pytorch 批次遍历数据集打印数据的例子

我就废话不多说了，直接上代码吧！

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, 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, total,
            done='Epoch [％s] ' ％ str(epoch) +
              ', time: ％.2f s' ％ (time() - t0)
            )

来源：https://blog.csdn.net/luolinll1212/article/details/82983520