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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.
# =============================================================================
"""Table-driven test for encode_proto op.
This test is run once with each of the *.TestCase.pbtxt files
in the test directory.
It tests that encode_proto is a lossless inverse of decode_proto
(for the specified fields).
"""
# Python3 readiness boilerplate
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from google.protobuf import text_format
from tensorflow.contrib.proto.python.kernel_tests import test_case
from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2
from tensorflow.contrib.proto.python.ops import decode_proto_op
from tensorflow.contrib.proto.python.ops import encode_proto_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import flags
from tensorflow.python.platform import test
FLAGS = flags.FLAGS
flags.DEFINE_string('message_text_file', None,
'A file containing a text serialized TestCase protobuf.')
class EncodeProtoOpTest(test_case.ProtoOpTestCase):
def testBadInputs(self):
# Invalid field name
with self.test_session():
with self.assertRaisesOpError('Unknown field: non_existent_field'):
encode_proto_op.encode_proto(
sizes=[[1]],
values=[np.array([[0.0]], dtype=np.int32)],
message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue',
field_names=['non_existent_field']).eval()
# Incorrect types.
with self.test_session():
with self.assertRaisesOpError(
'Incompatible type for field double_value.'):
encode_proto_op.encode_proto(
sizes=[[1]],
values=[np.array([[0.0]], dtype=np.int32)],
message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue',
field_names=['double_value']).eval()
# Incorrect shapes of sizes.
with self.test_session():
with self.assertRaisesOpError(
r'sizes should be batch_size \+ \[len\(field_names\)\]'):
sizes = array_ops.placeholder(dtypes.int32)
values = array_ops.placeholder(dtypes.float64)
encode_proto_op.encode_proto(
sizes=sizes,
values=[values],
message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue',
field_names=['double_value']).eval(feed_dict={
sizes: [[[0, 0]]],
values: [[0.0]]
})
# Inconsistent shapes of values.
with self.test_session():
with self.assertRaisesOpError(
'Values must match up to the last dimension'):
sizes = array_ops.placeholder(dtypes.int32)
values1 = array_ops.placeholder(dtypes.float64)
values2 = array_ops.placeholder(dtypes.int32)
(encode_proto_op.encode_proto(
sizes=[[1, 1]],
values=[values1, values2],
message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue',
field_names=['double_value', 'int32_value']).eval(feed_dict={
values1: [[0.0]],
values2: [[0], [0]]
}))
def _testRoundtrip(self, in_bufs, message_type, fields):
field_names = [f.name for f in fields]
out_types = [f.dtype for f in fields]
with self.test_session() as sess:
sizes, field_tensors = decode_proto_op.decode_proto(
in_bufs,
message_type=message_type,
field_names=field_names,
output_types=out_types)
out_tensors = encode_proto_op.encode_proto(
sizes,
field_tensors,
message_type=message_type,
field_names=field_names)
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):
in_obj = test_example_pb2.RepeatedPrimitiveValue()
in_obj.ParseFromString(in_buf)
out_obj = test_example_pb2.RepeatedPrimitiveValue()
out_obj.ParseFromString(out_buf)
# Check that the deserialized objects are identical.
self.assertEqual(in_obj, out_obj)
# 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 testRoundtrip(self):
with open(FLAGS.message_text_file, 'r') as fp:
case = text_format.Parse(fp.read(), test_example_pb2.TestCase())
in_bufs = [primitive.SerializeToString() for primitive in case.primitive]
# np.array silently truncates strings if you don't specify dtype=object.
in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shape))
return self._testRoundtrip(
in_bufs, 'tensorflow.contrib.proto.RepeatedPrimitiveValue', case.field)
def testRoundtripPacked(self):
with open(FLAGS.message_text_file, 'r') as fp:
case = text_format.Parse(fp.read(), test_example_pb2.TestCase())
# Now try with the packed serialization.
# We test the packed representations by loading the same test cases
# using PackedPrimitiveValue instead of RepeatedPrimitiveValue.
# To do this we rely on the text format being the same for packed and
# unpacked fields, and reparse the test message using the packed version
# of the proto.
in_bufs = [
# Note: float_format='.17g' is necessary to ensure preservation of
# doubles and floats in text format.
text_format.Parse(
text_format.MessageToString(
primitive, float_format='.17g'),
test_example_pb2.PackedPrimitiveValue()).SerializeToString()
for primitive in case.primitive
]
# np.array silently truncates strings if you don't specify dtype=object.
in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shape))
return self._testRoundtrip(
in_bufs, 'tensorflow.contrib.proto.PackedPrimitiveValue', case.field)
if __name__ == '__main__':
test.main()
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