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1# Copyright 2021 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.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.ops import operations as P
23
24
25class NetConv3dTranspose(nn.Cell):
26    def __init__(self):
27        super(NetConv3dTranspose, self).__init__()
28        in_channel = 2
29        out_channel = 2
30        kernel_size = 2
31        self.conv_trans = P.Conv3DTranspose(in_channel, out_channel,
32                                            kernel_size,
33                                            pad_mode="pad",
34                                            pad=1,
35                                            stride=1,
36                                            dilation=1,
37                                            group=1)
38
39    def construct(self, x, w):
40        return self.conv_trans(x, w)
41
42
43@pytest.mark.level0
44@pytest.mark.platform_x86_gpu_training
45@pytest.mark.env_onecard
46def test_conv3d_transpose():
47    x = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
48    w = Tensor(np.ones((2, 2, 2, 2, 2)).astype(np.float32))
49    expect = np.array([[[[[320., 336.],
50                          [368., 384.]],
51                         [[464., 480.],
52                          [512., 528.]]],
53                        [[[320., 336.],
54                          [368., 384.]],
55                         [[464., 480.],
56                          [512., 528.]]]]]).astype(np.float32)
57
58    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
59    conv3dtranspose = NetConv3dTranspose()
60    output = conv3dtranspose(x, w)
61    assert (output.asnumpy() == expect).all()
62    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
63    conv3dtranspose = NetConv3dTranspose()
64    output = conv3dtranspose(x, w)
65    assert (output.asnumpy() == expect).all()
66