[API 2.0] add pool2d3d API,test=develop (#26331)

* add pool2d3d API,test=develop

* add api unitest,test=develop

* fix unittest, test=develop

* fix reviews, test=develop

* return one element when return indices is true, test=develop

* fix low converage; to_variable to to_tensor, test=develop

* sort API params, test=develop

* fix en doc, merge PR#26108 to here, test=develop

* fix en doc, test=develop

python/paddle/fluid/tests/unittests/test_pool1d_api.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+import paddle
+import paddle.nn.functional as F
+import paddle.fluid as fluid
+
+
+def adaptive_start_index(index, input_size, output_size):
+    return int(np.floor(index * input_size / output_size))
+
+
+def adaptive_end_index(index, input_size, output_size):
+    return int(np.ceil((index + 1) * input_size / output_size))
+
+
+def max_pool1D_forward_naive(x,
+                             ksize,
+                             strides,
+                             paddings,
+                             global_pool=0,
+                             ceil_mode=False,
+                             exclusive=False,
+                             adaptive=False,
+                             data_type=np.float64):
+    N, C, L = x.shape
+    if global_pool == 1:
+        ksize = [L]
+    if adaptive:
+        L_out = ksize[0]
+    else:
+        L_out = (L - ksize[0] + 2 * paddings[0] + strides[0] - 1
+                 ) // strides[0] + 1 if ceil_mode else (
+                     L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
+
+    out = np.zeros((N, C, L_out))
+    for i in range(L_out):
+        if adaptive:
+            r_start = adaptive_start_index(i, L, ksize[0])
+            r_end = adaptive_end_index(i, L, ksize[0])
+        else:
+            r_start = np.max((i * strides[0] - paddings[0], 0))
+            r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
+        x_masked = x[:, :, r_start:r_end]
+
+        out[:, :, i] = np.max(x_masked, axis=(2))
+    return out
+
+
+def avg_pool1D_forward_naive(x,
+                             ksize,
+                             strides,
+                             paddings,
+                             global_pool=0,
+                             ceil_mode=False,
+                             exclusive=False,
+                             adaptive=False,
+                             data_type=np.float64):
+    N, C, L = x.shape
+    if global_pool == 1:
+        ksize = [L]
+    if adaptive:
+        L_out = ksize[0]
+    else:
+        L_out = (L - ksize[0] + 2 * paddings[0] + strides[0] - 1
+                 ) // strides[0] + 1 if ceil_mode else (
+                     L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
+
+    out = np.zeros((N, C, L_out))
+    for i in range(L_out):
+        if adaptive:
+            r_start = adaptive_start_index(i, L, ksize[0])
+            r_end = adaptive_end_index(i, L, ksize[0])
+        else:
+            r_start = np.max((i * strides[0] - paddings[0], 0))
+            r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
+        x_masked = x[:, :, r_start:r_end]
+
+        field_size = (r_end - r_start) \
+            if (exclusive or adaptive) else (ksize[0])
+        if data_type == np.int8 or data_type == np.uint8:
+            out[:, :, i] = (np.rint(
+                np.sum(x_masked, axis=(2, 3)) / field_size)).astype(data_type)
+        else:
+            out[:, :, i] = (np.sum(x_masked, axis=(2)) /
+                            field_size).astype(data_type)
+    return out
+
+
+class TestPool1d_API(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(123)
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(fluid.CUDAPlace(0))
+
+    def check_avg_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0], ceil_mode=False)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_avg_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=[0])
+
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool1d_dg = paddle.nn.layer.AvgPool1d(
+                kernel_size=2, stride=None, padding=0)
+            result = avg_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.max_pool1d(input, kernel_size=2, stride=2, padding=[0])
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_max_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.max_pool1d(input, kernel_size=2, stride=2, padding=0)
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool1d_dg = paddle.nn.layer.MaxPool1d(
+                kernel_size=2, stride=None, padding=0)
+            result = max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_adaptive_max_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.adaptive_max_pool1d(input, output_size=16)
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            ada_max_pool1d_dg = paddle.nn.layer.AdaptiveMaxPool1d(
+                output_size=16)
+            result = ada_max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_adaptive_avg_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.adaptive_avg_pool1d(input, output_size=16)
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1d(
+                output_size=16)
+            result = ada_max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_adaptive_max_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.adaptive_max_pool1d(input, output_size=16)
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[2], paddings=[0], adaptive=True)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_adaptive_avg_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.adaptive_avg_pool1d(input, output_size=16)
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[2], paddings=[0], adaptive=True)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_max_dygraph_padding_same(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.max_pool1d(
+                input, kernel_size=2, stride=2, padding="SAME")
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_dygraph_padding_same(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.avg_pool1d(
+                input, kernel_size=2, stride=2, padding="SAME")
+
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def test_pool1d(self):
+        for place in self.places:
+
+            self.check_max_dygraph_results(place)
+            self.check_avg_dygraph_results(place)
+            self.check_max_static_results(place)
+            self.check_avg_static_results(place)
+            self.check_adaptive_max_dygraph_results(place)
+            self.check_adaptive_avg_dygraph_results(place)
+            self.check_adaptive_max_static_results(place)
+            self.check_adaptive_avg_static_results(place)
+            self.check_max_dygraph_padding_same(place)
+            self.check_avg_dygraph_padding_same(place)
+
+
+class TestPool2dError_API(unittest.TestCase):
+    def test_error_api(self):
+        def run1():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[2]]
+                res_pd = F.max_pool1d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run1)
+
+        def run2():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[2]]
+                res_pd = F.max_pool1d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run2)
+
+        def run3():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "padding"
+                res_pd = F.max_pool1d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run3)
+
+        def run4():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = F.max_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run4)
+
+        def run5():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = F.max_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run5)
+
+        def run6():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = F.avg_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run6)
+
+        def run7():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "paddle"
+                res_pd = F.avg_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run7)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_pool2d_api.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from test_pool2d_op import adaptive_start_index, adaptive_end_index, pool2D_forward_naive
+import unittest
+from op_test import OpTest
+import numpy as np
+import paddle.fluid.core as core
+from paddle.nn.functional import *
+import paddle.fluid as fluid
+import paddle
+
+
+class TestPool2d_API(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(123)
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(fluid.CUDAPlace(0))
+
+    def check_avg_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(
+                name="input", shape=[2, 3, 32, 32], dtype="float32")
+            result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='avg')
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_avg_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='avg')
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool2d_dg = paddle.nn.layer.AvgPool2d(
+                kernel_size=2, stride=2, padding=0)
+            result = avg_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(
+                name="input", shape=[2, 3, 32, 32], dtype="float32")
+            result = max_pool2d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='max')
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_max_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = max_pool2d(
+                input, kernel_size=2, stride=2, padding=0, return_indices=False)
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='max')
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool2d_dg = paddle.nn.layer.MaxPool2d(
+                kernel_size=2, stride=2, padding=0)
+            result = max_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_dygraph_stride_is_none(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result, indices = max_pool2d(
+                input,
+                kernel_size=2,
+                stride=None,
+                padding="SAME",
+                return_indices=True)
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='max',
+                padding_algorithm="SAME")
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool2d_dg = paddle.nn.layer.MaxPool2d(
+                kernel_size=2, stride=2, padding=0)
+            result = max_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_dygraph_stride_is_none(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool2d(
+                input, kernel_size=2, stride=None, padding="SAME")
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='avg',
+                padding_algorithm="SAME")
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool2d_dg = paddle.nn.layer.AvgPool2d(
+                kernel_size=2, stride=2, padding=0)
+            result = avg_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_dygraph_padding(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            padding = [[0, 0], [0, 0], [0, 0], [0, 0]]
+            result = max_pool2d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=padding,
+                return_indices=False)
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='max')
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool2d_dg = paddle.nn.layer.MaxPool2d(
+                kernel_size=2, stride=2, padding=0)
+            result = max_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_divisor(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            padding = [[0, 0], [0, 0], [0, 0], [0, 0]]
+            result = avg_pool2d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=padding,
+                divisor_override=4)
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='avg')
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool2d_dg = paddle.nn.layer.AvgPool2d(
+                kernel_size=2, stride=2, padding=0)
+            result = avg_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def test_pool2d(self):
+        for place in self.places:
+
+            self.check_max_dygraph_results(place)
+            self.check_avg_dygraph_results(place)
+            self.check_max_static_results(place)
+            self.check_avg_static_results(place)
+            self.check_max_dygraph_stride_is_none(place)
+            self.check_avg_dygraph_stride_is_none(place)
+            self.check_max_dygraph_padding(place)
+            self.check_avg_divisor(place)
+
+
+class TestPool2dError_API(unittest.TestCase):
+    def test_error_api(self):
+        def run1():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[0, 1], [0, 0], [0, 0], [0, 0]]
+                res_pd = max_pool2d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run1)
+
+        def run2():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[0, 1], [0, 0], [0, 0], [0, 0]]
+                res_pd = max_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run2)
+
+        def run3():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "padding"
+                res_pd = max_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run3)
+
+        def run3_avg():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "padding"
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run3_avg)
+
+        def run4():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = max_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run4)
+
+        def run4_avg():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run4_avg)
+
+        def run5():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "padding"
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run5)
+
+        def run6():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run6)
+
+        def run7():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=False,
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run7)
+
+        def run8():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = max_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=False,
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run8)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_pool3d_api.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+from __future__ import division
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.nn.functional import avg_pool3d, max_pool3d
+from test_pool3d_op import adaptive_start_index, adaptive_end_index, pool3D_forward_naive
+
+
+class TestPool3d_API(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(123)
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(fluid.CUDAPlace(0))
+
+    def check_avg_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(
+                name="input", shape=[2, 3, 32, 32, 32], dtype="float32")
+            result = avg_pool3d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='avg')
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_avg_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool3d(input, kernel_size=2, stride=2, padding="SAME")
+
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='avg',
+                padding_algorithm="SAME")
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool3d_dg = paddle.nn.layer.AvgPool3d(
+                kernel_size=2, stride=None, padding="SAME")
+            result = avg_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(
+                name="input", shape=[2, 3, 32, 32, 32], dtype="float32")
+            result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='max')
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_max_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
+
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='max')
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+            max_pool3d_dg = paddle.nn.layer.MaxPool3d(
+                kernel_size=2, stride=None, padding=0)
+            result = max_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_dygraph_stride_is_none(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result, indices = max_pool3d(
+                input,
+                kernel_size=2,
+                stride=None,
+                padding="SAME",
+                return_indices=True)
+
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='max',
+                padding_algorithm="SAME")
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+            max_pool3d_dg = paddle.nn.layer.MaxPool3d(
+                kernel_size=2, stride=2, padding=0)
+            result = max_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_dygraph_padding(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            padding = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]
+            result = max_pool3d(input, kernel_size=2, stride=2, padding=padding)
+
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='max')
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+            max_pool3d_dg = paddle.nn.layer.MaxPool3d(
+                kernel_size=2, stride=2, padding=0)
+            result = max_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            padding = [0, 0, 0, 0, 0, 0]
+            result = max_pool3d(input, kernel_size=2, stride=2, padding=padding)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_divisor(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            padding = 0
+            result = avg_pool3d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=padding,
+                divisor_override=8)
+
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='avg')
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+            avg_pool3d_dg = paddle.nn.layer.AvgPool3d(
+                kernel_size=2, stride=2, padding=0)
+            result = avg_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            padding = [0, 0, 0, 0, 0, 0]
+            result = avg_pool3d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=padding,
+                divisor_override=8)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def test_pool3d(self):
+        for place in self.places:
+
+            self.check_max_dygraph_results(place)
+            self.check_avg_dygraph_results(place)
+            self.check_max_static_results(place)
+            self.check_avg_static_results(place)
+            self.check_max_dygraph_stride_is_none(place)
+            self.check_max_dygraph_padding(place)
+            self.check_avg_divisor(place)
+
+
+class TestPool3dError_API(unittest.TestCase):
+    def test_error_api(self):
+        def run1():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
+                res_pd = avg_pool3d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run1)
+
+        def run2():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    data_format='NCDHW')
+
+        self.assertRaises(ValueError, run2)
+
+        def run3():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    data_format='NDHWC')
+
+        self.assertRaises(ValueError, run3)
+
+        def run4():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=0,
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run4)
+
+        def run5():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = max_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=0,
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run5)
+
+        def run6():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding="padding",
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run6)
+
+        def run7():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = max_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding="padding",
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run7)
+
+        def run8():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding="VALID",
+                    ceil_mode=True,
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run8)
+
+        def run9():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = max_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding="VALID",
+                    ceil_mode=True,
+                    data_format='NNNN')
+
+        self.assertRaises(ValueError, run9)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/functional/__init__.py

