src/processer/transforms.py

from torchvision import transforms
from PIL import Image


def get_transform(type='clip', keep_ratio=True, image_size=224):
    if type == 'clip':
        transform = []
        if keep_ratio:
            transform.extend([
                transforms.Resize(image_size),
                transforms.CenterCrop(image_size),
            ])
        else:
            transform.append(transforms.Resize((image_size, image_size)))
        transform.extend([
            transforms.ToTensor(),
            transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
        ])

        return transforms.Compose(transform)
    elif type == 'clipa':
        transform = []
        if keep_ratio:
            transform.extend([
                transforms.Resize(image_size),
                transforms.CenterCrop(image_size),
            ])
        else:
            transform.append(transforms.Resize((image_size, image_size)))
        transform.extend([transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))])

        return transforms.Compose(transform)
    elif type == 'clipb':
        transform = []

        if keep_ratio:
            def expand2square(pil_img, background_color):
                width, height = pil_img.size
                if width == height:
                    return pil_img
                elif width > height:
                    result = Image.new(pil_img.mode, (width, width),
                                    background_color)
                    result.paste(pil_img, (0, (width - height) // 2))
                    return result
                else:
                    result = Image.new(pil_img.mode, (height, height),
                                    background_color)
                    result.paste(pil_img, ((height - width) // 2, 0))
                    return result

            background_color = tuple(int(x * 255) for x in (0.48145466, 0.4578275, 0.40821073))
        
            transform.append(
                transforms.Lambda(
                    lambda img: expand2square(img, background_color)))

            transform.append(transforms.Resize((image_size, image_size)))
        else:
            transform.append(transforms.Resize((image_size, image_size)))

        transform.extend([
            transforms.ToTensor(),
            transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073),
                                std=(0.26862954, 0.26130258, 0.27577711))
        ])

        return transforms.Compose(transform)
    
    elif type == 'sd':
        transform = []
        if keep_ratio:
            transform.extend([
                transforms.Resize(image_size, interpolation=transforms.InterpolationMode.BICUBIC),
                transforms.CenterCrop(image_size),
            ])
        else:
            transform.append(transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC))
        transform.extend([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])

        return transforms.Compose(transform)
    else:
        raise NotImplementedError