def testNestedLists(self):
a = tracking.Checkpointable()
a.l = []
b = tracking.Checkpointable()
a.l.append([b])
c = tracking.Checkpointable()
a.l[0].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
a.l[0].append(1)
d = tracking.Checkpointable()
a.l[0].append(d)
a_deps = util.list_objects(a)
self.assertIn(d, a_deps)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertNotIn(1, a_deps)
e = tracking.Checkpointable()
f = tracking.Checkpointable()
a.l1 = [[], [e]]
a.l1[0].append(f)
a_deps = util.list_objects(a)
self.assertIn(e, a_deps)
self.assertIn(f, a_deps)
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l[0].append(data_structures.NoDependency([]))
a.l[0][-1].append(5)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
# Dirtying the inner list means the root object is unsaveable.
a.l[0][1] = 2
with self.assertRaisesRegexp(ValueError,
"A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def testNestedLists(self):
a = tracking.Checkpointable()
a.l = []
b = tracking.Checkpointable()
a.l.append([b])
c = tracking.Checkpointable()
a.l[0].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
a.l[0].append(1)
d = tracking.Checkpointable()
a.l[0].append(d)
a_deps = util.list_objects(a)
self.assertIn(d, a_deps)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertNotIn(1, a_deps)
e = tracking.Checkpointable()
f = tracking.Checkpointable()
a.l1 = [[], [e]]
a.l1[0].append(f)
a_deps = util.list_objects(a)
self.assertIn(e, a_deps)
self.assertIn(f, a_deps)
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l[0].append(data_structures.NoDependency([]))
a.l[0][-1].append(5)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
# Dirtying the inner list means the root object is unsaveable.
a.l[0][1] = 2
with self.assertRaisesRegexp(ValueError, "A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def get_non_optimizer_objects(m, g):
"""Gather set of model and optimizer checkpointable objects."""
# Set default graph because optimizer.variables() returns optimizer
# variables defined in the default graph.
with g.as_default():
all_objects = set(checkpointable_utils.list_objects(m))
optimizer_and_variables = set()
for obj in all_objects:
if isinstance(obj, optimizers.TFOptimizer):
optimizer_and_variables.update(checkpointable_utils.list_objects(obj))
optimizer_and_variables.update(set(obj.optimizer.variables()))
return all_objects - optimizer_and_variables
def testAddVariableOverwrite(self):
root = base.Checkpointable()
a = root._add_variable_with_custom_getter(
name="v", shape=[], getter=variable_scope.get_variable)
self.assertEqual([root, a], util.list_objects(root))
with ops.Graph().as_default():
b = root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=True,
getter=variable_scope.get_variable)
self.assertEqual([root, b], util.list_objects(root))
with ops.Graph().as_default():
with self.assertRaisesRegexp(
ValueError, "already declared as a dependency"):
root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=False,
getter=variable_scope.get_variable)
def testAddVariableOverwrite(self):
root = base.CheckpointableBase()
a = root._add_variable_with_custom_getter(
name="v", shape=[], getter=variable_scope.get_variable)
self.assertEqual([root, a], util.list_objects(root))
with ops.Graph().as_default():
b = root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=True,
getter=variable_scope.get_variable)
self.assertEqual([root, b], util.list_objects(root))
with ops.Graph().as_default():
with self.assertRaisesRegexp(
ValueError, "already declared as a dependency"):
root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=False,
getter=variable_scope.get_variable)
def testDictWrapperNoDependency(self):
a = tracking.Checkpointable()
a.d = data_structures.NoDependency({})
a.d[1] = [3]
self.assertEqual([a], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNonStringKeyNotCheckpointableValue(self):
a = tracking.Checkpointable()
a.d = {}
a.d["a"] = [3]
a.d[1] = data_structures.NoDependency([3])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def test_checkpointable_save_restore(self):
def _templated():
v = variable_scope.get_variable(
"v",
shape=[1],
initializer=init_ops.zeros_initializer(),
use_resource=True)
v2 = variable_scope.get_variable(
"v2",
shape=[1],
initializer=init_ops.zeros_initializer(),
use_resource=True)
manual = _ManualScope()
return v, v + 1., v2, manual, manual()
save_template = template.make_template("s1", _templated)
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
six.assertCountEqual(
self,
[v1_save, v2_save, manual_scope, manual_scope_v, save_template],
checkpointable_utils.list_objects(save_template))
manual_dep, = manual_scope._checkpoint_dependencies
self.assertEqual("in_manual_scope", manual_dep.name)
self.assertIs(manual_scope_v, manual_dep.ref)
optimizer = adam.AdamOptimizer(0.0)
save_root = checkpointable_utils.Checkpoint(my_template=save_template,
optimizer=optimizer)
optimizer.minimize(v1_save.read_value)
self.evaluate([v.initializer for v in save_template.variables])
self.evaluate([v.initializer for v in optimizer.variables()])
self.evaluate(v1_save.assign([12.]))
