def testNumberedPath(self): root = checkpointable.Checkpointable() leaf = checkpointable.Checkpointable() root.track_checkpointable(leaf) leaf.add_variable(name="v", shape=[]) named_variables, _ = checkpointable._serialize_object_graph(root) variable_name, = named_variables.keys() self.assertEqual(r"_0/v", variable_name)
def _get_checkpoint_name(self, name):
root = checkpointable.Checkpointable()
root.add_variable(name=name, shape=[1, 2], dtype=dtypes.float64)
named_variables, _ = checkpointable._serialize_object_graph(root)
checkpoint_name, = named_variables.keys()
with ops.name_scope("root/" + checkpoint_name):
pass # Make sure we can use this as an op name if we prefix it.
return checkpoint_name
def testNumberedPath(self): root = checkpointable.Checkpointable() leaf = checkpointable.Checkpointable() root.track_checkpointable(leaf, name="leaf") leaf.add_variable(name="v", shape=[]) named_variables, _ = checkpointable._serialize_object_graph(root) variable_name, = named_variables.keys() self.assertEqual(r"leaf/v", variable_name)
def _get_checkpoint_name(self, name):
root = checkpointable.Checkpointable()
root.add_variable(name=name, shape=[1, 2], dtype=dtypes.float64)
named_variables, _ = checkpointable._serialize_object_graph(root)
checkpoint_name, = named_variables.keys()
with ops.name_scope("root/" + checkpoint_name):
pass # Make sure we can use this as an op name if we prefix it.
return checkpoint_name
def testAddVariable(self):
obj = NonLayerCheckpointable()
with self.assertRaisesRegexp(ValueError, "do not specify shape"):
obj.add_variable(name="shape_specified_twice",
shape=[],
initializer=1)
constant_initializer = obj.add_variable(name="constant_initializer",
initializer=1)
with variable_scope.variable_scope("some_variable_scope"):
ones_initializer = obj.add_variable(
name="ones_initializer",
shape=[2],
initializer=init_ops.ones_initializer(dtype=dtypes.float32))
bare_initializer = obj.add_variable(
name="bare_initializer",
shape=[2, 2],
dtype=dtypes.float64,
initializer=init_ops.zeros_initializer)
# Even in graph mode, there are no naming conflicts between objects, only
# naming conflicts within an object.
other_duplicate = resource_variable_ops.ResourceVariable(
name="duplicate", initial_value=1.)
duplicate = obj.add_variable(name="duplicate", shape=[])
with self.assertRaisesRegexp(ValueError, "'duplicate' already exists"):
obj.add_variable(name="duplicate", shape=[])
if context.in_graph_mode():
self.evaluate(variables.global_variables_initializer())
self.assertEqual("constant_initializer:0", constant_initializer.name)
self.assertEqual(1, self.evaluate(constant_initializer))
self.assertEqual("some_variable_scope/ones_initializer:0",
ones_initializer.name)
self.assertAllEqual([1, 1], self.evaluate(ones_initializer))
self.assertAllEqual([[0., 0.], [0., 0.]],
self.evaluate(bare_initializer))
self.assertEqual("a_variable:0", obj.a_variable.name)
self.assertEqual("duplicate:0", other_duplicate.name)
if context.in_graph_mode():
# The .name attribute may be globally influenced, but the checkpoint name
# won't be (tested below).
self.assertEqual("duplicate_1:0", duplicate.name)
else:
# When executing eagerly, there's no uniquification of variable names. The
# checkpoint name will be the same.
self.assertEqual("duplicate:0", duplicate.name)
named_variables, _ = checkpointable._serialize_object_graph(obj)
expected_checkpoint_names = (
"a_variable",
"bare_initializer",
"constant_initializer",
"duplicate",
"ones_initializer",
)
six.assertCountEqual(self, expected_checkpoint_names,
named_variables.keys())
def testAddVariable(self):
obj = NonLayerCheckpointable()
with self.assertRaisesRegexp(ValueError, "do not specify shape"):
obj.add_variable(
name="shape_specified_twice", shape=[], initializer=1)
constant_initializer = obj.add_variable(
name="constant_initializer", initializer=1)
with variable_scope.variable_scope("some_variable_scope"):
ones_initializer = obj.add_variable(
name="ones_initializer",
shape=[2],
initializer=init_ops.ones_initializer(dtype=dtypes.float32))
bare_initializer = obj.add_variable(
name="bare_initializer",
shape=[2, 2],
dtype=dtypes.float64,
initializer=init_ops.zeros_initializer)
# Even in graph mode, there are no naming conflicts between objects, only
# naming conflicts within an object.
other_duplicate = resource_variable_ops.ResourceVariable(
name="duplicate", initial_value=1.)
