diff --git a/python/paddle/fluid/tests/unittests/test_spectral_norm_op.py b/python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
index f286296fe5560699108e0388c7862307e6cde876..033ee7908866d7468e773e68befe519f560cd68b 100644
--- a/python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
+++ b/python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
@@ -149,6 +149,24 @@ class TestSpectralNormOpError(unittest.TestCase):
             # the data type of type must be float32 or float64
             self.assertRaises(TypeError, test_weight_dtype)
 
+            def test_dim_out_of_range_1():
+                weight_3 = np.random.random((2, 4)).astype("float32")
+                tensor_3 = paddle.to_tensor(weight_3)
+                paddle.static.nn.spectral_norm(
+                    tensor_3, dim=1382376303, power_iters=2
+                )
+
+            # the dim must be 0 or 1
+            self.assertRaises(ValueError, test_dim_out_of_range_1)
+
+            def test_dim_out_of_range_2():
+                weight_4 = np.random.random((2, 4)).astype("float32")
+                tensor_4 = paddle.to_tensor(weight_4)
+                paddle.static.nn.spectral_norm(tensor_4, dim=-1, power_iters=2)
+
+            # the dim must be 0 or 1
+            self.assertRaises(ValueError, test_dim_out_of_range_2)
+
 
 class TestDygraphSpectralNormOpError(unittest.TestCase):
     def test_errors(self):
diff --git a/python/paddle/static/nn/common.py b/python/paddle/static/nn/common.py
index 0b278eefa1551fe9e7bf8dd8be48738687843603..5da81feb3369d9e4894dbef35fa0f36d0f6a5feb 100644
--- a/python/paddle/static/nn/common.py
+++ b/python/paddle/static/nn/common.py
@@ -3418,11 +3418,12 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
     # create intput and parameters
     input_shape = weight.shape
     assert weight.numel() > 0, "Any dimension of input cannot be equal to 0."
-    assert dim < len(input_shape), (
-        "The input `dim` should be less than the "
-        "rank of `weight`, but received dim="
-        "{}".format(dim)
-    )
+
+    if dim not in [0, 1]:
+        raise ValueError(
+            f"The input `dim` must be 0 (if weight in fc) or 1 (if weight in conv), but received dim={dim}"
+        )
+
     h = input_shape[dim]
     w = np.prod(input_shape) // h