[API2.0]Unify pooling function and add adaptive max pooling function (#26483)

python/paddle/fluid/layers/nn.py

@@ -1858,6 +1858,7 @@ def conv3d(input,
     return helper.append_activation(pre_act)
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.pool2d")
 @templatedoc()
 def pool2d(input,
            pool_size=-1,
@@ -2075,6 +2076,7 @@ def pool2d(input,
     return pool_out
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.pool3d")
 @templatedoc()
 def pool3d(input,
            pool_size=-1,
@@ -2303,6 +2305,7 @@ def pool3d(input,
     return pool_out
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.adaptive_pool2d")
 @templatedoc(op_type="pool2d")
 def adaptive_pool2d(input,
                     pool_size,
@@ -2450,6 +2453,7 @@ def adaptive_pool2d(input,
     return (pool_out, mask) if require_index else pool_out
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.adaptive_pool3d")
 @templatedoc(op_type="pool3d")
 def adaptive_pool3d(input,
                     pool_size,

python/paddle/fluid/tests/unittests/test_adaptive_avg_pool1d.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 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_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_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 test_adaptive_avg_pool1d(self):
+        for place in self.places:
+            self.check_adaptive_avg_dygraph_results(place)
+            self.check_adaptive_avg_static_results(place)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_adaptive_max_pool1d.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
+from op_test import OpTest
+import paddle.fluid.core as core
+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
+
+
+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_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_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 test_adaptive_max_pool1d(self):
+        for place in self.places:
+            self.check_adaptive_max_dygraph_results(place)
+            self.check_adaptive_max_static_results(place)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.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.fluid.core as core
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+
+
+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 adaptive_pool2d_forward(x, output_size, data_format='NCHW',
+                            pool_type="max"):
+
+    N = x.shape[0]
+    C, H, W = [x.shape[1], x.shape[2], x.shape[3]] if data_format == 'NCHW' \
+        else [x.shape[3], x.shape[1], x.shape[2]]
+
+    if (isinstance(output_size, int) or output_size == None):
+        H_out = output_size
+        W_out = output_size
+        output_size = [H_out, W_out]
+    else:
+        H_out, W_out = output_size
+
+    if output_size[0] == None:
+        output_size[0] = H
+        H_out = H
+    if output_size[1] == None:
+        output_size[1] = W
+        W_out = W
+
+    out = np.zeros((N, C, H_out, W_out)) if data_format=='NCHW' \
+        else np.zeros((N, H_out, W_out, C))
+
+    for i in range(H_out):
+        in_h_start = adaptive_start_index(i, H, output_size[0])
+        in_h_end = adaptive_end_index(i, H, output_size[0])
+
+        for j in range(W_out):
+            in_w_start = adaptive_start_index(j, W, output_size[1])
+            in_w_end = adaptive_end_index(j, W, output_size[1])
+
+            if data_format == 'NCHW':
+                x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
+                if pool_type == 'avg':
+                    field_size = (
+                        (in_h_end - in_h_start) * (in_w_end - in_w_start))
+                    out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
+                elif pool_type == 'max':
+                    out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
+            elif data_format == 'NHWC':
+                x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
+                if pool_type == 'avg':
+                    field_size = (
+                        (in_h_end - in_h_start) * (in_w_end - in_w_start))
+                    out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
+                elif pool_type == 'max':
+                    out[:, i, j, :] = np.max(x_masked, axis=(1, 2))
+    return out
+
+
+class TestAdaptiveMaxPool2dAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
+        self.res_1_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[3, 3], pool_type="max")
+
+        self.res_2_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=5, pool_type="max")
+
+        self.res_3_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[2, 5], pool_type="max")
