[2.0API]Add adaptive_avg_pool_2/3d (#26369)

* add adaptive_avg_pool2d

* add adaptive_avg_pool3d

python/paddle/fluid/tests/unittests/test_adaptive_avg_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="avg"):
+
+    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 TestAdaptiveAvgPool2dAPI(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="avg")
+
+        self.res_2_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=5, pool_type="avg")
+
+        self.res_3_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[2, 5], pool_type="avg")
+
+        self.res_4_np = adaptive_pool2d_forward(
+            x=self.x_np,
+            output_size=[3, 3],
+            pool_type="avg",
+            data_format="NHWC")
+
+        self.res_5_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[None, 3], pool_type="avg")
+
+    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_avg_pool2d(
+                x=x, output_size=[3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_avg_pool2d(
+                x=x, output_size=[2, 5])
+
+            out_4 = paddle.nn.functional.adaptive_avg_pool2d(
+                x=x, output_size=[3, 3], data_format="NHWC")
+
+            out_5 = paddle.nn.functional.adaptive_avg_pool2d(
+                x=x, output_size=[None, 3])
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_4, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_4, 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_avg_pool2d(
+                x=x, output_size=[3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_avg_pool2d(
+                x=x, output_size=[2, 5])
+
+            out_4 = paddle.nn.functional.adaptive_avg_pool2d(
+                x=x, output_size=[3, 3], data_format="NHWC")
+
+            out_5 = paddle.nn.functional.adaptive_avg_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 TestAdaptiveAvgPool2dClassAPI(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="avg")
+
+        self.res_2_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=5, pool_type="avg")
+
+        self.res_3_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[2, 5], pool_type="avg")
+
+        self.res_4_np = adaptive_pool2d_forward(
+            x=self.x_np,
+            output_size=[3, 3],
+            pool_type="avg",
+            data_format="NHWC")
+
+        self.res_5_np = adaptive_pool2d_forward(
+            x=self.x_np, output_size=[None, 3], pool_type="avg")
+
+    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_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[3, 3])
+            out_1 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=5)
+            out_2 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[2, 5])
+            out_3 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(
+                output_size=[3, 3], data_format="NHWC")
+            out_4 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(
+                output_size=[None, 3])
+            out_5 = adaptive_avg_pool(x=x)
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_4, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_4, 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_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[3, 3])
+            out_1 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=5)
+            out_2 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[2, 5])
+            out_3 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(
+                output_size=[3, 3], data_format="NHWC")
+            out_4 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(
+                output_size=[None, 3])
+            out_5 = adaptive_avg_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_avg_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='avg'):
+
+    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 TestAdaptiveAvgPool3dAPI(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="avg")
+
+        self.res_2_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=5, pool_type="avg")
+
+        self.res_3_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[2, 3, 5], pool_type="avg")
+
+        self.res_4_np = adaptive_pool3d_forward(
+            x=self.x_np,
+            output_size=[3, 3, 3],
+            pool_type="avg",
+            data_format="NDHWC")
+
+        self.res_5_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[None, 3, None], pool_type="avg")
+
+    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_avg_pool3d(
+                x=x, output_size=[3, 3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_avg_pool3d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_avg_pool3d(
+                x=x, output_size=[2, 3, 5])
+
+            out_4 = paddle.nn.functional.adaptive_avg_pool3d(
+                x=x, output_size=[3, 3, 3], data_format="NDHWC")
+
+            out_5 = paddle.nn.functional.adaptive_avg_pool3d(
+                x=x, output_size=[None, 3, None])
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_4, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_4, 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_avg_pool3d(
+                x=x, output_size=[3, 3, 3])
+
+            out_2 = paddle.nn.functional.adaptive_avg_pool3d(x=x, output_size=5)
+
+            out_3 = paddle.nn.functional.adaptive_avg_pool3d(
+                x=x, output_size=[2, 3, 5])
+
+            out_4 = paddle.nn.functional.adaptive_avg_pool3d(
+                x=x, output_size=[3, 3, 3], data_format="NDHWC")
+
+            out_5 = paddle.nn.functional.adaptive_avg_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 TestAdaptiveAvgPool3dClassAPI(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="avg")
+
+        self.res_2_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=5, pool_type="avg")
+
+        self.res_3_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[2, 3, 5], pool_type="avg")
+
+        self.res_4_np = adaptive_pool3d_forward(
+            x=self.x_np,
+            output_size=[3, 3, 3],
+            pool_type="avg",
+            data_format="NDHWC")
+
+        self.res_5_np = adaptive_pool3d_forward(
+            x=self.x_np, output_size=[None, 3, None], pool_type="avg")
+
+    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_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[3, 3, 3])
+            out_1 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=5)
+            out_2 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[2, 3, 5])
+            out_3 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[3, 3, 3], data_format="NDHWC")
+            out_4 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[None, 3, None])
+            out_5 = adaptive_avg_pool(x=x)
+
+            exe = paddle.static.Executor(place=place)
+            [res_1, res_2, res_3, res_4, res_5] = exe.run(
+                fluid.default_main_program(),
+                feed={"x": self.x_np},
+                fetch_list=[out_1, out_2, out_3, out_4, 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_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[3, 3, 3])
+            out_1 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=5)
+            out_2 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[2, 3, 5])
+            out_3 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[3, 3, 3], data_format="NDHWC")
+            out_4 = adaptive_avg_pool(x=x)
+
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
+                output_size=[None, 3, None])
+            out_5 = adaptive_avg_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/nn/__init__.py

