Function build_transform

data/build.py:125–162

(is_train, config)

def build_transform(is_train, config):
    resize_im = config.DATA.IMG_SIZE > 32
    if is_train:
        # this should always dispatch to transforms_imagenet_train
        transform = create_transform(
            input_size=config.DATA.IMG_SIZE,
            is_training=True,
            color_jitter=config.AUG.COLOR_JITTER if config.AUG.COLOR_JITTER > 0 else None,
            auto_augment=config.AUG.AUTO_AUGMENT if config.AUG.AUTO_AUGMENT != 'none' else None,
            re_prob=config.AUG.REPROB,
            re_mode=config.AUG.REMODE,
            re_count=config.AUG.RECOUNT,
            interpolation=config.DATA.INTERPOLATION,
        )
        if not resize_im:
            # replace RandomResizedCropAndInterpolation with
            # RandomCrop
            transform.transforms[0] = transforms.RandomCrop(config.DATA.IMG_SIZE, padding=4)
        return transform

    t = []
    if resize_im:
        if config.TEST.CROP:
            size = int((256 / 224) * config.DATA.IMG_SIZE)
            t.append(
                transforms.Resize(size, interpolation=_pil_interp(config.DATA.INTERPOLATION)),
                # to maintain same ratio w.r.t. 224 images
            )
            t.append(transforms.CenterCrop(config.DATA.IMG_SIZE))
        else:
            t.append(
                transforms.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE),
                                  interpolation=_pil_interp(config.DATA.INTERPOLATION))
            )

    t.append(transforms.ToTensor())
    t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
    return transforms.Compose(t)