@@ -82,3 +82,18 @@ def flip_tensor(src_tensor, flip_direction):
     else:
         out_tensor = torch.flip(src_tensor, [2, 3])
     return out_tensor
+
+
+def collect_mlvl_tensor_single(mlvl_tensors, batch_id, detach=False):
+    assert isinstance(mlvl_tensors, (list, tuple))
+    num_levels = len(mlvl_tensors)
+
+    if detach:
+        mlvl_tensor_list = [
+            mlvl_tensors[i][batch_id].detach() for i in range(num_levels)
+        ]
+    else:
+        mlvl_tensor_list = [
+            mlvl_tensors[i][batch_id] for i in range(num_levels)
+        ]
+    return mlvl_tensor_list
from functools import partial

import numpy as np
import torch
from six.moves import map, zip

from ..mask.structures import BitmapMasks, PolygonMasks


def multi_apply(func, *args, **kwargs):
    """Apply function to a list of arguments.

    Note:
        This function applies the ``func`` to multiple inputs and
        map the multiple outputs of the ``func`` into different
        list. Each list contains the same type of outputs corresponding
        to different inputs.

    Args:
        func (Function): A function that will be applied to a list of
            arguments

    Returns:
        tuple(list): A tuple containing multiple list, each list contains \
            a kind of returned results by the function
    """
    pfunc = partial(func, **kwargs) if kwargs else func
    map_results = map(pfunc, *args)
    return tuple(map(list, zip(*map_results)))


def unmap(data, count, inds, fill=0):
    """Unmap a subset of item (data) back to the original set of items (of size
    count)"""
    if data.dim() == 1:
        ret = data.new_full((count, ), fill)
        ret[inds.type(torch.bool)] = data
    else:
        new_size = (count, ) + data.size()[1:]
        ret = data.new_full(new_size, fill)
        ret[inds.type(torch.bool), :] = data
    return ret


def mask2ndarray(mask):
    """Convert Mask to ndarray..

    Args:
        mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or
        torch.Tensor or np.ndarray): The mask to be converted.

    Returns:
        np.ndarray: Ndarray mask of shape (n, h, w) that has been converted
    """
    if isinstance(mask, (BitmapMasks, PolygonMasks)):
        mask = mask.to_ndarray()
    elif isinstance(mask, torch.Tensor):
        mask = mask.detach().cpu().numpy()
    elif not isinstance(mask, np.ndarray):
        raise TypeError(f'Unsupported {type(mask)} data type')
    return mask


def flip_tensor(src_tensor, flip_direction):
    """flip tensor base on flip_direction.

    Args:
        src_tensor (Tensor): input feature map, shape (B, C, H, W).
        flip_direction (str): The flipping direction. Options are
          'horizontal', 'vertical', 'diagonal'.

    Returns:
        out_tensor (Tensor): Flipped tensor.
    """
    assert src_tensor.ndim == 4
    valid_directions = ['horizontal', 'vertical', 'diagonal']
    assert flip_direction in valid_directions
    if flip_direction == 'horizontal':
        out_tensor = torch.flip(src_tensor, [3])
    elif flip_direction == 'vertical':
        out_tensor = torch.flip(src_tensor, [2])
    else:
        out_tensor = torch.flip(src_tensor, [2, 3])
    return out_tensor

add docs for this function