self.evaluate(v2_save.assign([14.]))
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = save_root.save(checkpoint_prefix)
load_template = template.make_template("s2", _templated)
load_optimizer = adam.AdamOptimizer(0.0)
load_root = checkpointable_utils.Checkpoint(my_template=load_template,
optimizer=load_optimizer)
status = load_root.restore(save_path)
var, var_plus_one, var2, _, _ = load_template()
load_optimizer.minimize(var.read_value)
self.assertEqual(3, len(load_template._checkpoint_dependencies))
self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
self.assertEqual("ManualScope",
load_template._checkpoint_dependencies[2].name)
status.assert_consumed().run_restore_ops()
self.assertAllEqual([12.], self.evaluate(var))
self.assertAllEqual([13.], self.evaluate(var_plus_one))
self.assertAllEqual([14.], self.evaluate(var2))
def testListBasic(self):
a = tracking.Checkpointable()
b = tracking.Checkpointable()
a.l = [b]
c = tracking.Checkpointable()
a.l.append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
direct_a_dep, = a._checkpoint_dependencies
self.assertEqual("l", direct_a_dep.name)
self.assertIn(b, direct_a_dep.ref)
self.assertIn(c, direct_a_dep.ref)
def testShallowCopyCheckpointable(self):
original = tracking.Checkpointable()
original_sub = tracking.Checkpointable()
original.a = [[1.]]
original.b = {"a": original_sub}
shallow_copied = copy.copy(original)
self.assertIs(original_sub, shallow_copied.b["a"])
self.assertIsNot(original, shallow_copied)
self.assertEqual([[1.]], shallow_copied.a)
shallow_deps = util.list_objects(shallow_copied)
self.assertIn(shallow_copied.a, shallow_deps)
self.assertIn(shallow_copied.b, shallow_deps)
self.assertIn(shallow_copied.b["a"], shallow_deps)
def testListBasic(self):
a = tracking.Checkpointable()
b = tracking.Checkpointable()
a.l = [b]
c = tracking.Checkpointable()
a.l.append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
direct_a_dep, = a._checkpoint_dependencies
self.assertEqual("l", direct_a_dep.name)
self.assertIn(b, direct_a_dep.ref)
self.assertIn(c, direct_a_dep.ref)
def test_checkpointable_save_restore(self):
def _templated():
v = variable_scope.get_variable(
"v", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
v2 = variable_scope.get_variable(
"v2", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
manual = _ManualScope()
return v, v + 1., v2, manual, manual()
save_template = template.make_template("s1", _templated)
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
six.assertCountEqual(
self,
[v1_save, v2_save, manual_scope, manual_scope_v, save_template],
checkpointable_utils.list_objects(save_template))
manual_dep, = manual_scope._checkpoint_dependencies
self.assertEqual("in_manual_scope", manual_dep.name)
self.assertIs(manual_scope_v, manual_dep.ref)
optimizer = adam.AdamOptimizer(0.0)
save_root = checkpointable_utils.Checkpoint(
my_template=save_template, optimizer=optimizer)
optimizer.minimize(v1_save.read_value)
self.evaluate([v.initializer for v in save_template.variables])
self.evaluate([v.initializer for v in optimizer.variables()])
self.evaluate(v1_save.assign([12.]))
self.evaluate(v2_save.assign([14.]))
