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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 model_coverage_lib.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
from tensorflow.contrib.lite.python import lite
from tensorflow.contrib.lite.testing.model_coverage import model_coverage_lib as model_coverage
from tensorflow.python import keras
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
from tensorflow.python.saved_model import saved_model
from tensorflow.python.training.training_util import write_graph
class EvaluateFrozenGraph(test.TestCase):
def _saveFrozenGraph(self, sess):
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
write_graph(sess.graph_def, '', graph_def_file, False)
return graph_def_file
def testFloat(self):
with session.Session().as_default() as sess:
in_tensor = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32)
_ = in_tensor + in_tensor
filename = self._saveFrozenGraph(sess)
model_coverage.test_frozen_graph(filename, ['Placeholder'], ['add'])
def testMultipleOutputs(self):
with session.Session().as_default() as sess:
in_tensor_1 = array_ops.placeholder(
shape=[1, 16], dtype=dtypes.float32, name='inputA')
in_tensor_2 = array_ops.placeholder(
shape=[1, 16], dtype=dtypes.float32, name='inputB')
weight = constant_op.constant(-1.0, shape=[16, 16])
bias = constant_op.constant(-1.0, shape=[16])
layer = math_ops.matmul(in_tensor_1, weight) + bias
_ = math_ops.reduce_mean(math_ops.square(layer - in_tensor_2))
filename = self._saveFrozenGraph(sess)
model_coverage.test_frozen_graph(filename, ['inputA', 'inputB'],
['add', 'Mean'])
class EvaluateSavedModel(test.TestCase):
def testFloat(self):
saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
with session.Session().as_default() as sess:
in_tensor_1 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
in_tensor_2 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
out_tensor = in_tensor_1 + in_tensor_2
inputs = {'x': in_tensor_1, 'y': in_tensor_2}
outputs = {'z': out_tensor}
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
model_coverage.test_saved_model(saved_model_dir)
class EvaluateKerasModel(test.TestCase):
def _getSingleInputKerasModel(self):
"""Returns single input Sequential tf.keras model."""
keras.backend.clear_session()
xs = [-1, 0, 1, 2, 3, 4]
ys = [-3, -1, 1, 3, 5, 7]
model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.train_on_batch(xs, ys)
return model
def _saveKerasModel(self, model):
try:
fd, keras_file = tempfile.mkstemp('.h5')
keras.models.save_model(model, keras_file)
finally:
os.close(fd)
return keras_file
def testFloat(self):
model = self._getSingleInputKerasModel()
keras_file = self._saveKerasModel(model)
model_coverage.test_keras_model(keras_file)
def testPostTrainingQuantize(self):
model = self._getSingleInputKerasModel()
keras_file = self._saveKerasModel(model)
model_coverage.test_keras_model(keras_file, post_training_quantize=True)
def testConverterMode(self):
model = self._getSingleInputKerasModel()
keras_file = self._saveKerasModel(model)
model_coverage.test_keras_model(
keras_file, converter_mode=lite.ConverterMode.TOCO_FLEX)
if __name__ == '__main__':
test.main()
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