SeResNet分类模型
时间: 2023-12-04 09:36:59 浏览: 36
SeResNet是一种基于ResNet的分类模型,它在ResNet的基础上增加了SE模块,用于提高模型的性能。SE模块可以自适应地调整每个通道的权重,从而提高模型的表现。
SE模块由两个步骤组成:squeeze和excitation。squeeze步骤通过全局平均池化来压缩每个通道的特征图,得到一个通道描述符;excitation步骤则通过两个全连接层来学习每个通道的权重,从而得到一个通道权重向量。最后,将通道权重向量与原始特征图相乘,得到加权特征图。
SeResNet相比于ResNet,在ImageNet数据集上的表现有了显著提升,同时参数量也没有明显增加。
相关问题
如何用senet搭配神经网络模型框架搭建一个图像识别模型,所使用的训练集和测试集是本地路径的图片,并进行分类处理,并且保存模型,写一个python文件能够让这个模型打印出分类的结果
首先,需要安装好PyTorch和torchvision,然后导入需要用到的库:
```python
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision.models import resnet18
```
接着,定义SENet模块:
```python
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
```
然后,定义使用SENet的ResNet18模型:
```python
class SEResNet18(nn.Module):
def __init__(self, num_classes=10):
super(SEResNet18, self).__init__()
self.resnet18 = resnet18(pretrained=True)
self.selu = nn.SELU(inplace=True)
self.se1 = SELayer(64)
self.se2 = SELayer(128)
self.se3 = SELayer(256)
self.se4 = SELayer(512)
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.resnet18.conv1(x)
x = self.resnet18.bn1(x)
x = self.resnet18.relu(x)
x = self.resnet18.maxpool(x)
x = self.se1(self.resnet18.layer1(x))
x = self.se2(self.resnet18.layer2(x))
x = self.se3(self.resnet18.layer3(x))
x = self.se4(self.resnet18.layer4(x))
x = self.resnet18.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(self.selu(x))
return x
```
接下来,设置训练参数、数据集、数据预处理和模型训练:
```python
# 设置训练参数
batch_size = 32
learning_rate = 1e-3
num_epochs = 10
# 定义数据集
train_dataset = datasets.ImageFolder(root='train/', transform=transforms.ToTensor())
test_dataset = datasets.ImageFolder(root='test/', transform=transforms.ToTensor())
# 定义数据加载器
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
# 定义模型
model = SEResNet18(num_classes=10)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=learning_rate)
scheduler = ReduceLROnPlateau(optimizer, mode='min')
# 训练模型
for epoch in range(num_epochs):
train_loss = 0
train_acc = 0
model.train()
for images, labels in train_loader:
images, labels = images.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item() * images.size(0)
_, pred = torch.max(outputs, 1)
train_acc += (pred == labels).sum().item()
train_loss = train_loss / len(train_loader.dataset)
train_acc = train_acc / len(train_loader.dataset)
test_loss = 0
test_acc = 0
model.eval()
with torch.no_grad():
for images, labels in test_loader:
images, labels = images.cuda(), labels.cuda()
outputs = model(images)
loss = criterion(outputs, labels)
test_loss += loss.item() * images.size(0)
_, pred = torch.max(outputs, 1)
test_acc += (pred == labels).sum().item()
test_loss = test_loss / len(test_loader.dataset)
test_acc = test_acc / len(test_loader.dataset)
print("Epoch: {}, Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}".format(
epoch + 1, train_loss, train_acc, test_loss, test_acc))
scheduler.step(test_loss)
```
最后,保存模型并使用模型进行预测:
```python
# 保存模型
torch.save(model.state_dict(), 'seresnet18.pth')
# 使用模型进行预测
model.load_state_dict(torch.load('seresnet18.pth'))
model.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
image = transform(Image.open('test_image.jpg')).unsqueeze(0)
output = model(image.cuda())
_, pred = torch.max(output, 1)
print("Predicted class: {}".format(pred.item()))
```
如何使用SEnet制作一个图像识别模型代码
SENet(Squeeze-and-Excitation Network)是一种用于图像识别的卷积神经网络架构,它通过自适应地调整不同通道的权重来提高模型的性能。下面是使用SENet构建图像分类模型的示例代码:
```python
import torch.nn as nn
from senet import SEBottleneck, SEResNet
class SEModel(nn.Module):
def __init__(self, num_classes):
super(SEModel, self).__init__()
self.base_model = SEResNet(block=SEBottleneck, layers=[3, 4, 6, 3], groups=64, reduction=16,
dropout_p=None, inplanes=64, input_3x3=False, downsample_kernel_size=1,
downsample_padding=0, num_classes=1000)
# 修改最后一层全连接层
self.base_model.avg_pool = nn.AdaptiveAvgPool2d(1)
self.base_model.last_linear = nn.Linear(self.base_model.last_linear.in_features, num_classes)
def forward(self, x):
x = self.base_model(x)
return x
```
在这个示例中,我们使用SEResNet作为基础模型,并修改了最后一层全连接层,以适应特定的分类任务。如果你需要进行其他类型的图像识别任务,你需要对模型的结构进行相应的修改。
接下来,你需要定义数据集和训练过程,这部分的代码可以根据你的具体需求进行编写。最后,使用PyTorch提供的优化器和损失函数进行模型训练和测试即可。
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