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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for type_info module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.autograph.pyct import anno
from tensorflow.python.autograph.pyct import cfg
from tensorflow.python.autograph.pyct import parser
from tensorflow.python.autograph.pyct import qual_names
from tensorflow.python.autograph.pyct import transformer
from tensorflow.python.autograph.pyct.static_analysis import activity
from tensorflow.python.autograph.pyct.static_analysis import live_values
from tensorflow.python.autograph.pyct.static_analysis import reaching_definitions
from tensorflow.python.autograph.pyct.static_analysis import type_info
from tensorflow.python.client import session
from tensorflow.python.platform import test
from tensorflow.python.training import training
class ScopeTest(test.TestCase):
def test_basic(self):
scope = type_info.Scope(None)
self.assertFalse(scope.hasval('foo'))
scope.setval('foo', 'bar')
self.assertTrue(scope.hasval('foo'))
self.assertFalse(scope.hasval('baz'))
def test_nesting(self):
scope = type_info.Scope(None)
scope.setval('foo', '')
child = type_info.Scope(scope)
self.assertTrue(child.hasval('foo'))
self.assertTrue(scope.hasval('foo'))
child.setval('bar', '')
self.assertTrue(child.hasval('bar'))
self.assertFalse(scope.hasval('bar'))
class TypeInfoResolverTest(test.TestCase):
def _parse_and_analyze(self,
test_fn,
namespace,
arg_types=None):
node, source = parser.parse_entity(test_fn)
entity_info = transformer.EntityInfo(
source_code=source,
source_file=None,
namespace=namespace,
arg_values=None,
arg_types=arg_types,
owner_type=None)
node = qual_names.resolve(node)
graphs = cfg.build(node)
node = activity.resolve(node, entity_info)
node = reaching_definitions.resolve(node, entity_info, graphs,
reaching_definitions.Definition)
node = live_values.resolve(node, entity_info, {})
node = type_info.resolve(node, entity_info)
node = live_values.resolve(node, entity_info, {})
return node
def test_constructor_detection(self):
def test_fn():
opt = training.GradientDescentOptimizer(0.1)
return opt
node = self._parse_and_analyze(test_fn, {'training': training})
call_node = node.body[0].body[0].value
self.assertEquals(training.GradientDescentOptimizer,
anno.getanno(call_node, 'type'))
self.assertEquals((training.__name__, 'GradientDescentOptimizer'),
anno.getanno(call_node, 'type_fqn'))
def test_class_members_of_detected_constructor(self):
def test_fn():
opt = training.GradientDescentOptimizer(0.1)
opt.minimize(0)
node = self._parse_and_analyze(test_fn, {'training': training})
method_call = node.body[0].body[1].value.func
self.assertEquals(training.GradientDescentOptimizer.minimize,
anno.getanno(method_call, 'live_val'))
def test_class_members_in_with_stmt(self):
def test_fn(x):
with session.Session() as sess:
sess.run(x)
node = self._parse_and_analyze(test_fn, {'session': session})
constructor_call = node.body[0].body[0].items[0].context_expr
self.assertEquals(session.Session, anno.getanno(constructor_call, 'type'))
self.assertEquals((session.__name__, 'Session'),
anno.getanno(constructor_call, 'type_fqn'))
method_call = node.body[0].body[0].body[0].value.func
self.assertEquals(session.Session.run, anno.getanno(method_call,
'live_val'))
def test_constructor_data_dependent(self):
def test_fn(x):
if x > 0:
opt = training.GradientDescentOptimizer(0.1)
else:
opt = training.GradientDescentOptimizer(0.01)
opt.minimize(0)
node = self._parse_and_analyze(test_fn, {'training': training})
method_call = node.body[0].body[1].value.func
self.assertFalse(anno.hasanno(method_call, 'live_val'))
def test_parameter_class_members(self):
def test_fn(opt):
opt.minimize(0)
node = self._parse_and_analyze(test_fn, {})
method_call = node.body[0].body[0].value.func
self.assertFalse(anno.hasanno(method_call, 'live_val'))
def test_parameter_class_members_with_value_hints(self):
def test_fn(opt):
opt.minimize(0)
node = self._parse_and_analyze(
test_fn, {},
arg_types={
'opt': (training.GradientDescentOptimizer.__name__,
training.GradientDescentOptimizer)
})
method_call = node.body[0].body[0].value.func
self.assertEquals(training.GradientDescentOptimizer.minimize,
anno.getanno(method_call, 'live_val'))
def test_function_variables(self):
def bar():
pass
def test_fn():
foo = bar
foo()
node = self._parse_and_analyze(test_fn, {'bar': bar})
method_call = node.body[0].body[1].value.func
self.assertFalse(anno.hasanno(method_call, 'live_val'))
def test_nested_members(self):
def test_fn():
foo = training.GradientDescentOptimizer(0.1)
foo.bar.baz()
node = self._parse_and_analyze(test_fn, {'training': training})
method_call = node.body[0].body[1].value.func
self.assertFalse(anno.hasanno(method_call, 'live_val'))
def test_nested_unpacking(self):
class Foo(object):
pass
class Bar(object):
pass
def test_fn():
a, (b, c) = (Foo(), (Bar(), Foo()))
return a, b, c
node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'Bar': Bar})
a, b, c = node.body[0].body[1].value.elts
self.assertEquals(anno.getanno(a, 'type'), Foo)
self.assertEquals(anno.getanno(b, 'type'), Bar)
self.assertEquals(anno.getanno(c, 'type'), Foo)
self.assertFalse(anno.hasanno(a, 'live_val'))
self.assertFalse(anno.hasanno(b, 'live_val'))
self.assertFalse(anno.hasanno(c, 'live_val'))
if __name__ == '__main__':
test.main()
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