+        """
+        self.res_4_np = adaptive_pool2d_forward(
+            x=self.x_np,
+            output_size=[3, 3],
+            pool_type="max",
+            data_format="NHWC")
+        """
+        self.res_5_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[None, 3], pool_type="max")
+
+    def test_static_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.enable_static()
+            x = paddle.data(name="x", shape=[2, 3, 7, 7], dtype="float32")
+
+            out_1 = paddle.nn.functional.adaptive_max_pool2d(
+                x=x, output_size=[3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_max_pool2d(
+                x=x, output_size=[2, 5])
+
+            #out_4 = paddle.nn.functional.adaptive_max_pool2d(
+            #    x=x, output_size=[3, 3], data_format="NHWC")
+
+            out_5 = paddle.nn.functional.adaptive_max_pool2d(
+                x=x, output_size=[None, 3])
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_5])
+
+            assert np.allclose(res_1, self.res_1_np)
+
+            assert np.allclose(res_2, self.res_2_np)
+
+            assert np.allclose(res_3, self.res_3_np)
+
+            #assert np.allclose(res_4, self.res_4_np)
+
+            assert np.allclose(res_5, self.res_5_np)
+
+    def test_dynamic_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.disable_static(place=place)
+            x = paddle.to_variable(self.x_np)
+
+            out_1 = paddle.nn.functional.adaptive_max_pool2d(
+                x=x, return_indices=False, output_size=[3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_max_pool2d(
+                x=x, output_size=[2, 5])
+
+            #out_4 = paddle.nn.functional.adaptive_max_pool2d(
+            #    x=x, output_size=[3, 3], data_format="NHWC")
+
+            out_5 = paddle.nn.functional.adaptive_max_pool2d(
+                x=x, output_size=[None, 3])
+
+            assert np.allclose(out_1.numpy(), self.res_1_np)
+
+            assert np.allclose(out_2.numpy(), self.res_2_np)
+
+            assert np.allclose(out_3.numpy(), self.res_3_np)
+
+            #assert np.allclose(out_4.numpy(), self.res_4_np)
+
+            assert np.allclose(out_5.numpy(), self.res_5_np)
+
+
+class TestAdaptiveMaxPool2dClassAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
+        self.res_1_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[3, 3], pool_type="max")
+
+        self.res_2_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=5, pool_type="max")
+
+        self.res_3_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[2, 5], pool_type="max")
+
+        #self.res_4_np = adaptive_pool2d_forward(
+        #    x=self.x_np,
+        #    output_size=[3, 3],
+        #    pool_type="max",
+        #    data_format="NHWC")
+
+        self.res_5_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[None, 3], pool_type="max")
+
+    def test_static_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.enable_static()
+            x = paddle.data(name="x", shape=[2, 3, 7, 7], dtype="float32")
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[3, 3])
+            out_1 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=5)
+            out_2 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[2, 5])
+            out_3 = adaptive_max_pool(x=x)
+
+            #    adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(
+            #        output_size=[3, 3], data_format="NHWC")
+            #    out_4 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(
+                output_size=[None, 3])
+            out_5 = adaptive_max_pool(x=x)
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_5])
+
+            assert np.allclose(res_1, self.res_1_np)
+
+            assert np.allclose(res_2, self.res_2_np)
+
+            assert np.allclose(res_3, self.res_3_np)
+
+            #assert np.allclose(res_4, self.res_4_np)
+
+            assert np.allclose(res_5, self.res_5_np)
+
+    def test_dynamic_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.disable_static(place=place)
+            x = paddle.to_variable(self.x_np)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[3, 3])
+            out_1 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=5)
+            out_2 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[2, 5])
+            out_3 = adaptive_max_pool(x=x)
+
+            #adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(
+            #    output_size=[3, 3], data_format="NHWC")
+            #out_4 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(
+                output_size=[None, 3])
+            out_5 = adaptive_max_pool(x=x)
+
