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1# Copyright 2022 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
16import numpy as np
17import pytest
18
19import mindspore as ms
20import mindspore.nn as nn
21import mindspore.ops as ops
22
23
24class Net(nn.Cell):
25    def __init__(self, kernel_size=1, stride=1, pad_mode="valid", padding=0, ceil_mode=False, count_include_pad=True,
26                 divisor_override=None, data_format='NCHW'):
27        super(Net, self).__init__()
28        self.pool = nn.AvgPool2d(kernel_size=kernel_size, stride=stride, pad_mode=pad_mode, padding=padding,
29                                 ceil_mode=ceil_mode, count_include_pad=count_include_pad,
30                                 divisor_override=divisor_override, data_format=data_format)
31
32    def construct(self, x):
33        out = self.pool(x)
34        return out
35
36
37@pytest.mark.level2
38@pytest.mark.platform_x86_gpu_training
39@pytest.mark.platform_x86_cpu
40@pytest.mark.platform_arm_cpu
41@pytest.mark.env_onecard
42@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
43def test_avgpool2d_normal(mode):
44    """
45    Feature: AvgPool2d
46    Description: Verify the result of AvgPool2d
47    Expectation: success
48    """
49    ms.set_context(mode=mode)
50    x1 = ms.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), ms.float32)
51    pool1 = Net(kernel_size=3, stride=1)
52    output1 = pool1(x1)
53
54    x2 = ops.randn(6, 6, 8, 8)
55    pool2 = Net(kernel_size=4, stride=1, pad_mode='pad', padding=2, divisor_override=5)
56    output2 = pool2(x2)
57
58    assert output1.shape == (1, 2, 2, 2)
59    assert output2.shape == (6, 6, 9, 9)
60