SENet代码及解释（pytorch）
1.SEblock搭建
全局平均池化+1*1卷积核+ReLu+1*1卷积核+Sigmoid
import torch.nn as nn
#全局平均池化+1*1卷积核+ReLu+1*1卷积核+Sigmoid
class SE_Block(nn.Module):
def __init__(self, inchannel, ratio=16):
super(SE_Block, self).__init__()
# 全局平均池化(Fsq操作)
self.gap = nn.AdaptiveAvgPool2d((1, 1))
# 两个全连接层(Fex操作)
self.fc = nn.Sequential(
nn.Linear(inchannel, inchannel // ratio, bias=False), # 从 c -> c/r
nn.ReLU(),
nn.Linear(inchannel // ratio, inchannel, bias=False), # 从 c/r -> c
nn.Sigmoid()
)
def forward(self, x):
# 读取批数据图片数量及通道数
b, c, h, w = x.size()
# Fsq操作：经池化后输出b*c的矩阵
y = self.gap(x).view(b, c)
# Fex操作：经全连接层输出（b，c，1，1）矩阵
y = self.fc(y).view(b, c, 1, 1)
# Fscale操作：将得到的权重乘以原来的特征图x
return x * y.expand_as(x)2.将SEblock嵌入残差模块
将SE模块分别嵌入ResNet的BasicBlock和Bottleneck中，得到 SEBasicBlock和SEBottleneck
BasicBlock模块
'''-------------二、BasicBlock模块-----------------------------'''
# 左侧的 residual block 结构（18-layer、34-layer）
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inchannel, outchannel, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(outchannel)
self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(outchannel)
# SE_Block放在BN之后，shortcut之前
self.SE = SE_Block(outchannel)
self.shortcut = nn.Sequential()
if stride != 1 or inchannel != self.expansion*outchannel:
self.shortcut = nn.Sequential(
nn.Conv2d(inchannel, self.expansion*outchannel,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*outchannel)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
SE_out = self.SE(out)
out = out * SE_out
out += self.shortcut(x)
out = F.relu(out)
return outBottleneck模块
'''-------------三、Bottleneck模块-----------------------------'''
# 右侧的 residual block 结构（50-layer、101-layer、152-layer）
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inchannel, outchannel, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(outchannel)
self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(outchannel)
self.conv3 = nn.Conv2d(outchannel, self.expansion*outchannel,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*outchannel)
# SE_Block放在BN之后，shortcut之前
self.SE = SE_Block(self.expansion*outchannel)
self.shortcut = nn.Sequential()
if stride != 1 or inchannel != self.expansion*outchannel:
self.shortcut = nn.Sequential(
nn.Conv2d(inchannel, self.expansion*outchannel,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*outchannel)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
SE_out = self.SE(out)
out = out * SE_out
out += self.shortcut(x)
out = F.relu(out)
return out3.搭建SE_ResNet结构
'''-------------四、搭建SE_ResNet结构-----------------------------'''
class SE_ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(SE_ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
stride=1, padding=1, bias=False) # conv1
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) # conv2_x
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) # conv3_x
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) # conv4_x
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) # conv5_x
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
out = self.linear(x)
return out4.完整代码
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
'''-------------一、SE模块-----------------------------'''
#全局平均池化+1*1卷积核+ReLu+1*1卷积核+Sigmoid
class SE_Block(nn.Module):
def __init__(self, inchannel, ratio=16):
super(SE_Block, self).__init__()
# 全局平均池化(Fsq操作)
self.gap = nn.AdaptiveAvgPool2d((1, 1))
# 两个全连接层(Fex操作)
self.fc = nn.Sequential(
nn.Linear(inchannel, inchannel // ratio, bias=False), # 从 c -> c/r
nn.ReLU(),
nn.Linear(inchannel // ratio, inchannel, bias=False), # 从 c/r -> c
nn.Sigmoid()
)
def forward(self, x):
# 读取批数据图片数量及通道数
b, c, h, w = x.size()
# Fsq操作：经池化后输出b*c的矩阵
y = self.gap(x).view(b, c)
# Fex操作：经全连接层输出（b，c，1，1）矩阵
y = self.fc(y).view(b, c, 1, 1)
# Fscale操作：将得到的权重乘以原来的特征图x
return x * y.expand_as(x)
'''-------------二、BasicBlock模块-----------------------------'''
# 左侧的 residual block 结构（18-layer、34-layer）
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inchannel, outchannel, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(outchannel)
self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(outchannel)
# SE_Block放在BN之后，shortcut之前
self.SE = SE_Block(outchannel)
self.shortcut = nn.Sequential()
if stride != 1 or inchannel != self.expansion*outchannel:
self.shortcut = nn.Sequential(
nn.Conv2d(inchannel, self.expansion*outchannel,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*outchannel)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
SE_out = self.SE(out)
out = out * SE_out
out += self.shortcut(x)
out = F.relu(out)
return out
'''-------------三、Bottleneck模块-----------------------------'''
# 右侧的 residual block 结构（50-layer、101-layer、152-layer）
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inchannel, outchannel, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(outchannel)
self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(outchannel)
self.conv3 = nn.Conv2d(outchannel, self.expansion*outchannel,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*outchannel)
# SE_Block放在BN之后，shortcut之前
self.SE = SE_Block(self.expansion*outchannel)
self.shortcut = nn.Sequential()
if stride != 1 or inchannel != self.expansion*outchannel:
self.shortcut = nn.Sequential(
nn.Conv2d(inchannel, self.expansion*outchannel,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*outchannel)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
SE_out = self.SE(out)
out = out * SE_out
out += self.shortcut(x)
out = F.relu(out)
return out
'''-------------四、搭建SE_ResNet结构-----------------------------'''
class SE_ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(SE_ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
stride=1, padding=1, bias=False) # conv1
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) # conv2_x
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) # conv3_x
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) # conv4_x
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) # conv5_x
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
out = self.linear(x)
return out
def SE_ResNet18():
return SE_ResNet(BasicBlock, [2, 2, 2, 2])
def SE_ResNet34():
return SE_ResNet(BasicBlock, [3, 4, 6, 3])
def SE_ResNet50():
return SE_ResNet(Bottleneck, [3, 4, 6, 3])
def SE_ResNet101():
return SE_ResNet(Bottleneck, [3, 4, 23, 3])
def SE_ResNet152():
return SE_ResNet(Bottleneck, [3, 8, 36, 3])
'''
if __name__ == '__main__':
model = SE_ResNet50()
print(model)
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)
# test()
'''
if __name__ == '__main__':
net = SE_ResNet50().cuda()
summary(net, (3, 224, 224))