一、深度学习与图像分类基础
图像分类是计算机视觉的核心任务之一。传统方法依赖手工特征（如SIFT、HOG），而深度学习通过卷积神经网络（CNN）自动学习特征层次。以ResNet为例，其残差连接结构有效缓解了深层网络的梯度消失问题，成为当前主流模型。
关键公式：残差块的计算
若原始映射为 \( H(x) \)，ResNet学习的是残差 \( F(x) = H(x) - x \)，输出变为：
\( H(x) = F(x) + x \)
二、代码实现：PyTorch全流程
1. 环境准备
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
# 检查GPU可用性
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")2. 数据加载与增强
使用CIFAR-10数据集，包含10类物体图像：
transform = torchvision.transforms.Compose([
torchvision.transforms.RandomHorizontalFlip(), # 数据增强
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_data = torchvision.datasets.CIFAR10(
root="data", train=True, download=True, transform=transform
)
test_data = torchvision.datasets.CIFAR10(
root="data", train=False, download=True, transform=transform
)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64)3. 定义ResNet模型
简化版ResNet-18实现：
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
residual = self.shortcut(x)
x = nn.ReLU()(self.bn1(self.conv1(x)))
x = self.bn2(self.conv2(x))
x += residual # 残差连接
return nn.ReLU()(x)
class ResNet(nn.Module):
def __init__(self, num_classes=10):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(64, 64, stride=1)
self.layer2 = self._make_layer(64, 128, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(128, num_classes)
def _make_layer(self, in_channels, out_channels, stride):
return nn.Sequential(
ResidualBlock(in_channels, out_channels, stride),
ResidualBlock(out_channels, out_channels, stride=1)
)
def forward(self, x):
x = nn.ReLU()(self.bn1(self.conv1(x)))
x = self.layer1(x)
x = self.layer2(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
return self.fc(x)4. 训练与评估
model = ResNet().to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
def train(epochs):
for epoch in range(epochs):
model.train()
for X, y in train_loader:
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 验证集测试
model.eval（)
test_loss, correct = 0, 0
with torch.no_grad():
for X, y in test_loader:
X, y = X.to(device), y.to(device)
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
print(f"Epoch {epoch+1}: Test Accuracy = {correct/len(test_loader.dataset):.3f}")
train(epochs=10)三、优化策略与部署建议
- 超参数调优：
• 学习率调度（如CosineAnnealingLR）
• 数据增强扩展（CutMix、AutoAugment） - 模型轻量化：
# 使用预训练模型并微调
model = torchvision.models.resnet18(pretrained=True)
model.fc = nn.Linear(512, 10) # 修改最后一层- 部署到生产环境：
• 使用ONNX格式导出模型
• 通过TorchScript实现跨平台推理
四、总结
本文实现了ResNet在CIFAR-10上的分类任务，代码完整覆盖数据加载、模型定义、训练评估全流程。实际应用中需根据场景调整模型深度和数据增强策略。