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82 lines
2.5 KiB
82 lines
2.5 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""test conv"""
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from ..ut_filter import non_graph_engine
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weight = Tensor(np.ones([2, 2]))
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in_channels = 3
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out_channels = 64
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self,
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cin,
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cout,
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kernel_size,
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stride=1,
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pad_mode='pad',
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padding=0,
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dilation=1,
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group=1,
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has_bias=False,
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weight_init='normal',
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bias_init='zeros'):
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super(Net, self).__init__()
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Tensor(np.ones([6, 3, 3, 3]).astype(np.float32) * 0.01)
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self.conv = nn.Conv2d(cin,
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cout,
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kernel_size,
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stride,
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pad_mode,
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padding,
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dilation,
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group,
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has_bias,
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weight_init,
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bias_init)
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def construct(self, input_x):
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return self.conv(input_x)
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@non_graph_engine
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def test_compile():
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net = Net(3, 6, (3, 3), bias_init='zeros')
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input_data = Tensor(np.ones([3, 3, 32, 32]).astype(np.float32) * 0.01)
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output = net(input_data)
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print(output.asnumpy())
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@non_graph_engine
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def test_compile2():
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net = Net(3, 1, (3, 3), bias_init='zeros')
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input_data = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
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output = net(input_data)
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print(output.asnumpy())
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@non_graph_engine
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def test_compile3():
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net = Net(3, 1, (3, 3), weight_init='ONES')
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input_data = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
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output = net(input_data)
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print(output.asnumpy())
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