cbam resnet

CBAM-ResNet是一种基于残差网络（ResNet）和通道注意力模块（CBAM）的深度神经网络架构。CBAM模块可以自适应地学习图像中每个通道的重要性，并对通道特征进行加权，以提高图像分类性能。在CBAM-ResNet中，CBAM模块被嵌入到ResNet的每个残差块中，以提高网络的特征表达能力。实验证明，CBAM-ResNet相比于传统的ResNet，可以在图像分类、目标检测等领域取得更好的性能。

相关问题

CBAM-ResNet tf实现

以下是CBAM-ResNet的TensorFlow实现：

import tensorflow as tf

def conv2d(inputs, filters, kernel_size, strides=1, padding='same', activation=None, name=None):
    return tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=kernel_size,
                            strides=strides, padding=padding, activation=activation, name=name)

def batch_norm(inputs, training, momentum=0.997, epsilon=1e-5, name=None):
    return tf.layers.batch_normalization(inputs=inputs, momentum=momentum, epsilon=epsilon,
                                         scale=True, training=training, name=name)

def relu(inputs, name=None):
    return tf.nn.relu(inputs, name=name)

def max_pool2d(inputs, pool_size, strides, padding='same', name=None):
    return tf.layers.max_pooling2d(inputs=inputs, pool_size=pool_size, strides=strides,
                                   padding=padding, name=name)

def avg_pool2d(inputs, pool_size, strides, padding='same', name=None):
    return tf.layers.average_pooling2d(inputs=inputs, pool_size=pool_size, strides=strides,
                                       padding=padding, name=name)

def cbam_block(inputs, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        # Channel attention
        channels = inputs.get_shape()[-1]
        avg_pool = tf.reduce_mean(inputs, axis=[1, 2], keepdims=True)
        assert avg_pool.get_shape()[1:] == (1, 1, channels)
        max_pool = tf.reduce_max(inputs, axis=[1, 2], keepdims=True)
        assert max_pool.get_shape()[1:] == (1, 1, channels)

        fc1 = conv2d(avg_pool, int(channels * reduction_ratio), kernel_size=1, name='fc1')
        assert fc1.get_shape()[1:] == (1, 1, int(channels * reduction_ratio))
        relu1 = relu(fc1, name='relu1')
        fc2 = conv2d(relu1, channels, kernel_size=1, name='fc2')
        assert fc2.get_shape()[1:] == (1, 1, channels)
        # channel attention的权重
        ch_attention = tf.sigmoid(fc2 + max_pool)

        # Spatial attention
        max_pool2d = tf.reduce_max(ch_attention, axis=-1, keepdims=True)
        assert max_pool2d.get_shape()[1:] == (1, 1, 1)
        avg_pool2d = tf.reduce_mean(ch_attention, axis=-1, keepdims=True)
        assert avg_pool2d.get_shape()[1:] == (1, 1, 1)
        # spatial attention的权重
        sp_attention = tf.sigmoid(max_pool2d + avg_pool2d)

        # 输出加权后的特征
        output = inputs * ch_attention * sp_attention
        return output

def cbam_resnet_block(inputs, filters, strides, training, projection_shortcut, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        shortcut = inputs
        if projection_shortcut is not None:
            shortcut = projection_shortcut(inputs)

        inputs = conv2d(inputs, filters, kernel_size=1, strides=1, name='conv1')
        inputs = batch_norm(inputs, training=training, name='bn1')
        inputs = relu(inputs, name='relu1')

        inputs = conv2d(inputs, filters, kernel_size=3, strides=strides, name='conv2')
        inputs = batch_norm(inputs, training=training, name='bn2')
        inputs = relu(inputs, name='relu2')

        inputs = cbam_block(inputs, reduction_ratio=reduction_ratio, name='cbam_block')

        inputs += shortcut
        inputs = relu(inputs, name='relu_output')
        return inputs

def cbam_resnet(inputs, num_blocks, filters, training, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        # 第一层
        inputs = conv2d(inputs, filters[0], kernel_size=7, strides=2, name='conv1')
        inputs = batch_norm(inputs, training=training, name='bn1')
        inputs = relu(inputs, name='relu1')
        inputs = max_pool2d(inputs, pool_size=3, strides=2, name='max_pool1')

        # resnet blocks
        for i in range(num_blocks):
            filters_block = filters[i+1]
            strides = 1
            if i == 0:
                strides = 2
            inputs = cbam_resnet_block(inputs, filters_block, strides, training,
                                        projection_shortcut=cbam_projection_shortcut if strides > 1 else None,
                                        reduction_ratio=reduction_ratio, name='block{}'.format(i+1))

        inputs = batch_norm(inputs, training=training, name='bn_last')
        inputs = relu(inputs, name='relu_last')
        inputs = avg_pool2d(inputs, pool_size=inputs.get_shape()[1:-1], strides=1, name='avg_pool')
        inputs = tf.layers.flatten(inputs, name='flatten')
        return inputs


def cbam_projection_shortcut(inputs, filters, strides, training, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        shortcut = conv2d(inputs, filters, kernel_size=1, strides=strides, name='conv_projection')
        shortcut = batch_norm(shortcut, training=training, name='bn_projection')
        shortcut = cbam_block(shortcut, reduction_ratio=reduction_ratio, name='cbam_projection')
        return shortcut

其中，`cbam_block` 和 `cbam_resnet_block` 实现CBAM模块和CBAM-ResNet模块，`cbam_resnet` 实现CBAM-ResNet网络的整体架构，`cbam_projection_shortcut` 实现CBAM-ResNet中的投影shortcut。

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。