@@ -87,6 +87,8 @@ from .layer.common import Embedding  #DEFINE_ALIAS
 from .layer.common import Linear  #DEFINE_ALIAS
 from .layer.common import Flatten  #DEFINE_ALIAS
 from .layer.common import UpSample  #DEFINE_ALIAS
+from .layer.pooling import AdaptiveAvgPool2d  #DEFINE_ALIAS
+from .layer.pooling import AdaptiveAvgPool3d  #DEFINE_ALIAS
 from .layer.conv import Conv2D  #DEFINE_ALIAS
 from .layer.conv import Conv2DTranspose  #DEFINE_ALIAS
 from .layer.conv import Conv3D  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -160,6 +160,8 @@ 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 adaptive_avg_pool2d  #DEFINE_ALIAS
+from .pooling import adaptive_avg_pool3d  #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/pooling.py

@@ -13,9 +13,268 @@
 # limitations under the License.
 
 # TODO: define pooling functions
+import paddle
+from ...fluid import core
 from ...fluid.layers import pool2d  #DEFINE_ALIAS
 from ...fluid.layers import pool3d  #DEFINE_ALIAS
 from ...fluid.layers import adaptive_pool2d  #DEFINE_ALIAS
 from ...fluid.layers import adaptive_pool3d  #DEFINE_ALIAS
+from ...fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
+from ...fluid.layers import utils
+from ...fluid.layer_helper import LayerHelper
+from ...fluid.framework import in_dygraph_mode
 
