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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"""test conv"""
16import numpy as np
17
18import mindspore.nn as nn
19from mindspore import Tensor
20from ..ut_filter import non_graph_engine
21
22weight = Tensor(np.ones([2, 2]))
23in_channels = 3
24out_channels = 64
25
26
27class Net(nn.Cell):
28    """Net definition"""
29
30    def __init__(self,
31                 cin,
32                 cout,
33                 kernel_size,
34                 stride=1,
35                 pad_mode='pad',
36                 padding=0,
37                 dilation=1,
38                 group=1,
39                 has_bias=False,
40                 weight_init='normal',
41                 bias_init='zeros'):
42        super(Net, self).__init__()
43        Tensor(np.ones([6, 3, 3, 3]).astype(np.float32) * 0.01)
44        self.conv = nn.Conv2d(cin,
45                              cout,
46                              kernel_size,
47                              stride,
48                              pad_mode,
49                              padding,
50                              dilation,
51                              group,
52                              has_bias,
53                              weight_init,
54                              bias_init)
55
56    def construct(self, input_x):
57        return self.conv(input_x)
58
59
60@non_graph_engine
61def test_compile():
62    net = Net(3, 6, (3, 3), bias_init='zeros')
63    input_data = Tensor(np.ones([3, 3, 32, 32]).astype(np.float32) * 0.01)
64    output = net(input_data)
65    print(output.asnumpy())
66
67
68@non_graph_engine
69def test_compile2():
70    net = Net(3, 1, (3, 3), bias_init='zeros')
71    input_data = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
72    output = net(input_data)
73    print(output.asnumpy())
74
75
76@non_graph_engine
77def test_compile3():
78    net = Net(3, 1, (3, 3), weight_init='ONES')
79    input_data = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
80    output = net(input_data)
81    print(output.asnumpy())
82