@@ -25,6 +25,8 @@ from . import extension
 __all__ += extension.__all__
 from . import common
 __all__ += common.__all__
+from . import pooling
+__all__ += pooling.__all__
 from . import loss
 __all__ += loss.__all__
 from .activation import brelu  #DEFINE_ALIAS
@@ -166,10 +168,18 @@ from .norm import l2_normalize  #DEFINE_ALIAS
 from .norm import lrn  #DEFINE_ALIAS
 from .norm import normalize  #DEFINE_ALIAS
 # from .norm import spectral_norm        #DEFINE_ALIAS
+from .pooling import max_pool1d  #DEFINE_ALIAS
+from .pooling import avg_pool1d  #DEFINE_ALIAS
+from .pooling import adaptive_max_pool1d  #DEFINE_ALIAS
+from .pooling import adaptive_avg_pool1d  #DEFINE_ALIAS
 from .pooling import pool2d  #DEFINE_ALIAS
 from .pooling import pool3d  #DEFINE_ALIAS
 from .pooling import adaptive_pool2d  #DEFINE_ALIAS
 from .pooling import adaptive_pool3d  #DEFINE_ALIAS
+from .pooling import avg_pool2d  #DEFINE_ALIAS
+from .pooling import max_pool2d  #DEFINE_ALIAS
+from .pooling import avg_pool3d  #DEFINE_ALIAS
+from .pooling import max_pool3d  #DEFINE_ALIAS
 from .pooling import adaptive_avg_pool2d  #DEFINE_ALIAS
 from .pooling import adaptive_avg_pool3d  #DEFINE_ALIAS
 # from .rnn import gru_unit        #DEFINE_ALIAS

python/paddle/nn/functional/pooling.py

@@ -13,23 +13,1208 @@
 # limitations under the License.
 
 # TODO: define pooling functions
-import paddle
-from ...fluid import core
 from ...fluid.layers import pool2d  #DEFINE_ALIAS
 from ...fluid.layers import pool3d  #DEFINE_ALIAS
 from ...fluid.layers import adaptive_pool2d  #DEFINE_ALIAS
 from ...fluid.layers import adaptive_pool3d  #DEFINE_ALIAS
-from ...fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
-from ...fluid.layers import utils
-from ...fluid.layer_helper import LayerHelper
-from ...fluid.framework import in_dygraph_mode
+from ...fluid import core
+from ...fluid.framework import in_dygraph_mode, convert_np_dtype_to_dtype_
+from ...fluid.layers import utils, LayerHelper
+from ...fluid.data_feeder import check_type, check_variable_and_dtype, check_type, check_dtype, convert_dtype
+from ...fluid.layers import unsqueeze, squeeze
 
 __all__ = [
-    'pool2d', 'pool3d', 'adaptive_pool2d', 'adaptive_pool3d',
-    'adaptive_avg_pool2d', 'adaptive_avg_pool3d'
+    'pool2d',
+    'pool3d',
+    'avg_pool1d',
+    'max_pool1d',
+    'adaptive_avg_pool1d',
+    'adaptive_max_pool1d',
+    'adaptive_avg_pool2d',
+    'adaptive_avg_pool3d',
+    'adaptive_pool2d',
+    'adaptive_pool3d',
+    'max_pool2d',
+    'avg_pool2d',
+    'max_pool3d',
+    'avg_pool3d',
 ]
 
