pytorch训练EfficientNetB0十分类任务的代码

以下是使用PyTorch训练EfficientNetB0进行十分类任务的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from efficientnet_pytorch import EfficientNet

# 定义数据预处理
transform_train = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = datasets.ImageFolder('/path/to/train', transform=transform_train)
test_dataset = datasets.ImageFolder('/path/to/test', transform=transform_test)

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False, num_workers=4)

# 定义模型
model = EfficientNet.from_pretrained('efficientnet-b0', num_classes=10)

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

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy: %d %%' % (100 * correct / total))

在上述代码中，我们首先定义了数据预处理方式，包括随机裁剪、随机水平翻转和归一化等操作。然后加载了训练集和测试集，并定义了数据加载器。接着，我们使用`from_pretrained()`方法加载了预训练的EfficientNet-B0模型，并修改最后一层的输出类别数为10。我们使用交叉熵损失函数和Adam优化器来进行训练，最后对模型进行测试并统计准确率。