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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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
import tensorflow as tf
from tensorflow.contrib.lite.toco import model_flags_pb2
from tensorflow.contrib.lite.toco import toco_flags_pb2
from tensorflow.contrib.lite.toco import types_pb2
from tensorflow.python.platform import googletest
from tensorflow.python.platform import resource_loader
def TensorName(x):
"""Get the canonical (non foo:0 name)."""
return x.name.split(":")[0]
class TocoFromProtosTest(googletest.TestCase):
def _run(self, sess, in_tensor, out_tensor, should_succeed):
"""Use toco binary to check conversion from graphdef to tflite.
Args:
sess: Active TensorFlow session containing graph.
in_tensor: TensorFlow tensor to use as input.
out_tensor: TensorFlow tensor to use as output.
should_succeed: Whether this is a valid conversion.
"""
# Build all protos and extract graphdef
graph_def = sess.graph_def
toco_flags = toco_flags_pb2.TocoFlags()
toco_flags.input_format = toco_flags_pb2.TENSORFLOW_GRAPHDEF
toco_flags.output_format = toco_flags_pb2.TFLITE
toco_flags.inference_input_type = types_pb2.FLOAT
toco_flags.inference_type = types_pb2.FLOAT
toco_flags.allow_custom_ops = True
model_flags = model_flags_pb2.ModelFlags()
input_array = model_flags.input_arrays.add()
input_array.name = TensorName(in_tensor)
input_array.shape.dims.extend(map(int, in_tensor.get_shape()))
model_flags.output_arrays.append(TensorName(out_tensor))
# Shell out to run toco (in case it crashes)
with tempfile.NamedTemporaryFile() as fp_toco, \
tempfile.NamedTemporaryFile() as fp_model, \
tempfile.NamedTemporaryFile() as fp_input, \
tempfile.NamedTemporaryFile() as fp_output:
fp_model.write(model_flags.SerializeToString())
fp_toco.write(toco_flags.SerializeToString())
fp_input.write(graph_def.SerializeToString())
fp_model.flush()
fp_toco.flush()
fp_input.flush()
tflite_bin = resource_loader.get_path_to_datafile("toco_from_protos")
cmdline = " ".join([
tflite_bin, fp_model.name, fp_toco.name, fp_input.name, fp_output.name
])
exitcode = os.system(cmdline)
if exitcode == 0:
stuff = fp_output.read()
self.assertEqual(stuff is not None, should_succeed)
else:
self.assertFalse(should_succeed)
def test_toco(self):
"""Run a couple of TensorFlow graphs against TOCO through the python bin."""
with tf.Session() as sess:
img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
out = tf.identity(val, name="out")
out2 = tf.sin(val, name="out2")
# This is a valid mdoel
self._run(sess, img, out, True)
# This uses an invalid function.
# TODO(aselle): Check to make sure a warning is included.
self._run(sess, img, out2, True)
# This is an identity graph, which doesn't work
self._run(sess, img, img, False)
if __name__ == "__main__":
googletest.main()
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