再写一个行人属性识别训练少量图片代码

好的，以下是一个简单的行人属性识别训练代码，使用的是 PyTorch 框架：

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from PIL import Image

# 定义数据集
class PedestrianDataset(Dataset):
    def __init__(self, img_paths, labels, transform=None):
        self.img_paths = img_paths
        self.labels = labels
        self.transform = transform
    
    def __getitem__(self, index):
        img_path = self.img_paths[index]
        label = self.labels[index]
        img = Image.open(img_path).convert('RGB')
        if self.transform is not None:
            img = self.transform(img)
        return img, label
    
    def __len__(self):
        return len(self.img_paths)

# 定义模型
class PedestrianClassifier(nn.Module):
    def __init__(self):
        super(PedestrianClassifier, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.fc = nn.Sequential(
            nn.Linear(128 * 8 * 8, 512),
            nn.ReLU(inplace=True),
            nn.Linear(512, 2)
        )
    
    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 定义超参数
batch_size = 16
learning_rate = 0.001
num_epochs = 10

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomCrop((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 加载数据
train_img_paths = ['path/to/image1.jpg', 'path/to/image2.jpg', ...]
train_labels = [0, 1, ...]  # 0表示非行人，1表示行人
train_dataset = PedestrianDataset(train_img_paths, train_labels, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 定义模型、损失函数和优化器
model = PedestrianClassifier()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 向前传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 向后传播并优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        # 打印训练信息
        if (i + 1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

这个代码可以处理少量的行人属性识别训练数据，但是需要注意的是，如果训练数据过少，模型可能会过拟合，导致泛化性能不佳，因此需要尽量收集更多的训练数据。此外，还需要注意数据的质量，如光照、角度、遮挡等因素，对训练结果会产生很大的影响。