 
+def check_input(x, dimension):
+    if len(x.shape) != dimension:
+        raise ValueError("Excepted Input X is 3-D tensor, but received {}-D {}".
+                         format(len(x.shape), type(x)))
+
+
+def check_instance(x, x_name, types=(int, float)):
+
+    if not isinstance(x, types):
+        raise ValueError("Excepted {} type for {} but received type: {}. ".
+                         format(types, x_name, type(x)))
+
+
+def update_padding1d(padding, pool_type='avg'):
+    def is_list_or_tuple(ele):
+        if isinstance(ele, list) or isinstance(ele, tuple):
+            return True
+        return False
+
+    if is_list_or_tuple(padding):
+        if padding.__len__() == 1 and not is_list_or_tuple(padding[0]):
+            return [0, padding[0]]
+        else:
+            raise ValueError(
+                "{}_pool1d() argument 'padding' should contain one int (got {})".
+                format(pool_type, padding.__len__()))
+    else:
+        padding = [0, padding]
+
+    return padding
+
+
+def update_padding2d(padding, data_format):
+    def is_list_or_tuple(ele):
+        if isinstance(ele, list) or isinstance(ele, tuple):
+            return True
+        return False
+
+    if is_list_or_tuple(padding) and len(padding) == 4:
+        if is_list_or_tuple(padding[0]) and (data_format == "NCHW"):
+            if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
+                raise ValueError(
+                    "Non-zero pool_padding(%s) in the batch or channel dimensions "
+                    "is not supported." % str(padding))
+            padding = padding[2:4]
+            padding = [ele for a_list in padding for ele in a_list]
+        elif is_list_or_tuple(padding[0]) and (data_format == "NHWC"):
+            if not (padding[0] == [0, 0] and padding[3] == [0, 0]):
+                raise ValueError(
+                    "Non-zero pool_padding(%s) in the batch or channel dimensions "
+                    "is not supported." % str(padding))
+            padding = padding[1:3]
+            padding = [ele for a_list in padding for ele in a_list]
+        padding = utils.convert_to_list(padding, 4, 'padding')
+
+        if utils._is_symmetric_padding(padding, 2):
+            padding = [padding[0], padding[2]]
+    else:
+        padding = utils.convert_to_list(padding, 2, 'padding')
+
+    return padding
+
+
+def update_padding3d(padding, data_format):
+    def is_list_or_tuple(ele):
+        if isinstance(ele, (list, tuple)):
+            return True
+        return False
+
+    if is_list_or_tuple(padding) and len(padding) == 5:
+        if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"):
+            if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
+                raise ValueError(
+                    "Non-zero pool_padding(%s) in the batch or channel dimensions "
+                    "is not supported." % str(padding))
+            padding = padding[2:5]
+            padding = [ele for a_list in padding for ele in a_list]
+        elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"):
+            if not (padding[0] == [0, 0] and padding[4] == [0, 0]):
+                raise ValueError(
+                    "Non-zero pool_padding(%s) in the batch or channel dimensions "
+                    "is not supported." % str(padding))
+            padding = padding[1:4]
+            padding = [ele for a_list in padding for ele in a_list]
+        padding = utils.convert_to_list(padding, 6, 'padding')
+        if utils._is_symmetric_padding(padding, 3):
+            padding = [padding[0], padding[2], padding[4]]
+
+    elif is_list_or_tuple(padding) and len(padding) == 6:
+        padding = utils.convert_to_list(padding, 6, 'padding')
+        if utils._is_symmetric_padding(padding, 3):
+            padding = [padding[0], padding[2], padding[4]]
+    else:
+        padding = utils.convert_to_list(padding, 3, 'padding')
+
+    return padding
+
+
+def avg_pool1d(x,
+               kernel_size,
+               stride=None,
+               padding=0,
+               count_include_pad=True,
+               ceil_mode=False,
+               name=None):
+    """
+
+    This operation applies a 1D average pooling over an input signal composed
+    of several input planes, based on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+    The output tensor shape will be [N, C, output_size].
+
+    The output value of the layer with input size (N, C, L),
+    output (N, C, L_{out}) and kernel_size k can be precisely described as
+    For average pool1d:
+
+    ..  math::
+
+       Output(N_i, C_i, l) &= mean(Input[N_i, C_i, stride \times l:stride \times l+k])
+
+
+    Args:
+        x (Tensor): The input tensor of pooling operator which is a 3-D tensor with
+                          shape [N, C, L]. where `N` is batch size, `C` is the number of channels,
+                          `L` is the length of the feature. The data type if float32 or float64.
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain one integers.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain one integers.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be the following forms: `[pad_left, pad_right]`. If padding is non-zero,
+            then the input is implicitly zero-padded on both sides for padding number of points.
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is `true`.
+        ceil_mode (bool): ${ceil_mode_comment}Whether to use the ceil function to calculate output height and width.
+            If it is set to False, the floor function will be used. Default False
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of pooling result. The data type is same as input tensor.
+
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ValueError: If `padding` is a list or tuple but its length greater than 1.
+        ShapeError: If the input is not a 3-D.
+        ShapeError: If the output's shape calculated is not greater than 0.
+
+
+    Examples:
+
+        .. code-block:: python
+
+          import paddle
+          import paddle.nn.functional as F
+          paddle.disable_static()
+
+          data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+          pool_out = F.avg_pool1d(data, kernel_size=2, stride=2, padding=0)
+          # pool_out shape: [1, 3, 16]
+
+    """
+    """NCL to NCHW"""
+    data_format = "NCHW"
+    check_variable_and_dtype(x, 'input', ['float32', 'float64'], 'avg_pool1d')
+    check_input(x, 3)
+    x = unsqueeze(x, [2])
+    kernel_size = utils.convert_to_list(kernel_size, 1, 'pool_size')
+    kernel_size = [1] + kernel_size
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = utils.convert_to_list(stride, 1, 'pool_stride')
+        stride = [1] + stride
+
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown Attr(padding): '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding))
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0]
+            if ceil_mode != False:
+                raise ValueError(
+                    "When Attr(padding) is \"VALID\", Attr(ceil_mode) must be False. "
+                    "Received ceil_mode: True.")
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0]
+
+    padding = update_padding1d(padding, "avg")
+
+    if in_dygraph_mode():
+        output = core.ops.pool2d(
+            x, 'pooling_type', 'avg', 'ksize', kernel_size, 'global_pooling',
+            False, 'strides', stride, 'paddings', padding, 'padding_algorithm',
+            padding_algorithm, 'use_cudnn', not count_include_pad, 'ceil_mode',
+            ceil_mode, 'use_mkldnn', False, 'exclusive', True, 'data_format',
+            data_format)
+        return squeeze(output, [2])
+
+    op_type = 'pool2d'
+    helper = LayerHelper(op_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+
+    helper.append_op(
+        type=op_type,
+        inputs={"X": x},
+        outputs={"Out": pool_out},
+        attrs={
+            "pooling_type": 'avg',
+            "ksize": kernel_size,
+            "global_pooling": False,
+            "strides": stride,
+            "paddings": padding,
+            "padding_algorithm": padding_algorithm,
+            "use_cudnn": True,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": False,
+            "exclusive": not count_include_pad,
+            "data_format": data_format,
+        })
+
+    return squeeze(pool_out, [2])
+
+
+def max_pool1d(x,
+               kernel_size,
+               stride=None,
+               padding=0,
+               return_indices=False,
+               ceil_mode=False,
+               name=None):
+    """
+
+    Applies a 1D max pooling over an input signal composed of several input planes based
+    on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+
+    The output value of the layer with input size (N, C, L),
+    output (N, C, L_{out}) and kernel_size k can be precisely described as
+    For average pool1d:
+
+    ..  math::
+
+       Output(N_i, C_i, l) &=  max(Input[N_i, C_i, stride \times l:stride \times l+k])}
+
+    Args:
+        x (Tensor): The input tensor of pooling operator which is a 3-D tensor with
+                          shape [N, C, L], where `N` is batch size, `C` is the number of channels,
+                          `L` is the length of the feature. The data type if float32 or float64.
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain one integers.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain one integers.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be the following forms: `[pad_left, pad_right]`.
+        return_indices (bool): Whether return the max indices along with the outputs. default is `False`.
+        ceil_mode (bool): Whether to use the ceil function to calculate output height and width. False is the default.
+            If it is set to False, the floor function will be used. Default False.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of pooling result. The data type is same as input tensor.
+
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ValueError: If `padding` is a list or tuple but its length greater than 1.
+        ShapeError: If the input is not a 3-D.
+        ShapeError: If the output's shape calculated is not greater than 0.
+
+
+    Examples:
+
+        .. code-block:: python
+
+          import paddle
+          import paddle.nn.functional as F
+          paddle.disable_static()
+
+          data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+          pool_out = F.max_pool1d(data, kernel_size=2, stride=2, padding=0)
+          # pool_out shape: [1, 3, 16]
+
+          pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_indices=True)
+          # pool_out shape: [1, 3, 16],  indices shape: [1, 3, 16]
+
+    """
+    """NCL to NCHW"""
+    data_format = "NCHW"
+    check_variable_and_dtype(x, 'input', ['float32', 'float64'], 'max_pool1d')
+    check_input(x, 3)
+    x = unsqueeze(x, [2])
+    kernel_size = [1] + utils.convert_to_list(kernel_size, 1, 'pool_size')
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = [1] + utils.convert_to_list(stride, 1, 'pool_stride')
+
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown Attr(padding): '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding))
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0]
+            if ceil_mode != False:
+                raise ValueError(
+                    "When Attr(padding) is \"VALID\", Attr(ceil_mode) must be False. "
+                    "Received ceil_mode: True.")
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0]
+
+    padding = update_padding1d(padding, 'max')
+
+    if in_dygraph_mode():
+        pool_out = core.ops.max_pool2d_with_index(
+            x, 'ksize', kernel_size, 'global_pooling', False, 'strides', stride,
+            'paddings', padding, 'padding_algorithm', padding_algorithm,
+            'use_cudnn', True, 'ceil_mode', ceil_mode, 'use_mkldnn', False,
+            'exclusive', True, 'data_format', data_format)
+        return (squeeze(pool_out[0], [2]), squeeze(
+            pool_out[1], [2])) if return_indices else squeeze(pool_out[0], [2])
+
+    op_type = 'max_pool2d_with_index'
+    helper = LayerHelper(op_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    mask = helper.create_variable_for_type_inference(dtype)
+    outputs = {"Out": pool_out, "Mask": mask}
+
+    helper.append_op(
+        type=op_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": 'max',
+            "ksize": kernel_size,
+            "global_pooling": False,
+            "strides": stride,
+            "paddings": padding,
+            "padding_algorithm": padding_algorithm,
+            "use_cudnn": True,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": False,
+            "exclusive": True,
+            "data_format": data_format,
+        })
+
+    return (squeeze(pool_out, [2]),
+            squeeze(mask, [2])) if return_indices else squeeze(pool_out, [2])
+
+
+def adaptive_avg_pool1d(x, output_size, name=None):
+    """
+
+    This operation applies a 1D adaptive average pooling over an input signal composed
+    of several input planes, based on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+    The output tensor shape will be [N, C, output_size].
+
+    For average adaptive pool1d:
+
+    ..  math::
+
+        lstart &= floor(i * L_{in} / L_{out})
+
+        lend &= ceil((i + 1) * L_{in} / L_{out})
+
+        Output(i) &= \\frac{sum(Input[lstart:lend])}{(lstart - lend)}
+
+    Args:
+        x (Tensor): The input tensor of pooling operator, which is a 3-D tensor
