error message of SpectralNorm OP enhancement (#23516)

python/paddle/fluid/dygraph/nn.py

@@ -2989,6 +2989,8 @@ class SpectralNorm(layers.Layer):
         self.weight_v.stop_gradient = True
 
     def forward(self, weight):
+        check_variable_and_dtype(weight, "weight", ['float32', 'float64'],
+                                 'SpectralNorm')
         inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_v}
         out = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(

python/paddle/fluid/tests/unittests/test_spectral_norm_op.py

@@ -16,9 +16,11 @@ from __future__ import division
 
 import unittest
 import numpy as np
+import paddle.fluid as fluid
 from op_test import OpTest, skip_check_grad_ci
 
 from paddle.fluid import core
+from paddle.fluid.framework import program_guard, Program
 
 
 def spectral_norm(weight, u, v, dim, power_iters, eps):
@@ -125,5 +127,46 @@ class TestSpectralNormOp2(TestSpectralNormOp):
         self.eps = 1e-12
 
 
+class TestSpectralNormOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_Variable():
+                weight_1 = np.random.random((2, 4)).astype("float32")
+                fluid.layers.spectral_norm(weight_1, dim=1, power_iters=2)
+
+            # the weight type of spectral_norm must be Variable
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_weight_dtype():
+                weight_2 = np.random.random((2, 4)).astype("int32")
+                fluid.layers.spectral_norm(weight_2, dim=1, power_iters=2)
+
+            # the data type of type must be float32 or float64
+            self.assertRaises(TypeError, test_weight_dtype)
+
+
+class TestDygraphSpectralNormOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            shape = (2, 4, 3, 3)
+            spectralNorm = fluid.dygraph.nn.SpectralNorm(
+                shape, dim=1, power_iters=2)
+
+            def test_Variable():
+                weight_1 = np.random.random((2, 4)).astype("float32")
+                spectralNorm(weight_1)
+
+            # the weight type of SpectralNorm must be Variable
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_weight_dtype():
+                weight_2 = np.random.random((2, 4)).astype("int32")
+                spectralNorm(weight_2)
+
+            # the data type of type must be float32 or float64
+            self.assertRaises(TypeError, test_weight_dtype)
+
+
 if __name__ == "__main__":
     unittest.main()