import numpy as np
import torch
from torch.nn.modules.utils import _pair
[docs]def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
cfg):
""" Compute mask target for positive proposals in multiple images.
Args:
pos_proposals_list (list[Tensor]): Positive proposals in multiple
images.
pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each
positive proposals.
gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of
each image.
cfg (dict): Config dict that specifies the mask size.
Returns:
list[Tensor]: Mask target of each image.
"""
cfg_list = [cfg for _ in range(len(pos_proposals_list))]
mask_targets = map(mask_target_single, pos_proposals_list,
pos_assigned_gt_inds_list, gt_masks_list, cfg_list)
mask_targets = list(mask_targets)
if len(mask_targets) > 0:
mask_targets = torch.cat(mask_targets)
return mask_targets
def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):
"""Compute mask target for each positive proposal in the image.
Args:
pos_proposals (Tensor): Positive proposals.
pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals.
gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap
or Polygon.
cfg (dict): Config dict that indicate the mask size.
Returns:
Tensor: Mask target of each positive proposals in the image.
"""
device = pos_proposals.device
mask_size = _pair(cfg.mask_size)
num_pos = pos_proposals.size(0)
if num_pos > 0:
proposals_np = pos_proposals.cpu().numpy()
maxh, maxw = gt_masks.height, gt_masks.width
proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw)
proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh)
pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
mask_targets = gt_masks.crop_and_resize(
proposals_np, mask_size, device=device,
inds=pos_assigned_gt_inds).to_ndarray()
mask_targets = torch.from_numpy(mask_targets).float().to(device)
else:
mask_targets = pos_proposals.new_zeros((0, ) + mask_size)
return mask_targets