[API 2.0] add pool2d3d API,test=develop

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

@@ -0,0 +1,158 @@
+# 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=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=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)
+
+            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 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)
+
+
+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)
+                res_pd = avg_pool2d(
+                    input_np, kernel_size=2, stride=2, padding=0)
+
+        def run2():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.uint8)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool2d(
+                    input_pd, kernel_size=2, stride=2, padding=0)
+
+        def run3():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                res_pd = max_pool2d(
+                    input_np, kernel_size=2, stride=2, padding=0)
+
+        def run4():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.uint8)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = max_pool2d(
+                    input_pd, kernel_size=2, stride=2, padding=0)
+
+        #self.assertRaises(ValueError, run1)
+        #self.assertRaises(TypeError, run2)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -0,0 +1,124 @@
+#   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=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=0)
+
+            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))
+
+    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=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=2, padding=0)
+            result = max_pool3d_dg(input)
+            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)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/functional/__init__.py

@@ -25,6 +25,8 @@
 __all__ += extension.__all__
 from . import common
 __all__ += common.__all__
+from . import pooling
+__all__ += pooling.__all__
 from .activation import brelu  #DEFINE_ALIAS
 from .activation import elu  #DEFINE_ALIAS
 from .activation import erf  #DEFINE_ALIAS
@@ -155,6 +157,10 @@
 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
+from .pooling import max_pool2d
+from .pooling import avg_pool3d
+from .pooling import max_pool3d
 # from .rnn import gru_unit        #DEFINE_ALIAS
 # from .rnn import lstm        #DEFINE_ALIAS
 # from .rnn import lstm_unit        #DEFINE_ALIAS