def test_one_channel(self):
mnist = input_data.read_data_sets("tmp/MNIST_data/")
x = np.reshape(mnist.test.images[0:2], (-1, 28, 28, 1))
preprocess = JpegCompression()
compressed_x = preprocess(x, quality=70)
self.assertTrue((compressed_x.shape == x.shape))
self.assertTrue((compressed_x <= 1.0).all())
self.assertTrue((compressed_x >= 0.0).all())
def test_one_channel(self):
(x_train, _), (_, _), _, _ = load_mnist()
x_train = x_train[:2]
preprocess = JpegCompression()
compressed_x, _ = preprocess(x_train, quality=70)
self.assertTrue((compressed_x.shape == x_train.shape))
self.assertTrue((compressed_x <= 1.0).all())
self.assertTrue((compressed_x >= 0.0).all())
def test_three_channels(self):
(train_features, _), (_, _) = cifar10.load_data()
x = train_features[:2] / 255.0
preprocess = JpegCompression()
compressed_x = preprocess(x, quality=80)
self.assertTrue((compressed_x.shape == x.shape))
self.assertTrue((compressed_x <= 1.0).all())
self.assertTrue((compressed_x >= 0.0).all())
def test_channel_index(self):
(train_features, _), (_, _) = cifar10.load_data()
x = train_features[:2] / 255.0
x = np.swapaxes(x, 1, 3)
preprocess = JpegCompression(channel_index=1)
compressed_x = preprocess(x, quality=80)
self.assertTrue((compressed_x.shape == x.shape))
self.assertTrue((compressed_x <= 1.0).all())
self.assertTrue((compressed_x >= 0.0).all())
def test_failure_feature_vectors(self):
x = np.random.rand(10, 3)
preprocess = JpegCompression(channel_index=1, quality=80)
# Assert that value error is raised for feature vectors
with self.assertRaises(ValueError) as context:
preprocess(x)
self.assertTrue('Feature vectors detected.' in str(context.exception))