duplicate = obj.add_variable(name="duplicate", shape=[])
with self.assertRaisesRegexp(ValueError, "'duplicate' already exists"):
obj.add_variable(name="duplicate", shape=[])
if context.in_graph_mode():
self.evaluate(variables.global_variables_initializer())
self.assertEqual("constant_initializer:0", constant_initializer.name)
self.assertEqual(1, self.evaluate(constant_initializer))
self.assertEqual("some_variable_scope/ones_initializer:0",
ones_initializer.name)
self.assertAllEqual([1, 1], self.evaluate(ones_initializer))
self.assertAllEqual([[0., 0.],
[0., 0.]], self.evaluate(bare_initializer))
self.assertEqual("a_variable:0", obj.a_variable.name)
self.assertEqual("duplicate:0", other_duplicate.name)
if context.in_graph_mode():
# The .name attribute may be globally influenced, but the checkpoint name
# won't be (tested below).
self.assertEqual("duplicate_1:0", duplicate.name)
else:
# When executing eagerly, there's no uniquification of variable names. The
# checkpoint name will be the same.
self.assertEqual("duplicate:0", duplicate.name)
named_variables, _ = checkpointable._serialize_object_graph(obj)
expected_checkpoint_names = (
"a_variable",
"bare_initializer",
"constant_initializer",
"duplicate",
"ones_initializer",
)
six.assertCountEqual(
self, expected_checkpoint_names, named_variables.keys())
def _get_checkpoint_name(self, name):
root = checkpointable.Checkpointable()
with variable_scope.variable_scope("get_checkpoint_name"):
# Create the variable in a variable scope so that we get more relaxed
# naming rules (variables outside a scope may not start with "_", "/" or
# "-"). Since we don't use the scope part of the name, these cases are
# somewhat annoying.
root.add_variable(name=name, shape=[1, 2], dtype=dtypes.float64)
named_variables, _ = checkpointable._serialize_object_graph(root)
checkpoint_name, = named_variables.keys()
with ops.name_scope("root/" + checkpoint_name):
pass # Make sure we can use this as an op name if we prefix it.
return checkpoint_name
def _get_checkpoint_name(self, name):
root = checkpointable.Checkpointable()
with variable_scope.variable_scope("get_checkpoint_name"):
# Create the variable in a variable scope so that we get more relaxed
# naming rules (variables outside a scope may not start with "_", "/" or
# "-"). Since we don't use the scope part of the name, these cases are
# somewhat annoying.
root.add_variable(name=name, shape=[1, 2], dtype=dtypes.float64)
named_variables, _ = checkpointable._serialize_object_graph(root)
checkpoint_name, = named_variables.keys()
with ops.name_scope("root/" + checkpoint_name):
pass # Make sure we can use this as an op name if we prefix it.
return checkpoint_name
def testNamingWithOptimizer(self):
input_value = constant_op.constant([[3.]])
network = MyNetwork()
# A nuisance Network using the same optimizer. Its slot variables should not
# go in the checkpoint, since it is never depended on.
other_network = MyNetwork()
optimizer = CheckpointableAdam(0.001)
root_checkpointable = Root(optimizer=optimizer, network=network)
if context.in_eager_mode():
optimizer.minimize(
lambda: network(input_value),
global_step=root_checkpointable.global_step)
optimizer.minimize(
lambda: other_network(input_value),
global_step=root_checkpointable.global_step)
else:
train_op = optimizer.minimize(
network(input_value), global_step=root_checkpointable.global_step)
optimizer.minimize(
other_network(input_value),
global_step=root_checkpointable.global_step)
self.evaluate(variables.global_variables_initializer())
self.evaluate(train_op)
named_variables, serialized_graph = checkpointable._serialize_object_graph(
root_checkpointable)
expected_checkpoint_names = (
# Created in the root node, so no prefix.
"global_step",
# No name provided to track_checkpointable(), so the position (1, after
# the named track_checkpointable() which is 0) is used instead.
"network/_1/kernel",
# track_checkpointable() with a name provided, so that's used
"network/named_dense/kernel",
"network/named_dense/bias",
# The optimizer creates two non-slot variables
"optimizer/beta1_power",
"optimizer/beta2_power",
# Slot variables
"network/_1/kernel/_OPTIMIZER_SLOT/optimizer/m",
"network/_1/kernel/_OPTIMIZER_SLOT/optimizer/v",
"network/named_dense/kernel/_OPTIMIZER_SLOT/optimizer/m",
"network/named_dense/kernel/_OPTIMIZER_SLOT/optimizer/v",
"network/named_dense/bias/_OPTIMIZER_SLOT/optimizer/m",
"network/named_dense/bias/_OPTIMIZER_SLOT/optimizer/v",
)
six.assertCountEqual(self, expected_checkpoint_names,
named_variables.keys())
# Check that we've mapped to the right variable objects (not exhaustive)
self.assertEqual("global_step:0", named_variables["global_step"].name)
self.assertEqual("my_network/checkpointable_dense_layer_1/kernel:0",
named_variables["network/_1/kernel"].name)
self.assertEqual("my_network/checkpointable_dense_layer/kernel:0",
named_variables["network/named_dense/kernel"].name)
self.assertEqual("beta1_power:0",
named_variables["optimizer/beta1_power"].name)
self.assertEqual("beta2_power:0",
named_variables["optimizer/beta2_power"].name)
