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