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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 | |