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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
21
22
23class Net(nn.Cell):
24    def __init__(self, kernel_size=1, stride=1, pad_mode="valid", padding=0, dilation=1, return_indices=False,
25                 ceil_mode=False):
26        super(Net, self).__init__()
27        self.pool = nn.MaxPool1d(kernel_size=kernel_size, stride=stride, pad_mode=pad_mode, padding=padding,
28                                 dilation=dilation, return_indices=return_indices, ceil_mode=ceil_mode)
29
30    def construct(self, x):
31        out = self.pool(x)
32        return out
33
34
35@pytest.mark.level2
36@pytest.mark.platform_arm_ascend_training
37@pytest.mark.platform_x86_gpu_training
38@pytest.mark.platform_x86_cpu
39@pytest.mark.platform_arm_cpu
40@pytest.mark.env_onecard
41@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
42def test_maxpool1d_normal(mode):
43    """
44    Feature: MaxPool1d
45    Description: Verify the result of MaxPool1d
46    Expectation: success
47    """
48    ms.set_context(mode=mode)
49    x1 = ms.Tensor(np.random.randint(0, 10, [1, 2, 4]), dtype=ms.float32)
50    pool1 = Net(kernel_size=3, stride=1)
51    output1 = pool1(x1)
52
53    x2 = ms.Tensor(np.random.randint(0, 10, [5, 3, 4]), dtype=ms.float32)
54    pool2 = Net(kernel_size=2, stride=1, pad_mode='pad', padding=1, dilation=1, return_indices=True)
55    output2 = pool2(x2)
56
57    assert output1.shape == (1, 2, 2)
58    assert output2[0].shape == (5, 3, 5)
59    assert output2[1].shape == (5, 3, 5)
60