[API 2.0] add functional pool1d API

python/paddle/fluid/tests/unittests/test_pool1d_op.py

@@ -0,0 +1,373 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+import paddle
+import paddle.nn.functional as F
+import paddle.fluid as fluid
+
+
+def adaptive_start_index(index, input_size, output_size):
+    return int(np.floor(index * input_size / output_size))
+
+
+def adaptive_end_index(index, input_size, output_size):
+    return int(np.ceil((index + 1) * input_size / output_size))
+
+
+def max_pool1D_forward_naive(x,
+                             ksize,
+                             strides,
+                             paddings,
+                             global_pool=0,
+                             ceil_mode=False,
+                             exclusive=False,
+                             adaptive=False,
+                             data_type=np.float64):
+    N, C, L = x.shape
+    if global_pool == 1:
+        ksize = [L]
+    if adaptive:
+        L_out = ksize[0]
+    else:
+        L_out = (L - ksize[0] + 2 * paddings[0] + strides[0] - 1
+                 ) // strides[0] + 1 if ceil_mode else (
+                     L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
+
+    out = np.zeros((N, C, L_out))
+    for i in range(L_out):
+        if adaptive:
+            r_start = adaptive_start_index(i, L, ksize[0])
+            r_end = adaptive_end_index(i, L, ksize[0])
+        else:
+            r_start = np.max((i * strides[0] - paddings[0], 0))
+            r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
+        x_masked = x[:, :, r_start:r_end]
+
+        out[:, :, i] = np.max(x_masked, axis=(2))
+    return out
+
+
+def avg_pool1D_forward_naive(x,
+                             ksize,
+                             strides,
+                             paddings,
+                             global_pool=0,
+                             ceil_mode=False,
+                             exclusive=False,
+                             adaptive=False,
+                             data_type=np.float64):
+    N, C, L = x.shape
+    if global_pool == 1:
+        ksize = [L]
+    if adaptive:
+        L_out = ksize[0]
+    else:
+        L_out = (L - ksize[0] + 2 * paddings[0] + strides[0] - 1
+                 ) // strides[0] + 1 if ceil_mode else (
+                     L - ksize[0] + 2 * paddings[0]) // strides[0] + 1
+
+    out = np.zeros((N, C, L_out))
+    for i in range(L_out):
+        if adaptive:
+            r_start = adaptive_start_index(i, L, ksize[0])
+            r_end = adaptive_end_index(i, L, ksize[0])
+        else:
+            r_start = np.max((i * strides[0] - paddings[0], 0))
+            r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L))
+        x_masked = x[:, :, r_start:r_end]
+
+        field_size = (r_end - r_start) \
+            if (exclusive or adaptive) else (ksize[0])
+        if data_type == np.int8 or data_type == np.uint8:
+            out[:, :, i] = (np.rint(
+                np.sum(x_masked, axis=(2, 3)) / field_size)).astype(data_type)
+        else:
+            out[:, :, i] = (np.sum(x_masked, axis=(2)) /
+                            field_size).astype(data_type)
+    return out
+
+
+class TestPool1d_API(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(123)
+        self.places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.places.append(fluid.CUDAPlace(0))
+
+    def check_avg_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0], ceil_mode=False)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_avg_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=[0])
+
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool1d_dg = paddle.nn.layer.AvgPool1d(
+                kernel_size=2, stride=None, padding=0)
+            result = avg_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.max_pool1d(input, kernel_size=2, stride=2, padding=[0])
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_max_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.max_pool1d(input, kernel_size=2, stride=2, padding=0)
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool1d_dg = paddle.nn.layer.MaxPool1d(
+                kernel_size=2, stride=None, padding=0)
+            result = max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_adaptive_max_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.adaptive_max_pool1d(input, output_size=16)
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            ada_max_pool1d_dg = paddle.nn.layer.AdaptiveMaxPool1d(
+                output_size=16)
+            result = ada_max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_adaptive_avg_dygraph_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.adaptive_avg_pool1d(input, output_size=16)
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1d(
+                output_size=16)
+            result = ada_max_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_adaptive_max_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.adaptive_max_pool1d(input, output_size=16)
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[2], paddings=[0], adaptive=True)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_adaptive_avg_static_results(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
+            result = F.adaptive_avg_pool1d(input, output_size=16)
+
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[16], strides=[2], paddings=[0], adaptive=True)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input": input_np},
+                              fetch_list=[result])
+            self.assertTrue(np.allclose(fetches[0], result_np))
+
+    def check_max_dygraph_padding_same(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.max_pool1d(
+                input, kernel_size=2, stride=2, padding="SAME")
+
+            result_np = max_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_dygraph_padding_same(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.avg_pool1d(
+                input, kernel_size=2, stride=2, padding="SAME")
+
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[0])
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def test_pool1d(self):
+        for place in self.places:
+
+            self.check_max_dygraph_results(place)
+            self.check_avg_dygraph_results(place)
+            self.check_max_static_results(place)
+            self.check_avg_static_results(place)
+            self.check_adaptive_max_dygraph_results(place)
+            self.check_adaptive_avg_dygraph_results(place)
+            self.check_adaptive_max_static_results(place)
+            self.check_adaptive_avg_static_results(place)
+            self.check_max_dygraph_padding_same(place)
+            self.check_avg_dygraph_padding_same(place)
+
+
+class TestPool2dError_API(unittest.TestCase):
+    def test_error_api(self):
+        def run1():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[2]]
+                res_pd = F.max_pool1d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run1)
+
+        def run2():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = [[2]]
+                res_pd = F.max_pool1d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run2)
+
+        def run3():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "padding"
+                res_pd = F.max_pool1d(
+                    input_pd, kernel_size=2, stride=2, padding=padding)
+
+        self.assertRaises(ValueError, run3)
+
+        def run4():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = F.max_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run4)
+
+        def run5():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = F.max_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run5)
+
+        def run6():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "VALID"
+                res_pd = F.avg_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run6)
+
+        def run7():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = "paddle"
+                res_pd = F.avg_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run7)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/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
@@ -157,6 +159,10 @@
 from .pooling import pool3d  #DEFINE_ALIAS
 from .pooling import adaptive_pool2d  #DEFINE_ALIAS
 from .pooling import adaptive_pool3d  #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 .rnn import gru_unit        #DEFINE_ALIAS
 # from .rnn import lstm        #DEFINE_ALIAS
 # from .rnn import lstm_unit        #DEFINE_ALIAS