def load(self, sess, tags, import_scope=None, **saver_kwargs):
"""Load the MetaGraphDef graph and restore variable values into the session.
Args:
sess: tf.compat.v1.Session to restore variable values.
tags: a set of string tags identifying a MetaGraphDef.
import_scope: Optional `string` -- if specified, prepend this string
followed by '/' to all loaded tensor names. This scope is applied to
tensor instances loaded into the passed session, but it is *not* written
through to the static `MetaGraphDef` protocol buffer that is returned.
**saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph.
Returns:
`MetagraphDef` proto of the graph that was loaded.
"""
saved_model_proto = parse_saved_model(self._export_dir)
metrics.IncrementReadApi(_LOADER_LABEL)
with sess.graph.as_default():
saver, _ = self.load_graph(sess.graph, tags, import_scope,
**saver_kwargs)
self.restore_variables(sess, saver, import_scope)
self.run_init_ops(sess, tags, import_scope)
meta_graph_def = self.get_meta_graph_def_from_tags(tags)
if (len(saved_model_proto.meta_graphs) == 1 and
saved_model_proto.meta_graphs[0].HasField("object_graph_def")):
metrics.IncrementRead(write_version="2")
else:
metrics.IncrementRead(write_version="1")
return meta_graph_def
def load(export_dir, tags):
"""Load a v1-style SavedModel as an object."""
metrics.IncrementReadApi(_LOAD_V1_V2_LABEL)
loader = _EagerSavedModelLoader(export_dir)
result = loader.load(tags=tags)
metrics.IncrementRead(write_version="1")
return result
def test_SM_increment_read(self):
self.assertEqual(metrics.GetRead(write_version="2"), 0)
metrics.IncrementReadApi("bar")
self.assertEqual(metrics.GetReadApi("bar"), 1)
metrics.IncrementRead(write_version="2")
self.assertEqual(metrics.GetRead(write_version="2"), 1)
def load_internal(export_dir,
tags=None,
options=None,
loader_cls=Loader,
filters=None):
"""Loader implementation."""
options = options or load_options.LoadOptions()
if tags is not None and not isinstance(tags, set):
# Supports e.g. tags=SERVING and tags=[SERVING]. Sets aren't considered
# sequences for nest.flatten, so we put those through as-is.
tags = nest.flatten(tags)
saved_model_proto, debug_info = (
loader_impl.parse_saved_model_with_debug_info(export_dir))
if (len(saved_model_proto.meta_graphs) == 1
and saved_model_proto.meta_graphs[0].HasField("object_graph_def")):
metrics.IncrementReadApi(_LOAD_V2_LABEL)
meta_graph_def = saved_model_proto.meta_graphs[0]
# tensor_content field contains raw bytes in litle endian format
# which causes problems when loaded on big-endian systems
# requiring byteswap
if sys.byteorder == "big":
saved_model_utils.swap_function_tensor_content(
meta_graph_def, "little", "big")
if (tags is not None
and set(tags) != set(meta_graph_def.meta_info_def.tags)):
raise ValueError(
"Got an incompatible argument to `tags`: {tags}. The SavedModel at "
f"{export_dir} has one MetaGraph with tags "
f"{meta_graph_def.meta_info_def.tags}. You may omit the argument, "
"pass 'None', or pass matching tags.")
object_graph_proto = meta_graph_def.object_graph_def
ckpt_options = checkpoint_options.CheckpointOptions(
experimental_io_device=options.experimental_io_device)
with ops.init_scope():
try:
loader = loader_cls(object_graph_proto, saved_model_proto,
export_dir, ckpt_options, options, filters)
except errors.NotFoundError as err:
raise FileNotFoundError(
str(err) +
"\n You may be trying to load on a different device "
"from the computational device. Consider setting the "
"`experimental_io_device` option in `tf.saved_model.LoadOptions` "
"to the io_device such as '/job:localhost'.")
root = loader.get(0)
if isinstance(loader, Loader):
root.graph_debug_info = loader.adjust_debug_info_func_names(
debug_info)
root.tensorflow_version = meta_graph_def.meta_info_def.tensorflow_version
root.tensorflow_git_version = (
meta_graph_def.meta_info_def.tensorflow_git_version)
metrics.IncrementRead(write_version="2")
else:
if filters:
raise ValueError(
"SavedModels saved from Tensorflow 1.x or Estimator (any"
" version) cannot be loaded with node filters.")
with ops.init_scope():
root = load_v1_in_v2.load(export_dir, tags)
root.graph_debug_info = debug_info
if filters:
return {node_id: loader.get(node_id) for node_id in filters}
else:
return {"root": root}
def load_partial(export_dir, filters, tags=None, options=None):
"""Partially load a SavedModel (saved from V2).
Similar to `tf.saved_model.load`, but with an additional argument that
lets you specify which nodes to load.
`tf.saved_model.load_partial(export_dir, ["root"])` and
`tf.saved_model.load(export_dir)` are equivalent.
Note: This only works for SavedModels saved with TensorFlow V2 from
`tf.saved_model.save` or Keras. This will not load SavedModels save from
the Estimator API.
