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Fix test cases #202

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Apr 23, 2019
Merged

Fix test cases #202

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58 changes: 21 additions & 37 deletions tensorflow_addons/image/filters_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,72 +22,56 @@
from tensorflow_addons.utils import test_utils


@test_utils.run_all_in_graph_and_eager_modes
class MedianFilter2dTest(tf.test.TestCase):
def _validate_median_filter2d(self,
inputs,
expected_values,
filter_shape=(3, 3)):
output = median_filter2d(inputs, filter_shape)
self.assertAllClose(output, expected_values)

values_op = median_filter2d(inputs)
with self.test_session(use_gpu=False) as sess:
if tf.executing_eagerly():
expected_values = expected_values.numpy()
values = values_op.numpy()
else:
expected_values = expected_values.eval()
values = values_op.eval()
self.assertShapeEqual(values, inputs)
self.assertShapeEqual(expected_values, values_op)
self.assertAllClose(expected_values, values)

@test_utils.run_in_graph_and_eager_modes
def test_filter_tuple(self):
tf_img = tf.zeros([3, 4, 3], tf.int32)

with self.assertRaisesRegexp(TypeError,
'Filter shape must be a tuple'):
median_filter2d(tf_img, 3)
median_filter2d(tf_img, 3.5)
median_filter2d(tf_img, 'dt')
median_filter2d(tf_img, None)
for filter_shape in [3, 3.5, 'dt', None]:
with self.assertRaisesRegexp(TypeError,
'Filter shape must be a tuple'):
median_filter2d(tf_img, filter_shape)

filter_shape = (3, 3, 3)
msg = 'Filter shape must be a tuple of 2 integers. ' \
'Got %s values in tuple' % len(filter_shape)
msg = ('Filter shape must be a tuple of 2 integers. '
'Got %s values in tuple' % len(filter_shape))
with self.assertRaisesRegexp(ValueError, msg):
median_filter2d(tf_img, filter_shape)

with self.assertRaisesRegexp(TypeError,
'Size of the filter must be Integers'):
median_filter2d(tf_img, (3.5, 3))
median_filter2d(tf_img, (None, 3))
msg = 'Size of the filter must be Integers'
for filter_shape in [(3.5, 3), (None, 3)]:
with self.assertRaisesRegexp(TypeError, msg):
median_filter2d(tf_img, filter_shape)

@test_utils.run_in_graph_and_eager_modes
def test_filter_value(self):
tf_img = tf.zeros([3, 4, 3], tf.int32)

with self.assertRaises(ValueError):
median_filter2d(tf_img, (4, 3))

@test_utils.run_deprecated_v1
def test_dimension(self):
tf.compat.v1.disable_eager_execution()
tf_img = tf.compat.v1.placeholder(tf.int32, shape=[3, 4, None])
tf_img1 = tf.compat.v1.placeholder(tf.int32, shape=[3, None, 4])
tf_img2 = tf.compat.v1.placeholder(tf.int32, shape=[None, 3, 4])

with self.assertRaises(TypeError):
median_filter2d(tf_img)
median_filter2d(tf_img1)
median_filter2d(tf_img2)
for image_shape in [(3, 4, None), (3, None, 4), (None, 3, 4)]:
with self.assertRaises(TypeError):
tf_img = tf.compat.v1.placeholder(tf.int32, shape=image_shape)
median_filter2d(tf_img)

@test_utils.run_in_graph_and_eager_modes
def test_image_vs_filter(self):
tf_img = tf.zeros([3, 4, 3], tf.int32)
m = tf_img.shape[0]
no = tf_img.shape[1]
ch = tf_img.shape[2]
filter_shape = (3, 5)
with self.assertRaises(ValueError):
median_filter2d(tf_img, filter_shape)

@test_utils.run_in_graph_and_eager_modes
def test_three_channels(self):
tf_img = [[[0.32801723, 0.08863795, 0.79119259],
[0.35526001, 0.79388736, 0.55435993],
Expand Down