[API 2.0] add pool2d3d API,test=develop

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

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