+            assert np.allclose(out_1.numpy(), self.res_1_np)
+
+            assert np.allclose(out_2.numpy(), self.res_2_np)
+
+            assert np.allclose(out_3.numpy(), self.res_3_np)
+
+            #assert np.allclose(out_4.numpy(), self.res_4_np)
+
+            assert np.allclose(out_5.numpy(), self.res_5_np)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.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.fluid.core as core
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+
+
+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 adaptive_pool3d_forward(x,
+                            output_size,
+                            adaptive=True,
+                            data_format='NCDHW',
+                            pool_type='max'):
+
+    N = x.shape[0]
+    C, D, H, W = [x.shape[1], x.shape[2], x.shape[3], x.shape[4]] \
+        if data_format == 'NCDHW' else [x.shape[4], x.shape[1], x.shape[2],x.shape[3]]
+
+    if (isinstance(output_size, int) or output_size == None):
+        H_out = output_size
+        W_out = output_size
+        D_out = output_size
+        output_size = [D_out, H_out, W_out]
+    else:
+        D_out, H_out, W_out = output_size
+
+    if output_size[0] == None:
+        output_size[0] = D
+        D_out = D
+    if output_size[1] == None:
+        output_size[1] = H
+        H_out = H
+    if output_size[2] == None:
+        output_size[2] = W
+        W_out = W
+
+    out = np.zeros((N, C, D_out, H_out, W_out)) if data_format=='NCDHW' \
+        else np.zeros((N, D_out, H_out, W_out, C))
+    for k in range(D_out):
+        d_start = adaptive_start_index(k, D, output_size[0])
+        d_end = adaptive_end_index(k, D, output_size[0])
+
+        for i in range(H_out):
+            h_start = adaptive_start_index(i, H, output_size[1])
+            h_end = adaptive_end_index(i, H, output_size[1])
+
+            for j in range(W_out):
+                w_start = adaptive_start_index(j, W, output_size[2])
+                w_end = adaptive_end_index(j, W, output_size[2])
+
+                if data_format == 'NCDHW':
+                    x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:
+                                 w_end]
+                    if pool_type == 'avg':
+                        field_size = (d_end - d_start) * (h_end - h_start) * (
+                            w_end - w_start)
+                        out[:, :, k, i, j] = np.sum(x_masked,
+                                                    axis=(2, 3, 4)) / field_size
+                    elif pool_type == 'max':
+                        out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4))
+
+                elif data_format == 'NDHWC':
+                    x_masked = x[:, d_start:d_end, h_start:h_end, w_start:
+                                 w_end, :]
+                    if pool_type == 'avg':
+                        field_size = (d_end - d_start) * (h_end - h_start) * (
+                            w_end - w_start)
+                        out[:, k, i, j, :] = np.sum(x_masked,
+                                                    axis=(1, 2, 3)) / field_size
+                    elif pool_type == 'max':
+                        out[:, k, i, j, :] = np.max(x_masked, axis=(1, 2, 3))
+    return out
+
+
+class TestAdaptiveMaxPool3dAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32")
+        self.res_1_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[3, 3, 3], pool_type="max")
+
+        self.res_2_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=5, pool_type="max")
+
+        self.res_3_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[2, 3, 5], pool_type="max")
+
+        self.res_4_np = adaptive_pool3d_forward(
+            x=self.x_np,
+            output_size=[3, 3, 3],
+            pool_type="max",
+            data_format="NDHWC")
+
+        self.res_5_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[None, 3, None], pool_type="max")
+
+    def test_static_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.enable_static()
+            x = paddle.data(name="x", shape=[2, 3, 5, 7, 7], dtype="float32")
+
+            out_1 = paddle.nn.functional.adaptive_max_pool3d(
+                x=x, output_size=[3, 3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_max_pool3d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_max_pool3d(
+                x=x, output_size=[2, 3, 5])
+
+            #out_4 = paddle.nn.functional.adaptive_max_pool3d(
+            #    x=x, output_size=[3, 3, 3], data_format="NDHWC")
+
+            out_5 = paddle.nn.functional.adaptive_max_pool3d(
+                x=x, output_size=[None, 3, None])
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_5])
