sys.stdout = Logger(log_path + suffix + '_os.txt')
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0
feature_dim = args.low_dim
print('==> Loading data..')
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.ToPILImage(),
#transforms.Pad(10),
transforms.RectScale(args.img_h, args.img_w),
transforms.RandomCrop((args.img_h, args.img_w)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
transform_test = transforms.Compose([
transforms.ToPILImage(),
#transforms.Resize((args.img_h,args.img_w)),
transforms.RectScale(args.img_h, args.img_w),
transforms.ToTensor(),
normalize,
])
end = time.time()
if dataset == 'sysu':
# training set
