mask_former/modeling/heads/mask_former_head.py [29:55]:
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            scratch = True
            logger = logging.getLogger(__name__)
            for k in list(state_dict.keys()):
                newk = k
                if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
                    newk = k.replace(prefix, prefix + "pixel_decoder.")
                    # logger.debug(f"{k} ==> {newk}")
                if newk != k:
                    state_dict[newk] = state_dict[k]
                    del state_dict[k]
                    scratch = False

            if not scratch:
                logger.warning(
                    f"Weight format of {self.__class__.__name__} have changed! "
                    "Please upgrade your models. Applying automatic conversion now ..."
                )

    @configurable
    def __init__(
        self,
        input_shape: Dict[str, ShapeSpec],
        *,
        num_classes: int,
        pixel_decoder: nn.Module,
        loss_weight: float = 1.0,
        ignore_value: int = -1,
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mask_former/modeling/heads/per_pixel_baseline.py [29:55]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            scratch = True
            logger = logging.getLogger(__name__)
            for k in list(state_dict.keys()):
                newk = k
                if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
                    newk = k.replace(prefix, prefix + "pixel_decoder.")
                    # logger.warning(f"{k} ==> {newk}")
                if newk != k:
                    state_dict[newk] = state_dict[k]
                    del state_dict[k]
                    scratch = False

            if not scratch:
                logger.warning(
                    f"Weight format of {self.__class__.__name__} have changed! "
                    "Please upgrade your models. Applying automatic conversion now ..."
                )

    @configurable
    def __init__(
        self,
        input_shape: Dict[str, ShapeSpec],
        *,
        num_classes: int,
        pixel_decoder: nn.Module,
        loss_weight: float = 1.0,
        ignore_value: int = -1,
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