自定义数据加载器，用于pytorch中的图像和标签。损失函数中输入的困难

import torch
from torch.utils.data import Dataset

class CustomDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        image = self.data[index]
        label = self.labels[index]
        return image, label

    def hard_example_mining(self, loss_values, num_hard_examples):
        sorted_indices = torch.argsort(loss_values, descending=True)
        hard_indices = sorted_indices[:num_hard_examples]
        return hard_indices

# 使用自定义数据加载器
dataset = CustomDataset(data, labels)
loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)

# 计算损失函数
for images, labels in loader:
    outputs = model(images)
    loss = criterion(outputs, labels)

    # 获取损失值
    loss_values = loss.detach()

    # 进行难例挖掘
    hard_indices = dataset.hard_example_mining(loss_values, num_hard_examples=10)

    # 使用难例样本进行训练
    hard_images = images[hard_indices]
    hard_labels = labels[hard_indices]

    # 继续进行模型的训练
    # ...