请给出行人重识别的python可执行代码

由于行人重识别是一个复杂的算法，代码较长，无法在此一一列举。以下是一个基于PyTorch实现的行人重识别代码示例，供参考：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from dataset import PersonReID_Dataset
from model import PersonReID_Model

# 设置超参数
lr = 1e-3
epochs = 50
batch_size = 32
num_workers = 4

# 加载数据集
train_dataset = PersonReID_Dataset('train', transforms.Compose([
    transforms.Resize((256, 128)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]))
test_dataset = PersonReID_Dataset('test', transforms.Compose([
    transforms.Resize((256, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]))

# 加载数据集到DataLoader
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)

# 定义模型和损失函数
net = PersonReID_Model(num_classes=751)
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.Adam(net.parameters(), lr=lr)

# 训练模型
for epoch in range(epochs):
    running_loss = 0.0
    running_corrects = 0

    for inputs, labels in train_loader:
        optimizer.zero_grad()

        outputs = net(inputs)
        _, preds = torch.max(outputs, 1)
        loss = criterion(outputs, labels)

        loss.backward()
        optimizer.step()

        running_loss += loss.item() * inputs.size(0)
        running_corrects += torch.sum(preds == labels.data)

    epoch_loss = running_loss / len(train_dataset)
    epoch_acc = running_corrects.double() / len(train_dataset)

    print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, epochs, epoch_loss, epoch_acc))

# 测试模型
net.eval()
with torch.no_grad():
    running_corrects = 0
    for inputs, labels in test_loader:
        outputs = net(inputs)
        _, preds = torch.max(outputs, 1)
        running_corrects += torch.sum(preds == labels.data)

    test_acc = running_corrects.double() / len(test_dataset)

    print('Test Accuracy: {:.4f}'.format(test_acc))