写一段可运行的Python代码：基于Pytorch实现中药材中草药分类识别。

以下是一个基于PyTorch实现中药材分类识别的示例代码。该代码使用了ResNet-18模型，并使用了一个包含5个类别的数据集进行训练和测试。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义数据集路径和批处理大小
data_path = 'path/to/dataset'
batch_size = 32

# 定义数据增强和预处理的转换
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])
])

# 加载训练集和测试集
trainset = torchvision.datasets.ImageFolder(root=data_path+'/train', transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)

testset = torchvision.datasets.ImageFolder(root=data_path+'/test', transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)

# 定义ResNet-18模型
class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet18 = torchvision.models.resnet18(pretrained=True)
        self.fc = nn.Linear(1000, 5)

    def forward(self, x):
        x = self.resnet18(x)
        x = self.fc(x)
        return x

# 定义模型和损失函数
net = ResNet18()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(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 testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (
    100 * correct / total))

该代码使用了ResNet-18模型进行中药材分类识别。在训练过程中，使用了随机裁剪、水平翻转和归一化等数据增强和预处理技术。训练完成后，使用测试集对模型进行评估，并计算出了模型的准确率