In Tensorflow V2, SavedModel stores the **object graph** of the saved object.
The graph contains nodes (`tf.Module`, `tf.Variable`, `tf.function`, Keras
layers, etc.) and edges that are the name of the attributes connecting the
objects.
*Example 1*
```
model = tf.Module()
model.child_layer = tf.Module()
model.child_layer.v = tf.Variable(5.)
tf.saved_model.save(model, '/tmp/model')
loaded = tf.__internal__.saved_model.load_partial(
... '/tmp/model',
... ['root.child_layer', 'root.child_layer.v'])
loaded['root.child_layer'].v.numpy()
5.
loaded['root.child_layer'].v is loaded['root.child_layer.v']
True
*Example 2*
model = tf.Module()
model.child_layer = tf.Module()
model.child_layer.v = tf.Variable(5.)
>>>
tf.saved_model.save(model, '/tmp/model')
# Create a variable
new_variable = tf.Variable(0.)
loaded = tf.__internal__.saved_model.load_partial(
... '/tmp/model',
... {'root.child_layer': None, 'root.child_layer.v': new_variable})
loaded['root.child_layer'].v.numpy()
5.
new_variable.numpy()
5.
```
**Loading under different distribution strategies**
You can load different parts of the model under different distribution
strategies. Note that this is very experimental so use with care.
```
model = tf.Module()
model.layer_1 = tf.Module()
model.layer_1.v = tf.Variable(5.)
model.layer_2 = tf.Module()
model.layer_2.v = tf.Variable(7.)
tf.saved_model.save(model, '/tmp/model')
# Load with no strategy
loaded = tf.__internal__.saved_model.load_partial(
... '/tmp/model',
... ['root.layer_1'])
loaded['root.layer_1'].v
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=5.0>
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
... loaded2 = tf.__internal__.saved_model.load_partial(
... '/tmp/model',
... ['root.layer_2'])
loaded2['root.layer_2'].v
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=7.0>
}
```
Args:
export_dir: The SavedModel directory to load from.
filters: A list or dictionary where each element or key is a string
path to nodes that should be loaded. Node paths consist of all the child
attribute names to reach that node in the form: `root.{attribute_name}`.
The loader will load all of the specified nodes and their recursive
descendants. When this option is defined, the loader will return a
dictionary mapping the node paths to the loaded objects.
tags: A tag or sequence of tags identifying the MetaGraph to load. Optional
if the SavedModel contains a single MetaGraph, as for those exported from
`tf.saved_model.save`.
options: `tf.saved_model.LoadOptions` object that specifies options for
loading.
Returns:
A dictionary mapping node paths from the filter to loaded objects.
"""
options = options or load_options.LoadOptions()
if tags is not None and not isinstance(tags, set):
# Supports e.g. tags=SERVING and tags=[SERVING]. Sets aren't considered
# sequences for nest.flatten, so we put those through as-is.
tags = nest.flatten(tags)
saved_model_proto, debug_info = (
loader_impl.parse_saved_model_with_debug_info(export_dir))
if (len(saved_model_proto.meta_graphs) == 1 and
saved_model_proto.meta_graphs[0].HasField("object_graph_def")):
metrics.IncrementReadApi(_LOAD_V2_LABEL)
meta_graph_def = saved_model_proto.meta_graphs[0]
# tensor_content field contains raw bytes in litle endian format
# which causes problems when loaded on big-endian systems
# requiring byteswap
if sys.byteorder == "big":
saved_model_utils.swap_function_tensor_content(meta_graph_def, "little",
"big")
if (tags is not None
and set(tags) != set(meta_graph_def.meta_info_def.tags)):
raise ValueError(
f"Got an incompatible argument to `tags`: {tags}. The SavedModel at "
f"{export_dir} has one MetaGraph with tags "
f"{meta_graph_def.meta_info_def.tags}. You may omit the argument, "
"pass 'None', or pass matching tags.")
object_graph_proto = meta_graph_def.object_graph_def
ckpt_options = checkpoint_options.CheckpointOptions(
experimental_io_device=options.experimental_io_device)
with ops.init_scope():
try:
loader = Loader(object_graph_proto, saved_model_proto, export_dir,
ckpt_options, options, filters)
except errors.NotFoundError as err:
raise FileNotFoundError(
str(err) + "\n You may be trying to load on a different device "
"from the computational device. Consider setting the "
"`experimental_io_device` option in `tf.saved_model.LoadOptions` "
"to the io_device such as '/job:localhost'.")
root = loader.get(0)
root.graph_debug_info = loader.adjust_debug_info_func_names(debug_info)
root.tensorflow_version = meta_graph_def.meta_info_def.tensorflow_version
root.tensorflow_git_version = (
meta_graph_def.meta_info_def.tensorflow_git_version)
metrics.IncrementRead(write_version="2")
else:
if filters:
raise ValueError("SavedModels saved from Tensorflow 1.x or Estimator (any"
" version) cannot be loaded with node filters.")
with ops.init_scope():
root = load_v1_in_v2.load(export_dir, tags)
root.graph_debug_info = debug_info
if filters:
return {node_id: loader.get(node_id) for node_id in filters}
else:
return {"root": root}
