code: import os
  import torch
  import csv
  import numpy as np
  from torch.utils.data import Dataset
  from PIL import Image
  
  def split_csv(file):
      data = []
      a_train_file = r'/home/huangyinyue/ISIC_2019/train.csv'
      a_test_file = r'/home/huangyinyue/ISIC_2019/test.csv'
  
      seed = 3
      np.random.seed(seed)
      train_indices = np.random.choice(25331, 20265, replace=False)  # 设置随机数生成从0-150中随机挑选120个随机数
      test_indices = np.array(list(set(range(25331)) - set(train_indices)))
      # test_indices = np.random.choice(len(residue), 30, replace=False)  # 如果训练集和测试集综合的数据加起来就是一整个数据集则不需要这个操作
  
      with open(file)as afile:
          a_reader = csv.reader(afile)  # 从原始数据集中将所有数据读取出来并保存到a_reader中
          labels = next(a_reader)  # 提取第一行设置为labels
          for row in a_reader:  # 将a_reader中每一行的数据提取出来并保存到data的列表中
              data.append(row)
  
      # 生成训练数据集
      if not os.path.exists(a_train_file):
          with open(a_train_file, "w", newline='') as a_trian:
              writer = csv.writer(a_trian)
              writer.writerows([labels])  # 第一行为标签行
              writer.writerows(np.array(data)[train_indices])
              a_trian.close()
  
      # 生成测试数据集
      if not os.path.exists(a_test_file):
          with open(a_test_file, "w", newline='')as a_test:
              writer = csv.writer(a_test)
              writer.writerows([labels])  # 第一行为标签行
              writer.writerows(np.array(data)[test_indices])
              a_test.close()
  
  
  def read_labels_csv(file,header=True):
      images = []
      num_categories = 0
      with open(file, 'r') as f:
          reader = csv.reader(f)
          rownum = 0
          for row in reader:
              if header and rownum == 0:
                  header = row
              else:
                  if num_categories == 0:
                      num_categories = len(row) - 1
                  name = row[0]
                  labels = (np.asarray(row[1:num_categories + 1])).astype(np.float32)
                  labels = torch.from_numpy(labels)
                  item = (name, labels)
                  images.append(item)
              rownum += 1
      return images
  
  class ISICDataset(Dataset):
      def __init__(self,csv_file,image_path,transform=None):
          self.images = read_labels_csv(csv_file)
          self.root_dir = image_path
          self.transform = transform
  
      def __len__(self):
          return len(self.images)
  
      def __getitem__(self, index):
          image_name,target = self.images[index]
          # print(os.path.join(self.root_dir,image_name+'.jpg'))
          image = Image.open(os.path.join(self.root_dir,image_name+'.jpg')).convert('RGB')
          if self.transform is not None:
              image = self.transform(image)
  
          return image,target
  
  
  if __name__ == '__main__':
      split_csv(file=r"/home/huangyinyue/ISIC_2019/ISIC_2019_Training_GroundTruth.csv")
  
apis: numpy.random.seed numpy.asarray numpy.random.choice torch.from_numpy numpy.array