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74 lines
2.6 KiB
74 lines
2.6 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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"""Alexnet."""
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import mindspore.nn as nn
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.ops import operations as P
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid"):
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode=pad_mode)
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def fc_with_initialize(input_channels, out_channels):
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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return TruncatedNormal(0.02) # 0.02
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class AlexNet(nn.Cell):
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"""
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Alexnet
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"""
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def __init__(self, num_classes=10, channel=3):
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super(AlexNet, self).__init__()
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self.conv1 = conv(channel, 96, 11, stride=4)
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self.conv2 = conv(96, 256, 5, pad_mode="same")
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self.conv3 = conv(256, 384, 3, pad_mode="same")
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self.conv4 = conv(384, 384, 3, pad_mode="same")
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self.conv5 = conv(384, 256, 3, pad_mode="same")
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self.relu = nn.ReLU()
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self.max_pool2d = P.MaxPool(ksize=3, strides=2)
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self.flatten = nn.Flatten()
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self.fc1 = fc_with_initialize(6*6*256, 4096)
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self.fc2 = fc_with_initialize(4096, 4096)
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self.fc3 = fc_with_initialize(4096, num_classes)
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv3(x)
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x = self.relu(x)
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x = self.conv4(x)
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x = self.relu(x)
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x = self.conv5(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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