Pytorch使用shuffle打乱数据的操作

 def Shuffle(self, x, y,random=None, int=int):
if random is None:
random = self.random
  for i in range(len(x)):
j = int(random() * (i + 1))
if j<=len(x)-1:
 x[i],x[j]=x[j],x[i]
 y[i],y[j]=y[j],y[i]
 retrun x,y

 def Shuffle(self, x, y,random=None, int=int):
  """x, random=random.random -> shuffle list x in place; return None.
  Optional arg random is a 0-argument function returning a random
  float in [0.0, 1.0); by default, the standard random.random.
  """
  if random is None:
random = self.random #random=random.random
  #转成numpy
  if torch.is_tensor(x)==True:
if self.use_cuda==True:
x=x.cpu().numpy()
else:
x=x.numpy()
  if torch.is_tensor(y) == True:
if self.use_cuda==True:
y=y.cpu().numpy()
else:
y=y.numpy()
  #开始随机置换
  for i in range(len(x)):
j = int(random() * (i + 1))
if j<=len(x)-1:#交换
 x[i],x[j]=x[j],x[i]
 y[i],y[j]=y[j],y[i]
  #转回tensor
  if self.use_cuda == True:
x=torch.from_numpy(x).cuda()
y=torch.from_numpy(y).cuda()
  else:
x = torch.from_numpy(x)
y = torch.from_numpy(y)
  return x,y

image_list=[]  # list of images
label_list=[]  # list of labels
 
temp = np.array([image_list, label_list])
temp = temp.transpose()
np.random.shuffle(temp)
 
images = temp[:, 0]  # array of images(N,)
labels = temp[:, 1]

image_list=[]  # list of images
label_list=[]  # list of labels
 
##如果image_list存的是读取的特征数据，而不是图片路径，不要注释后面两句（list无法索引内部list）
#[list indices must be integers or slices, not list]
#image_list = np.array(image_list)
#label_list = np.array(label_list)
 
index = [i for i in range(len(image_list))]
np.random.shuffle(index)
images = image_list[index]
labels = label_list[index]