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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# ============================================================================
15import numpy as np
16import pytest
17
18import mindspore.context as context
19import mindspore.nn as nn
20import mindspore.common.dtype as mstype
21from mindspore.common.initializer import Normal
22from mindspore import Tensor
23
24
25context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
26
27
28@pytest.mark.level1
29@pytest.mark.platform_x86_ascend_training
30@pytest.mark.platform_arm_ascend_training
31@pytest.mark.env_onecard
32def test_conv2d_depthwiseconv2d_str():
33    net = nn.Conv2d(128, 128, (2, 3), stride=4, pad_mode='valid', padding=0, group=128, weight_init='normal')
34    input_data = Tensor(np.ones([3, 128, 127, 114]), dtype=mstype.float32)
35    output = net(input_data)
36    assert output.shape == (3, 128, 32, 28)
37
38
39@pytest.mark.level1
40@pytest.mark.platform_x86_ascend_training
41@pytest.mark.platform_arm_ascend_training
42@pytest.mark.env_onecard
43def test_conv2d_depthwiseconv2d_initializer():
44    net = nn.Conv2d(128, 128, (2, 3), stride=4, pad_mode='valid', padding=0, group=128, weight_init=Normal())
45    input_data = Tensor(np.ones([3, 128, 127, 114]), dtype=mstype.float32)
46    output = net(input_data)
47    assert output.shape == (3, 128, 32, 28)
48
49
50@pytest.mark.level0
51@pytest.mark.platform_x86_ascend_training
52@pytest.mark.platform_arm_ascend_training
53@pytest.mark.env_onecard
54def test_conv2d_depthwiseconv2d_tensor():
55    weight_init = Tensor(np.random.randn(128, 1, 2, 3).astype(np.float32))
56    net = nn.Conv2d(128, 128, (2, 3), stride=4, pad_mode='valid', padding=0, group=128, weight_init=weight_init)
57    input_data = Tensor(np.ones([3, 128, 127, 114]), dtype=mstype.float32)
58    output = net(input_data)
59    assert output.shape == (3, 128, 32, 28)
60