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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 Inception V3 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.keras.applications.imagenet_utils import preprocess_input
from tensorflow.python.platform import test
class ImageNetUtilsTest(test.TestCase):
def test_preprocess_input(self):
# Test batch of images
x = np.random.uniform(0, 255, (2, 10, 10, 3))
self.assertEqual(preprocess_input(x).shape, x.shape)
out1 = preprocess_input(x, 'channels_last')
out2 = preprocess_input(np.transpose(x, (0, 3, 1, 2)), 'channels_first')
self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
# Test single image
x = np.random.uniform(0, 255, (10, 10, 3))
self.assertEqual(preprocess_input(x).shape, x.shape)
out1 = preprocess_input(x, 'channels_last')
out2 = preprocess_input(np.transpose(x, (2, 0, 1)), 'channels_first')
self.assertAllClose(out1, out2.transpose(1, 2, 0))
def test_preprocess_input_symbolic(self):
# Test image batch
x = np.random.uniform(0, 255, (2, 10, 10, 3))
inputs = keras.layers.Input(shape=x.shape[1:])
outputs = keras.layers.Lambda(
preprocess_input, output_shape=x.shape[1:])(inputs)
model = keras.models.Model(inputs, outputs)
assert model.predict(x).shape == x.shape
# pylint: disable=g-long-lambda
outputs1 = keras.layers.Lambda(lambda x:
preprocess_input(x, 'channels_last'),
output_shape=x.shape[1:])(inputs)
model1 = keras.models.Model(inputs, outputs1)
out1 = model1.predict(x)
x2 = np.transpose(x, (0, 3, 1, 2))
inputs2 = keras.layers.Input(shape=x2.shape[1:])
# pylint: disable=g-long-lambda
outputs2 = keras.layers.Lambda(lambda x:
preprocess_input(x, 'channels_first'),
output_shape=x2.shape[1:])(inputs2)
model2 = keras.models.Model(inputs2, outputs2)
out2 = model2.predict(x2)
self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
# Test single image
x = np.random.uniform(0, 255, (10, 10, 3))
inputs = keras.layers.Input(shape=x.shape)
outputs = keras.layers.Lambda(preprocess_input,
output_shape=x.shape)(inputs)
model = keras.models.Model(inputs, outputs)
assert model.predict(x[np.newaxis])[0].shape == x.shape
# pylint: disable=g-long-lambda
outputs1 = keras.layers.Lambda(lambda x:
preprocess_input(x, 'channels_last'),
output_shape=x.shape)(inputs)
model1 = keras.models.Model(inputs, outputs1)
out1 = model1.predict(x[np.newaxis])[0]
x2 = np.transpose(x, (2, 0, 1))
inputs2 = keras.layers.Input(shape=x2.shape)
outputs2 = keras.layers.Lambda(lambda x:
preprocess_input(x, 'channels_first'),
output_shape=x2.shape)(inputs2) # pylint: disable=g-long-lambda
model2 = keras.models.Model(inputs2, outputs2)
out2 = model2.predict(x2[np.newaxis])[0]
self.assertAllClose(out1, out2.transpose(1, 2, 0))
if __name__ == '__main__':
test.main()
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