图像分类,增量训练示例代码
时间: 2023-08-21 20:02:57 浏览: 54
以下是一个简单的示例代码,展示如何使用增量训练来进行图像分类任务:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import Subset, DataLoader
# 定义模型
class MyModel(nn.Module):
def __init__(self, num_classes):
super(MyModel, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.classifier = nn.Linear(128 * 7 * 7, num_classes)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
# 加载原有模型和数据
model = MyModel(num_classes=10)
model.load_state_dict(torch.load('pretrained_model.pth')) # 加载原有模型的参数
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
# 原有分类的样本和新分类的样本索引
old_class_indices = [0, 1, 2, 3, 4]
new_class_indices = [5, 6, 7, 8, 9]
# 创建新的数据加载器
old_train_subset = Subset(train_dataset, old_class_indices)
new_train_subset = Subset(train_dataset, new_class_indices)
old_train_loader = DataLoader(old_train_subset, batch_size=64, shuffle=True)
new_train_loader = DataLoader(new_train_subset, batch_size=64, shuffle=True)
# 定义优化器和损失函数
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss()
# 原有分类的训练
model.train()
for epoch in range(10):
for images, labels in old_train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 新分类的训练
for epoch in range(10):
for images, labels in new_train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 保存增量训练后的模型
torch.save(model.state_dict(), 'incremental_trained_model.pth')
```
以上代码为示例代码,仅供参考。实际使用时,需要根据具体的数据集和模型进行相应的调整。
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