+                              with shape [N, C, L].  The format of input tensor is NCL,
+                              where N is batch size, C is the number of channels, L is the
+                              length of the feature. The data type is float32 or float64.
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+                it must contain one int.
+        name(str, optional): For detailed information, please refer
+                                 to :ref:`api_guide_Name`. Usually name is no need to set and
+                                 None by default.
+
+    Returns:
+            Tensor: The output tensor of adaptive average pooling result. The data type is same
+                      as input tensor.
+
+    Raises:
+            ValueError: 'output_size' should be a integer or list or tuple with length as 1.
+
+    Examples:
+        .. code-block:: python
+
+              # average adaptive pool1d
+              # suppose input data in shape of [N, C, L], `output_size` is m or [m],
+              # output shape is [N, C, m], adaptive pool divide L dimension
+              # of input data into m grids averagely and performs poolings in each
+              # grid to get output.
+              # adaptive max pool performs calculations as follow:
+              #
+              #     for i in range(m):
+              #         lstart = floor(i * L / m)
+              #         lend = ceil((i + 1) * L / m)
+              #         output[:, :, i] = sum(input[:, :, lstart: lend])/(lstart - lend)
+              #
+              import paddle
+              import paddle.nn.functional as F
+              paddle.disable_static()
+
+              data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+              pool_out = F.adaptive_average_pool1d(data, output_size=16)
+              # pool_out shape: [1, 3, 16])
+    """
+    pool_type = 'avg'
+    check_variable_and_dtype(x, 'input', ['float32', 'float64'],
+                             'adaptive_pool2d')
+    check_input(x, 3)
+    check_type(output_size, 'pool_size', (int), 'adaptive_pool1d')
+
+    pool_size = [1] + utils.convert_to_list(output_size, 1, 'pool_size')
+
+    l_type = "pool2d"
+    x = unsqueeze(x, [2])
+    if in_dygraph_mode():
+        pool_out = core.ops.pool2d(x, 'pooling_type', pool_type, 'ksize',
+                                   pool_size, 'adaptive', True)
+        return squeeze(pool_out, [2])
+
+    helper = LayerHelper(l_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+
+    outputs = {"Out": pool_out}
+    helper.append_op(
+        type=l_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": pool_type,
+            "ksize": pool_size,
+            "adaptive": True,
+        })
+
+    return squeeze(pool_out, [2])
+
+
+def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
+    """
+    This operation applies a 1D adaptive max pooling over an input signal composed
+    of several input planes, based on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+    The output tensor shape will be [N, C, output_size].
+
+    For max adaptive pool1d:
+
+    ..  math::
+
+        lstart &= floor(i * L_{in} / L_{out})
+
+        lend &= ceil((i + 1) * L_{in} / L_{out})
+
+        Output(i) &= max(Input[lstart:lend])}
+
+    Args:
+        x (Tensor): The input tensor of pooling operator, which is a 3-D tensor
+                              with shape [N, C, L].  The format of input tensor is NCL,
+                              where N is batch size, C is the number of channels, L is the
+                              length of the feature. The data type is float32 or float64.
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+                it must contain one int.
+        return_indices (bool): If true, the index of max pooling point will be returned along
+                with outputs. It cannot be set in average pooling type. Default False.
+        name(str, optional): For detailed information, please refer
+                                 to :ref:`api_guide_Name`. Usually name is no need to set and
+                                 None by default.
+
+    Returns:
+            Tensor: The output tensor of adaptive pooling result. The data type is same
+                      as input tensor.
+
+    Raises:
+            ValueError: 'output_size' should be a integer or list or tuple with length as 1.
+
+    Examples:
+        .. code-block:: python
+
+              # max adaptive pool1d
+              # suppose input data in shape of [N, C, L], `output_size` is m or [m],
+              # output shape is [N, C, m], adaptive pool divide L dimension
+              # of input data into m grids averagely and performs poolings in each
+              # grid to get output.
+              # adaptive max pool performs calculations as follow:
+              #
+              #     for i in range(m):
+              #         lstart = floor(i * L / m)
+              #         lend = ceil((i + 1) * L / m)
+              #         output[:, :, i] = max(input[:, :, lstart: lend])
+              #
+              import paddle
+              import paddle.nn.functional as F
+              paddle.disable_static()
+
+              data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+              pool_out = F.adaptive_max_pool1d(data, output_size=16)
+              # pool_out shape: [1, 3, 16])
+
+              pool_out, indices = F.adaptive_max_pool1d(data, output_size=16, return_indices=True)
+              # pool_out shape: [1, 3, 16] indices  shape: [1, 3, 16]
+
+    """
+    pool_type = 'max'
+    check_variable_and_dtype(x, 'input', ['float32', 'float64'],
+                             'adaptive_max_pool1d')
+    check_input(x, 3)
+    check_type(output_size, 'pool_size', (int), 'adaptive_max_pool1d')
+    check_type(return_indices, 'return_indices', bool, 'adaptive_max_pool1d')
+
+    pool_size = [1] + utils.convert_to_list(output_size, 1, 'pool_size')
+
+    l_type = 'max_pool2d_with_index'
+
+    x = unsqueeze(x, [2])
+    if in_dygraph_mode():
+        pool_out = core.ops.max_pool2d_with_index(
+            x, 'pooling_type', pool_type, 'ksize', pool_size, 'adaptive', True)
+        return (squeeze(pool_out[0], [2]), squeeze(
+            pool_out[1], [2])) if return_indices else squeeze(pool_out[0], [2])
+
+    helper = LayerHelper(l_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+
+    mask = helper.create_variable_for_type_inference(dtype)
+    outputs = {"Out": pool_out, "Mask": mask}
+
+    helper.append_op(
+        type=l_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": pool_type,
+            "ksize": pool_size,
+            "adaptive": True,
+        })
+
+    return (squeeze(pool_out, [2]),
+            squeeze(mask, [2])) if return_indices else squeeze(pool_out, [2])
+
+
+def max_pool2d(x,
+               kernel_size,
+               stride=None,
+               padding=0,
+               return_indices=False,
+               ceil_mode=False,
+               data_format="NCHW",
+               name=None):
+    """
+    This operation applies 2D max pooling over input feature based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCHW format, 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.
+
+    Example:
+      Input:
+           X shape: $(N, C, H_{in}, W_{in})$
+      Attr:
+           kernel_size: ksize
+           stride: stride
+
+      Output:
+           Out shape: $(N, C, H_{out}, W_{out})$
+           $$
+           out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, ksize[0] -1} \max_{n=0, \ldots, ksize[1]-1} \\
+                                    & \text{input}(N_i, C_j, \text{stride[0]} \times h + m,
+                                                   \text{stride[1]} \times w + n)
+           $$
+
+    Args:
+        x (Tensor): The input tensor of pooling operator which is a 4-D tensor with
+                          shape [N, C, H, W]. The format of input tensor is `"NCHW"` or
+                          `"NHWC"`, 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. The data type if float32 or float64.
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_height, pad_width]` or
+            `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`,
+            `pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+            Otherwise, the pool padding size will be a square of an int.
+        ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
+        return_indices (bool): Whether to return the max indices along with the outputs.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+                        The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of pooling result. The data type is same as input tensor.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn.functional as F
+          import numpy as np
+          paddle.disable_static()
+
+          # max pool2d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32))
+          output = F.max_pool2d(input,
+                                kernel_size=2,
+                                stride=2, padding=0)
+          # output.shape [1, 3, 16, 16]
+
+          # for return_indices=True
+          output, max_indices = F.max_pool2d(input,
+                                             kernel_size=2,
+                                             stride=2,
+                                             padding=0,
+                                             return_indices=True)
+          # output.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
+    """
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool2d')
+    kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = utils.convert_to_list(stride, 2, 'pool_stride')
+
+    if data_format not in ["NCHW", "NHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
+            "Attr(data_format): %s." % str(data_format))
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown Attr(padding): '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding))
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0, 0]
+            if ceil_mode != False:
+                raise ValueError(
+                    "When Attr(padding) is \"VALID\", Attr(ceil_mode) must be False. "
+                    "Received ceil_mode: True.")
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0, 0]
+
+    padding = update_padding2d(padding, data_format)
+
+    if in_dygraph_mode():
+        output = core.ops.max_pool2d_with_index(
+            x, 'ksize', kernel_size, 'global_pooling', False, 'strides', stride,
+            'paddings', padding, 'padding_algorithm', padding_algorithm,
+            'use_cudnn', True, 'ceil_mode', ceil_mode, 'use_mkldnn', False,
+            'exclusive', True, 'data_format', data_format)
+        return output if return_indices else output[0]
+
+    op_type = 'max_pool2d_with_index'
+    helper = LayerHelper(op_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    mask = helper.create_variable_for_type_inference(dtype)
+    outputs = {"Out": pool_out, "Mask": mask}
+
+    helper.append_op(
+        type=op_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": 'max',
+            "ksize": kernel_size,
+            "global_pooling": False,
+            "strides": stride,
+            "paddings": padding,
+            "padding_algorithm": padding_algorithm,
+            "use_cudnn": True,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": False,
+            "exclusive": True,
+            "data_format": data_format,
+        })
+
+    return (pool_out, mask) if return_indices else pool_out
+
+
+def avg_pool2d(x,
+               kernel_size,
+               stride=None,
+               padding=0,
+               ceil_mode=False,
+               count_include_pad=True,
+               divisor_override=None,
+               data_format="NCHW",
+               name=None):
+    """
+    This operation applies 2D average pooling over input features based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCHW format, 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.
+
+    Example:
+      Input:
+           X shape: $(N, C, H_{in}, W_{in})$
+      Attr:
+           kernel_size: ksize
+
+      Output:
+           Out shape: $(N, C, H_{out}, W_{out})$
+           $$
+           out(N_i, C_j, h, w)  = \frac{1}{ksize[0] * ksize[1]} \sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1}
+                               input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
+           $$
+
+    Args:
+        x (Tensor): The input tensor of pooling operator which is a 4-D tensor with
+                          shape [N, C, H, W]. The format of input tensor is `"NCHW"` or
+                          `"NHWC"`, 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. The data type if float32 or float64.
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_height, pad_width]` or
+            `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`,
+            `pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+            Otherwise, the pool padding size will be a square of an int.
+        ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is `true`.
+        divisor_override (float): if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+                        The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of pooling result. The data type is same as input tensor.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn.functional as F
+          import numpy as np
+          paddle.disable_static()
+
+          # avg pool2d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32))
+          output = F.avg_pool2d(input,
