diff --git a/python/paddle/fluid/tests/unittests/test_unpool3d_op.py b/python/paddle/fluid/tests/unittests/test_unpool3d_op.py
index abbabb43a51850306c4242b7700f38a68718d7be..aa4da0b7c107857eabc4c296074fd86d194a5970 100644
--- a/python/paddle/fluid/tests/unittests/test_unpool3d_op.py
+++ b/python/paddle/fluid/tests/unittests/test_unpool3d_op.py
@@ -195,6 +195,20 @@ class TestUnpool3DOpException(unittest.TestCase):
             ).astype("int32")
             F.max_unpool3d(data, indices, kernel_size=2, stride=2)
 
+        def x_rank_error():
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 27), shape=[1, 1, 3, 3, 3]
+            ).astype("int32")
+            F.max_unpool3d(data, indices, kernel_size=2, stride=2)
+
+        def indices_rank_error():
+            data = paddle.rand(shape=[1, 1, 3, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 27), shape=[1, 3, 3, 3]
+            ).astype("int32")
+            F.max_unpool3d(data, indices, kernel_size=2, stride=2)
+
         def indices_value_error():
             data = paddle.rand(shape=[1, 1, 3, 3, 3])
             indices = paddle.reshape(
@@ -238,6 +252,16 @@ class TestUnpool3DOpException(unittest.TestCase):
             r"The dimensions of Input\(X\) must equal to",
             indices_size_error,
         )
+        self.assertRaisesRegex(
+            ValueError,
+            r"The x should have \[N, C, D, H, W\] format",
+            x_rank_error,
+        )
+        self.assertRaisesRegex(
+            ValueError,
+            r"The indices should have \[N, C, D, H, W\] format",
+            indices_rank_error,
+        )
         if not core.is_compiled_with_cuda():
             self.assertRaisesRegex(
                 ValueError,
diff --git a/python/paddle/fluid/tests/unittests/test_unpool_op.py b/python/paddle/fluid/tests/unittests/test_unpool_op.py
index 4963f93c67e77852164d0b299eafc6dddbee16e6..e5eefc067e89206f518de39b5a4cce43c2c991ab 100644
--- a/python/paddle/fluid/tests/unittests/test_unpool_op.py
+++ b/python/paddle/fluid/tests/unittests/test_unpool_op.py
@@ -193,6 +193,20 @@ class TestUnpoolOpException(unittest.TestCase):
             ).astype("int32")
             F.max_unpool2d(data, indices, kernel_size=2, stride=2)
 
+        def x_rank_error():
+            data = paddle.rand(shape=[1, 1, 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)
+
+        def indices_rank_error():
+            data = paddle.rand(shape=[1, 1, 3, 3])
+            indices = paddle.reshape(
+                paddle.arange(0, 9), shape=[1, 3, 3]
+            ).astype("int32")
+            F.max_unpool2d(data, indices, kernel_size=2, stride=2)
+
         def indices_value_error():
             data = paddle.rand(shape=[1, 1, 3, 3])
             indices = paddle.reshape(
@@ -232,6 +246,16 @@ class TestUnpoolOpException(unittest.TestCase):
             r"The dimensions of Input\(X\) must equal to",
             indices_size_error,
         )
+        self.assertRaisesRegex(
+            ValueError,
+            r"The x should have \[N, C, H, W\] format",
+            x_rank_error,
+        )
+        self.assertRaisesRegex(
+            ValueError,
+            r"The indices should have \[N, C, H, W\] format",
+            indices_rank_error,
+        )
         if not core.is_compiled_with_cuda():
             self.assertRaisesRegex(
                 ValueError,
diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py
index 6aab78ec116e93d3e484310d1fb4c2df3549272b..03e7f202c531a6613543920ea07896b6a4e1d1ba 100755
--- a/python/paddle/nn/functional/pooling.py
+++ b/python/paddle/nn/functional/pooling.py
@@ -927,6 +927,15 @@ def max_unpool2d(
             # unpool_out shape: [1, 1, 7, 7]
 
     """
+    if x.ndim != 4:
+        raise ValueError(
+            f'The x should have [N, C, H, W] format, but received {x.shape}.'
+        )
+    if indices.ndim != 4:
+        raise ValueError(
+            f'The indices should have [N, C, H, W] format, but received {indices.shape}.'
+        )
+
     kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
     if stride is None:
         stride = kernel_size
@@ -1061,6 +1070,15 @@ def max_unpool3d(
             # unpool_out shape: [1, 1, 4, 4, 6]
 
     """
+    if x.ndim != 5:
+        raise ValueError(
+            f'The x should have [N, C, D, H, W] format, but received {x.shape}.'
+        )
+    if indices.ndim != 5:
+        raise ValueError(
+            f'The indices should have [N, C, D, H, W] format, but received {indices.shape}.'
+        )
+
     kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
     if stride is None:
         stride = kernel_size