def force_fp32(apply_to=None, out_fp16=False):
    """Decorator to convert input arguments to fp32 in force.

    This decorator is useful when you write custom modules and want to support
    mixed precision training. If there are some inputs that must be processed
    in fp32 mode, then this decorator can handle it. If inputs arguments are
    fp16 tensors, they will be converted to fp32 automatically. Arguments other
    than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
    torch.cuda.amp is used as the backend, otherwise, original mmcv
    implementation will be adopted.

    Args:
        apply_to (Iterable, optional): The argument names to be converted.
            `None` indicates all arguments.
        out_fp16 (bool): Whether to convert the output back to fp16.

    Example:

        >>> import torch.nn as nn
        >>> class MyModule1(nn.Module):
        >>>
        >>>     # Convert x and y to fp32
        >>>     @force_fp32()
        >>>     def loss(self, x, y):
        >>>         pass

        >>> import torch.nn as nn
        >>> class MyModule2(nn.Module):
        >>>
        >>>     # convert pred to fp32
        >>>     @force_fp32(apply_to=('pred', ))
        >>>     def post_process(self, pred, others):
        >>>         pass
    """

    def force_fp32_wrapper(old_func):

        @functools.wraps(old_func)
        def new_func(*args, **kwargs):
            # check if the module has set the attribute `fp16_enabled`, if not,
            # just fallback to the original method.
            if not isinstance(args[0], torch.nn.Module):
                raise TypeError('@force_fp32 can only be used to decorate the '
                                'method of nn.Module')
            if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
                return old_func(*args, **kwargs)
            # get the arg spec of the decorated method
            args_info = getfullargspec(old_func)
            # get the argument names to be casted
            args_to_cast = args_info.args if apply_to is None else apply_to
            # convert the args that need to be processed
            new_args = []
            if args:
                arg_names = args_info.args[:len(args)]
                for i, arg_name in enumerate(arg_names):
                    if arg_name in args_to_cast:
                        new_args.append(
                            cast_tensor_type(args[i], torch.half, torch.float))
                    else:
                        new_args.append(args[i])
            # convert the kwargs that need to be processed
            new_kwargs = dict()
            if kwargs:
                for arg_name, arg_value in kwargs.items():
                    if arg_name in args_to_cast:
                        new_kwargs[arg_name] = cast_tensor_type(
                            arg_value, torch.half, torch.float)
                    else:
                        new_kwargs[arg_name] = arg_value
            # apply converted arguments to the decorated method
            if (TORCH_VERSION != 'parrots' and
                    digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
                with autocast(enabled=False):
                    output = old_func(*new_args, **new_kwargs)
            else:
                output = old_func(*new_args, **new_kwargs)
            # cast the results back to fp32 if necessary
            if out_fp16:
                output = cast_tensor_type(output, torch.float, torch.half)
            return output

        return new_func

    return force_fp32_wrapper
Decorator to convert input arguments to fp32 in force.

    This decorator is useful when you write custom modules and want to support
    mixed precision training. If there are some inputs that must be processed
    in fp32 mode, then this decorator can handle it. If inputs arguments are
    fp16 tensors, they will be converted to fp32 automatically. Arguments other
    than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
    torch.cuda.amp is used as the backend, otherwise, original mmcv
    implementation will be adopted.

    Args:
        apply_to (Iterable, optional): The argument names to be converted.
            `None` indicates all arguments.
        out_fp16 (bool): Whether to convert the output back to fp16.

    Example:

        >>> import torch.nn as nn
        >>> class MyModule1(nn.Module):
        >>>
        >>>     # Convert x and y to fp32
        >>>     @force_fp32()
        >>>     def loss(self, x, y):
        >>>         pass

        >>> import torch.nn as nn
        >>> class MyModule2(nn.Module):
        >>>
        >>>     # convert pred to fp32
        >>>     @force_fp32(apply_to=('pred', ))
        >>>     def post_process(self, pred, others):
        >>>         pass
    
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/runner/fp16_utils.py