+                                kernel_size=2,
+                                stride=2, padding=0)
+          # output.shape [1, 3, 16, 16]
+
+    """
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'avg_pool2d')
+    kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = utils.convert_to_list(stride, 2, 'pool_stride')
+
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown Attr(pool_padding): '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding))
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0, 0]
+            if ceil_mode != False:
+                raise ValueError(
+                    "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode) must be False. "
+                    "Received ceil_mode: True.")
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0, 0]
+
+    if data_format not in ["NCHW", "NHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
+            "Attr(data_format): %s." % str(data_format))
+    pool_padding = update_padding2d(padding, data_format)
+
+    if in_dygraph_mode():
+        output = core.ops.pool2d(
+            x, 'pooling_type', 'avg', 'ksize', kernel_size, 'global_pooling',
+            False, 'padding_algorithm', padding_algorithm, 'strides', stride,
+            'paddings', pool_padding, 'use_cudnn', True, 'ceil_mode', ceil_mode,
+            'use_mkldnn', False, 'exclusive', not count_include_pad,
+            'data_format', data_format)
+        if divisor_override is None:
+            return output
+        else:
+            check_instance(divisor_override, "divisor_override")
+            return output * (kernel_size[0] * kernel_size[1]) / divisor_override
+
+    op_type = 'pool2d'
+    helper = LayerHelper(op_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+
+    helper.append_op(
+        type=op_type,
+        inputs={"X": x},
+        outputs={"Out": pool_out},
+        attrs={
+            "pooling_type": "avg",
+            "ksize": kernel_size,
+            "global_pooling": False,
+            "strides": stride,
+            "paddings": pool_padding,
+            "padding_algorithm": padding_algorithm,
+            "use_cudnn": True,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": False,
+            "exclusive": not count_include_pad,
+            "data_format": data_format,
+        })
+
+    if divisor_override is None:
+        return pool_out
+    else:
+        check_instance(divisor_override, "divisor_override")
+        return pool_out * (kernel_size[0] * kernel_size[1]) / divisor_override
+
+
+def max_pool3d(x,
+               kernel_size,
+               stride=None,
+               padding=0,
+               return_indices=False,
+               ceil_mode=False,
+               data_format="NCDHW",
+               name=None):
+    """
+    This operation applies 3D max pooling over input features based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCDHW format, where N is batch size, C is the number of channels,
+    H is the height of the feature,  D is the depth of the feature, and W is the width of the feature.
+
+    Example:
+      Input:
+           X shape: $(N, C, D_{in}, H_{in}, W_{in})$
+      Attr:
+           kernel_size: ksize
+
+      Output:
+           Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
+           $$
+           \text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, ksize[0]-1} \max_{m=0, \ldots, ksize[1]-1} \max_{n=0, \ldots, ksize[2]-1} \\
+                                              & \text{input}(N_i, C_j, \text{stride[0]} \times d + k,
+                                                             \text{stride[1]} \times h + m, \text{stride[2]} \times w + n)
+           $$
+
+    Args:
+        x (Tensor): The input tensor of pooling operator, which is a 5-D tensor with
+                          shape [N, C, D, H, W]. The format of
+                          input tensor is `"NCDHW"` or `"NDHWC"`, where `N` is batch size, `C` is
+                          the number of channels, `D` is the depth of the feature,
+                          `H` is the height of the feature, and `W` is the width
+                          of the feature.
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size
+            is a tuple or list, it must contain three integers,
+            (pool_size_Depth, pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be the cube of an int.
+        stride (string|int|list|tuple)): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool stride size is a tuple or list,
+            it must contain three integers, `[stride_Depth, stride_Height, stride_Width]`.
+            Otherwise, the pool stride size will be a cube of an int.
+        padding (int|list|tuple): The pool padding size. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
+            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
+            and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
+            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+        ceil_mode (bool): ${ceil_mode_comment}
+        return_indices (bool): Whether to return the max indices along with the outputs.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
+                        The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_depth, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of pooling result. The data type is same as input tensor.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn.functional as F
+          import numpy as np
+          paddle.disable_static()
+
+          # max pool3d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
+          output = F.max_pool2d(input,
+                                kernel_size=2,
+                                stride=2, padding=0)
+          output.shape [1, 3, 16, 16, 16]
+
+          # for return_indices=True
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
+          output, max_indices = paddle.nn.functional.max_pool3d(input,
+                                        kernel_size = 2,
+                                        stride = 2,
+                                        padding=0,
+                                        return_indices=True)
+          # output.shape [None, 3, 16, 16, 16], max_indices.shape [None, 3, 16, 16, 16],
+
+    """
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool3d')
+    kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = utils.convert_to_list(stride, 3, 'pool_stride')
+
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown Attr(pool_padding): '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding))
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0, 0, 0]
+            if ceil_mode != False:
+                raise ValueError(
+                    "When Attr(pool_padding) is \"VALID\", ceil_mode must be False. "
+                    "Received ceil_mode: True.")
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0, 0, 0]
+
+    if data_format not in ["NCDHW", "NDHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
+            "Attr(data_format): %s" % str(data_format))
+    padding = update_padding3d(padding, data_format)
+
+    if in_dygraph_mode():
+        output = core.ops.max_pool3d_with_index(
+            x, 'pooling_type', 'max', 'ksize', kernel_size, 'strides', stride,
+            'paddings', padding, 'global_pooling', False, 'padding_algorithm',
+            padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
+            'use_mkldnn', False, 'exclusive', True, 'data_format', data_format)
+        return output if return_indices else output[0]
+
+    op_type = "max_pool3d_with_index"
+    helper = LayerHelper(op_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    mask = helper.create_variable_for_type_inference(dtype)
+    outputs = {"Out": pool_out, "Mask": mask}
+
+    helper.append_op(
+        type=op_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": 'max',
+            "ksize": kernel_size,
+            "global_pooling": False,
+            "strides": stride,
+            "paddings": padding,
+            "padding_algorithm": padding_algorithm,
+            "use_cudnn": True,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": False,
+            "exclusive": False,
+            "data_format": data_format,
+        })
+
+    return (pool_out, mask) if return_indices else pool_out
+
+
+def avg_pool3d(x,
+               kernel_size,
+               stride=None,
+               padding=0,
+               ceil_mode=False,
+               count_include_pad=False,
+               divisor_override=None,
+               data_format="NCDHW",
+               name=None):
+    """
+    This operation applies 3D max pooling over input features based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCDHW format, where N is batch size, C is the number of channels,
+    H is the height of the feature,  D is the depth of the feature, and W is the width of the feature.
+
+    Args:
+        input (Tensor): The input tensor of pooling operator, which is a 5-D tensor with
+                          shape [N, C, D, H, W], where `N` is batch size, `C` is
+                          the number of channels, `D` is the depth of the feature,
+                          `H` is the height of the feature, and `W` is the width
+                          of the feature.
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size
+            is a tuple or list, it must contain three integers,
+            (pool_size_Depth, pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be the cube of an int.
+        stride (string|int|list|tuple)): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool stride size is a tuple or list,
+            it must contain three integers, `[stride_Depth, stride_Height, stride_Width]`.
+            Otherwise, the pool stride size will be a cube of an int.
+        padding (int|list|tuple): The pool padding size. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
+            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
+            and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
+            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+        ceil_mode (bool): ${ceil_mode_comment}
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is True.
+        divisor_override (int|float) if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
+                        The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_depth, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+
+    Returns:
+        Tensor: The output tensor of pooling result. The data type is same as input tensor.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle.fluid as fluid
+          import paddle
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
+          # avg pool3d
+          pool3d = paddle.nn.functional.avg_pool3d(
+                                            input,
+                                            kernel_size = 2,
+                                            stride = 2,
+                                            padding=0)
+          # pool3d.shape: [1, 3, 16, 16, 16]
+    """
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool3d')
+    kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = utils.convert_to_list(stride, 3, 'pool_stride')
+
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown Attr(pool_padding): '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding))
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0, 0, 0]
+            if ceil_mode != False:
+                raise ValueError(
+                    "When Attr(pool_padding) is \"VALID\", ceil_mode must be False. "
+                    "Received ceil_mode: True.")
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0, 0, 0]
+
+    if data_format not in ["NCDHW", "NDHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
+            "Attr(data_format): %s" % str(data_format))
+    padding = update_padding3d(padding, data_format)
+
+    if in_dygraph_mode():
+        output = core.ops.pool3d(
+            x, 'pooling_type', 'avg', 'ksize', kernel_size, 'strides', stride,
+            'paddings', padding, 'global_pooling', False, 'padding_algorithm',
+            padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
+            'use_mkldnn', False, 'exclusive', not count_include_pad,
+            'data_format', data_format)
+        if divisor_override is None:
+            return output
+        else:
+            check_instance(divisor_override, "divisor_override")
+            return output * (kernel_size[0] * kernel_size[1] *
+                             kernel_size[2]) / divisor_override
+
+    op_type = "pool3d"
+    helper = LayerHelper(op_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    outputs = {"Out": pool_out}
+
+    helper.append_op(
+        type=op_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": 'avg',
+            "ksize": kernel_size,
+            "global_pooling": False,
+            "strides": stride,
+            "paddings": padding,
+            "padding_algorithm": padding_algorithm,
+            "use_cudnn": True,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": False,
+            "exclusive": not count_include_pad,
+            "data_format": data_format,
+        })
+
+    if divisor_override is None:
+        return pool_out
+    else:
+        check_instance(divisor_override, "divisor_override")
+        return pool_out * (kernel_size[0] * kernel_size[1] *
+                           kernel_size[2]) / divisor_override
+
+
 def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
     """
 

