[CodeStyle][F821] fix test_exception in test_unpool3d_op and test_unpool_op (#47756)

* [Fix][F821] fix TestUnpoolOpException

* fix TestUnpoolOpException

* fix TestUnpool3DOpException

* remove unused variables

* fix the regexp does not match the C++ traceback

* add missing error message for gpu unpool_kernel

* Revert "add missing error message for gpu unpool_kernel"

This reverts commit 17ef7a127e1c3ee00f9102c37ad8cea35953f20c.

* assertion indices_value_error errors are only reported on the CPU

* for test

* run test_exception in dygraph mode

python/paddle/fluid/tests/unittests/test_unpool3d_op.py

@@ -17,6 +17,7 @@ import numpy as np
 from op_test import OpTest
 import paddle
 import paddle.nn.functional as F
+from paddle.fluid import core
 
 paddle.enable_static()
 paddle.seed(2022)
@@ -178,36 +179,42 @@ class TestUnpool3DOpOutput(TestUnpool3DOp):
 
 
 class TestUnpool3DOpException(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+
+    def tearDown(self):
+        paddle.enable_static()
+
     def test_exception(self):
         def indices_size_error():
-            data = paddle.randint(shape=[1, 1, 3, 3, 3])
+            data = paddle.rand(shape=[1, 1, 3, 3, 3])
             indices = paddle.reshape(
                 paddle.arange(0, 36), shape=[1, 1, 3, 3, 4]
-            )
-            MaxUnPool3D = F.maxunpool3d(data, indices, kernel_size=2, stride=2)
+            ).astype("int32")
+            F.max_unpool3d(data, indices, kernel_size=2, stride=2)
 
         def indices_value_error():
-            data = paddle.randint(shape=[1, 1, 3, 3, 3])
+            data = paddle.rand(shape=[1, 1, 3, 3, 3])
             indices = paddle.reshape(
-                paddle.arange(4, 40), shape=[1, 1, 3, 3, 3]
-            )
-            MaxUnPool3D = F.maxunpool3d(data, indices, kernel_size=2, stride=2)
+                paddle.arange(195, 222), shape=[1, 1, 3, 3, 3]
+            ).astype("int32")
+            F.max_unpool3d(data, indices, kernel_size=2, stride=2)
 
         def data_format_error():
-            data = paddle.randint(shape=[1, 1, 3, 3, 3])
+            data = paddle.rand(shape=[1, 1, 3, 3, 3])
             indices = paddle.reshape(
                 paddle.arange(0, 27), shape=[1, 1, 3, 3, 3]
-            )
-            MaxUnPool3D = F.maxunpool3d(
+            ).astype("int32")
+            F.max_unpool3d(
                 data, indices, kernel_size=2, stride=2, data_format="NDHWC"
             )
 
         def data_outputsize_error():
-            data = paddle.randint(shape=[1, 1, 3, 3, 3])
+            data = paddle.rand(shape=[1, 1, 3, 3, 3])
             indices = paddle.reshape(
                 paddle.arange(0, 27), shape=[1, 1, 3, 3, 3]
-            )
-            MaxUnPool3D = F.maxunpool3d(
+            ).astype("int32")
+            F.max_unpool3d(
                 data,
                 indices,
                 kernel_size=2,
@@ -216,19 +223,36 @@ class TestUnpool3DOpException(unittest.TestCase):
             )
 
         def data_outputsize_error2():
-            data = paddle.randint(shape=[1, 1, 3, 3, 3])
+            data = paddle.rand(shape=[1, 1, 3, 3, 3])
             indices = paddle.reshape(
                 paddle.arange(0, 27), shape=[1, 1, 3, 3, 3]
             )
-            MaxUnPool3D = F.maxunpool3d(
+            F.max_unpool3d(
                 data, indices, kernel_size=2, stride=2, output_size=[10, 10, 10]
             )
 
-        self.assertRaises(ValueError, indices_size_error)
-        self.assertRaises(ValueError, indices_value_error)
-        self.assertRaises(ValueError, data_format_error)
-        self.assertRaises(ValueError, data_outputsize_error)
-        self.assertRaises(ValueError, data_outputsize_error2)
+        self.assertRaisesRegex(
+            ValueError,
+            r"The dimensions of Input\(X\) must equal to",
+            indices_size_error,
+        )
+        if not core.is_compiled_with_cuda():
+            self.assertRaisesRegex(
+                ValueError,
+                r"index should less than output",
+                indices_value_error,
+            )
+        self.assertRaisesRegex(
+            ValueError,
+            r"Attr\(data_format\) should be 'NCDHW'",
+            data_format_error,
+        )
+        self.assertRaisesRegex(
+            ValueError, r"invalid output_size", data_outputsize_error
+        )
+        self.assertRaisesRegex(
+            ValueError, r"invalid output_size", data_outputsize_error2
+        )
 
 
 class TestUnpool3DOpAPI_dygraph(unittest.TestCase):