+
+            assert np.allclose(res_1, self.res_1_np)
+
+            assert np.allclose(res_2, self.res_2_np)
+
+            assert np.allclose(res_3, self.res_3_np)
+
+            #assert np.allclose(res_4, self.res_4_np)
+
+            assert np.allclose(res_5, self.res_5_np)
+
+    def test_dynamic_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.disable_static(place=place)
+            x = paddle.to_variable(self.x_np)
+
+            out_1 = paddle.nn.functional.adaptive_max_pool3d(
+                x=x, output_size=[3, 3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_max_pool3d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_max_pool3d(
+                x=x, output_size=[2, 3, 5])
+
+            #out_4 = paddle.nn.functional.adaptive_max_pool3d(
+            #    x=x, output_size=[3, 3, 3], data_format="NDHWC")
+
+            out_5 = paddle.nn.functional.adaptive_max_pool3d(
+                x=x, output_size=[None, 3, None])
+
+            assert np.allclose(out_1.numpy(), self.res_1_np)
+
+            assert np.allclose(out_2.numpy(), self.res_2_np)
+
+            assert np.allclose(out_3.numpy(), self.res_3_np)
+
+            #assert np.allclose(out_4.numpy(), self.res_4_np)
+
+            assert np.allclose(out_5.numpy(), self.res_5_np)
+
+
+class TestAdaptiveMaxPool3dClassAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32")
+        self.res_1_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[3, 3, 3], pool_type="max")
+
+        self.res_2_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=5, pool_type="max")
+
+        self.res_3_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[2, 3, 5], pool_type="max")
+
+        # self.res_4_np = adaptive_pool3d_forward(
+        #     x=self.x_np,
+        #     output_size=[3, 3, 3],
+        #     pool_type="max",
+        #     data_format="NDHWC")
+
+        self.res_5_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[None, 3, None], pool_type="max")
+
+    def test_static_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.enable_static()
+            x = paddle.data(name="x", shape=[2, 3, 5, 7, 7], dtype="float32")
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+                output_size=[3, 3, 3])
+            out_1 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(output_size=5)
+            out_2 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+                output_size=[2, 3, 5])
+            out_3 = adaptive_max_pool(x=x)
+
+            #     adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+            #         output_size=[3, 3, 3], data_format="NDHWC")
+            #     out_4 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+                output_size=[None, 3, None])
+            out_5 = adaptive_max_pool(x=x)
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_5])
+
+            assert np.allclose(res_1, self.res_1_np)
+
+            assert np.allclose(res_2, self.res_2_np)
+
+            assert np.allclose(res_3, self.res_3_np)
+
+            #     assert np.allclose(res_4, self.res_4_np)
+
+            assert np.allclose(res_5, self.res_5_np)
+
+    def test_dynamic_graph(self):
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+            paddle.disable_static(place=place)
+            x = paddle.to_variable(self.x_np)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+                output_size=[3, 3, 3])
+            out_1 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(output_size=5)
+            out_2 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+                output_size=[2, 3, 5])
+            out_3 = adaptive_max_pool(x=x)
+
+            #     adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+            #         output_size=[3, 3, 3], data_format="NDHWC")
+            #     out_4 = adaptive_max_pool(x=x)
+
+            adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
+                output_size=[None, 3, None])
+            out_5 = adaptive_max_pool(x=x)
+
+            assert np.allclose(out_1.numpy(), self.res_1_np)
+
+            assert np.allclose(out_2.numpy(), self.res_2_np)
+
+            assert np.allclose(out_3.numpy(), self.res_3_np)
+
+            #     assert np.allclose(out_4.numpy(), self.res_4_np)
+
+            assert np.allclose(out_5.numpy(), self.res_5_np)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -174,66 +174,6 @@ class TestPool1d_API(unittest.TestCase):
             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")
@@ -265,10 +205,6 @@ class TestPool1d_API(unittest.TestCase):
             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)
 

