计算机视觉四：resnet50实现cifar10分类

'''
@File    :     resnet50.py
@Contact :     zhangzhilong2022@gmail.com
@Modify Time   2024/3/18 17:39
@Author        huahai2022
@Desciption
'''
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torchvision.models as models

# 定义数据预处理的转换
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载CIFAR-10数据集
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

# 定义数据加载器
batch_size = 128
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义ResNet-50模型
model = models.resnet50(pretrained=True)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 10)  # 替换最后一层全连接层，使其适配CIFAR-10的类别数

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 定义训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    for images, labels in train_loader:
        images = images.cuda()
        labels = labels.cuda()

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# 定义测试函数
def test(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images = images.cuda()
            labels = labels.cuda()

            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    accuracy = 100 * correct / total
    return accuracy

# 将模型移动到GPU上（如果可用）
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# 训练和测试模型
num_epochs = 10
for epoch in range(num_epochs):
    train(model, train_loader, criterion, optimizer)
    accuracy = test(model, test_loader)
    print(f'Epoch [{epoch+1}/{num_epochs}], Test Accuracy: {accuracy:.2f}%')

在跨越相加的时候，有一个地方就是输入和输出的形状不一定是一样的，采用了两个方法，1是使用下采样改变矩阵的形状，2是使用广播机制。