1.简单测import sys

import sys
import torch
import random
import argparse
import numpy as np
import pandas as pd
import torch.nn as nn
from torch.nn import functional as F
from torch.optim import lr_scheduler
from torchvision import datasets, transforms
from torch.utils.data import TensorDataset, DataLoader, Dataset
 
class DealDataset(Dataset):
    def __init__(self):
        xy = np.random.randn(4,3)
        print(xy)
        print("
")
        self.x_data = torch.from_numpy(xy[:, 0:-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])
        self.len = xy.shape[0]
    
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]
 
    def __len__(self):
        return self.len
   
dealDataset = DealDataset()
 
train_loader2 = DataLoader(dataset=dealDataset,
                          batch_size=2,
                          shuffle=True)
for j in range(5):
    for i, data in enumerate(train_loader2):
        inputs, labels = data

        #inputs, labels = Variable(inputs), Variable(labels)
        print(inputs)
    print("
")
        #print("epoch：", epoch, "的第" , i, "个inputs", inputs.data.size(), "labels", labels.data.size())

输出：

[[ 1.35870858 -0.74676435 -0.4181123 ]
 [ 0.14165115 -1.55553785 -2.03821185]
 [ 0.46154706  1.36100343 -0.13686081]
 [ 0.59683626  1.60361944 -0.90266193]]


tensor([[ 0.4615,  1.3610],
        [ 0.1417, -1.5555]], dtype=torch.float64)
tensor([[ 0.5968,  1.6036],
        [ 1.3587, -0.7468]], dtype=torch.float64)


tensor([[ 1.3587, -0.7468],
        [ 0.4615,  1.3610]], dtype=torch.float64)
tensor([[ 0.5968,  1.6036],
        [ 0.1417, -1.5555]], dtype=torch.float64)


tensor([[ 0.1417, -1.5555],
        [ 1.3587, -0.7468]], dtype=torch.float64)
tensor([[0.5968, 1.6036],
        [0.4615, 1.3610]], dtype=torch.float64)


tensor([[ 0.5968,  1.6036],
        [ 1.3587, -0.7468]], dtype=torch.float64)
tensor([[ 0.4615,  1.3610],
        [ 0.1417, -1.5555]], dtype=torch.float64)


tensor([[ 0.5968,  1.6036],
        [ 0.1417, -1.5555]], dtype=torch.float64)
tensor([[ 1.3587, -0.7468],
        [ 0.4615,  1.3610]], dtype=torch.float64)

说明每次调用dataloader都是重新打乱，而不是在定义的时候只打乱一次。