python/paddle/nn/layer/__init__.py

@@ -60,6 +60,14 @@ from .common import Dropout3D  #DEFINE_ALIAS
 from .common import AlphaDropout  #DEFINE_ALIAS
 from .pooling import AdaptiveAvgPool2d  #DEFINE_ALIAS
 from .pooling import AdaptiveAvgPool3d  #DEFINE_ALIAS
+from .pooling import AvgPool1d  #DEFINE_ALIAS
+from .pooling import MaxPool1d  #DEFINE_ALIAS
+from .pooling import AdaptiveAvgPool1d  #DEFINE_ALIAS
+from .pooling import AdaptiveMaxPool1d  #DEFINE_ALIAS
+from .pooling import AvgPool2d  #DEFINE_ALIAS
+from .pooling import MaxPool2d  #DEFINE_ALIAS
+from .pooling import AvgPool3d  #DEFINE_ALIAS
+from .pooling import MaxPool3d  #DEFINE_ALIAS
 from .conv import Conv1d  #DEFINE_ALIAS
 from .conv import Conv2d  #DEFINE_ALIAS
 from .conv import Conv3d  #DEFINE_ALIAS

python/paddle/nn/layer/pooling.py

@@ -23,6 +23,14 @@ from .. import functional as F
 __all__ = [
     'AdaptiveAvgPool2d',
     'AdaptiveAvgPool3d',
+    'AvgPool1d',
+    'maxPool1d',
+    'AdaptiveMaxPool1d',
+    'AdaptiveAvgPool1d',
+    'AvgPool2d',
+    'MaxPool2d',
+    'AvgPool3d',
+    'MaxPool3d',
 ]
 
