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# =============================================================================
# Copyright 2018 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 proto ops reading descriptors from other sources."""
# Python3 preparedness imports.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from google.protobuf.descriptor_pb2 import FieldDescriptorProto
from google.protobuf.descriptor_pb2 import FileDescriptorSet
from tensorflow.contrib.proto.python.kernel_tests import proto_op_test_base as test_base
from tensorflow.python.framework import dtypes
from tensorflow.python.platform import test
class DescriptorSourceTestBase(test.TestCase):
"""Base class for testing descriptor sources."""
def __init__(self, decode_module, encode_module, methodName='runTest'): # pylint: disable=invalid-name
"""DescriptorSourceTestBase initializer.
Args:
decode_module: a module containing the `decode_proto_op` method
encode_module: a module containing the `encode_proto_op` method
methodName: the name of the test method (same as for test.TestCase)
"""
super(DescriptorSourceTestBase, self).__init__(methodName)
self._decode_module = decode_module
self._encode_module = encode_module
# NOTE: We generate the descriptor programmatically instead of via a compiler
# because of differences between different versions of the compiler.
#
# The generated descriptor should capture the subset of `test_example.proto`
# used in `test_base.simple_test_case()`.
def _createDescriptorFile(self):
set_proto = FileDescriptorSet()
file_proto = set_proto.file.add(
name='types.proto',
package='tensorflow',
syntax='proto3')
enum_proto = file_proto.enum_type.add(name='DataType')
enum_proto.value.add(name='DT_DOUBLE', number=0)
enum_proto.value.add(name='DT_BOOL', number=1)
file_proto = set_proto.file.add(
name='test_example.proto',
package='tensorflow.contrib.proto',
dependency=['types.proto'])
message_proto = file_proto.message_type.add(name='TestCase')
message_proto.field.add(
name='values',
number=1,
type=FieldDescriptorProto.TYPE_MESSAGE,
type_name='.tensorflow.contrib.proto.TestValue',
label=FieldDescriptorProto.LABEL_REPEATED)
message_proto.field.add(
name='shapes',
number=2,
type=FieldDescriptorProto.TYPE_INT32,
label=FieldDescriptorProto.LABEL_REPEATED)
message_proto.field.add(
name='sizes',
number=3,
type=FieldDescriptorProto.TYPE_INT32,
label=FieldDescriptorProto.LABEL_REPEATED)
message_proto.field.add(
name='fields',
number=4,
type=FieldDescriptorProto.TYPE_MESSAGE,
type_name='.tensorflow.contrib.proto.FieldSpec',
label=FieldDescriptorProto.LABEL_REPEATED)
message_proto = file_proto.message_type.add(
name='TestValue')
message_proto.field.add(
name='double_value',
number=1,
type=FieldDescriptorProto.TYPE_DOUBLE,
label=FieldDescriptorProto.LABEL_REPEATED)
message_proto.field.add(
name='bool_value',
number=2,
type=FieldDescriptorProto.TYPE_BOOL,
label=FieldDescriptorProto.LABEL_REPEATED)
message_proto = file_proto.message_type.add(
name='FieldSpec')
message_proto.field.add(
name='name',
number=1,
type=FieldDescriptorProto.TYPE_STRING,
label=FieldDescriptorProto.LABEL_OPTIONAL)
message_proto.field.add(
name='dtype',
number=2,
type=FieldDescriptorProto.TYPE_ENUM,
type_name='.tensorflow.DataType',
label=FieldDescriptorProto.LABEL_OPTIONAL)
message_proto.field.add(
name='value',
number=3,
type=FieldDescriptorProto.TYPE_MESSAGE,
type_name='.tensorflow.contrib.proto.TestValue',
label=FieldDescriptorProto.LABEL_OPTIONAL)
fn = os.path.join(self.get_temp_dir(), 'descriptor.pb')
with open(fn, 'wb') as f:
f.write(set_proto.SerializeToString())
return fn
def _testRoundtrip(self, descriptor_source):
# Numpy silently truncates the strings if you don't specify dtype=object.
in_bufs = np.array(
[test_base.ProtoOpTestBase.simple_test_case().SerializeToString()],
dtype=object)
message_type = 'tensorflow.contrib.proto.TestCase'
field_names = ['values', 'shapes', 'sizes', 'fields']
tensor_types = [dtypes.string, dtypes.int32, dtypes.int32, dtypes.string]
with self.test_session() as sess:
sizes, field_tensors = self._decode_module.decode_proto(
in_bufs,
message_type=message_type,
field_names=field_names,
output_types=tensor_types,
descriptor_source=descriptor_source)
out_tensors = self._encode_module.encode_proto(
sizes,
field_tensors,
message_type=message_type,
field_names=field_names,
descriptor_source=descriptor_source)
out_bufs, = sess.run([out_tensors])
# Check that the re-encoded tensor has the same shape.
self.assertEqual(in_bufs.shape, out_bufs.shape)
# Compare the input and output.
for in_buf, out_buf in zip(in_bufs.flat, out_bufs.flat):
# Check that the input and output serialized messages are identical.
# If we fail here, there is a difference in the serialized
# representation but the new serialization still parses. This could
# be harmless (a change in map ordering?) or it could be bad (e.g.
# loss of packing in the encoding).
self.assertEqual(in_buf, out_buf)
def testWithFileDescriptorSet(self):
# First try parsing with a local proto db, which should fail.
with self.assertRaisesOpError('No descriptor found for message type'):
self._testRoundtrip('local://')
# Now try parsing with a FileDescriptorSet which contains the test proto.
descriptor_file = self._createDescriptorFile()
self._testRoundtrip(descriptor_file)
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