diff --git a/tests/python/frontend/mxnet/test_forward.py b/tests/python/frontend/mxnet/test_forward.py
index 34e3fdd4e760..453058556880 100644
--- a/tests/python/frontend/mxnet/test_forward.py
+++ b/tests/python/frontend/mxnet/test_forward.py
@@ -833,7 +833,7 @@ def verify(data_shape, out_shape, begin, end):
 
 def test_forward_convolution():
     def verify(data_shape, kernel_size, stride, pad, num_filter):
-        weight_shape=(num_filter,1,) + kernel_size
+        weight_shape=(num_filter, data_shape[1],) + kernel_size
         x = np.random.uniform(size=data_shape).astype("float32")
         weight = np.random.uniform(size=weight_shape).astype("float32")
         bias = np.random.uniform(size=num_filter).astype("float32")
@@ -852,11 +852,17 @@ def verify(data_shape, kernel_size, stride, pad, num_filter):
                 tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3)
 
     verify(data_shape=(1,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
+    verify(data_shape=(20,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
+    verify(data_shape=(1,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
+    verify(data_shape=(20,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
     verify(data_shape=(1, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
+    verify(data_shape=(20, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
+    verify(data_shape=(1, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
+    verify(data_shape=(20, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
 
 def test_forward_deconvolution():
     def verify(data_shape, kernel_size, stride, pad, num_filter):
-        weight_shape=(1, num_filter) + kernel_size
+        weight_shape=(data_shape[1], num_filter) + kernel_size
         x = np.random.uniform(size=data_shape).astype("float32")
         weight = np.random.uniform(size=weight_shape).astype("float32")
         bias = np.random.uniform(size=num_filter).astype("float32")
@@ -875,7 +881,13 @@ def verify(data_shape, kernel_size, stride, pad, num_filter):
                 tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3)
 
     verify(data_shape=(1,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
+    verify(data_shape=(20,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
+    verify(data_shape=(1,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
+    verify(data_shape=(20,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
     verify(data_shape=(1, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
+    verify(data_shape=(20, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
+    verify(data_shape=(1, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
+    verify(data_shape=(20, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
 
 
 if __name__ == '__main__':