简介
Xception的名称源自于"Extreme Inception",它是在Inception架构的基础上进行了扩展和改进。Inception架构是Google团队提出的一种经典的卷积神经网络架构,用于解决深度卷积神经网络中的计算和参数增长问题。
与Inception不同,Xception的主要创新在于使用了深度可分离卷积(Depthwise Separable Convolution)来替代传统的卷积操作。深度可分离卷积将卷积操作分解为两个步骤:深度卷积和逐点卷积。
深度卷积是一种在每个输入通道上分别应用卷积核的操作,它可以有效地减少计算量和参数数量。逐点卷积是一种使用1x1卷积核进行通道间的线性组合的操作,用于增加模型的表示能力。通过使用深度可分离卷积,Xception网络能够更加有效地学习特征表示,并在相同计算复杂度下获得更好的性能。
Xception 网络结构
一个标准的Inception模块(Inception V3)
简化后的Inception模块
简化后的Inception的等价结构
采用深度可分离卷积的思想,使 3×3 卷积的数量与 1×1卷积输出通道的数量相等
Xception模型,一共可以分为3个flow,分别是Entry flow、Middle flow、Exit flow。
在这里 Entry 与 Exit 都具有相同的部分,Middle 与这二者有所不同。
Xception模型的pytorch复现
(1)深度可分离卷积
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, dilation=1, bias=False):
super(SeparableConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding,
dilation, groups=in_channels, bias=bias)
self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0,
dilation=1, groups=1, bias=False)
def forward(self, x):
x = self.conv(x)
x = self.pointwise(x)
return x
(2)构建三个flow结构
class EntryFlow(nn.Module):
def __init__(self):
super(EntryFlow, self).__init__()
self.headconv = nn.Sequential(
nn.Conv2d(3, 32, 3, 2, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, 3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.residual_block1 = nn.Sequential(
SeparableConv2d(64, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
SeparableConv2d(128, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.MaxPool2d(3, stride=2, padding=1),
)
self.residual_block2 = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
SeparableConv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.MaxPool2d(3, stride=2, padding=1)
)
self.residual_block3 = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(256, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.MaxPool2d(3, stride=2, padding=1)
)
def shortcut(self, inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 2, bias=False),
nn.BatchNorm2d(oup)
)
def forward(self, x):
x = self.headconv(x)
residual = self.residual_block1(x)
shortcut_block1 = self.shortcut(64, 128)
x = residual + shortcut_block1(x)
residual = self.residual_block2(x)
shortcut_block2 = self.shortcut(128, 256)
x = residual + shortcut_block2(x)
residual = self.residual_block3(x)
shortcut_block3 = self.shortcut(256, 728)
x = residual + shortcut_block3(x)
return x
class MiddleFlow(nn.Module):
def __init__(self):
super(MiddleFlow, self).__init__()
self.shortcut = nn.Sequential()
self.conv1 = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728)
)
def forward(self, x):
residual = self.conv1(x)
input = self.shortcut(x)
return input + residual
class ExitFlow(nn.Module):
def __init__(self):
super(ExitFlow, self).__init__()
self.residual_with_exit = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.MaxPool2d(3, stride=2, padding=1)
)
self.endconv = nn.Sequential(
SeparableConv2d(1024, 1536, 3, 1, 1),
nn.BatchNorm2d(1536),
nn.ReLU(inplace=True),
SeparableConv2d(1536, 2048, 3, 1, 1),
nn.BatchNorm2d(2048),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
)
def shortcut(self, inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 2, bias=False),
nn.BatchNorm2d(oup)
)
def forward(self, x):
residual = self.residual_with_exit(x)
shortcut_block = self.shortcut(728, 1024)
output = residual + shortcut_block(x)
return self.endconv(output)
(3)构建网络(完整代码)
"""
Copyright (c) 2023, Auorui.
All rights reserved.
Xception: Deep Learning with Depthwise Separable Convolutions
<https://arxiv.org/pdf/1610.02357.pdf>
"""
import torch
import torch.nn as nn
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, dilation=1, bias=False):
super(SeparableConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding,
dilation, groups=in_channels, bias=bias)
self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0,
dilation=1, groups=1, bias=False)
def forward(self, x):
x = self.conv(x)
x = self.pointwise(x)
return x
class EntryFlow(nn.Module):
def __init__(self):
super(EntryFlow, self).__init__()
self.headconv = nn.Sequential(
nn.Conv2d(3, 32, 3, 2, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, 3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.residual_block1 = nn.Sequential(
SeparableConv2d(64, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
SeparableConv2d(128, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.MaxPool2d(3, stride=2, padding=1),
)
self.residual_block2 = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
SeparableConv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.MaxPool2d(3, stride=2, padding=1)
)
self.residual_block3 = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(256, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.MaxPool2d(3, stride=2, padding=1)
)
def shortcut(self, inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 2, bias=False),
nn.BatchNorm2d(oup)
)
def forward(self, x):
x = self.headconv(x)
residual = self.residual_block1(x)
shortcut_block1 = self.shortcut(64, 128)
x = residual + shortcut_block1(x)
residual = self.residual_block2(x)
shortcut_block2 = self.shortcut(128, 256)
x = residual + shortcut_block2(x)
residual = self.residual_block3(x)
shortcut_block3 = self.shortcut(256, 728)
x = residual + shortcut_block3(x)
return x
class MiddleFlow(nn.Module):
def __init__(self):
super(MiddleFlow, self).__init__()
self.shortcut = nn.Sequential()
self.conv1 = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728)
)
def forward(self, x):
residual = self.conv1(x)
input = self.shortcut(x)
return input + residual
class ExitFlow(nn.Module):
def __init__(self):
super(ExitFlow, self).__init__()
self.residual_with_exit = nn.Sequential(
nn.ReLU(inplace=True),
SeparableConv2d(728, 728, 3, padding=1),
nn.BatchNorm2d(728),
nn.ReLU(inplace=True),
SeparableConv2d(728, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.MaxPool2d(3, stride=2, padding=1)
)
self.endconv = nn.Sequential(
SeparableConv2d(1024, 1536, 3, 1, 1),
nn.BatchNorm2d(1536),
nn.ReLU(inplace=True),
SeparableConv2d(1536, 2048, 3, 1, 1),
nn.BatchNorm2d(2048),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
)
def shortcut(self, inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 2, bias=False),
nn.BatchNorm2d(oup)
)
def forward(self, x):
residual = self.residual_with_exit(x)
shortcut_block = self.shortcut(728, 1024)
output = residual + shortcut_block(x)
return self.endconv(output)
class Xception(nn.Module):
def __init__(self, num_classes=1000):
super().__init__()
self.num_classes = num_classes
self.entry_flow = EntryFlow()
self.middle_flow = MiddleFlow()
self.exit_flow = ExitFlow()
self.fc = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.entry_flow(x)
for i in range(8):
x = self.middle_flow(x)
x = self.exit_flow(x)
x = x.view(x.size(0), -1)
out = self.fc(x)
return out
if __name__=='__main__':
import torchsummary
device = 'cuda' if torch.cuda.is_available() else 'cpu'
input = torch.ones(2, 3, 224, 224).to(device)
net = Xception(num_classes=4)
net = net.to(device)
out = net(input)
print(out)
print(out.shape)
torchsummary.summary(net, input_size=(3, 224, 224))
# Xception Total params: 19,838,076
参考文章
[ 轻量级网络 ] 经典网络模型4——Xception 详解与复现-CSDN博客
神经网络学习小记录22——Xception模型的复现详解_xception timm-CSDN博客
【卷积神经网络系列】十七、Xception_xception模块-CSDN博客
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