行人重识别秒杀

import torch
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# 定义一个简单的CNN模型
class SimpleReIDModel(nn.Module):
    def __init__(self):
        super(SimpleReIDModel, self).__init__()
        self.base_model = models.resnet50(pretrained=True)
        self.base_model.fc = nn.Linear(2048, 128)  # 替换最后一层全连接层

    def forward(self, x):
        return self.base_model(x)

# 数据预处理
transform = 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
train_dataset = datasets.ImageFolder(root='path_to_train_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 初始化模型、损失函数和优化器
model = SimpleReIDModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练循环
for epoch in range(10):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    print(f'Epoch {epoch+1}, Loss: {loss.item()}')