diff --git a/tests/python/relay/test_op_level2.py b/tests/python/relay/test_op_level2.py
index 130edf2d21d4..85fc466bc927 100644
--- a/tests/python/relay/test_op_level2.py
+++ b/tests/python/relay/test_op_level2.py
@@ -362,10 +362,8 @@ def test_conv2d_transpose_nhwc_run():
     data = np.random.uniform(size=dshape_nhwc).astype(dtype)
     kernel = np.random.uniform(size=kshape_hwoi).astype(dtype)
     # use true kshape layout here - HWOI
-    c_np = topi.testing.conv2d_transpose_nhwc_python(data, kernel, 'HWOI', 2, 1)
-    d_np = np.zeros(shape=oshape_nhwc)
-    d_np[:,0:c_np.shape[1],0:c_np.shape[2],:] = c_np
-    ref_res = d_np
+    ref_res = topi.testing.conv2d_transpose_nhwc_python(data, kernel, 'HWOI',
+                                                        2, 1, output_padding=(2, 2))
 
     for target, ctx in ctx_list():
         intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
diff --git a/topi/python/topi/testing/conv2d_transpose_python.py b/topi/python/topi/testing/conv2d_transpose_python.py
index 92e08120c2ec..7c56cf86c627 100644
--- a/topi/python/topi/testing/conv2d_transpose_python.py
+++ b/topi/python/topi/testing/conv2d_transpose_python.py
@@ -76,7 +76,8 @@ def conv2d_transpose_nchw_python(a_np, w_np, stride, padding, output_padding=(0,
     return b_np
 
 
-def conv2d_transpose_nhwc_python(a_nhwc, weight, weight_format, stride, padding):
+def conv2d_transpose_nhwc_python(a_nhwc, weight, weight_format, stride, padding,
+                                 output_padding=(0, 0)):
     """Transposed convolution operator in NHWC layout.
 
     Parameters
@@ -118,6 +119,7 @@ def conv2d_transpose_nhwc_python(a_nhwc, weight, weight_format, stride, padding)
     else:
         raise ValueError('Valid weight_formats are HWIO, HWOI, OIHW or IOHW')
 
-    res_nchw = conv2d_transpose_nchw_python(a_nchw, w_iohw, stride, padding)
+    res_nchw = conv2d_transpose_nchw_python(a_nchw, w_iohw, stride, padding,
+                                            output_padding=output_padding)
     res_nhwc = np.transpose(res_nchw, (0, 2, 3, 1))
     return res_nhwc