test=develop

python/paddle/fluid/layers/nn.py

@@ -2139,8 +2139,9 @@ def pool2d(input,
                           input tensor is NCHW, where N is batch size, C is
                           the number of channels, H is the height of the
                           feature, and W is the width of the feature.
-        pool_size (int): The side length of pooling windows. All pooling
-                         windows are squares with pool_size on a side.
+        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
         pool_type: ${pooling_type_comment}
         pool_stride (int): stride of the pooling layer.
         pool_padding (int): padding size.

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

@@ -202,6 +202,12 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
         print(str(program))
 
+    def test_pool2d(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
+            self.assertIsNotNone(layers.pool2d(x, pool_size=[5, 3]))
+
     def test_lstm_unit(self):
         program = Program()
         with program_guard(program):