diff --git a/tests/st/ops/gpu/test_hswish_op.py b/tests/st/ops/gpu/test_hswish_op.py
index 7de5f6a7b39..3c8f5b4e174 100644
--- a/tests/st/ops/gpu/test_hswish_op.py
+++ b/tests/st/ops/gpu/test_hswish_op.py
@@ -47,28 +47,28 @@ def expect_hswish_forward_result(x):
 
 
 def expect_hswish_backward_result(x, dout):
-    return np.where(x <= -3, 0, np.where(x >= 3, 1, x / 3 + 0.5)) * dout
+    return np.where(x <= -3, 0, np.where(x >= 3, 1, (x * 2 + 3) / 6)) * dout
 
 
-def judge_result_correct(result, expect):
+def judge_result_correct(result, expect, loss):
     assert result.dtype == expect.dtype
     assert result.shape == expect.shape
-    assert np.allclose(result, expect)
+    assert np.allclose(result, expect, loss, loss)
 
 
-def generate_test_cases(np_type, mode):
+def generate_test_cases(np_type, mode, loss):
     context.set_context(mode=mode, device_target="GPU")
     x = np.array([-1, -2, 0, 4, 5]).astype(np_type)
     net = Net()
     output = net(Tensor(x))
     expect = expect_hswish_forward_result(x)
-    judge_result_correct(output.asnumpy(), expect)
+    judge_result_correct(output.asnumpy(), expect, loss)
 
     sens = np.array([-1.45, 0.63, 0.34, 6.43, 34.6]).astype(np_type)
     backward_net = Grad(Net())
     output = backward_net(Tensor(x), Tensor(sens))
     expect = expect_hswish_backward_result(x, sens)
-    judge_result_correct(output[0].asnumpy(), expect)
+    judge_result_correct(output[0].asnumpy(), expect, loss)
 
 
 @pytest.mark.level1
@@ -79,4 +79,5 @@ def test_hswish_forward_and_backward():
     dtypes = (np.float32, np.float16)
     for mode in modes:
         for dtype in dtypes:
-            generate_test_cases(dtype, mode)
+            loss = 1e-4 if (dtype == np.float32) else 1e-3
+            generate_test_cases(dtype, mode, loss)