# Spot check the generated protocol buffers.
self.assertEqual(0, serialized_graph.nodes[0].children[0].local_uid)
self.assertEqual("optimizer",
serialized_graph.nodes[0].children[0].local_name)
optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[
0].node_id]
self.assertEqual("beta1_power", optimizer_node.variables[0].local_name)
self.assertEqual("beta1_power", optimizer_node.variables[0].full_name)
self.assertEqual(
"kernel", optimizer_node.slot_variables[0].original_variable_local_name)
original_variable_owner = serialized_graph.nodes[
optimizer_node.slot_variables[0].original_variable_node_id]
self.assertEqual("kernel", original_variable_owner.variables[0].local_name)
self.assertEqual("m", optimizer_node.slot_variables[0].slot_name)
# We strip off the :0 suffix, as variable.name-based saving does.
self.assertEqual("my_network/checkpointable_dense_layer/kernel/Adam",
optimizer_node.slot_variables[0].full_name)
self.assertEqual("my_network/checkpointable_dense_layer/kernel/Adam:0",
optimizer.get_slot(
var=named_variables["network/named_dense/kernel"],
name="m").name)
def testNamingWithOptimizer(self):
input_value = constant_op.constant([[3.]])
network = MyNetwork()
# A nuisance Network using the same optimizer. Its slot variables should not
# go in the checkpoint, since it is never depended on.
other_network = MyNetwork()
optimizer = CheckpointableAdam(0.001)
root_checkpointable = Root(optimizer=optimizer, network=network)
if context.in_eager_mode():
optimizer.minimize(
lambda: network(input_value),
global_step=root_checkpointable.global_step)
optimizer.minimize(
lambda: other_network(input_value),
global_step=root_checkpointable.global_step)
else:
train_op = optimizer.minimize(
network(input_value), global_step=root_checkpointable.global_step)
optimizer.minimize(
other_network(input_value),
global_step=root_checkpointable.global_step)
self.evaluate(variables.global_variables_initializer())
self.evaluate(train_op)
named_variables, serialized_graph = checkpointable._serialize_object_graph(
root_checkpointable)
expected_checkpoint_names = (
# Created in the root node, so no prefix.
"global_step",
# No name provided to track_checkpointable(), so the position is used
# instead (one-based).
"network/via_track_layer/kernel",
# track_checkpointable() with a name provided, so that's used
"network/_named_dense/kernel",
"network/_named_dense/bias",
# non-Layer dependency of the network
"network/_non_layer/a_variable",
# The optimizer creates two non-slot variables
"_optimizer/beta1_power",
"_optimizer/beta2_power",
# Slot variables
"network/via_track_layer/kernel/-OPTIMIZER_SLOT/_optimizer/m",
"network/via_track_layer/kernel/-OPTIMIZER_SLOT/_optimizer/v",
"network/_named_dense/kernel/-OPTIMIZER_SLOT/_optimizer/m",
"network/_named_dense/kernel/-OPTIMIZER_SLOT/_optimizer/v",
"network/_named_dense/bias/-OPTIMIZER_SLOT/_optimizer/m",
"network/_named_dense/bias/-OPTIMIZER_SLOT/_optimizer/v",
)
six.assertCountEqual(self, expected_checkpoint_names,
named_variables.keys())
# Check that we've mapped to the right variable objects (not exhaustive)
self.assertEqual("global_step:0", named_variables["global_step"].name)
self.assertEqual("my_network/checkpointable_dense_layer_1/kernel:0",
named_variables["network/via_track_layer/kernel"].name)
self.assertEqual("my_network/checkpointable_dense_layer/kernel:0",
named_variables["network/_named_dense/kernel"].name)
self.assertEqual("beta1_power:0",
named_variables["_optimizer/beta1_power"].name)
self.assertEqual("beta2_power:0",
named_variables["_optimizer/beta2_power"].name)
# Spot check the generated protocol buffers.
self.assertEqual("_optimizer",
serialized_graph.nodes[0].children[0].local_name)
optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[
0].node_id]
self.assertEqual("beta1_power", optimizer_node.variables[0].local_name)
self.assertEqual("beta1_power", optimizer_node.variables[0].full_name)
# Variable ordering is arbitrary but deterministic (alphabetized)
self.assertEqual(
"bias", optimizer_node.slot_variables[0].original_variable_local_name)
original_variable_owner = serialized_graph.nodes[
optimizer_node.slot_variables[0].original_variable_node_id]
self.assertEqual("network/_named_dense/bias",
original_variable_owner.variables[0].checkpoint_key)
self.assertEqual("bias", original_variable_owner.variables[0].local_name)
self.assertEqual("m", optimizer_node.slot_variables[0].slot_name)
self.assertEqual("network/_named_dense/bias/-OPTIMIZER_SLOT/_optimizer/m",
optimizer_node.slot_variables[0].checkpoint_key)
# We strip off the :0 suffix, as variable.name-based saving does.
self.assertEqual("my_network/checkpointable_dense_layer/bias/Adam",
optimizer_node.slot_variables[0].full_name)
self.assertEqual("my_network/checkpointable_dense_layer/bias/Adam:0",
optimizer.get_slot(
var=named_variables["network/_named_dense/bias"],
name="m").name)
