def _recreate_asset(self, proto):
filename = os.path.join(
saved_model_utils.get_assets_dir(self._export_dir),
self._asset_file_def[proto.asset_file_def_index].filename)
asset = tracking.Asset(filename)
if not context.executing_eagerly():
ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, asset.asset_path)
return asset, setattr
def __init__(self):
self.asset = tracking.Asset(
test.test_src_dir_path(
"cc/saved_model/testdata/static_hashtable_asset.txt"))
self.table = lookup_ops.StaticHashTable(
lookup_ops.TextFileInitializer(
self.asset, dtypes.string, lookup_ops.TextFileIndex.WHOLE_LINE,
dtypes.int64, lookup_ops.TextFileIndex.LINE_NUMBER), -1)
def test_unused_asset(self):
root = tracking.AutoTrackable()
root.f = def_function.function(
lambda x: 2. * x,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
root.asset = tracking.Asset(self._vocab_path)
export_dir = os.path.join(self.get_temp_dir(), "save_dir")
save.save(root, export_dir)
self.assertAllClose({"output_0": [0.2]},
_import_and_infer(export_dir, {"x": [0.1]}))
def test_asset_path_returned(self):
root = tracking.AutoTrackable()
root.path = tracking.Asset(self._vocab_path)
save_dir = os.path.join(self.get_temp_dir(), "saved_model")
root.get_asset = def_function.function(lambda: root.path.asset_path)
save.save(root, save_dir, signatures=root.get_asset.get_concrete_function())
second_dir = os.path.join(self.get_temp_dir(), "second_dir")
file_io.rename(save_dir, second_dir)
imported_path = _import_and_infer(second_dir, {})["output_0"]
self.assertIn(
compat.as_str_any(second_dir), compat.as_str_any(imported_path))
def __init__(self,
resource_name,
filename,
maximum_cached_engines,
device="GPU"):
super(_TRTEngineResource, self).__init__(
device=device, deleter=_TRTEngineResourceDeleter(resource_name, device))
self._resource_name = resource_name
# Track the serialized engine file in the SavedModel.
self._filename = self._track_trackable(
tracking.Asset(filename), "_serialized_trt_resource_filename")
self._maximum_cached_engines = maximum_cached_engines
def _recreate_asset(self, proto):
filename = os.path.join(
saved_model_utils.get_assets_dir(self._export_dir),
self._asset_file_def[proto.asset_file_def_index].filename)
return tracking.Asset(filename), setattr
def __init__(self):
self.asset = tracking.Asset(
test.test_src_dir_path("cc/saved_model/testdata/test_asset.txt"))
def load(self, tags):
"""Creates an object from the MetaGraph identified by `tags`."""
meta_graph_def = self.get_meta_graph_def_from_tags(tags)
load_shared_name_suffix = "_load_{}".format(ops.uid())
functions = function_deserialization.load_function_def_library(
meta_graph_def.graph_def.library,
load_shared_name_suffix=load_shared_name_suffix)
# Replace existing functions in the MetaGraphDef with renamed functions so
# we don't have duplicates or name collisions.
meta_graph_def.graph_def.library.Clear()
for function in functions.values():
meta_graph_def.graph_def.library.function.add().CopyFrom(
function.function_def)
# We've renamed functions and shared names. We need the same operation on
# the GraphDef itself for consistency.
for node_def in meta_graph_def.graph_def.node:
function_deserialization.fix_node_def(
node_def,
functions,
load_shared_name_suffix,
debug_name="MetaGraph import")
load_graph_returns = [None]
wrapped = wrap_function.wrap_function(functools.partial(
self.load_graph, load_graph_returns, meta_graph_def),
signature=[])
saver, = load_graph_returns
self.restore_variables(wrapped, saver)
with wrapped.graph.as_default():
init_op = loader_impl.get_init_op(
meta_graph_def
) or monitored_session.Scaffold.default_local_init_op()
# Add a dummy Tensor we know we can fetch to add control dependencies to.
init_anchor = constant_op.constant(0., name="dummy_fetch")
root = tracking.AutoTrackable()
asset_feed_tensors = []
asset_paths = []
for tensor_name, value in loader_impl.get_asset_tensors(
self._export_dir, meta_graph_def).items():
asset_feed_tensors.append(
wrapped.graph.as_graph_element(tensor_name))
asset_paths.append(tracking.Asset(value))
init_fn = wrapped.prune(
feeds=asset_feed_tensors,
fetches=[init_anchor,
wrapped.graph.as_graph_element(init_op)])
initializer = _Initializer(init_fn, asset_paths)
# pylint: disable=protected-access
local_init_op, _ = initializer._initialize()
# pylint: enable=protected-access
with ops.init_scope():
if not context.executing_eagerly():
ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS,
local_init_op)
for variable in wrapped.graph.get_collection_ref(
ops.GraphKeys.LOCAL_VARIABLES):
# pylint: disable=protected-access
variable._initializer_op = local_init_op
# pylint: enable=protected-access
root.initializer = initializer
root.asset_paths = asset_paths
signature_functions = self._extract_signatures(wrapped, meta_graph_def)
root.signatures = signature_serialization.create_signature_map(
signature_functions)
root.variables = list(wrapped.graph.variables)
root.tensorflow_version = (
meta_graph_def.meta_info_def.tensorflow_version)
root.tensorflow_git_version = (
meta_graph_def.meta_info_def.tensorflow_git_version)
root.graph = wrapped.graph
root.prune = wrapped.prune
return root
