def nested_template():
nested1 = template.make_template_internal(
"nested",
variable_scoped_function_no_return_value,
create_graph_function_=True)
nested2 = template.make_template_internal(
"nested",
variable_scoped_function_no_return_value,
create_graph_function_=True)
nested1()
nested2()
v1 = nested1.variables
v2 = nested2.variables
# nested1 and nested2 should not share variables
self.assertNotEqual(v1, v2)
# Variables created by nested1 should be isolated from variables
# created by nested2.
self.assertEqual(nested1.variables, v1)
self.assertEqual(nested2.variables, v2)
self.assertEqual(nested1.trainable_variables, v1)
self.assertEqual(nested2.trainable_variables, v2)
self.assertEqual(len(nested1.non_trainable_variables), 0)
self.assertEqual(len(nested2.non_trainable_variables), 0)
def test_make_template_with_defun(self):
def variable_scoped_function_no_return_value(scope_name):
# defun cannot compile functions that return non-Tensor objects
with variable_scope.variable_scope(scope_name):
_ = variable_scope.get_variable(
"dummy",
shape=[1],
initializer=init_ops.zeros_initializer())
tmpl = template.make_template_internal(
"s1",
variable_scoped_function_no_return_value,
create_graph_function_=True,
scope_name="test")
# The first invocation of tmpl1 creates variables, the second should
# be executed as a graph function.
tmpl()
v1 = tmpl.variables
tmpl()
v2 = tmpl.variables
self.assertEqual(len(v1), len(v2))
for v, w in zip(v1, v2):
self.assertIs(v, w)
self.assertEqual("s1/test/dummy:0", v1[0].name)
def test_graph_function_no_name(self):
with context.eager_mode():
def f(_, y):
return y + 1
partial = functools.partial(f, 1.0)
tmpl = template.make_template_internal(
"a", partial, create_graph_function_=True)
self.assertAllEqual(tmpl(ops.convert_to_tensor(1.0)), 2.0)
def nested_template():
nested1 = template.make_template_internal(
"nested",
variable_scoped_function_no_return_value,
create_graph_function_=True)
nested2 = template.make_template_internal(
"nested",
variable_scoped_function_no_return_value,
create_graph_function_=True)
nested1()
nested2()
v1 = nested1.variables
v2 = nested2.variables
self.assertEqual(len(v1), 1)
self.assertEqual(len(v2), 1)
# nested1 and nested2 should not share variables
self.assertIsNot(v1[0], v2[0])
self.assertIs(nested1.trainable_variables[0], v1[0])
self.assertIs(nested2.trainable_variables[0], v2[0])
self.assertEqual(len(nested1.non_trainable_variables), 0)
self.assertEqual(len(nested2.non_trainable_variables), 0)
def test_make_template_with_defun(self):
def variable_scoped_function_no_return_value(scope_name):
# defun cannot compile functions that return non-Tensor objects
with variable_scope.variable_scope(scope_name):
_ = variable_scope.get_variable(
"dummy", shape=[1], initializer=init_ops.zeros_initializer())
tmpl = template.make_template_internal(
"s1",
variable_scoped_function_no_return_value,
create_graph_function_=True,
scope_name="test")
# The first invocation of tmpl1 creates variables, the second should
# be executed as a graph function.
tmpl()
v1 = tmpl.variables
tmpl()
v2 = tmpl.variables
self.assertSequenceEqual(v1, v2)
self.assertEqual("s1/test/dummy:0", v1[0].name)
