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mmdet.core.mask.mask_target 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from torch.nn.modules.utils import _pair


[文档]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. Example: >>> import mmcv >>> import mmdet >>> from mmdet.core.mask import BitmapMasks >>> from mmdet.core.mask.mask_target import * >>> H, W = 17, 18 >>> cfg = mmcv.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: >>> import mmcv >>> import mmdet >>> from mmdet.core.mask import BitmapMasks >>> from mmdet.core.mask.mask_target import * # NOQA >>> H, W = 32, 32 >>> cfg = mmcv.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
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