python/paddle/nn/__init__.py

@@ -97,8 +97,20 @@ from .layer.common import Dropout  #DEFINE_ALIAS
 from .layer.common import Dropout2D  #DEFINE_ALIAS
 from .layer.common import Dropout3D  #DEFINE_ALIAS
 from .layer.common import AlphaDropout  #DEFINE_ALIAS
+
+from .layer.pooling import AvgPool1d  #DEFINE_ALIAS
+from .layer.pooling import AvgPool2d  #DEFINE_ALIAS
+from .layer.pooling import AvgPool3d  #DEFINE_ALIAS
+from .layer.pooling import MaxPool1d  #DEFINE_ALIAS
+from .layer.pooling import MaxPool2d  #DEFINE_ALIAS
+from .layer.pooling import MaxPool3d  #DEFINE_ALIAS
+from .layer.pooling import AdaptiveAvgPool1d  #DEFINE_ALIAS
 from .layer.pooling import AdaptiveAvgPool2d  #DEFINE_ALIAS
 from .layer.pooling import AdaptiveAvgPool3d  #DEFINE_ALIAS
+
+from .layer.pooling import AdaptiveMaxPool1d  #DEFINE_ALIAS
+from .layer.pooling import AdaptiveMaxPool2d  #DEFINE_ALIAS
+from .layer.pooling import AdaptiveMaxPool3d  #DEFINE_ALIAS
 from .layer.conv import Conv1d  #DEFINE_ALIAS
 from .layer.conv import Conv2d  #DEFINE_ALIAS
 from .layer.conv import Conv3d  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -170,22 +170,28 @@ from .norm import layer_norm  #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 avg_pool1d  #DEFINE_ALIAS
 from .pooling import adaptive_pool2d  #DEFINE_ALIAS
 from .pooling import adaptive_pool3d  #DEFINE_ALIAS
-from .rnn import rnn  #DEFINE_ALIAS
-from .rnn import birnn  #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_pool1d  #DEFINE_ALIAS
+from .pooling import max_pool2d  #DEFINE_ALIAS
 from .pooling import max_pool3d  #DEFINE_ALIAS
+
+from .pooling import adaptive_pool2d  #DEFINE_ALIAS
+from .pooling import adaptive_pool3d  #DEFINE_ALIAS
+from .pooling import adaptive_max_pool1d  #DEFINE_ALIAS
+from .pooling import adaptive_max_pool2d  #DEFINE_ALIAS
+from .pooling import adaptive_max_pool3d  #DEFINE_ALIAS
+from .pooling import adaptive_avg_pool1d  #DEFINE_ALIAS
 from .pooling import adaptive_avg_pool2d  #DEFINE_ALIAS
 from .pooling import adaptive_avg_pool3d  #DEFINE_ALIAS
+
+from .rnn import rnn  #DEFINE_ALIAS
+from .rnn import birnn  #DEFINE_ALIAS
 # from .rnn import gru_unit        #DEFINE_ALIAS
 # from .rnn import lstm        #DEFINE_ALIAS
 # from .rnn import lstm_unit        #DEFINE_ALIAS

python/paddle/nn/functional/conv.py

@@ -158,7 +158,7 @@ def conv1d(x,
         bias (Tensor, optional): The bias with shape [M,]. Default: None.
         stride (int or tuple, optional): The stride size. If stride is a tuple, it must
             contain one integers, (stride_size). Default: 1.
-        padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
+        padding(int|str|tuple|list, optional): The padding size. Padding could be in one of the following forms.
             1. a string in ['valid', 'same'].
             2. an int, which means the feature map is zero paded by size of `padding` on both sides.
             3. a list[int] or tuple[int] whose length is 1, which means the feature map is zero paded by size of `padding[0]` on both sides.
@@ -185,7 +185,7 @@ def conv1d(x,
         same with input.
 
     Raises:
-        ValueError: If the channel dimmention of the input is less than or equal to zero.
+        ValueError: If the channel dimension of the input is less than or equal to zero.
         ValueError: If `data_format` is not "NCL" or "NLC".
         ValueError: If `padding` is a string, but not "SAME" or "VALID".
         ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
@@ -238,7 +238,7 @@ def conv1d(x,
     num_channels = x.shape[channel_dim]
     num_filters = weight.shape[0]
     if num_channels < 0:
-        raise ValueError("The channel dimmention of the input({}) "
+        raise ValueError("The channel dimension of the input({}) "
                          "should be defined. Received: {}.".format(
                              x.shape, num_channels))
     if num_channels % groups != 0:
@@ -260,7 +260,7 @@ def conv1d(x,
         padding = padding + [0]
     else:
         raise ValueError(
-            "The size of padding's dimmention should 1 or 2. But got padding={}".
+            "The size of padding's dimension should be 1 or 2. But got padding={}".
             format(padding))
 
     stride = utils.convert_to_list(stride, 1, 'stride') + [1]
@@ -424,7 +424,7 @@ def conv2d(x,
 