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = save_root.save(checkpoint_prefix)
load_template = template.make_template("s2", _templated)
load_optimizer = adam.AdamOptimizer(0.0)
load_root = checkpointable_utils.Checkpoint(
my_template=load_template, optimizer=load_optimizer)
status = load_root.restore(save_path)
var, var_plus_one, var2, _, _ = load_template()
load_optimizer.minimize(var.read_value)
self.assertEqual(3, len(load_template._checkpoint_dependencies))
self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
self.assertEqual("ManualScope",
load_template._checkpoint_dependencies[2].name)
status.assert_consumed().run_restore_ops()
self.assertAllEqual([12.], self.evaluate(var))
self.assertAllEqual([13.], self.evaluate(var_plus_one))
self.assertAllEqual([14.], self.evaluate(var2))
def testDeepCopyCheckpointable(self):
original = tracking.Checkpointable()
original_sub = tracking.Checkpointable()
original.a = [[1.]]
original.b = {"a": original_sub}
deep_copied = copy.deepcopy(original)
self.assertIsNot(original, deep_copied)
self.assertIsNot(original_sub, deep_copied.b["a"])
self.assertEqual([[1.]], deep_copied.a)
self.assertIsInstance(deep_copied.b["a"], tracking.Checkpointable)
deps = util.list_objects(deep_copied)
self.assertIn(deep_copied.a, deps)
self.assertIn(deep_copied.b, deps)
self.assertIn(deep_copied.b["a"], deps)
self.assertNotIn(original_sub, deps)
def test_checkpointable_dependencies(self):
rnn = keras.layers.SimpleRNN
x = np.random.random((2, 2, 2))
y = np.random.random((2, 2))
model = keras.models.Sequential()
model.add(rnn(2))
model.compile(optimizer=rmsprop.RMSPropOptimizer(learning_rate=0.001),
loss='mse')
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# checkpointable list of objects
checkpointed_objects = set(checkpointable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_checkpointable_dependencies(self):
rnn = keras.layers.SimpleRNN
with self.test_session():
x = np.random.random((2, 2, 2))
y = np.random.random((2, 2))
model = keras.models.Sequential()
model.add(rnn(2))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# checkpointable list of objects
checkpointed_objects = set(checkpointable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_timedistributed_dense(self):
model = keras.models.Sequential()
model.add(
keras.layers.TimeDistributed(keras.layers.Dense(2),
input_shape=(3, 4)))
model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
checkpointed_objects = set(checkpointable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_timedistributed_dense(self):
model = keras.models.Sequential()
model.add(
keras.layers.TimeDistributed(
keras.layers.Dense(2), input_shape=(3, 4)))
model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
model.fit(
np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
checkpointed_objects = set(checkpointable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def testNoDependency(self):
root = tracking.Checkpointable()
hasdep = tracking.Checkpointable()
root.hasdep = hasdep
nodep = tracking.Checkpointable()
root.nodep = data_structures.NoDependency(nodep)
self.assertEqual(1, len(root._checkpoint_dependencies))
self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep)
self.assertIs(root.hasdep, hasdep)
self.assertIs(root.nodep, nodep)
class NoDependencyModel(training.Model):
@base.no_automatic_dependency_tracking
def __init__(self):
super(NoDependencyModel, self).__init__()
self.a = []
self.b = tracking.Checkpointable()
nodeps = NoDependencyModel()
self.assertEqual([nodeps], util.list_objects(nodeps))
def testNonAppendNotCheckpointable(self):
