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Make tests compatible with graph mode #291

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Merged
merged 1 commit into from
Jun 16, 2019
Merged

Make tests compatible with graph mode #291

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3 changes: 0 additions & 3 deletions tensorflow_addons/image/distort_image_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,9 +139,6 @@ def adjust_hsv_in_yiq(image,
with tf.name_scope(name or "adjust_hsv_in_yiq"):
image = tf.convert_to_tensor(image, name="image")

if image.shape.rank < 3:
raise ValueError("input must be at least rank 3.")

# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
flt_image = tf.image.convert_image_dtype(image, tf.dtypes.float32)
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24 changes: 12 additions & 12 deletions tensorflow_addons/image/distort_image_ops_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,18 +102,18 @@ def test_adjust_random_hue_in_yiq(self):
y_tf = self._adjust_hue_in_yiq_tf(x_np, delta_h)
self.assertAllClose(y_tf, y_np, rtol=2e-4, atol=1e-4)

# TODO: run in both graph and eager modes
@test_utils.run_in_graph_and_eager_modes
def test_invalid_shapes(self):
x_np = np.random.rand(2, 3) * 255.
delta_h = np.random.rand() * 2.0 - 1.0
with self.assertRaisesRegexp(ValueError,
"input must be at least rank 3."):
self._adjust_hue_in_yiq_tf(x_np, delta_h)
"Shape must be at least rank 3"):
self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
x_np = np.random.rand(4, 2, 4) * 255.
delta_h = np.random.rand() * 2.0 - 1.0
with self.assertRaisesOpError("input must have 3 channels "
"but instead has 4 channels"):
self._adjust_hue_in_yiq_tf(x_np, delta_h)
self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))


class AdjustValueInYiqTest(tf.test.TestCase):
Expand Down Expand Up @@ -163,18 +163,18 @@ def test_adjust_random_value_in_yiq(self):
y_tf = self._adjust_value_in_yiq_tf(x_np, scale)
self.assertAllClose(y_tf, y_np, rtol=2e-4, atol=1e-4)

# TODO: run in both graph and eager modes
@test_utils.run_in_graph_and_eager_modes
def test_invalid_shapes(self):
x_np = np.random.rand(2, 3) * 255.
scale = np.random.rand() * 2.0 - 1.0
with self.assertRaisesRegexp(ValueError,
"input must be at least rank 3."):
self._adjust_value_in_yiq_tf(x_np, scale)
"Shape must be at least rank 3"):
self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
x_np = np.random.rand(4, 2, 4) * 255.
scale = np.random.rand() * 2.0 - 1.0
with self.assertRaisesOpError("input must have 3 channels "
"but instead has 4 channels"):
self._adjust_value_in_yiq_tf(x_np, scale)
self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))


class AdjustSaturationInYiqTest(tf.test.TestCase):
Expand Down Expand Up @@ -228,18 +228,18 @@ def test_adjust_random_saturation_in_yiq(self):
y_tf = self._adjust_saturation_in_yiq_tf(x_np, scale)
self.assertAllClose(y_tf, y_baseline, rtol=2e-4, atol=1e-4)

# TODO: run in both graph and eager modes
@test_utils.run_in_graph_and_eager_modes
def test_invalid_shapes(self):
x_np = np.random.rand(2, 3) * 255.
scale = np.random.rand() * 2.0 - 1.0
with self.assertRaisesRegexp(ValueError,
"input must be at least rank 3."):
self._adjust_saturation_in_yiq_tf(x_np, scale)
"Shape must be at least rank 3"):
self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
x_np = np.random.rand(4, 2, 4) * 255.
scale = np.random.rand() * 2.0 - 1.0
with self.assertRaisesOpError("input must have 3 channels "
"but instead has 4 channels"):
self._adjust_saturation_in_yiq_tf(x_np, scale)
self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))


# TODO: get rid of sessions
Expand Down