 
@@ -194,3 +202,676 @@ class AdaptiveAvgPool3d(layers.Layer):
             output_size=self._output_size,
             data_format=self._data_format,
             name=self._name)
+
+
+class AvgPool1d(layers.Layer):
+    """
+    This operation applies a 1D average pooling over an input signal composed
+    of several input planes, based on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+    The output tensor shape will be [N, C, output_size].
+
+    The output value of the layer with input size (N, C, L),
+    output (N, C, L_{out}) and kernel_size k can be precisely described as
+    For average pool1d:
+
+    ..  math::
+
+       Output(N_i, C_i, l) &= mean(Input[N_i, C_i, stride \times l:stride \times l+k])
+
+
+    Args:
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain one integers.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain one integers.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be the following forms: `[pad_left, pad_right]`. If padding is non-zero,
+            then the input is implicitly zero-padded on both sides for padding number of points.
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is `true`.
+        ceil_mode (bool): ${ceil_mode_comment}Whether to use the ceil function to calculate output height and width.
+            If it is set to False, the floor function will be used. Default False
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        None.
+
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ValueError: If `padding` is a list or tuple but its length greater than 1.
+        ShapeError: If the input is not a 3-D.
+        ShapeError: If the output's shape calculated is not greater than 0.
+
+
+    Examples:
+
+        .. code-block:: python
+          import paddle
+          import paddle.nn as nn
+          paddle.disable_static()
+
+          data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+          AvgPool1d = nn.AvgPool1d(kernel_size=2, stride=2, padding=0)
+          pool_out = AvgPool1d(data)
+          # pool_out shape: [1, 3, 16]
+
+    """
+
+    def __init__(self,
+                 kernel_size,
+                 stride=None,
+                 padding=0,
+                 count_include_pad=True,
+                 ceil_mode=False,
+                 name=None):
+        super(AvgPool1d, self).__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.ceil_mode = ceil_mode
+        self.count_include_pad = count_include_pad
+        self.name = name
+
+    def forward(self, x):
+        out = F.avg_pool1d(x, self.kernel_size, self.stride, self.padding,
+                           self.count_include_pad, self.ceil_mode, self.name)
+        return out
+
+
+class MaxPool1d(layers.Layer):
+    """
+    Applies a 1D max pooling over an input signal composed of several input planes based
+    on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+
+    The output value of the layer with input size (N, C, L),
+    output (N, C, L_{out}) and kernel_size k can be precisely described as
+    For average pool1d:
+
+    ..  math::
+
+       Output(N_i, C_i, l) &=  max(Input[N_i, C_i, stride \times l:stride \times l+k])}
+
+    Args:
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain one integers.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain one integers.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be the following forms: `[pad_left, pad_right]`.
+        return_indices (bool): Whether return the max indices along with the outputs. default is `False`.
+        ceil_mode (bool): Whether to use the ceil function to calculate output height and width. False is the default.
+            If it is set to False, the floor function will be used. Default False
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        None.
+
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ValueError: If `padding` is a list or tuple but its length greater than 1.
+        ShapeError: If the input is not a 3-D.
+        ShapeError: If the output's shape calculated is not greater than 0.
+
+
+    Examples:
+
+        .. code-block:: python
+
+          import paddle
+          import paddle.nn as nn
+          paddle.disable_static()
+
+          data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+          MaxPool1d = nn.MaxPool1d(kernel_size=2, stride=2, padding=0)
+          pool_out = MaxPool1d(data)
+          # pool_out shape: [1, 3, 16]
+
+          MaxPool1d = nn.MaxPool1d(kernel_size=2, stride=2, padding=0, return_indices=True)
+          pool_out, indices = MaxPool1d(data)
+          # pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
+
+    """
+
+    def __init__(self,
+                 kernel_size,
+                 stride=None,
+                 padding=0,
+                 return_indices=False,
+                 ceil_mode=False,
+                 name=None):
+        super(MaxPool1d, self).__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.ceil_mode = ceil_mode
+        self.return_indices = return_indices
+        self.name = name
+
+    def forward(self, input):
+        out = F.max_pool1d(input, self.kernel_size, self.stride, self.padding,
+                           self.return_indices, self.ceil_mode, self.name)
+        return out
+
+
+class AdaptiveAvgPool1d(layers.Layer):
+    """
+
+    This operation applies a 1D adaptive average pooling over an input signal composed
+    of several input planes, based on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+    The output tensor shape will be [N, C, output_size].
+
+    For average adaptive pool1d:
+
+    ..  math::
+
+       lstart &= floor(i * L_{in} / L_{out})
+
+       lend &= ceil((i + 1) * L_{in} / L_{out})
+
+       Output(i) &= \\frac{sum(Input[lstart:lend])}{(lstart - lend)}
+
+    Args:
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain one int.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        None.
+
+    Raises:
+        ValueError: 'pool_size' should be a integer or list or tuple with length as 1.
+
+    Examples:
+        .. code-block:: python
+
+          # average adaptive pool1d
+          # suppose input data in shape of [N, C, L], `output_size` is m or [m],
+          # output shape is [N, C, m], adaptive pool divide L dimension
+          # of input data into m grids averagely and performs poolings in each
+          # grid to get output.
+          # adaptive max pool performs calculations as follow:
+          #
+          #     for i in range(m):
+          #         lstart = floor(i * L / m)
+          #         lend = ceil((i + 1) * L / m)
+          #         output[:, :, i] = sum(input[:, :, lstart: lend])/(lstart - lend)
+          #
+          import paddle
+          import paddle.nn as nn
+          paddle.disable_static()
+
+          data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+          AdaptiveAvgPool1d = nn.AdaptiveAvgPool1d(output_size=16)
+          pool_out = AdaptiveAvgPool1d(data)
+          # pool_out shape: [1, 3, 16]
+    """
+
+    def __init__(self, output_size, name=None):
+        super(AdaptiveAvgPool1d, self).__init__()
+        self.output_size = output_size
+        self.name = name
+
+    def forward(self, input):
+        return F.adaptive_avg_pool1d(input, self.output_size, self.name)
+
+
+class AdaptiveMaxPool1d(layers.Layer):
+    """
+
+    This operation applies a 1D adaptive max pooling over an input signal composed
+    of several input planes, based on the input, output_size, return_indices parameters.
+    Input(X) and output(Out) are in NCL format, where N is batch
+    size, C is the number of channels, L is the length of the feature.
+    The output tensor shape will be [N, C, output_size].
+
+    For max adaptive pool1d:
+
+    ..  math::
+
+       lstart &= floor(i * L_{in} / L_{out})
+
+       lend &= ceil((i + 1) * L_{in} / L_{out})
+
+       Output(i) &= max(Input[lstart:lend])}
+
+    Args:
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+             it must contain one int.
+        return_indices (bool): If true, the index of max pooling point will be returned along
+            with outputs. It cannot be set in average pooling type. Default False.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+    Returns:
+        None.
+
+    Raises:
+        ValueError: 'pool_size' should be a integer or list or tuple with length as 1.
+
+    Examples:
+        .. code-block:: python
+
+          # max adaptive pool1d
+          # suppose input data in shape of [N, C, L], `output_size` is m or [m],
+          # output shape is [N, C, m], adaptive pool divide L dimension
+          # of input data into m grids averagely and performs poolings in each
+          # grid to get output.
+          # adaptive max pool performs calculations as follow:
+          #
+          #     for i in range(m):
+          #         lstart = floor(i * L / m)
+          #         lend = ceil((i + 1) * L / m)
+          #         output[:, :, i] = max(input[:, :, lstart: lend])
+          #
+                    import paddle
+          import paddle.nn as nn
+          paddle.disable_static()
+
+          data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
+          AdaptiveMaxPool1d = nn.AdaptiveMaxPool1d(output_size=16)
+          pool_out = AdaptiveMaxPool1d(data)
+          # pool_out shape: [1, 3, 16]
+
+          # for return_indices = true
+          AdaptiveMaxPool1d = nn.AdaptiveMaxPool1d(output_size=16, return_indices=True)
+          pool_out, indices = AdaptiveMaxPool1d(data)
+          # pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
+
+    """
+
+    def __init__(self, output_size, return_indices=False, name=None):
+        super(AdaptiveMaxPool1d, self).__init__()
+        self.output_size = output_size
+        self.return_indices = return_indices
+        self.name = name
+
+    def forward(self, input):
+        return F.adaptive_max_pool1d(input, self.output_size,
+                                     self.return_indices, self.name)
+
+
+class AvgPool2d(layers.Layer):
+    """
+    This operation applies 2D average pooling over input features based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCHW format, 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.
+
+    Example:
+      Input:
+           X shape: $(N, C, H_{in}, W_{in})$
+      Attr:
+           kernel_size: ksize
+
+      Output:
+           Out shape: $(N, C, H_{out}, W_{out})$
+           $$
+           out(N_i, C_j, h, w)  = \frac{1}{ksize[0] * ksize[1]} \sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1}
+                               input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
+           $$
+
+    Args:
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int. Default: kernel_size.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_height, pad_width]` or
+            `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`,
+            `pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+            Otherwise, the pool padding size will be a square of an int.
+        ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is `true`.
