import timm
import torchvision

data_config = {'input_size': (3, 384, 384),
               'interpolation': 'bicubic',
               'mean': (0.48145466, 0.4578275, 0.40821073),
               'std': (0.26862954, 0.26130258, 0.27577711),
               'crop_pct': 1.0,
               'crop_mode': 'squash'}

transform_synthetic = timm.data.create_transform(**data_config, is_training=False)

transform_200M = torchvision.transforms.Compose([
    torchvision.transforms.Resize((640, 640)),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225]),
])

transform_5M = torchvision.transforms.Compose([
    torchvision.transforms.Resize((224, 224)),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225]),
])