Hello！This is my first experience with image processing.I don’t know why the process is wrong.
Here is my code:
import os
from skimage import io
import torchvision.datasets.mnist as mnist
root="C:/fashion_mnist/"
train_set = (mnist.read_image_file(os.path.join(root, 'train-images-idx3-ubyte')),mnist.read_label_file(os.path.join(root, 'train-labels-idx1-ubyte')))
test_set = (mnist.read_image_file(os.path.join(root, 't10k-images-idx3-ubyte')),mnist.read_label_file(os.path.join(root, 't10k-labels-idx1-ubyte')))
print("training set :",train_set[0].size())
training set : torch.Size([60000, 28, 28])
print("test set :",test_set[0].size())
test set : torch.Size([10000, 28, 28])
def convert_to_img(train=True):
   if(train):
     f=open(root+'train.txt','w')
     data_path=root+'/train/'
     if(not os.path.exists(data_path)):
       os.makedirs(data_path)
     for i, (img,label) in enumerate(zip(train_set[0],train_set[1])):
       img_path=data_path+str(i)+'.jpg'
       io.imsave(img_path,img.numpy())
       f.write(img_path+' '+str(label)+'\n')
     f.close()
   else:
     f = open(root + 'test.txt', 'w')
     data_path = root + '/test/'
     if (not os.path.exists(data_path)):
       os.makedirs(data_path)
     for i, (img,label) in enumerate(zip(test_set[0],test_set[1])):
       img_path = data_path+ str(i) + '.jpg'
       io.imsave(img_path, img.numpy())
       f.write(img_path + ' ' + str(label) + '\n')
     f.close()

convert_to_img(True)
convert_to_img(False)
import torch
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
root="C:/fashion_mnist/"
def default_loader(path):
   return Image.open(path).convert('RGB')

class MyDataset(Dataset):
    def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
      fh = open(txt, 'r')
      imgs = []
      for line in fh:
        line = line.strip('\n')
        line = line.rstrip()
        words = line.split()
        imgs.append((words[0],words[1]))
      self.imgs = imgs
      self.transform = transform
      self.target_transform = target_transform
      self.loader = loader
    def __getitem__(self, index):
      fn, label = self.imgs[index]
      img = self.loader(fn)
      if self.transform is not None:
        img = self.transform(img)
      return img,label
    def __len__(self):
      return len(self.imgs)

train_data=MyDataset(txt=root+'train.txt', transform=transforms.ToTensor())
test_data=MyDataset(txt=root+'test.txt', transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=64)


class Net(torch.nn.Module):
   def __init__(self):
     super(Net, self).__init__()
     self.conv1 = torch.nn.Sequential(torch.nn.Conv2d(3, 32, 3, 1, 1),torch.nn.ReLU(),torch.nn.MaxPool2d(2))
     self.conv2 = torch.nn.Sequential(torch.nn.Conv2d(32, 64, 3, 1, 1),torch.nn.ReLU(),torch.nn.MaxPool2d(2))
     self.conv3 = torch.nn.Sequential(torch.nn.Conv2d(64, 64, 3, 1, 1),torch.nn.ReLU(),torch.nn.MaxPool2d(2))
     self.dense = torch.nn.Sequential(torch.nn.Linear(64 * 3 * 3, 128),torch.nn.ReLU(),torch.nn.Linear(128, 10))
   def forward(self, x):
     conv1_out = self.conv1(x)
     conv2_out = self.conv2(conv1_out)
     conv3_out = self.conv3(conv2_out)
     res = conv3_out.view(conv3_out.size(0), -1)
     out = self.dense(res)
     return out

model = Net()
print(model)
optimizer = torch.optim.Adam(model.parameters())
loss_func = torch.nn.CrossEntropyLoss()

for epoch in range(10):
   print('epoch {}'.format(epoch + 1))
   train_loss = 0.
   train_acc = 0.
   for batch_x, batch_y in train_loader:
     batch_x, batch_y = Variable(batch_x), Variable(batch_y)
     out = model(batch_x)
     loss = loss_func(out, batch_y)
     train_loss += loss.data[0]
     pred = torch.max(out, 1)[1]
     train_correct = (pred == batch_y).sum()
     train_acc += train_correct.data[0]
     optimizer.zero_grad()
     loss.backward()
     optimizer.step()
   print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(train_data)), train_acc / (len(train_data))))

Thanks for your help!!!