# Non-append mutations (deleting or overwriting values) are OK when the
# values aren't tracked.
a = tracking.Checkpointable()
a.d = {}
a.d["a"] = [3]
a.d[1] = 3
a.d[1] = 2
self.assertEqual(2, a.d[1])
del a.d[1]
a.d[2] = data_structures.NoDependency(tracking.Checkpointable())
second = tracking.Checkpointable()
a.d[2] = data_structures.NoDependency(second)
self.assertIs(second, a.d[2])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNoDependency(self):
root = tracking.Checkpointable()
hasdep = tracking.Checkpointable()
root.hasdep = hasdep
nodep = tracking.Checkpointable()
root.nodep = data_structures.NoDependency(nodep)
self.assertEqual(1, len(root._checkpoint_dependencies))
self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep)
self.assertIs(root.hasdep, hasdep)
self.assertIs(root.nodep, nodep)
class NoDependencyModel(training.Model):
@base.no_automatic_dependency_tracking
def __init__(self):
super(NoDependencyModel, self).__init__()
self.a = []
self.b = tracking.Checkpointable()
nodeps = NoDependencyModel()
self.assertEqual([nodeps], util.list_objects(nodeps))
def testDictionariesBasic(self):
a = training.Model()
b = training.Model()
a.attribute = {"b": b}
c = training.Model()
a.attribute["c"] = []
a.attribute["c"].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertIs(b, a.attribute["b"])
six.assertCountEqual(
self,
["b", "c"],
[dep.name for dep in a.attribute._checkpoint_dependencies])
self.assertEqual([b, c], a.layers)
self.assertEqual([b, c], a.attribute.layers)
self.assertEqual([c], a.attribute["c"].layers)
checkpoint = util.Checkpoint(a=a)
save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
checkpoint.restore(save_path).assert_consumed()
def testDictDeepCopy(self):
root = tracking.Checkpointable()
orig_dict = {"a": [1.]}
root.a = orig_dict
copied = copy.deepcopy(root.a)
self.assertAllEqual([1.], copied["a"])
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a["a"], copied["a"])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_dict["b"] = []
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def testListDeepCopy(self):
root = tracking.Checkpointable()
orig_list = [[1.]]
root.a = orig_list
copied = copy.deepcopy(root.a)
self.assertAllEqual([[1.]], copied)
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a[0], copied[0])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_list.append(1.)
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def _make_graph_def(root, signature_functions, object_saver):
"""Generates and exports call ops for `signature_functions`."""
signatures = {}
# List objects from the eager context to make sure Optimizers give us the
# right Graph-dependent variables.
accessible_objects = util.list_objects(root)
exported_graph = ops.Graph()
with exported_graph.as_default():
object_map, resource_map = _map_resources(accessible_objects)
# Saving an object-based checkpoint again gathers variables. We need to do the
# gathering from the eager context so Optimizers save the right set of
# variables, but want any operations associated with the save/restore to be in
# the exported graph (thus the `to_graph` argument).
saver = object_saver.freeze(object_map=object_map, to_graph=exported_graph)
with exported_graph.as_default():
signatures = _generate_signatures(signature_functions, resource_map)
saver_def = saver.to_proto()
graph_def = exported_graph.as_graph_def(add_shapes=True)
# Clean reference cycles so repeated export()s don't make work for the garbage
# collector.
ops.dismantle_graph(exported_graph)
return graph_def, signatures, saver_def
def _make_graph_def(root, signature_functions, object_saver):
"""Generates and exports call ops for `signature_functions`."""
signatures = {}
# List objects from the eager context to make sure Optimizers give us the
# right Graph-dependent variables.
accessible_objects = util.list_objects(root)
exported_graph = ops.Graph()
with exported_graph.as_default():
object_map, resource_map = _map_resources(accessible_objects)
# Saving an object-based checkpoint again gathers variables. We need to do the
# gathering from the eager context so Optimizers save the right set of
# variables, but want any operations associated with the save/restore to be in
# the exported graph (thus the `to_graph` argument).
saver = object_saver.freeze(object_map=object_map, to_graph=exported_graph)
with exported_graph.as_default():
signatures = _generate_signatures(signature_functions, resource_map)
saver_def = saver.to_proto()
graph_def = exported_graph.as_graph_def(add_shapes=True)
