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54 lines
1.7 KiB
54 lines
1.7 KiB
# Copyright 2019 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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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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context.set_context(device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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out_channel = 64
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kernel_size = 7
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self.conv = P.Conv2D(out_channel,
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kernel_size,
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mode=1,
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pad_mode="valid",
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pad=0,
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stride=1,
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dilation=1,
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group=1)
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self.w = Parameter(initializer(
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'normal', [64, 3, 7, 7]), name='w')
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@ms_function
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def construct(self, x):
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return self.conv(x, self.w)
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def test_net():
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x = np.random.randn(32, 3, 224, 224).astype(np.float32)
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conv = Net()
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output = conv(Tensor(x))
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print(output.asnumpy())
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