| |
| import numpy as np |
| import torch |
| from torch.nn.modules.utils import _pair |
|
|
|
|
| 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, each has shape (num_pos, 4). |
| pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each |
| positive proposals, each has shape (num_pos,). |
| gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of |
| each image. |
| cfg (dict): Config dict that specifies the mask size. |
| |
| Returns: |
| Tensor: Mask target of each image, has shape (num_pos, w, h). |
| |
| Example: |
| >>> from mmengine.config import Config |
| >>> import mmdet |
| >>> from mmdet.data_elements.mask import BitmapMasks |
| >>> from mmdet.data_elements.mask.mask_target import * |
| >>> H, W = 17, 18 |
| >>> cfg = Config({'mask_size': (13, 14)}) |
| >>> rng = np.random.RandomState(0) |
| >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image |
| >>> pos_proposals_list = [ |
| >>> torch.Tensor([ |
| >>> [ 7.2425, 5.5929, 13.9414, 14.9541], |
| >>> [ 7.3241, 3.6170, 16.3850, 15.3102], |
| >>> ]), |
| >>> torch.Tensor([ |
| >>> [ 4.8448, 6.4010, 7.0314, 9.7681], |
| >>> [ 5.9790, 2.6989, 7.4416, 4.8580], |
| >>> [ 0.0000, 0.0000, 0.1398, 9.8232], |
| >>> ]), |
| >>> ] |
| >>> # Corresponding class index for each proposal for each image |
| >>> pos_assigned_gt_inds_list = [ |
| >>> torch.LongTensor([7, 0]), |
| >>> torch.LongTensor([5, 4, 1]), |
| >>> ] |
| >>> # Ground truth mask for each true object for each image |
| >>> gt_masks_list = [ |
| >>> BitmapMasks(rng.rand(8, H, W), height=H, width=W), |
| >>> BitmapMasks(rng.rand(6, H, W), height=H, width=W), |
| >>> ] |
| >>> mask_targets = mask_target( |
| >>> pos_proposals_list, pos_assigned_gt_inds_list, |
| >>> gt_masks_list, cfg) |
| >>> assert mask_targets.shape == (5,) + cfg['mask_size'] |
| """ |
| 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. |
| |
| Example: |
| >>> from mmengine.config import Config |
| >>> import mmdet |
| >>> from mmdet.data_elements.mask import BitmapMasks |
| >>> from mmdet.data_elements.mask.mask_target import * # NOQA |
| >>> H, W = 32, 32 |
| >>> cfg = Config({'mask_size': (7, 11)}) |
| >>> rng = np.random.RandomState(0) |
| >>> # Masks for each ground truth box (relative to the image) |
| >>> gt_masks_data = rng.rand(3, H, W) |
| >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W) |
| >>> # Predicted positive boxes in one image |
| >>> pos_proposals = torch.FloatTensor([ |
| >>> [ 16.2, 5.5, 19.9, 20.9], |
| >>> [ 17.3, 13.6, 19.3, 19.3], |
| >>> [ 14.8, 16.4, 17.0, 23.7], |
| >>> [ 0.0, 0.0, 16.0, 16.0], |
| >>> [ 4.0, 0.0, 20.0, 16.0], |
| >>> ]) |
| >>> # For each predicted proposal, its assignment to a gt mask |
| >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1]) |
| >>> mask_targets = mask_target_single( |
| >>> pos_proposals, pos_assigned_gt_inds, gt_masks, cfg) |
| >>> assert mask_targets.shape == (5,) + cfg['mask_size'] |
| """ |
| device = pos_proposals.device |
| mask_size = _pair(cfg.mask_size) |
| binarize = not cfg.get('soft_mask_target', False) |
| 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, |
| binarize=binarize).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 |
|
|