# Clean reference cycles so repeated export()s don't make work for the garbage
# collector.
ops.dismantle_graph(exported_graph)
return graph_def, signatures, saver_def
def test_bidirectional(self):
rnn = keras.layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
with self.cached_session():
for mode in ['sum', 'concat', 'ave', 'mul']:
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = keras.models.Sequential()
model.add(
keras.layers.Bidirectional(rnn(output_dim),
merge_mode=mode,
input_shape=(timesteps, dim)))
model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# checkpointable list of objects
checkpointed_objects = set(
checkpointable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
# test compute output shape
ref_shape = model.layers[-1].output.get_shape()
shape = model.layers[-1].compute_output_shape(
(None, timesteps, dim))
self.assertListEqual(shape.as_list(), ref_shape.as_list())
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
def test_bidirectional(self):
rnn = keras.layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
with self.cached_session():
for mode in ['sum', 'concat', 'ave', 'mul']:
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = keras.models.Sequential()
model.add(
keras.layers.Bidirectional(
rnn(output_dim), merge_mode=mode, input_shape=(timesteps, dim)))
model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# checkpointable list of objects
checkpointed_objects = set(checkpointable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
# test compute output shape
ref_shape = model.layers[-1].output.get_shape()
shape = model.layers[-1].compute_output_shape(
(None, timesteps, dim))
self.assertListEqual(shape.as_list(), ref_shape.as_list())
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
def _fill_meta_graph_def(meta_graph_def, obj, signature_functions,
object_saver):
"""Generates a MetaGraph which calls `signature_functions`.
Args:
meta_graph_def: The MetaGraphDef proto to fill.
obj: The checkpointable object being exported.
signature_functions: A dictionary mapping signature keys to concrete
functions containing signatures to add to the MetaGraph.
object_saver: A CheckpointableSaver to add to the MetaGraph.
Returns:
An _AssetInfo, which contains information to help creating the SavedModel.
"""
signatures = {}
# List objects from the eager context to make sure Optimizers give us the
# right Graph-dependent variables.
accessible_objects = util.list_objects(obj)
resource_initializer_functions = _trace_resource_initializers(
accessible_objects)
exported_graph = ops.Graph()
resource_initializer_ops = []
with exported_graph.as_default():
object_map, resource_map, asset_info = _map_resources(
accessible_objects)
for resource_initializer_function in resource_initializer_functions:
asset_dependencies = []
for capture in resource_initializer_function.graph.external_captures:
asset_initializer = asset_info.asset_initializers_by_resource.get(
capture, None)
if asset_initializer is not None:
asset_dependencies.append(asset_initializer)
with ops.control_dependencies(asset_dependencies):
resource_initializer_ops.append(
_call_function_with_mapped_captures(
resource_initializer_function, [], resource_map))
with ops.control_dependencies(resource_initializer_ops):
init_op = control_flow_ops.no_op()
# Add the same op to the main_op collection and to the init_op
# signature. The collection is for compatibility with older loader APIs;
# only one will be executed.
meta_graph_def.collection_def[
constants.MAIN_OP_KEY].node_list.value.append(init_op.name)
meta_graph_def.signature_def[constants.INIT_OP_SIGNATURE_KEY].CopyFrom(
signature_def_utils.op_signature_def(
init_op, constants.INIT_OP_SIGNATURE_KEY))
# Saving an object-based checkpoint again gathers variables. We need to do the
# gathering from the eager context so Optimizers save the right set of
# variables, but want any operations associated with the save/restore to be in
# the exported graph (thus the `to_graph` argument).
saver = object_saver.freeze(object_map=object_map, to_graph=exported_graph)