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

@@ -18,7 +18,7 @@ import numpy as np
 from op_test import OpTest
 import paddle
 import paddle.nn.functional as F
-from paddle.fluid import Program, program_guard
+from paddle.fluid import Program, program_guard, core
 
 from test_attribute_var import UnittestBase
 
@@ -178,46 +178,76 @@ class TestUnpoolOpOutput(TestUnpoolOp):
 
 
 class TestUnpoolOpException(unittest.TestCase):
-    def test_exception(self):
-        import paddle.nn.functional as F
-        import paddle
+    def setUp(self):
+        paddle.disable_static()
+
+    def tearDown(self):
+        paddle.enable_static()
 
+    def test_exception(self):
         def indices_size_error():
-            data = paddle.randint(shape=[1, 1, 3, 3])
-            indices = paddle.reshape(paddle.arange(0, 12), shape[1, 1, 3, 4])
-            MaxPool2D = F.maxunpool2d(data, indices, kernel_size=2, stride=2)
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 12), shape=[1, 1, 3, 4]
+            ).astype("int32")
+            F.max_unpool2d(data, indices, kernel_size=2, stride=2)
 
         def indices_value_error():
-            data = paddle.randint(shape=[1, 1, 3, 3])
-            indices = paddle.reshape(paddle.arange(4, 40), shape[1, 1, 3, 4])
-            MaxPool2D = F.maxunpool2d(data, indices, kernel_size=2, stride=2)
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(31, 40), shape=[1, 1, 3, 3]
+            ).astype("int32")
+            F.max_unpool2d(data, indices, kernel_size=2, stride=2)
 
         def data_format_error():
-            data = paddle.randint(shape=[1, 1, 3, 3])
-            indices = paddle.reshape(paddle.arange(4, 40), shape[1, 1, 3, 4])
-            MaxPool2D = F.maxunpool2d(
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 9), shape=[1, 1, 3, 3]
+            ).astype("int32")
+            F.max_unpool2d(
                 data, indices, kernel_size=2, stride=2, data_format="NHWC"
             )
 
         def data_outputsize_error():
-            data = paddle.randint(shape=[1, 1, 3, 3])
-            indices = paddle.reshape(paddle.arange(4, 40), shape[1, 1, 3, 4])
-            MaxPool2D = F.maxunpool2d(
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 9), shape=[1, 1, 3, 3]
+            ).astype("int32")
+            F.max_unpool2d(
                 data, indices, kernel_size=2, stride=2, output_size=[5, 6, 7, 8]
             )
 
         def data_outputsize_error2():
-            data = paddle.randint(shape=[1, 1, 3, 3])
-            indices = paddle.reshape(paddle.arange(4, 40), shape[1, 1, 3, 4])
-            MaxPool2D = F.maxunpool2d(
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 9), shape=[1, 1, 3, 3]
+            ).astype("int32")
+            F.max_unpool2d(
                 data, indices, kernel_size=2, stride=2, output_size=[100, 100]
             )
 
-        self.assertRaises(ValueError, indices_size_error)
-        self.assertRaises(ValueError, indices_value_error)
-        self.assertRaises(ValueError, data_format_error)
-        self.assertRaises(ValueError, data_outputsize_error)
-        self.assertRaises(ValueError, data_outputsize_error2)
+        self.assertRaisesRegex(
+            ValueError,
+            r"The dimensions of Input\(X\) must equal to",
+            indices_size_error,
+        )
+        if not core.is_compiled_with_cuda():
+            self.assertRaisesRegex(
+                ValueError,
+                r"index should less than output",
+                indices_value_error,
+            )
+        self.assertRaisesRegex(
+            ValueError,
+            r"Attr\(data_format\) should be 'NCHW'",
+            data_format_error,
+        )
+        self.assertRaisesRegex(
+            ValueError, r"invalid output_size", data_outputsize_error
+        )
+        self.assertRaisesRegex(
+            ValueError, r"invalid output_size", data_outputsize_error2
+        )
 
 
 class TestUnpoolOpAPI_dy(unittest.TestCase):