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datasets.py
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import random
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import Dataset, SubsetRandomSampler
from utils.utils import *
class ImageDataset(object):
def __init__(self, args):
if args.dataset.lower() == 'cifar10':
Dt = datasets.CIFAR10
transform = transforms.Compose([
transforms.Resize(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_rot1 = transforms.Compose([
transforms.Resize(args.img_size),
MyRotationTransform(90),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_rot2 = transforms.Compose([
transforms.Resize(args.img_size),
MyRotationTransform(180),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_rot3 = transforms.Compose([
transforms.Resize(args.img_size),
MyRotationTransform(270),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
aug_transform = transforms.Compose([
transforms.Resize(args.img_size+ 8),
transforms.RandomCrop(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
args.n_classes = 10
elif args.dataset.lower() == 'stl10':
Dt = datasets.STL10
transform = transforms.Compose([
transforms.Resize(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
else:
raise NotImplementedError('Unknown dataset: {}'.format(args.dataset))
if args.dataset.lower() == 'stl10':
self.train = torch.utils.data.DataLoader(
Dt(root=args.data_path, split='train+unlabeled', transform=transform, download=True),
batch_size=args.dis_batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=True)
self.valid = torch.utils.data.DataLoader(
Dt(root=args.data_path, split='test', transform=transform),
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
self.test = self.valid
else:
dataset_idx = list(range(50000))
idx = random.sample(dataset_idx, args.data_size)
sampler = SubsetRandomSampler(idx)
# shuffled_dataset = torch.utils.data.Subset(Dt(root=args.data_path, train=True, transform=transform, download=True),
# idx)
# self.train = torch.utils.data.DataLoader(shuffled_dataset, batch_size=args.dis_batch_size, num_workers=args.num_workers, shuffle=False,
# drop_last=True, pin_memory=True)
#
# shuffled_dataset_rot1 = torch.utils.data.Subset(Dt(root=args.data_path, train=True, transform=transform_rot1, download=True),
# idx)
# self.train_rot1 = torch.utils.data.DataLoader(shuffled_dataset_rot1, batch_size=args.dis_batch_size, num_workers=args.num_workers, shuffle=False,
# drop_last=True, pin_memory=True)
#
# shuffled_dataset_rot2 = torch.utils.data.Subset(Dt(root=args.data_path, train=True, transform=transform_rot2, download=True),
# idx)
# self.train_rot2 = torch.utils.data.DataLoader(shuffled_dataset_rot2, batch_size=args.dis_batch_size, num_workers=args.num_workers, shuffle=False,
# drop_last=True, pin_memory=True)
#
# shuffled_dataset_rot3 = torch.utils.data.Subset(Dt(root=args.data_path, train=True, transform=transform_rot3, download=True),
# idx)
# self.train_rot3 = torch.utils.data.DataLoader(shuffled_dataset_rot3, batch_size=args.dis_batch_size, num_workers=args.num_workers, shuffle=False,
# drop_last=True, pin_memory=True)
# aug_shuffled_dataset = torch.utils.data.Subset(Dt(root=args.data_path, train=True, transform=aug_transform, download=True),
# idx)
# self.aug_train = torch.utils.data.DataLoader(aug_shuffled_dataset, batch_size=args.dis_batch_size, num_workers=args.num_workers, shuffle=False,
# drop_last=True, pin_memory=True)
self.train = torch.utils.data.DataLoader(
Dt(root=args.data_path, train=True, transform=transform, download=True),
batch_size=args.dis_batch_size, shuffle=False, drop_last=True, sampler=sampler,
num_workers=args.num_workers, pin_memory=True)
# self.aug_train = torch.utils.data.DataLoader(
# Dt(root=args.data_path, train=True, transform=aug_transform, download=True),
# batch_size=args.dis_batch_size, shuffle=False, drop_last=True, sampler=sampler,
# num_workers=args.num_workers, pin_memory=True)
self.valid = torch.utils.data.DataLoader(
Dt(root=args.data_path, train=False, transform=transform),
batch_size=args.dis_batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
self.test = self.valid