def build_plain_train_loader()

in utils/common.py [0:0]

• 47 lines of code
• 7 McCabe index (conditional complexity)

def build_plain_train_loader(args):  # for statistic during test
    if args.dataset in ['cifar10', 'cifar100']:
        train_transform = transforms.Compose([
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.RandomApply([
                transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)
            ], p=0.8),
            transforms.RandomGrayscale(p=0.2),
            transforms.ToTensor(),
        ])
        test_transform = transforms.Compose([
            transforms.ToTensor(),
        ])
    elif args.dataset == 'imagenet':
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        train_transform = transforms.Compose([
            transforms.RandomResizedCrop(224, scale=(0.2, 1.0)),
            transforms.RandomHorizontalFlip(),
            transforms.RandomApply([
                transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)
            ], p=0.8),
            transforms.RandomGrayscale(p=0.2),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ])
        test_transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ])
    if args.dataset == 'cifar10':
        num_classes = 10
        train_set = IMBALANCECIFAR10(train=True, transform=test_transform, imbalance_ratio=args.imbalance_ratio, root=args.data_root_path)
    elif args.dataset == 'cifar100':
        num_classes = 100
        train_set = IMBALANCECIFAR100(train=True, transform=test_transform, imbalance_ratio=args.imbalance_ratio, root=args.data_root_path)
    elif args.dataset == 'imagenet':
        num_classes = args.id_class_number
        train_set = LT_Dataset(
            osp.join(args.data_root_path, 'imagenet'), './datasets/ImageNet_LT/ImageNet_LT_train.txt', transform=test_transform, 
            subset_class_idx=np.arange(0,args.id_class_number))
        
    train_loader = DataLoader(train_set, batch_size=1, shuffle=False, num_workers=4,
                                drop_last=True, pin_memory=True)
    
    return train_loader