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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15"""
16test pooling api
17"""
18import numpy as np
19
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.common.api import _cell_graph_executor
23
24
25class AvgNet(nn.Cell):
26    def __init__(self,
27                 kernel_size,
28                 stride=None):
29        super(AvgNet, self).__init__()
30        self.avgpool = nn.AvgPool2d(kernel_size, stride)
31
32    def construct(self, x):
33        return self.avgpool(x)
34
35
36def test_compile_avg():
37    net = AvgNet(3, 1)
38    x = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32))
39    _cell_graph_executor.compile(net, x)
40
41
42class MaxNet(nn.Cell):
43    """ MaxNet definition """
44
45    def __init__(self,
46                 kernel_size,
47                 stride=None,
48                 padding=0):
49        _ = padding
50        super(MaxNet, self).__init__()
51        self.maxpool = nn.MaxPool2d(kernel_size,
52                                    stride)
53
54    def construct(self, x):
55        return self.maxpool(x)
56
57
58def test_compile_max():
59    net = MaxNet(3, stride=1, padding=0)
60    x = Tensor(np.random.randint(0, 255, [1, 3, 6, 6]).astype(np.float32))
61    _cell_graph_executor.compile(net, x)
62
63
64class Avg1dNet(nn.Cell):
65    def __init__(self,
66                 kernel_size,
67                 stride=None):
68        super(Avg1dNet, self).__init__()
69        self.avg1d = nn.AvgPool1d(kernel_size, stride)
70
71    def construct(self, x):
72        return self.avg1d(x)
73
74
75def test_avg1d():
76    net = Avg1dNet(6, 1)
77    input_ = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
78    _cell_graph_executor.compile(net, input_)
79