Function feature_take_indices

timm/models/_features.py:28–66

Determine the absolute feature indices to 'take' from. Note: This function can be called in forward() so must be torchscript compatible, which requires some incomplete typing and workaround hacks. Args: num_features: total number of features to select from indices: ind

(
        num_features: int,
        indices: Optional[Union[int, List[int]]] = None,
        as_set: bool = False,
)

def feature_take_indices(
        num_features: int,
        indices: Optional[Union[int, List[int]]] = None,
        as_set: bool = False,
) -> Tuple[List[int], int]:
    """ Determine the absolute feature indices to 'take' from.

    Note: This function can be called in forward() so must be torchscript compatible,
    which requires some incomplete typing and workaround hacks.

    Args:
        num_features: total number of features to select from
        indices: indices to select,
          None -> select all
          int -> select last n
          list/tuple of int -> return specified (-ve indices specify from end)
        as_set: return as a set

    Returns:
        List (or set) of absolute (from beginning) indices, Maximum index
    """
    if indices is None:
        indices = num_features  # all features if None

    if isinstance(indices, int):
        # convert int -> last n indices
        _assert(0 < indices <= num_features, f'last-n ({indices}) is out of range (1 to {num_features})')
        take_indices = [num_features - indices + i for i in range(indices)]
    else:
        take_indices: List[int] = []
        for i in indices:
            idx = num_features + i if i < 0 else i
            _assert(0 <= idx < num_features, f'feature index {idx} is out of range (0 to {num_features - 1})')
            take_indices.append(idx)

    if not torch.jit.is_scripting() and as_set:
        return set(take_indices), max(take_indices)

    return take_indices, max(take_indices)


def _out_indices_as_tuple(x: Union[int, Tuple[int, ...]]) -> Tuple[int, ...]: