Increase test coverages by adding invalid CNTK usecases (#10236)

tests/keras/backend/backend_test.py

@@ -1195,33 +1195,46 @@ class TestBackend(object):
             assert np.abs(np.std(rand) - std * 0.87962) < 0.015
 
     def test_conv_invalid_use(self):
+        dummy_x_1d = K.variable(np.ones((4, 8, 2)))
+        dummy_w_1d = K.variable(np.ones((3, 2, 3)))
+        dummy_x_2d = K.variable(np.ones((2, 3, 4, 5)))
+        dummy_w_2d = K.variable(np.ones((2, 2, 3, 4)))
+        dummy_x_3d = K.variable(np.ones((2, 3, 4, 5, 4)))
+        dummy_w_3d = K.variable(np.ones((2, 2, 2, 3, 4)))
+        dummy_w1x1_2d = K.variable(np.ones((1, 1, 12, 7)))
+
         with pytest.raises(ValueError):
-            K.conv1d(K.variable(np.ones((4, 8, 2))),
-                     K.variable(np.ones((3, 2, 3))),
-                     data_format='channels_middle')
+            K.conv1d(dummy_x_1d, dummy_w_1d, data_format='channels_middle')
 
         with pytest.raises(ValueError):
-            K.conv2d(K.variable(np.ones((2, 3, 4, 5))),
-                     K.variable(np.ones((2, 2, 3, 4))),
-                     data_format='channels_middle')
+            K.conv2d(dummy_x_2d, dummy_w_2d, data_format='channels_middle')
 
         with pytest.raises(ValueError):
-            K.conv3d(K.variable(np.ones((2, 3, 4, 5, 4))),
-                     K.variable(np.ones((2, 2, 2, 3, 4))),
-                     data_format='channels_middle')
+            K.conv3d(dummy_x_3d, dummy_w_3d, data_format='channels_middle')
 
         if K.backend() != 'theano':
             with pytest.raises(ValueError):
-                K.separable_conv2d(K.variable(np.ones((2, 3, 4, 5))),
-                                   K.variable(np.ones((2, 2, 3, 4))),
-                                   K.variable(np.ones((1, 1, 12, 7))),
+                K.separable_conv2d(dummy_x_2d, dummy_w_2d, dummy_w1x1_2d,
                                    data_format='channels_middle')
 
         with pytest.raises(ValueError):
-            K.depthwise_conv2d(K.variable(np.ones((2, 3, 4, 5))),
-                               K.variable(np.ones((2, 2, 3, 4))),
+            K.depthwise_conv2d(dummy_x_2d, dummy_w_2d,
                                data_format='channels_middle')
 
+        if K.backend() == 'cntk':
+            with pytest.raises(ValueError):
+                K.separable_conv2d(dummy_x_2d, dummy_w_2d, dummy_w1x1_2d,
+                                   dilation_rate=(1, 2))
+            with pytest.raises(ValueError):
+                K.separable_conv2d(dummy_x_2d, dummy_w_2d, dummy_w1x1_2d,
+                                   strides=(2, 2), dilation_rate=(1, 2))
+            with pytest.raises(ValueError):
+                K.depthwise_conv2d(dummy_x_2d, dummy_w_2d,
+                                   dilation_rate=(1, 2))
+            with pytest.raises(ValueError):
+                K.depthwise_conv2d(dummy_x_2d, dummy_w_2d,
+                                   strides=(2, 2), dilation_rate=(1, 2))
+
     def test_pooling_invalid_use(self):
         for (input_shape, pool_size) in zip([(5, 10, 12, 3), (5, 10, 12, 6, 3)], [(2, 2), (2, 2, 2)]):
             x = K.variable(np.random.random(input_shape))