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# Copyright 2016 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 MobileNet application."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.platform import test
class MobileNetTest(test.TestCase):
def test_with_top(self):
model = keras.applications.MobileNet(weights=None)
self.assertEqual(model.output_shape, (None, 1000))
def test_no_top(self):
model = keras.applications.MobileNet(weights=None, include_top=False)
self.assertEqual(model.output_shape, (None, None, None, 1024))
def test_with_pooling(self):
model = keras.applications.MobileNet(weights=None,
include_top=False,
pooling='avg')
self.assertEqual(model.output_shape, (None, 1024))
def test_weight_loading(self):
with self.assertRaises(ValueError):
keras.applications.MobileNet(weights='unknown',
include_top=False)
with self.assertRaises(ValueError):
keras.applications.MobileNet(weights='imagenet',
classes=2000)
def test_preprocess_input(self):
x = np.random.uniform(0, 255, (2, 300, 200, 3))
out1 = keras.applications.mobilenet.preprocess_input(x)
self.assertAllClose(np.mean(out1), 0., atol=0.1)
def test_mobilenet_variable_input_channels(self):
input_shape = (None, None, 1)
model = keras.applications.MobileNet(weights=None,
include_top=False,
input_shape=input_shape)
self.assertEqual(model.output_shape, (None, None, None, 1024))
input_shape = (None, None, 4)
model = keras.applications.MobileNet(weights=None,
include_top=False,
input_shape=input_shape)
self.assertEqual(model.output_shape, (None, None, None, 1024))
if __name__ == '__main__':
test.main()
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