add test to cover crop end or right is zero or not zero

onnx · Jul 2, 2019 · 2fa6a5c · 2fa6a5c
1 parent 30de251
commit 2fa6a5c
Showing 1 changed file with 24 additions and 2 deletions.
diff --git a/tests/test_backend.py b/tests/test_backend.py
@@ -2444,9 +2444,31 @@ def test_batch_to_spacend(self):
         self._run_test_case([_OUTPUT], {_INPUT: input_val})
 
     @check_opset_min_version(10, "Slice in opset 10 can accept dynamic 'starts' and 'ends'")
-    def test_batch_to_spacend_with_dynamic_crop(self):
+    def test_batch_to_spacend_with_dynamic_crop_top_is_zero(self):
         block_size = [2, 2]
-        crops_val = np.array([[0, 1], [2, 1]], dtype=np.int32)
+        crop_value = np.array([[0, 1], [2, 1]], dtype=np.int32)
+        input_val = np.random.random_sample([40, 3, 5, 100]).astype(np.float32)
+
+        input_x = tf.placeholder(dtype=tf.float32, shape=input_val.shape, name=_TFINPUT)
+        crop = tf.placeholder(dtype=tf.int32, shape=[2, 2], name=_TFINPUT1)
+        _ = tf.batch_to_space_nd(input_x, block_size, crop, name=_TFOUTPUT)
+        self._run_test_case([_OUTPUT], feed_dict={_INPUT: input_val, _INPUT1: crop_value})
+
+    @check_opset_min_version(10, "Slice in opset 10 can accept dynamic 'starts' and 'ends'")
+    def test_batch_to_spacend_with_dynamic_crop_right_is_zero(self):
+        block_size = [2, 2]
+        crops_val = np.array([[0, 1], [2, 0]], dtype=np.int32)
+        input_val = np.random.random_sample([40, 3, 5, 100]).astype(np.float32)
+
+        input_x = tf.placeholder(dtype=tf.float32, shape=input_val.shape, name=_TFINPUT)
+        crops = tf.placeholder(dtype=tf.int32, shape=[2, 2], name=_TFINPUT1)
+        _ = tf.batch_to_space_nd(input_x, block_size, crops, name=_TFOUTPUT)
+        self._run_test_case([_OUTPUT], feed_dict={_INPUT: input_val, _INPUT1: crops_val})
+
+    @check_opset_min_version(10, "Slice in opset 10 can accept dynamic 'starts' and 'ends'")
+    def test_batch_to_spacend_with_dynamic_crop_ends_are_zero(self):
+        block_size = [2, 2]
+        crops_val = np.array([[1, 0], [2, 0]], dtype=np.int32)
         input_val = np.random.random_sample([40, 3, 5, 100]).astype(np.float32)
 
         input_x = tf.placeholder(dtype=tf.float32, shape=input_val.shape, name=_TFINPUT)