87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
import math
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import init
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import numpy as np
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def conv_dw(inp, oup, stride):
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return nn.Sequential(
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# dw
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nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
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nn.BatchNorm2d(inp),
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nn.ReLU6(inplace=True),
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# pw-linear
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nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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)
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def conv_bn(inp, oup, stride):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
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nn.BatchNorm2d(oup),
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nn.ReLU6(inplace=True)
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)
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class ConvNet(nn.Module):
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def __init__(self, num_classes):
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super(ConvNet, self).__init__()
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self.conv1 = conv_bn(3, 8, 1) # 64x64x1
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self.conv2 = conv_bn(8, 16, 1) # 64x64x16
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self.conv3 = conv_dw(16, 32, 1) # 64x64x32
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self.conv4 = conv_dw(32, 32, 2) # 32x32x32
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self.conv5 = conv_dw(32, 64, 1) # 32x32x64
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self.conv6 = conv_dw(64, 64, 2) # 16x16x64
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self.conv7 = conv_dw(64, 128, 1) # 16x16x128
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self.conv8 = conv_dw(128, 128, 1) # 16x16x128
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self.conv9 = conv_dw(128, 128, 1) # 16x16x128
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self.conv10 = conv_dw(128, 128, 1) # 16x16x128
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self.conv11 = conv_dw(128, 128, 1) # 16x16x128
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self.conv12 = conv_dw(128, 256, 2) # 8x8x256
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self.classifier = nn.Sequential(
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nn.Linear(256 * 8 * 8, 4096),
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nn.Dropout(0.2),
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nn.ReLU(inplace=True),
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nn.Linear(4096, num_classes),
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)
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# self.weight_init()
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def forward(self, x):
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x1 = self.conv1(x)
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x2 = self.conv2(x1)
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x3 = self.conv3(x2)
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x4 = self.conv4(x3)
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x5 = self.conv5(x4)
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x6 = self.conv6(x5)
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x7 = self.conv7(x6)
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x8 = self.conv8(x7)
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x9 = self.conv9(x8)
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x9 = F.relu(x8 + x9)
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x10 = self.conv10(x9)
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x11 = self.conv11(x10)
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x11 = F.relu(x10 + x11)
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x12 = self.conv12(x11)
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x = x12.view(x12.size(0), -1)
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x = self.classifier(x)
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return x
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def weight_init(self):
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for layer in self.modules():
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self._layer_init(layer)
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def _layer_init(self, m):
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# 使用isinstance来判断m属于什么类型
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, np.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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# m中的weight，bias其实都是Variable，为了能学习参数以及后向传播
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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m.weight.data.normal_(0, 0.01)
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m.bias.data.zero_()
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