rebuild
This commit is contained in:
lee
271
model/resnet_attention.py
Normal file
271
model/resnet_attention.py
Normal file
@ -0,0 +1,271 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class ChannelAttention(nn.Module):
|
||||
"""通道注意力模块，通过全局平均池化和最大池化提取特征，经过MLP生成通道权重"""
|
||||
|
||||
def __init__(self, in_channels, reduction_ratio=16):
|
||||
super(ChannelAttention, self).__init__()
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.max_pool = nn.AdaptiveMaxPool2d(1)
|
||||
|
||||
# 共享的MLP层
|
||||
self.fc = nn.Sequential(
|
||||
nn.Conv2d(in_channels, in_channels // reduction_ratio, 1, bias=False),
|
||||
nn.ReLU(),
|
||||
nn.Conv2d(in_channels // reduction_ratio, in_channels, 1, bias=False)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
avg_out = self.fc(self.avg_pool(x))
|
||||
max_out = self.fc(self.max_pool(x))
|
||||
out = avg_out + max_out
|
||||
return torch.sigmoid(out)
|
||||
|
||||
|
||||
class SpatialAttention(nn.Module):
|
||||
"""空间注意力模块，通过通道维度的平均和最大值操作，生成空间权重"""
|
||||
|
||||
def __init__(self, kernel_size=7):
|
||||
super(SpatialAttention, self).__init__()
|
||||
self.conv = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
avg_out = torch.mean(x, dim=1, keepdim=True)
|
||||
max_out, _ = torch.max(x, dim=1, keepdim=True)
|
||||
out = torch.cat([avg_out, max_out], dim=1)
|
||||
out = self.conv(out)
|
||||
return torch.sigmoid(out)
|
||||
|
||||
|
||||
class CBAM(nn.Module):
|
||||
"""CBAM注意力模块，串联通道注意力和空间注意力"""
|
||||
|
||||
def __init__(self, in_channels, reduction_ratio=16, kernel_size=7):
|
||||
super(CBAM, self).__init__()
|
||||
self.channel_att = ChannelAttention(in_channels, reduction_ratio)
|
||||
self.spatial_att = SpatialAttention(kernel_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = x * self.channel_att(x)
|
||||
x = x * self.spatial_att(x)
|
||||
return x
|
||||
|
||||
|
||||
class BasicBlock(nn.Module):
|
||||
"""ResNet基础残差块，适用于ResNet18和ResNet34"""
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, in_channels, out_channels, stride=1, downsample=None, use_cbam=False):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(out_channels)
|
||||
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
# 是否使用CBAM注意力机制
|
||||
self.use_cbam = use_cbam
|
||||
if use_cbam:
|
||||
self.cbam = CBAM(out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
# # 如果使用注意力机制，应用CBAM
|
||||
if self.use_cbam:
|
||||
out = self.cbam(out)
|
||||
|
||||
# 如果有下采样，调整shortcut连接
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
# 残差连接
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
"""ResNet瓶颈残差块，适用于ResNet50及更深的网络"""
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, in_channels, out_channels, stride=1, downsample=None, use_cbam=False):
|
||||
super(Bottleneck, self).__init__()
|
||||
# 1x1卷积降维
|
||||
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
# 3x3卷积
|
||||
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(out_channels)
|
||||
# 1x1卷积升维
|
||||
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
# 是否使用CBAM注意力机制
|
||||
self.use_cbam = use_cbam
|
||||
if use_cbam:
|
||||
self.cbam = CBAM(out_channels * self.expansion)
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
# # 如果使用注意力机制，应用CBAM
|
||||
if self.use_cbam:
|
||||
out = self.cbam(out)
|
||||
|
||||
# 如果有下采样，调整shortcut连接
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
# 残差连接
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNet(nn.Module):
|
||||
"""集成了CBAM注意力机制的ResNet模型"""
|
||||
|
||||
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, use_cbam=True):
|
||||
super(ResNet, self).__init__()
|
||||
self.in_channels = 64
|
||||
self.use_cbam = use_cbam
|
||||
|
||||
# 初始卷积层
|
||||
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
|
||||
self.cbam1 = CBAM(64)
|
||||
self.bn1 = nn.BatchNorm2d(64)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
|
||||
# 残差块层
|
||||
self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
|
||||
|
||||
self.cbam2 = CBAM(512)
|
||||
# 全局平均池化和分类器
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
||||
|
||||
# 初始化权重
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
# 零初始化最后一个BN层的权重，使残差分支初始为0
|
||||
if zero_init_residual:
|
||||
for m in self.modules():
|
||||
if isinstance(m, Bottleneck):
|
||||
nn.init.constant_(m.bn3.weight, 0)
|
||||
elif isinstance(m, BasicBlock):
|
||||
nn.init.constant_(m.bn2.weight, 0)
|
||||
|
||||
def _make_layer(self, block, out_channels, blocks, stride=1):
|
||||
downsample = None
|
||||
# 如果通道数不匹配或需要调整步长，创建下采样层
|
||||
if stride != 1 or self.in_channels != out_channels * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
nn.BatchNorm2d(out_channels * block.expansion),
|
||||
)
|
||||
|
||||
layers = []
|
||||
# 第一个块可能需要下采样
|
||||
layers.append(block(self.in_channels, out_channels, stride, downsample, use_cbam=self.use_cbam))
|
||||
self.in_channels = out_channels * block.expansion
|
||||
|
||||
# 添加剩余的块
|
||||
for _ in range(1, blocks):
|
||||
layers.append(block(self.in_channels, out_channels, use_cbam=self.use_cbam))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
# 特征提取
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
# if self.use_cbam:
|
||||
# x = self.cbam1(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
# if self.use_cbam:
|
||||
# x = self.cbam2(x)
|
||||
# 分类
|
||||
x = self.avgpool(x)
|
||||
x = torch.flatten(x, 1)
|
||||
x = self.fc(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
# 工厂函数，创建不同深度的ResNet模型
|
||||
def resnet18_cbam(pretrained=False, **kwargs):
|
||||
return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
|
||||
|
||||
|
||||
def resnet34_cbam(pretrained=False, **kwargs):
|
||||
return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
|
||||
|
||||
|
||||
def resnet50_cbam(pretrained=False, **kwargs):
|
||||
return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
|
||||
|
||||
|
||||
def resnet101_cbam(pretrained=False, **kwargs):
|
||||
return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
|
||||
|
||||
|
||||
def resnet152_cbam(pretrained=False, **kwargs):
|
||||
return ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
|
||||
|
||||
|
||||
# 测试模型
|
||||
if __name__ == "__main__":
|
||||
# 创建一个带有CBAM注意力机制的ResNet50模型
|
||||
model = resnet50_cbam(num_classes=10)
|
||||
# 测试输入
|
||||
x = torch.randn(1, 3, 224, 224)
|
||||
y = model(x)
|
||||
print(f"输入形状: {x.shape}")
|
||||
print(f"输出形状: {y.shape}")
|
||||
Reference in New Issue
Block a user