Python Randomswap Examples

Example #1

File: config.py Project: zjj-2015/DCL

def load_data_transformers(resize_reso=512, crop_reso=448, swap_num=[7, 7]):
    center_resize = 600
    Normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    data_transforms = {
       	'swap': transforms.Compose([
            transforms.Randomswap((swap_num[0], swap_num[1])),
        ]),
        'common_aug': transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=15),
            transforms.RandomCrop((crop_reso,crop_reso)),
            transforms.RandomHorizontalFlip(),
        ]),
        'train_totensor': transforms.Compose([
            transforms.Resize((crop_reso, crop_reso)),
            # ImageNetPolicy(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]),
        'val_totensor': transforms.Compose([
            transforms.Resize((crop_reso, crop_reso)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]),
        'test_totensor': transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.CenterCrop((crop_reso, crop_reso)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]),
        'None': None,
    }
    return data_transforms

Example #2

def load_data_transformers(resize_reso=512, crop_reso=448, swap_num=[7, 7]):
    center_resize = 600
    Normalize = transforms.Normalize([0.485, 0.456, 0.406],
                                     [0.229, 0.224, 0.225])
    data_transforms = {
        'swap':
        transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=15),
            transforms.RandomCrop((crop_reso, crop_reso)),
            transforms.RandomHorizontalFlip(),
            transforms.Randomswap((swap_num[0], swap_num[1])),
        ]),
        'food_swap':
        transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=90),
            #transforms.RandomCrop((crop_reso, crop_reso)),
            transforms.RandomHorizontalFlip(),
            transforms.RandomVerticalFlip(),
            transforms.RandomResizedCrop(size=crop_reso, scale=(0.75, 1)),
            transforms.Randomswap((swap_num[0], swap_num[1])),
        ]),
        'food_unswap':
        transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=90),
            #transforms.RandomCrop((crop_reso, crop_reso)),
            transforms.RandomHorizontalFlip(),
            transforms.RandomVerticalFlip(),
            transforms.RandomResizedCrop(size=crop_reso, scale=(0.75, 1)),
        ]),
        'unswap':
        transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=15),
            transforms.RandomCrop((crop_reso, crop_reso)),
            transforms.RandomHorizontalFlip(),
        ]),
        'train_totensor':
        transforms.Compose([
            transforms.Resize((crop_reso, crop_reso)),
            #ImageNetPolicy(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]),
        'val_totensor':
        transforms.Compose([
            transforms.Resize((crop_reso, crop_reso)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]),
        'test_totensor':
        transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.CenterCrop((crop_reso, crop_reso)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]),
        'None':
        None,
        'Centered_swap':
        transforms.Compose([
            transforms.CenterCrop((center_resize, center_resize)),
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=15),
            transforms.RandomCrop((crop_reso, crop_reso)),
            transforms.RandomHorizontalFlip(),
            transforms.Randomswap((swap_num[0], swap_num[1])),
        ]),
        'Centered_unswap':
        transforms.Compose([
            transforms.CenterCrop((center_resize, center_resize)),
            transforms.Resize((resize_reso, resize_reso)),
            transforms.RandomRotation(degrees=15),
            transforms.RandomCrop((crop_reso, crop_reso)),
            transforms.RandomHorizontalFlip(),
        ]),
        'Tencrop':
        transforms.Compose([
            transforms.Resize((resize_reso, resize_reso)),
            transforms.TenCrop((crop_reso, crop_reso)),
            transforms.Lambda(lambda crops: torch.stack(
                [transforms.ToTensor()(crop) for crop in crops])),
        ])
    }

    return data_transforms

Example #3

print('train images:', train_pd.shape)
print('test images:', test_pd.shape)
print('num classes:', cfg['numcls'])

print('Set transform')

cfg['swap_num'] = 7

data_transforms = {
    'swap':
    transforms.Compose([
        transforms.Resize((512, 512)),
        transforms.RandomRotation(degrees=15),
        transforms.RandomCrop((448, 448)),
        transforms.RandomHorizontalFlip(),
        transforms.Randomswap((cfg['swap_num'], cfg['swap_num'])),
    ]),
    'unswap':
    transforms.Compose([
        transforms.Resize((512, 512)),
        transforms.RandomRotation(degrees=15),
        transforms.RandomCrop((448, 448)),
        transforms.RandomHorizontalFlip(),
    ]),
    'totensor':
    transforms.Compose([
        transforms.Resize((448, 448)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]),
    'None':