adaptive_pool support pool_size as int. test=develop

python/paddle/fluid/layers/nn.py

@@ -2570,12 +2570,7 @@ def adaptive_pool2d(input,
         raise ValueError(
             "invalid setting 'require_index' true when 'pool_type' is 'avg'.")
 
-    def _is_list_or_tuple_(data):
-        return (isinstance(data, list) or isinstance(data, tuple))
-
-    if not _is_list_or_tuple_(pool_size) or len(pool_size) != 2:
-        raise ValueError(
-            "'pool_size' should be a list or tuple with length as 2.")
+    pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
 
     if pool_type == "max":
         l_type = 'max_pool2d_with_index'
@@ -2671,12 +2666,7 @@ def adaptive_pool3d(input,
         raise ValueError(
             "invalid setting 'require_index' true when 'pool_type' is 'avg'.")
 
-    def _is_list_or_tuple_(data):
-        return (isinstance(data, list) or isinstance(data, tuple))
-
-    if not _is_list_or_tuple_(pool_size) or len(pool_size) != 3:
-        raise ValueError(
-            "'pool_size' should be a list or tuple with length as 3.")
+    pool_size = utils.convert_to_list(pool_size, 3, 'pool_size')
 
     if pool_type == "max":
         l_type = 'max_pool3d_with_index'

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

@@ -243,6 +243,10 @@ class TestBook(unittest.TestCase):
             pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True)
             self.assertIsNotNone(pool)
             self.assertIsNotNone(mask)
+            self.assertIsNotNone(layers.adaptive_pool2d(x, 3, pool_type='avg'))
+            pool, mask = layers.adaptive_pool2d(x, 3, require_index=True)
+            self.assertIsNotNone(pool)
+            self.assertIsNotNone(mask)
 
     def test_adaptive_pool3d(self):
         program = Program()
@@ -255,6 +259,10 @@ class TestBook(unittest.TestCase):
                 x, [3, 3, 3], require_index=True)
             self.assertIsNotNone(pool)
             self.assertIsNotNone(mask)
+            self.assertIsNotNone(layers.adaptive_pool3d(x, 3, pool_type='avg'))
+            pool, mask = layers.adaptive_pool3d(x, 3, require_index=True)
+            self.assertIsNotNone(pool)
+            self.assertIsNotNone(mask)
 
     def test_lstm_unit(self):
         program = Program()