[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/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/common.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# TODO: define the common classes to build a neural network  
+# TODO: define the common classes to build a neural network
 from ...fluid.dygraph import BilinearTensorProduct  #DEFINE_ALIAS
 from ...fluid.dygraph import Pool2D  #DEFINE_ALIAS
 from ...fluid.dygraph import Embedding  #DEFINE_ALIAS
@@ -583,8 +583,8 @@ class ReflectionPad1d(layers.Layer):
            Default is  "NCL"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -642,8 +642,8 @@ class ReplicationPad1d(layers.Layer):
            Default is  "NCL"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -657,7 +657,7 @@ class ReplicationPad1d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -702,8 +702,8 @@ class ConstantPad1d(layers.Layer):
            Default is  "NCL"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -718,7 +718,7 @@ class ConstantPad1d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -765,8 +765,8 @@ class ConstantPad2d(layers.Layer):
            Default is  "NCHW"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -781,7 +781,7 @@ class ConstantPad2d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -830,8 +830,8 @@ class ZeroPad2d(layers.Layer):
            Default is  "NCHW"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -845,7 +845,7 @@ class ZeroPad2d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -892,8 +892,8 @@ class ReplicationPad2d(layers.Layer):
            Default is  "NCHW"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -907,7 +907,7 @@ class ReplicationPad2d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -954,8 +954,8 @@ class ReflectionPad2d(layers.Layer):
            Default is  "NCHW"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -969,7 +969,7 @@ class ReflectionPad2d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -1019,8 +1019,8 @@ class ConstantPad3d(layers.Layer):
            Default is  "NCDHW"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -1035,7 +1035,7 @@ class ConstantPad3d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -1084,8 +1084,8 @@ class ReplicationPad3d(layers.Layer):
            Default is  "NCDHW"
         name (str, optional) : The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
-        
-    Returns: 
+
+    Returns:
         None
 
     Examples:
@@ -1099,7 +1099,7 @@ class ReplicationPad3d(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np
@@ -1141,7 +1141,7 @@ class CosineSimilarity(layers.Layer):
     Parameters:
         axis (int): Dimension of vectors to compute cosine similarity. Default is 1.
         eps(float): Small value to avoid division by zero. Default is 1e-8.
-    Returns: 
+    Returns:
         None
 
     Examples:
@@ -1162,7 +1162,7 @@ class CosineSimilarity(layers.Layer):
 
     Code Examples:
         .. code-block:: python
-        
+
             import paddle
             import paddle.nn as nn
             import numpy as np