DL-Art-School/codes/data/zip_file_dataset.py
2021-05-24 21:35:00 -06:00

65 lines
2.0 KiB
Python

import PIL.Image
import zipfile
import torch
import torchvision
from torch.utils.data import DataLoader
from torchvision.transforms import Compose, ToTensor, Normalize, Resize
class ZipFileDataset(torch.utils.data.Dataset):
def __init__(self, opt):
self.path = opt['path']
zip = zipfile.ZipFile(self.path)
self.all_files = list(zip.namelist())
self.resolution = opt['resolution']
self.paired_mode = opt['paired_mode']
self.transforms = Compose([ToTensor(),
Resize(self.resolution),
Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
self.zip = None
def __len__(self):
return len(self.all_files)
# Loaded on the fly because ZipFile does not tolerate pickling.
def get_zip(self):
if self.zip is None:
self.zip = zipfile.ZipFile(self.path)
return self.zip
def load_image(self, path):
file = self.get_zip().open(path, 'r')
pilimg = PIL.Image.open(file)
tensor = self.transforms(pilimg)
return tensor
def __getitem__(self, i):
fname = self.all_files[i]
out = {
'hq': self.load_image(fname),
'HQ_path': fname,
'has_alt': self.paired_mode
}
if self.paired_mode:
if fname.endswith('0.jpg'):
aname = fname.replace('0.jpg', '1.jpg')
else:
aname = fname.replace('1.jpg', '0.jpg')
out['alt_hq'] = self.load_image(aname)
return out
if __name__ == '__main__':
opt = {
'path': 'E:\\4k6k\\datasets\\images\\youtube-imagenet-paired\\output.zip',
'resolution': 224,
'paired_mode': True
}
dataset = ZipFileDataset(opt)
print(len(dataset))
loader = DataLoader(dataset, shuffle=True)
for i, d in enumerate(loader):
torchvision.utils.save_image(d['hq'], f'{i}_hq.png')
torchvision.utils.save_image(d['alt_hq'], f'{i}_althq.png')