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datasets.py
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datasets.py
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# -*- coding: utf-8 -*-
# @Date : 2019-07-25
# @Author : Xinyu Gong ([email protected])
# @Link : None
# @Version : 0.0
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from celeba import CelebA, FFHQ
class ImageDataset(object):
def __init__(self, args, cur_img_size=None, bs=None):
bs = args.dis_batch_size if bs == None else bs
img_size = cur_img_size if args.fade_in > 0 else args.img_size
if args.dataset.lower() == 'cifar10':
Dt = datasets.CIFAR10
transform = transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
args.n_classes = 0
train_dataset = Dt(root=args.data_path, train=True, transform=transform, download=True)
val_dataset = Dt(root=args.data_path, train=False, transform=transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
self.train_sampler = train_sampler
self.train = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.dis_batch_size, shuffle=(train_sampler is None),
num_workers=args.num_workers, pin_memory=True, sampler=train_sampler)
self.valid = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
self.test = self.valid
elif args.dataset.lower() == 'stl10':
Dt = datasets.STL10
transform = transforms.Compose([
transforms.Resize(img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
train_dataset = Dt(root=args.data_path, split='train+unlabeled', transform=transform, download=True)
val_dataset = Dt(root=args.data_path, split='test', transform=transform)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
else:
train_sampler = None
val_sampler = None
self.train_sampler = train_sampler
self.train = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.dis_batch_size, shuffle=(train_sampler is None),
num_workers=args.num_workers, pin_memory=True, sampler=train_sampler)
self.valid = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
self.test = self.valid
elif args.dataset.lower() == 'celeba':
Dt = CelebA
transform = transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
train_dataset = Dt(root=args.data_path, transform=transform)
val_dataset = Dt(root=args.data_path, transform=transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
self.train_sampler = train_sampler
self.train = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.dis_batch_size, shuffle=(train_sampler is None),
num_workers=args.num_workers, pin_memory=True, drop_last=True, sampler=train_sampler)
self.valid = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
self.test = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
elif args.dataset.lower() == 'ffhq':
Dt = FFHQ
transform = transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
train_dataset = Dt(root=args.data_path, transform=transform)
val_dataset = Dt(root=args.data_path, transform=transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
self.train_sampler = train_sampler
self.train = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.dis_batch_size, shuffle=(train_sampler is None),
num_workers=args.num_workers, pin_memory=True, drop_last=True, sampler=train_sampler)
self.valid = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
self.test = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
elif args.dataset.lower() == 'bedroom':
Dt = datasets.LSUN
transform = transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
train_dataset = Dt(root=args.data_path, classes=["bedroom_train"], transform=transform)
val_dataset = Dt(root=args.data_path, classes=["bedroom_val"], transform=transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
self.train_sampler = train_sampler
self.train = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.dis_batch_size, shuffle=(train_sampler is None),
num_workers=args.num_workers, pin_memory=True, drop_last=True, sampler=train_sampler)
self.valid = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
self.test = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
elif args.dataset.lower() == 'church':
Dt = datasets.LSUN
transform = transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
train_dataset = Dt(root=args.data_path, classes=["church_outdoor_train"], transform=transform)
val_dataset = Dt(root=args.data_path, classes=["church_outdoor_val"], transform=transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
self.train_sampler = train_sampler
self.train = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.dis_batch_size, shuffle=(train_sampler is None),
num_workers=args.num_workers, pin_memory=True, drop_last=True, sampler=train_sampler)
self.valid = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
self.test = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, sampler=val_sampler)
else:
raise NotImplementedError('Unknown dataset: {}'.format(args.dataset))