session() as session:
path = os.path.join(self.get_temp_dir(), "saved_model", str(ops.uid()))
b = builder_impl.SavedModelBuilder(path)
b.add_meta_graph_and_variables(
session,
tags=[tag_constants.SERVING],
signature_def_map={},
assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS))
b.save()
return path
def _export_variable(self, **kwargs_for_variable):
"""A 1.x SavedModel with a single variable."""
export_graph = ops.Graph()
with export_graph.as_default():
v = resource_variable_ops.ResourceVariable(3., **kwargs_for_variable)
with session_lib.Session() as session:
session.run([v.initializer])
path = os.path.join(self.get_temp_dir(), "saved_model", str(ops.uid()))
b = builder_impl.SavedModelBuilder(path)
b.add_meta_graph_and_variables(
session,
tags=[tag_constants.SERVING],
signature_def_map={})
b.save()
return path
def _unfed_placeholder_signature(self):
export_graph = ops.Graph()
with export_graph.as_default():
x = array_ops.placeholder(name="x", shape=[], dtype=dtypes.float32)
output = x * random_ops.random_normal([2])
with session_lib.Session() as session:
path = os.path.join(self.get_temp_dir(), "saved_model", str(ops.uid()))
b = builder_impl.SavedModelBuilder(path)
b.add_meta_graph_and_variables(
session,
tags=[tag_constants.SERVING],
signature_def_map={
"key": signature_def_utils.build_signature_def(
{}, dict(value=utils_impl.build_tensor_info(output)))})
b.save()
return path
def _v1_output_shape_saved_model(self):
export_graph = ops.Graph()
with export_graph.as_default():
start = array_ops.placeholder(
shape=[None], dtype=dtypes.float32, name="start")
output = array_ops.identity(start, name="output")
output.set_shape([1]) # Ok to use [1] because shape is only informational
with session_lib.Session() as session:
path = os.path.join(self.get_temp_dir(), "saved_model", str(ops.uid()))
builder = builder_impl.SavedModelBuilder(path)
builder.add_meta_graph_and_variables(
session,
tags=[tag_constants.SERVING],
signature_def_map={
"serving_default":
signature_def_utils.build_signature_def(
{"start": utils_impl.build_tensor_info(start)},
{"output": utils_impl.build_tensor_info(output)})
})
builder.save()
return path
