Make tests compatible with graph mode (#291)

Author: WindQAQ

tensorflow_addons/image/distort_image_ops.py

@@ -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)

tensorflow_addons/image/distort_image_ops_test.py

@@ -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):
@@ -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):
@@ -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