     Raises:
         ValueError: If `data_format` is not "NCHW" or "NHWC".
-        ValueError: If the channel dimmention of the input is less than or equal to zero.
+        ValueError: If the channel dimension of the input is less than or equal to zero.
         ValueError: If `padding` is a string, but not "SAME" or "VALID".
         ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
             or the element corresponding to the input's channel is not 0.
@@ -465,7 +465,7 @@ def conv2d(x,
     num_channels = x.shape[channel_dim]
     num_filters = weight.shape[0]
     if num_channels < 0:
-        raise ValueError("The channel dimmention of the input({}) "
+        raise ValueError("The channel dimension of the input({}) "
                          "should be defined. Received: {}.".format(
                              x.shape, num_channels))
     if num_channels % groups != 0:
@@ -710,7 +710,7 @@ def conv_transpose1d(x,
 
     num_channels = x.shape[channel_dim]
     if num_channels < 0:
-        raise ValueError("The channel dimmention of the input({}) "
+        raise ValueError("The channel dimension of the input({}) "
                          "should be defined. Received: {}.".format(
                              x.shape, num_channels))
     if num_channels % groups != 0:
@@ -728,7 +728,7 @@ def conv_transpose1d(x,
         padding = padding + [0]
     else:
         raise ValueError(
-            "The size of padding's dimmention should 1 or 2. But got padding={}".
+            "The size of padding's dimension should 1 or 2. But got padding={}".
             format(padding))
 
     stride = utils.convert_to_list(stride, 1, 'stride') + [1]
@@ -965,7 +965,7 @@ def conv_transpose2d(x,
     channel_dim = -1 if channel_last else 1
     num_channels = x.shape[channel_dim]
     if num_channels < 0:
-        raise ValueError("The channel dimmention of the input({}) "
+        raise ValueError("The channel dimension of the input({}) "
                          "should be defined. Received: {}.".format(
                              x.shape, num_channels))
     if num_channels % groups != 0:
@@ -1146,7 +1146,7 @@ def conv3d(x,
 
     Raises:
         ValueError: If `data_format` is not "NCDHW" or "NDHWC".
-        ValueError: If the channel dimmention of the input is less than or equal to zero.
+        ValueError: If the channel dimension of the input is less than or equal to zero.
         ValueError: If `padding` is a string, but not "SAME" or "VALID".
         ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
             or the element corresponding to the input's channel is not 0.
@@ -1187,7 +1187,7 @@ def conv3d(x,
     num_filters = weight.shape[0]
     if num_channels < 0:
         raise ValueError(
-            "The channel dimmention of the input({}) should be defined. "
+            "The channel dimension of the input({}) should be defined. "
             "Received: {}.".format(x.shape, num_channels))
     if num_channels % groups != 0:
         raise ValueError(
@@ -1422,7 +1422,7 @@ def conv_transpose3d(x,
     num_filters = weight.shape[1]
     if num_channels < 0:
         raise ValueError(
-            "The channel dimmention of the input({}) should be defined. "
+            "The channel dimension of the input({}) should be defined. "
             "Received: {}.".format(x.shape, num_channels))
     if num_channels % groups != 0:
         raise ValueError(

python/paddle/nn/layer/__init__.py

@@ -66,16 +66,18 @@ from .common import Dropout  #DEFINE_ALIAS
 from .common import Dropout2D  #DEFINE_ALIAS
 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 MaxPool1d  #DEFINE_ALIAS
+from .pooling import MaxPool2d  #DEFINE_ALIAS
 from .pooling import MaxPool3d  #DEFINE_ALIAS
+from .pooling import AdaptiveAvgPool1d  #DEFINE_ALIAS
+from .pooling import AdaptiveAvgPool2d  #DEFINE_ALIAS
+from .pooling import AdaptiveAvgPool3d  #DEFINE_ALIAS
+from .pooling import AdaptiveMaxPool1d  #DEFINE_ALIAS
+from .pooling import AdaptiveMaxPool2d  #DEFINE_ALIAS
+from .pooling import AdaptiveMaxPool3d  #DEFINE_ALIAS
 from .conv import Conv1d  #DEFINE_ALIAS
 from .conv import Conv2d  #DEFINE_ALIAS
 from .conv import Conv3d  #DEFINE_ALIAS