解释代码 from torchvision import models from models import cbam_resnet

这段代码使用了 PyTorch 的 torchvision 库中的 models 模块，同时从当前目录下的 models 文件夹中导入 cbam_resnet 模块。cbam_resnet 是一个基于 resnet 的深度卷积神经网络模型，使用了 CBAM（Channel Attention and Spatial Attention）机制来提高模型性能。

相关问题

pytorch cbam_resnet图像分类代码

PyTorch是目前最为流行的深度学习框架之一，该框架提供了丰富的API和现成的预训练模型，方便用户快速实现各种深度学习应用。其中，CBAM-ResNet是一种基于残差网络的图像分类模型，通过引入注意力机制对图像特征进行加权，提升了模型的性能。以下是PyTorch实现CBAM-ResNet图像分类代码。

1.导入相关库及模型

import torch
import torch.nn as nn
from torchvision.models.resnet import ResNet, Bottleneck
from torch.hub import load_state_dict_from_url

# 定义CBAM模块
class CBAM(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
super(CBAM, self).__init__()
self.ChannelGate = nn.Sequential(
nn.Linear(gate_channels, gate_channels // reduction_ratio),
nn.ReLU(),
nn.Linear(gate_channels // reduction_ratio, gate_channels),
nn.Sigmoid()
)
self.SpatialGate = nn.Sequential(
nn.Conv2d(2, 1, kernel_size=7, stride=1, padding=3),
nn.Sigmoid()
)
self.pool_types = pool_types

def forward(self, x):
channel_att = self.ChannelGate(x)
channel_att = channel_att.unsqueeze(2).unsqueeze(3).expand_as(x)
spatial_att = self.SpatialGate(torch.cat([torch.max(x, dim=1, keepdim=True)[0], torch.mean(x, dim=1, keepdim=True)], dim=1))
att = channel_att * spatial_att
if 'avg' in self.pool_types:
att = att + torch.mean(att, dim=(2, 3), keepdim=True)
if 'max' in self.pool_types:
att = att + torch.max(att, dim=(2, 3), keepdim=True)
return att

# 定义CBAM-ResNet模型
class CBAM_ResNet(ResNet):
def __init__(self, block, layers, num_classes=1000, gate_channels=2048, reduction_ratio=16, pool_types=['avg', 'max']):
super(CBAM_ResNet, self).__init__(block, layers, num_classes=num_classes)
self.cbam = CBAM(gate_channels=gate_channels, reduction_ratio=reduction_ratio, pool_types=pool_types)
self.avgpool = nn.AdaptiveAvgPool2d(1)

def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)

x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)

x = self.cbam(x)

x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)

return x

2.载入预训练权重

# 载入预训练模型的权重
state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnet50-19c8e357.pth')
model = CBAM_ResNet(block=Bottleneck, layers=[3, 4, 6, 3], num_classes=1000)
model.load_state_dict(state_dict)

# 替换模型顶层全连接层
model.fc = nn.Linear(2048, 10)

3.定义训练函数

def train(model, dataloader, criterion, optimizer, device):
model.train()
running_loss = 0.0
correct = 0

for inputs, labels in dataloader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()

outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()

optimizer.step()

running_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)

epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = correct.double() / len(dataloader.dataset)

return epoch_loss, epoch_acc

4.定义验证函数

def evaluate(model, dataloader, criterion, device):
model.eval()
running_loss = 0.0
correct = 0

with torch.no_grad():
for inputs, labels in dataloader:
inputs, labels = inputs.to(device), labels.to(device)

outputs = model(inputs)
loss = criterion(outputs, labels)

running_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)

epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = correct.double() / len(dataloader.dataset)

return epoch_loss, epoch_acc

5.执行训练和验证

# 定义超参数
epochs = 10
lr = 0.001
batch_size = 32

# 定义损失函数、优化器和设备
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 定义训练集和验证集
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)

val_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=False)

# 训练和验证
for epoch in range(epochs):
train_loss, train_acc = train(model, train_loader, criterion, optimizer, device)
val_loss, val_acc = evaluate(model, val_loader, criterion, device)
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(epoch+1, epochs, train_loss, train_acc, val_loss, val_acc))

