How to merge label classes without modifying the ground truth data about super-gradients HOT 4 CLOSED

    @staticmethod
    def target_transform(target):
        """
        target_transform - Transforms the sample image
        This function overrides the original function from SegmentationDataSet and changes target pixels with value
        255 to value = CITYSCAPES_IGNORE_LABEL. This was done since current IoU metric from torchmetrics does not
        support such a high ignore label value (crashed on OOM)

            :param target: The target mask to transform
            :return:       The transformed target mask
        """
        out = SegmentationDataSet.target_transform(target)
        out[out == 255] = CITYSCAPES_IGNORE_LABEL

        # if 4, 6, 7, 8 are values you would like to ignore:
        out[out == 4] = CITYSCAPES_IGNORE_LABEL 
        out[out == 6] = CITYSCAPES_IGNORE_LABEL
        out[out == 7] = CITYSCAPES_IGNORE_LABEL
        out[out == 8] = CITYSCAPES_IGNORE_LABEL
        return out

CITYSCAPES_IGNORE_LABEL = 1  # default: 19

@staticmethod
def target_transform(target):
    """
    target_transform - Transforms the sample image
    This function overrides the original function from SegmentationDataSet and changes target pixels with value
    255 to value = CITYSCAPES_IGNORE_LABEL. This was done since current IoU metric from torchmetrics does not
    support such a high ignore label value (crashed on OOM)

        :param target: The target mask to transform
        :return:       The transformed target mask
    """
    out = SegmentationDataSet.target_transform(target)

    keep_labels = (0,)  # road

    for lb in keep_labels:
        out[out != lb] = CITYSCAPES_IGNORE_LABEL

    # invert labels (works only for binary segmentation)
    out = (out - 1.) * (-1.)

    return out