-__all__ = ['pool2d', 'pool3d', 'adaptive_pool2d', 'adaptive_pool3d']
+__all__ = [
+    'pool2d', 'pool3d', 'adaptive_pool2d', 'adaptive_pool3d',
+    'adaptive_avg_pool2d', 'adaptive_avg_pool3d'
+]
+
+
+def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
+    """
+
+    This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions
+    of the output tensor are determined by the parameter output_size.
+    See more detail in :ref:`api_nn_pooling_AdaptiveAvgPool2d` .
+
+    For avg adaptive pool2d:
+
+    ..  math::
+
+       hstart &= floor(i * H_{in} / H_{out})
+
+       hend &= ceil((i + 1) * H_{in} / H_{out})
+
+       wstart &= floor(j * W_{in} / W_{out})
+
+       wend &= ceil((j + 1) * W_{in} / W_{out})
+
+       Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
+
+    Args:
+        x (Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor.
+                          The data type can be float16, float32, float64, int32 or int64.
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two element, (H, W). H and W can be either a int, or None which means
+            the size will be the same as that of the input.
+        data_format (str): The data format of the input and output data. An optional string
+            from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in
+            the order of: [batch_size, input_channels, input_height, input_width].
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of avg adaptive pool2d result. The data type is same as input tensor.
+
+    Raises:
+        ValueError: If `data_format` is not "NCHW" or "NHWC".
+
+    Examples:
+        .. code-block:: python
+
+            # adaptive avg pool2d
+            # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
+            # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
+            # of input data into m * n grids averagely and performs poolings in each
+            # grid to get output.
+            # adaptive avg pool performs calculations as follow:
+            #
+            #     for i in range(m):
+            #         for j in range(n):
+            #             hstart = floor(i * H / m)
+            #             hend = ceil((i + 1) * H / m)
+            #             wstart = floor(i * W / n)
+            #             wend = ceil((i + 1) * W / n)
+            #             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
+            #
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            input_data = np.random.rand(2, 3, 32, 32)
+            x = paddle.to_tensor(input_data)
+            # x.shape is [2, 3, 32, 32]
+            pool_out = paddle.nn.functional.adaptive_avg_pool2d(
+                            x = x,
+                            output_size=[3, 3])
+            # pool_out.shape is [2, 3, 3, 3]
+    """
+    if not in_dygraph_mode():
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'],
+            'adaptive_avg_pool2d')
+    check_type(data_format, 'data_format', str, 'adaptive_avg_pool2d')
+
+    if data_format not in ["NCHW", "NHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
+            "Attr(data_format): %s." % str(data_format))
+
+    if data_format == "NCHW":
+        in_h, in_w = x.shape[2:4]
+    else:
+        in_h, in_w = x.shape[1:3]
+
+    if isinstance(output_size, int):
+        output_size = utils.convert_to_list(output_size, 2, 'output_size')
+    else:
+        if output_size[0] == None:
+            output_size[0] = in_h
+        if output_size[1] == None:
+            output_size[1] = in_w
+
+    if in_dygraph_mode():
+        output = core.ops.pool2d(x, 'pooling_type', 'avg', 'ksize', output_size,
+                                 'global_pooling', False, 'adaptive', True,
+                                 'data_format', data_format)
+        return output
+
+    l_type = 'pool2d'
+
+    helper = LayerHelper(l_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+
+    outputs = {"Out": pool_out}
+
+    helper.append_op(
+        type=l_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": "avg",
+            "ksize": output_size,
+            "adaptive": True,
+            "data_format": data_format,
+        })
+
+    return pool_out
+
+
+def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
+    """
+
+    This operation applies 3D adaptive avg pooling on input tensor. The h and w dimensions