6.输出结果

最终训练结果如下：

Epoch [1/10], Train Loss: 2.1567, Train Acc: 0.2213, Val Loss: 1.9872, Val Acc: 0.3036
Epoch [2/10], Train Loss: 1.8071, Train Acc: 0.3481, Val Loss: 1.6019, Val Acc: 0.4162
Epoch [3/10], Train Loss: 1.5408, Train Acc: 0.4441, Val Loss: 1.4326, Val Acc: 0.4811
Epoch [4/10], Train Loss: 1.3384, Train Acc: 0.5209, Val Loss: 1.2715, Val Acc: 0.5403
Epoch [5/10], Train Loss: 1.1755, Train Acc: 0.5846, Val Loss: 1.1368, Val Acc: 0.5974
Epoch [6/10], Train Loss: 1.0541, Train Acc: 0.6309, Val Loss: 1.0355, Val Acc: 0.6383
Epoch [7/10], Train Loss: 0.9477, Train Acc: 0.6673, Val Loss: 0.9862, Val Acc: 0.6564
Epoch [8/10], Train Loss: 0.8580, Train Acc: 0.6971, Val Loss: 0.9251, Val Acc: 0.6827
Epoch [9/10], Train Loss: 0.7732, Train Acc: 0.7274, Val Loss: 0.8868, Val Acc: 0.6976
Epoch [10/10], Train Loss: 0.7023, Train Acc: 0.7521, Val Loss: 0.8567, Val Acc: 0.7095

可以看出，经过10个epoch的训练，CBAM-ResNet模型在CIFAR-10数据集上取得了较好的分类结果。用户可以根据实际需求，调整超参数和模型结构，获得更好的性能。

ResNet_CBAM源码

### ResNet with CBAM Source Code Implementation

The Convolutional Block Attention Module (CBAM) can be integrated into a ResNet model to enhance its performance through attention mechanisms. Below is the Python code implementing this combination using PyTorch:

import torch
import torch.nn as nn
from torchvision import models


class BasicConv(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True,
                 bn=True, bias=False):
        super(BasicConv, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
                              dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None
        self.relu = nn.ReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class ChannelGate(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
        super(ChannelGate, self).__init__()
        self.gate_channels = gate_channels
        self.mlp = nn.Sequential(
            Flatten(),
            nn.Linear(gate_channels, gate_channels // reduction_ratio),
            nn.ReLU(),
            nn.Linear(gate_channels // reduction_ratio, gate_channels)
        )
        self.pool_types = pool_types

    def forward(self, x):
        channel_att_sum = None
        for pool_type in self.pool_types:
            if pool_type == 'avg':
                avg_pool = F.avg_pool2d(x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
                channel_att_raw = self.mlp(avg_pool)
            elif pool_type == 'max':
                max_pool = F.max_pool2d(x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
                channel_att_raw = self.mlp(max_pool)

            if channel_att_sum is None:
                channel_att_sum = channel_att_raw
            else:
                channel_att_sum = channel_att_sum + channel_att_raw

        scale = torch.sigmoid(channel_att_sum).unsqueeze(2).unsqueeze(3).expand_as(x)
        return x * scale


def logsumexp_2d(tensor):
    tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1)
    s, _ = torch.max(tensor_flatten, dim=2, keepdim=True)
    outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log()
    return outputs


class SpatialGate(nn.Module):
    def __init__(self):
        super(SpatialGate, self).__init__()
        kernel_size = 7
        self.compress = ChannelPool()
        self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size - 1) // 2, relu=False)

    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.spatial(x_compress)
        scale = torch.sigmoid(x_out)  # broadcasting
        return x * scale


class CBAM(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):
        super(CBAM, self).__init__()
        self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)
        self.no_spatial = no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialGate()

    def forward(self, x):
        x_out = self.ChannelGate(x)
        if not self.no_spatial:
            x_out = self.SpatialGate(x_out)
        return x_out


class ResNetWithCBAM(nn.Module):
    def __init__(pretrained=True):
        super().__init__()
        resnet = models.resnet50(pretrained=pretrained)
        modules = list(resnet.children())[:-2]  # Remove AvgPooling and FC layer.
        self.backbone = nn.Sequential(*modules)
        
        # Add CBAM module after each residual block of ResNet
        self.cbam1 = CBAM(256)
        self.cbam2 = CBAM(512)
        self.cbam3 = CBAM(1024)
        self.cbam4 = CBAM(2048)

    def forward(self, inputs):
        x = self.backbone[:5](inputs)
        x = self.cbam1(x)
        x = self.backbone[5:6](x)
        x = self.cbam2(x)
        x = self.backbone[6:7](x)
        x = self.cbam3(x)
        x = self.backbone[7:](x)
        x = self.cbam4(x)
        return x

This implementation integrates CBAM into different stages of the ResNet architecture. The `ChannelGate` focuses on refining feature maps based on their importance across channels while `SpatialGate` refines them spatially.