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| # Copyright (c) OpenMMLab. All rights reserved. | |
| 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 | |