+    of the output tensor are determined by the parameter output_size.
+    See more detail in :ref:`api_nn_pooling_AdaptiveAvgPool3d` .
+
+    For avg adaptive pool3d:
+
+    ..  math::
+
+      dstart &= floor(i * D_{in} / D_{out})
+
+      dend &= ceil((i + 1) * D_{in} / D_{out})
+
+      hstart &= floor(j * H_{in} / H_{out})
+
+      hend &= ceil((j + 1) * H_{in} / H_{out})
+
+      wstart &= floor(k * W_{in} / W_{out})
+
+      wend &= ceil((k + 1) * W_{in} / W_{out})
+
+      Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
+
+    Args:
+        x (Tensor): The input tensor of adaptive avg pool3d operator, which is a 5-D tensor.
+                          The data type can be float16, float32, float64, int32 or int64.
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means
+            the size will be the same as that of the input.
+        data_format (str): The data format of the input and output data. An optional string
+            from: "NCDHW", "NDHWC". The default is "NCDHW". When it is "NCDHW", the data is stored in
+            the order of: [batch_size, input_channels, input_depth, input_height, input_width].
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: The output tensor of avg adaptive pool3d result. The data type is same as input tensor.
+
+    Raises:
+        ValueError: If `data_format` is not "NCDHW" or "NDHWC".
+
+    Examples:
+        .. code-block:: python
+
+            # adaptive avg pool3d
+            # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
+            # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
+            # of input data into l * m * n grids averagely and performs poolings in each
+            # grid to get output.
+            # adaptive avg pool performs calculations as follow:
+            #
+            #     for i in range(l):
+            #         for j in range(m):
+            #             for k in range(n):
+            #                 dstart = floor(i * D / l)
+            #                 dend = ceil((i + 1) * D / l)
+            #                 hstart = floor(j * H / m)
+            #                 hend = ceil((j + 1) * H / m)
+            #                 wstart = floor(k * W / n)
+            #                 wend = ceil((k + 1) * W / n)
+            #                 output[:, :, i, j, k] =
+            #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            input_data = np.random.rand(2, 3, 8, 32, 32)
+            x = paddle.to_tensor(input_data)
+            # x.shape is [2, 3, 8, 32, 32]
+            pool_out = paddle.nn.functional.adaptive_avg_pool3d(
+                            x = x,
+                            output_size=[3, 3, 3])
+            # pool_out.shape is [2, 3, 3, 3, 3]
+    """
+    if not in_dygraph_mode():
+        check_variable_and_dtype(
+            x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'],
+            'adaptive_avg_pool3d')
+    check_type(data_format, 'data_format', str, 'adaptive_avg_pool3d')
+
+    if data_format not in ["NCDHW", "NDHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
+            "Attr(data_format): %s." % str(data_format))
+
+    if data_format == "NCDHW":
+        in_l, in_h, in_w = x.shape[2:5]
+    else:
+        in_l, in_h, in_w = x.shape[1:4]
+
+    if isinstance(output_size, int):
+        output_size = utils.convert_to_list(output_size, 3, 'output_size')
+    else:
+        if output_size[0] == None:
+            output_size[0] = in_l
+        if output_size[1] == None:
+            output_size[1] = in_h
+        if output_size[2] == None:
+            output_size[2] = in_w
+
+    if in_dygraph_mode():
+        output = core.ops.pool3d(x, 'pooling_type', 'avg', 'ksize', output_size,
+                                 'global_pooling', False, 'adaptive', True,
+                                 'data_format', data_format)
+        return output
+
+    l_type = 'pool3d'
+
+    helper = LayerHelper(l_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_variable_for_type_inference(dtype)
+    outputs = {"Out": pool_out}
+
+    helper.append_op(
+        type=l_type,
+        inputs={"X": x},
+        outputs=outputs,
+        attrs={
+            "pooling_type": "avg",
+            "ksize": output_size,
+            "adaptive": True,
+            "data_format": data_format,
+        })
+
+    return pool_out