+        divisor_override (int|float) if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+                        The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_height, input_width]`.
+
+    Returns: None.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn as nn
+          import numpy as np
+          paddle.disable_static()
+
+          # max pool2d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32))
+          AvgPool2d = nn.AvgPool2d(kernel_size=2,
+                                stride=2, padding=0)
+          output = AvgPoo2d(input)
+          # output.shape [1, 3, 16, 16]
+
+    """
+
+    def __init__(self,
+                 kernel_size,
+                 stride=None,
+                 padding=0,
+                 ceil_mode=False,
+                 count_include_pad=True,
+                 divisor_override=None,
+                 data_format="NCHW",
+                 name=None):
+        super(AvgPool2d, self).__init__()
+        self.ksize = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.ceil_mode = ceil_mode
+        self.count_include_pad = count_include_pad
+        self.divisor = divisor_override
+        self.data_format = data_format
+        self.name = name
+
+    def forward(self, x):
+        return F.avg_pool2d(
+            x,
+            kernel_size=self.ksize,
+            stride=self.stride,
+            padding=self.padding,
+            ceil_mode=self.ceil_mode,
+            count_include_pad=self.count_include_pad,
+            divisor_override=self.divisor,
+            data_format=self.data_format,
+            name=self.name)
+
+
+class MaxPool2d(layers.Layer):
+    """
+    This operation applies 2D max pooling over input feature based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCHW format, 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.
+
+    Example:
+      Input:
+           X shape: $(N, C, H_{in}, W_{in})$
+      Attr:
+           kernel_size: ksize
+
+      Output:
+           Out shape: $(N, C, H_{out}, W_{out})$
+           $$
+           out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, ksize[0] -1} \max_{n=0, \ldots, ksize[1]-1} \\
+                                    & \text{input}(N_i, C_j, \text{stride[0]} \times h + m,
+                                                   \text{stride[1]} \times w + n)
+           $$
+
+    Args:
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
+        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int. Default: kernel_size.
+        padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_height, pad_width]` or
+            `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`,
+            `pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+            Otherwise, the pool padding size will be a square of an int.
+        ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
+        return_indices (bool): Whether to return the max indices along with the outputs.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+                        The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns: None
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn as nn
+          import numpy as np
+          paddle.disable_static()
+
+          # max pool2d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32))
+          MaxPool2d = nn.MaxPool2d(kernel_size=2,
+                                   stride=2, padding=0)
+          output = MaxPool2d(input)
+          # output.shape [1, 3, 16, 16]
+
+          # for return_indices=True
+          MaxPool2d = nn.MaxPool2d(kernel_size=2,stride=2, padding=0, return_indices=True)
+          output, max_indices = MaxPool2d(input)
+          # output.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
+    """
+
+    def __init__(self,
+                 kernel_size,
+                 stride=None,
+                 padding=0,
+                 return_indices=False,
+                 ceil_mode=False,
+                 data_format="NCHW",
+                 name=None):
+        super(MaxPool2d, self).__init__()
+        self.ksize = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.return_indices = return_indices
+        self.ceil_mode = ceil_mode
+        self.data_format = data_format
+        self.name = name
+
+    def forward(self, x):
+        return F.max_pool2d(
+            x,
+            kernel_size=self.ksize,
+            stride=self.stride,
+            padding=self.padding,
+            return_indices=self.return_indices,
+            data_format=self.data_format,
+            name=self.name)
+
+
+class MaxPool3d(layers.Layer):
+    """
+    This operation applies 3D max pooling over input features based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCDHW format, where N is batch size, C is the number of channels,
+    H is the height of the feature,  D is the depth of the feature, and W is the width of the feature.
+
+    Args:
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size
+            is a tuple or list, it must contain three integers,
+            (pool_size_Depth, pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be the cube of an int.
+        stride (string|int|list|tuple)): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool stride size is a tuple or list,
+            it must contain three integers, `[stride_Depth, stride_Height, stride_Width]`.
+            Otherwise, the pool stride size will be a cube of an int. Default kernel_size.
+        padding (int|list|tuple): The pool padding size. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
+            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
+            and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
+            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+        ceil_mode (bool): when True, will use ceil instead of floor to compute the output shape.
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is True.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+                        The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+
+    Returns:None.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn as nn
+          import numpy as np
+          paddle.disable_static()
+
+          # max pool3d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 2, 3, 32, 32]).astype(np.float32))
+          MaxPool3d = nn.MaxPool3d(kernel_size=2,
+                                   stride=2, padding=0)
+          output = MaxPool3d(input)
+          # output.shape [1, 2, 3, 16, 16]
+
+          # for return_indices=True
+          MaxPool3d = nn.MaxPool3d(kernel_size=2,stride=2, padding=0, return_indices=True)
+          output, max_indices = MaxPool3d(input)
+          # output.shape [1, 2, 3, 16, 16], max_indices.shape [1, 2, 3, 16, 16],
+    """
+
+    def __init__(self,
+                 kernel_size,
+                 stride,
+                 padding,
+                 return_indices=False,
+                 ceil_mode=False,
+                 data_format="NCDHW",
+                 name=None):
+        super(MaxPool3d, self).__init__()
+        self.ksize = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.return_indices = return_indices
+        self.ceil_mode = ceil_mode
+        self.data_format = data_format
+        self.name = name
+
+    def forward(self, x):
+        return F.max_pool3d(
+            x,
+            kernel_size=self.ksize,
+            stride=self.stride,
+            padding=self.padding,
+            return_indices=self.return_indices,
+            data_format=self.data_format,
+            name=self.name)
+
+
+class AvgPool3d(layers.Layer):
+    """
+    This operation applies 3D max pooling over input features based on the input,
+    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
+    in NCDHW format, where N is batch size, C is the number of channels,
+    H is the height of the feature,  D is the depth of the feature, and W is the width of the feature.
+
+    Args:
+        kernel_size (int|list|tuple): The pool kernel size. If pool kernel size
+            is a tuple or list, it must contain three integers,
+            (pool_size_Depth, pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be the cube of an int.
+        stride (string|int|list|tuple)): The pool padding. If `pool_padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If pool stride size is a tuple or list,
+            it must contain three integers, `[stride_Depth, stride_Height, stride_Width]`.
+            Otherwise, the pool stride size will be a cube of an int.
+        padding (int|list|tuple): The pool padding size. If pool padding size is a tuple or list,
+            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
+            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
+            and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
+            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+        ceil_mode (bool): ${ceil_mode_comment}
+        count_include_pad (bool): Whether to exclude padding points in average pooling
+                          mode, default is True.
+        divisor_override (int|float) if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+                        The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+                        `[batch_size, input_channels, input_height, input_width]`.
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns: None.
+    Raises:
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is "VALID", but `ceil_mode` is True.
+        ShapeError: If the output's shape calculated is not greater than 0.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.nn as nn
+          import numpy as np
+          paddle.disable_static()
+
+          # avg pool3d
+          input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 2, 3, 32, 32]).astype(np.float32))
+          AvgPool3d = nn.AvgPool3d(kernel_size=2,
+                                   stride=2, padding=0)
+          output = AvgPool3d(input)
+          # output.shape [1, 2, 3, 16, 16]
+
+    """
+
+    def __init__(self,
+                 kernel_size,
+                 stride,
+                 padding=0,
+                 ceil_mode=False,
+                 count_include_pad=True,
+                 divisor_override=None,
+                 data_format="NCDHW",
+                 name=None):
+        super(AvgPool3d, self).__init__()
+        self.ksize = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.ceil_mode = ceil_mode
+        self.count_include_pad = count_include_pad
+        self.divisor = divisor_override
+        self.data_format = data_format
+        self.name = name
+
+    def forward(self, x):
+        return F.avg_pool3d(
+            x,
+            kernel_size=self.ksize,
+            stride=self.stride,
+            padding=self.padding,
+            ceil_mode=self.ceil_mode,
+            count_include_pad=self.count_include_pad,
+            divisor_override=self.divisor,
+            data_format=self.data_format,
+            name=self.name)