How to add the augs in defaultpredictor??

[leesangjoon1]

class DefaultPredictor:
"""
Create a simple end-to-end predictor with the given config that runs on
single device for a single input image.

Compared to using the model directly, this class does the following additions:

1. Load checkpoint from `cfg.MODEL.WEIGHTS`.
2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`.
3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`.
4. Take one input image and produce a single output, instead of a batch.

This is meant for simple demo purposes, so it does the above steps automatically.
This is not meant for benchmarks or running complicated inference logic.
If you'd like to do anything more complicated, please refer to its source code as
examples to build and use the model manually.

Attributes:
    metadata (Metadata): the metadata of the underlying dataset, obtained from
        cfg.DATASETS.TEST.

Examples:
::
    pred = DefaultPredictor(cfg)
    inputs = cv2.imread("input.jpg")
    outputs = pred(inputs)
"""

def __init__(self, cfg):
    self.cfg = cfg.clone()  # cfg can be modified by model
    self.model = build_model(self.cfg)
    self.model.eval()
    if len(cfg.DATASETS.TEST):
        self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])

    checkpointer = DetectionCheckpointer(self.model)
    checkpointer.load(cfg.MODEL.WEIGHTS)

    self.aug = T.ResizeShortestEdge(
        [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST
    )

    self.input_format = cfg.INPUT.FORMAT
    assert self.input_format in ["RGB", "BGR"], self.input_format

[docs] def call(self, original_image):
"""
Args:
original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).

    Returns:
        predictions (dict):
            the output of the model for one image only.
            See :doc:`/tutorials/models` for details about the format.
    """
    with torch.no_grad():  # https://github.com/sphinx-doc/sphinx/issues/4258
        # Apply pre-processing to image.
        if self.input_format == "RGB":
            # whether the model expects BGR inputs or RGB
            original_image = original_image[:, :, ::-1]
        height, width = original_image.shape[:2]
        image = self.aug.get_transform(original_image).apply_image(original_image)
        image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))

        inputs = {"image": image, "height": height, "width": width}
        predictions = self.model([inputs])[0]
        return predictions

in this code, there is a self.aug T.ResizeShortEdge

but I want to use more augs.

I want to use more various augs.

How could I do???