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！fromosimportlistdirimportosfromtimeimporttimeimporttorch.utils.dataasdat

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)
 )

以上这篇pytorch 批次遍历数据集打印数据的例子就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。