我就廢話不多說了，直接上程式碼吧！

from os import listdir
import os
from time import time
 
import torch.utils.data as data
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
 
def printProgressBar(iteration,total,prefix='',suffix='',decimals=1,length=100,fill='=',empty=' ',tip='>',begin='[',end=']',done="[DONE]",clear=True):
  percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
  filledLength = int(length * iteration // total)
  bar = fill * filledLength
  if iteration != total:
    bar = bar + tip
  bar = bar + empty * (length - filledLength - len(tip))
  display = '\r{prefix}{begin}{bar}{end} {percent}%{suffix}' \
    .format(prefix=prefix,begin=begin,bar=bar,end=end,percent=percent,suffix=suffix)
  print(display,end=''),# comma after print() required for python 2
  if iteration == total: # print with newline on complete
    if clear: # display given complete message with spaces to 'erase' previous progress bar
      finish = '\r{prefix}{done}'.format(prefix=prefix,done=done)
      if hasattr(str,'decode'): # handle python 2 non-unicode strings for proper length measure
        finish = finish.decode('utf-8')
        display = display.decode('utf-8')
      clear = ' ' * max(len(display) - len(finish),0)
      print(finish + clear)
    else:
      print('')
 
 
class DatasetFromFolder(data.Dataset):
  def __init__(self,image_dir):
    super(DatasetFromFolder,self).__init__()
    self.photo_path = os.path.join(image_dir,"a")
    self.sketch_path = os.path.join(image_dir,"b")
    self.image_filenames = [x for x in listdir(self.photo_path) if is_image_file(x)]
 
    transform_list = [transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5))]
 
    self.transform = transforms.Compose(transform_list)
 
  def __getitem__(self,index):
    # Load Image
    input = load_img(os.path.join(self.photo_path,self.image_filenames[index]))
    input = self.transform(input)
    target = load_img(os.path.join(self.sketch_path,self.image_filenames[index]))
    target = self.transform(target)
 
    return input,target
 
  def __len__(self):
    return len(self.image_filenames)
 
if __name__ == '__main__':
  dataset = DatasetFromFolder("./dataset/facades/train")
  dataloader = DataLoader(dataset=dataset,num_workers=8,batch_size=1,shuffle=True)
  total = len(dataloader)
  for epoch in range(20):
    t0 = time()
    for i,batch in enumerate(dataloader):
      real_a,real_b = batch[0],batch[1]
      printProgressBar(i + 1,total + 1,length=20,prefix='Epoch %s ' % str(1),suffix=',d_loss: %d' % 1)
    printProgressBar(total,done='Epoch [%s] ' % str(epoch) +
               ',time: %.2f s' % (time() - t0)
             )

以上這篇pytorch 批次遍歷資料集列印資料的例子就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。