# We must resolve the concrete function to add to MetaGraph while in eager
# mode.
concrete_functions = []
for accessible_object in accessible_objects:
for function in function_serialization.list_all_polymorphic_functions(
accessible_object).values():
concrete_functions.extend(
function_serialization.list_all_concrete_functions(function))
with exported_graph.as_default():
signatures = _generate_signatures(signature_functions, resource_map)
for concrete_function in concrete_functions:
concrete_function.add_to_graph()
saver_def = saver.to_proto()
meta_graph_def.saver_def.CopyFrom(saver_def)
graph_def = exported_graph.as_graph_def(add_shapes=True)
# Clean reference cycles so repeated export()s don't make work for the garbage
# collector.
ops.dismantle_graph(exported_graph)
meta_graph_def.graph_def.CopyFrom(graph_def)
meta_graph_def.meta_info_def.tags.append(tag_constants.SERVING)
meta_graph_def.asset_file_def.extend(asset_info.asset_defs)
for signature_key, signature in signatures.items():
meta_graph_def.signature_def[signature_key].CopyFrom(signature)
meta_graph.strip_graph_default_valued_attrs(meta_graph_def)
return asset_info
def _fill_meta_graph_def(meta_graph_def, obj, signature_functions,
object_saver):
"""Generates a MetaGraph which calls `signature_functions`.
Args:
meta_graph_def: The MetaGraphDef proto to fill.
obj: The checkpointable object being exported.
signature_functions: A dictionary mapping signature keys to concrete
functions containing signatures to add to the MetaGraph.
object_saver: A CheckpointableSaver to add to the MetaGraph.
Returns:
asset_filename_map, a dictionary mapping from asset base names to
user-specified full asset paths, which should be copied to the SavedModel's
assets/ directory.
"""
signatures = {}
# List objects from the eager context to make sure Optimizers give us the
# right Graph-dependent variables.
accessible_objects = util.list_objects(obj)
resource_initializer_functions = _trace_resource_initializers(
accessible_objects)
exported_graph = ops.Graph()
resource_initializer_ops = []
with exported_graph.as_default():
object_map, resource_map, asset_info = _map_resources(accessible_objects)
for resource_initializer_function in resource_initializer_functions:
asset_dependencies = []
for capture in resource_initializer_function.graph.external_captures:
asset_initializer = asset_info.asset_initializers_by_resource.get(
capture, None)
if asset_initializer is not None:
asset_dependencies.append(asset_initializer)
with ops.control_dependencies(asset_dependencies):
resource_initializer_ops.append(
_call_function_with_mapped_captures(
resource_initializer_function, [], resource_map))
with ops.control_dependencies(resource_initializer_ops):
init_op = control_flow_ops.no_op()
# Add the same op to the main_op collection and to the init_op
# signature. The collection is for compatibility with older loader APIs;
# only one will be executed.
meta_graph_def.collection_def[constants.MAIN_OP_KEY].node_list.value.append(
init_op.name)
meta_graph_def.signature_def[constants.INIT_OP_SIGNATURE_KEY].CopyFrom(
signature_def_utils.op_signature_def(
init_op, constants.INIT_OP_SIGNATURE_KEY))
# Saving an object-based checkpoint again gathers variables. We need to do the
# gathering from the eager context so Optimizers save the right set of
# variables, but want any operations associated with the save/restore to be in
# the exported graph (thus the `to_graph` argument).
saver = object_saver.freeze(object_map=object_map, to_graph=exported_graph)
with exported_graph.as_default():
signatures = _generate_signatures(signature_functions, resource_map)
saver_def = saver.to_proto()
meta_graph_def.saver_def.CopyFrom(saver_def)
graph_def = exported_graph.as_graph_def(add_shapes=True)
# Clean reference cycles so repeated export()s don't make work for the garbage
# collector.
ops.dismantle_graph(exported_graph)
meta_graph_def.graph_def.CopyFrom(graph_def)
meta_graph_def.meta_info_def.tags.append(tag_constants.SERVING)
meta_graph_def.asset_file_def.extend(asset_info.asset_defs)
for signature_key, signature in signatures.items():
meta_graph_def.signature_def[signature_key].CopyFrom(signature)
meta_graph.strip_graph_default_valued_attrs(meta_graph_def)
return asset_info.asset_filename_map