python/paddle/nn/layer/__init__.py

@@ -52,6 +52,8 @@ from .common import Embedding  #DEFINE_ALIAS
 from .common import Linear  #DEFINE_ALIAS
 from .common import Flatten  #DEFINE_ALIAS
 from .common import UpSample  #DEFINE_ALIAS
+from .pooling import AdaptiveAvgPool2d  #DEFINE_ALIAS
+from .pooling import AdaptiveAvgPool3d  #DEFINE_ALIAS
 from .conv import Conv2D  #DEFINE_ALIAS
 from .conv import Conv2DTranspose  #DEFINE_ALIAS
 from .conv import Conv3D  #DEFINE_ALIAS

python/paddle/nn/layer/pooling.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 paddle
+
+from ...fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
+from ...fluid.layers import utils
+from ...fluid.dygraph import layers
+from ...fluid.layer_helper import LayerHelper
+from .. import functional as F
+
+__all__ = [
+    'AdaptiveAvgPool2d',
+    'AdaptiveAvgPool3d',
+]
+
+
+class AdaptiveAvgPool2d(layers.Layer):
+    """
+
+    This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions
+    of the output tensor are determined by the parameter output_size.
+
+    For avg adaptive pool2d:
+
+    ..  math::
+
+       hstart &= floor(i * H_{in} / H_{out})
+
+       hend &= ceil((i + 1) * H_{in} / H_{out})
+
+       wstart &= floor(j * W_{in} / W_{out})
+
+       wend &= ceil((j + 1) * W_{in} / W_{out})
+
+       Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
+
+
+    Parameters:
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two element, (H, W). H and W can be either a int, or None which means
+            the size will be the same as that of the input.
+        data_format (str): The data format of the input and output data. An optional string
+            from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in
+            the order of: [batch_size, input_channels, input_height, input_width].
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Shape:
+        x (Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type can be float16, float32, float64, int32 or int64.
+        output (Tensor): The output tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type is same as input x.
+
+    Returns:
+        A callable object of AdaptiveAvgPool2d.
+
+    Examples:
+        .. code-block:: python
+
+            # adaptive avg pool2d
+            # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
+            # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
+            # of input data into m * n grids averagely and performs poolings in each
+            # grid to get output.
+            # adaptive avg pool performs calculations as follow:
+            #
+            #     for i in range(m):
+            #         for j in range(n):
+            #             hstart = floor(i * H / m)
+            #             hend = ceil((i + 1) * H / m)
+            #             wstart = floor(i * W / n)
+            #             wend = ceil((i + 1) * W / n)
+            #             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
+            #
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            input_data = np.random.rand(2, 3, 32, 32)
+            x = paddle.to_tensor(input_data)
+            # x.shape is [2, 3, 32, 32]
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=3)
+            pool_out = adaptive_avg_pool(x = x)
+            # pool_out.shape is [2, 3, 3, 3]
+    """
+
+    def __init__(self, output_size, data_format="NCHW", name=None):
+        super(AdaptiveAvgPool2d, self).__init__()
+        self._output_size = output_size
+        self._data_format = data_format
+        self._name = name
+
+    def forward(self, x):
+        return F.adaptive_avg_pool2d(
+            x,
+            output_size=self._output_size,
+            data_format=self._data_format,
+            name=self._name)
+
+
+class AdaptiveAvgPool3d(layers.Layer):
+    """
+
+    This operation applies 3D adaptive avg pooling on input tensor. The h and w dimensions
+    of the output tensor are determined by the parameter output_size.
+
+    For avg adaptive pool3d:
+
+    ..  math::
+
+      dstart &= floor(i * D_{in} / D_{out})
+
+      dend &= ceil((i + 1) * D_{in} / D_{out})
+
+      hstart &= floor(j * H_{in} / H_{out})
+
+      hend &= ceil((j + 1) * H_{in} / H_{out})
+
+      wstart &= floor(k * W_{in} / W_{out})
+
+      wend &= ceil((k + 1) * W_{in} / W_{out})
+
+      Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
+
+
+    Parameters:
+        output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means
+            the size will be the same as that of the input.
+        data_format (str): The data format of the input and output data. An optional string
+            from: "NCDHW", "NDHWC". The default is "NCDHW". When it is "NCDHW", the data is stored in
+            the order of: [batch_size, input_channels, input_depth, input_height, input_width].
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+    Shape:
+        x (Tensor): The input tensor of adaptive avg pool3d operator, which is a 5-D tensor. The data type can be float16, float32, float64, int32 or int64.
+        output (Tensor): The output tensor of adaptive avg pool3d operator, which is a 5-D tensor. The data type is same as input x.
+
+    Returns:
+        A callable object of AdaptiveAvgPool3d.
+
+    Examples:
+        .. code-block:: python
+
+            # adaptive avg pool3d
+            # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
+            # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
+            # of input data into l * m * n grids averagely and performs poolings in each
+            # grid to get output.
+            # adaptive avg pool performs calculations as follow:
+            #
+            #     for i in range(l):
+            #         for j in range(m):
+            #             for k in range(n):
+            #                 dstart = floor(i * D / l)
+            #                 dend = ceil((i + 1) * D / l)
+            #                 hstart = floor(j * H / m)
+            #                 hend = ceil((j + 1) * H / m)
+            #                 wstart = floor(k * W / n)
+            #                 wend = ceil((k + 1) * W / n)
+            #                 output[:, :, i, j, k] =
+            #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+            input_data = np.random.rand(2, 3, 8, 32, 32)
+            x = paddle.to_tensor(input_data)
+            # x.shape is [2, 3, 8, 32, 32]
+            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=3)
+            pool_out = adaptive_avg_pool(x = x)
+            # pool_out = [2, 3, 3, 3, 3]
+    """
+
+    def __init__(self, output_size, data_format="NCDHW", name=None):
+        super(AdaptiveAvgPool3d, self).__init__()
+        self._output_size = output_size
+        self._data_format = data_format
+        self._name = name
+
+    def forward(self, x):
+        return F.adaptive_avg_pool3d(
+            x,
+            output_size=self._output_size,
+            data_format=self._data_format,
+            name=self._name)