小编Dev*_*shi的帖子

我正在尝试在 Pytorch 中运行以下代码：

import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data


class H5Dataset(data.Dataset):

    def __init__(self, trainx_path, trainy_path):

        super(H5Dataset, self).__init__()
        x_file = h5py.File(trainx_path)
        y_file = h5py.File(trainy_path)

        self.data = x_file.get('X')        
        self.target = y_file.get('y')

    def __getitem__(self, size):

      permutation1 = list(np.random.permutation(249000))  
      permutation2 = list(np.random.permutation(np.arange(249000,498000)))

      size1 = int(size/2)
      index1=list(permutation1[0:size1])
      index2=list(permutation2[0:size1])
      index = index1+index2
      labels=np.array(self.target).reshape(498000,-1)
      train_labels=labels[index]
      train_batch=[]

      for i in range(size):
          img=(self.data)[index[i]]
          train_batch.append(img)    
      train_batch=np.array(train_batch)

      return (torch.from_numpy(train_batch).float(), torch.from_numpy(train_labels).float())


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


dataset = H5Dataset('//content//drive//My …