PyTorch資料讀取

torch.utils.data.dataloader(torch.utils.data.dataset,batch_size,shuffle,num_workers,pin_memory)

關鍵是這兩個類：

torch.utils.data.dataloader

torch.utils.data.dataset

import torchvision.transforms as transforms

train_loader = torch.utils.data.dataloader(
imagelist(root=opt.root_path, filelist=opt.train_list, 
transform=transforms.compose([ 
transforms.totensor(),              #將讀取的變為tensor型別，很重要
])),
batch_size=opt.batch_size, shuffle=true,
num_workers=opt.workers, pin_memory=true)

#load_imglist.py

import torch.utils.data 
from pil import image
import os
def default_list_reader(filelist):
imglist = 
with open(filelist, 'r') as file:
for line in file.readlines():
imgpath, label = line.strip().split(' ')
return imglist
class imagelist(torch.utils.data.dataset):
def __init__(self, root, filelist, transform=none):
self.root      = root
self.imglist   = default_list_reader(filelist)
self.transform = transform
def __getitem__(self, index):
imgpath, target = self.imglist[index]
print(imgpath)
img_loc=os.path.join(self.root, imgpath)
img = image.open(img_loc).convert('l')  #預設讀取彩色圖象，這兒轉化為rgb影象
if self.transform is not none:
img = self.transform(img)
return img, target
def __len__(self):
return len(self.imglist)

for i,(input,target) in enumerate(train_loader):

print(i,target)
print(input.shape)

(1093, 

928[torch.longtensor of size 1]
)(1l, 1l, 64l, 64l)

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