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import torch
from mmdet.core import bbox2roi
from ..builder import HEADS, build_head
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class MaskScoringRoIHead(StandardRoIHead):
"""Mask Scoring RoIHead for Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(self, mask_iou_head, **kwargs):
assert mask_iou_head is not None
super(MaskScoringRoIHead, self).__init__(**kwargs)
self.mask_iou_head = build_head(mask_iou_head)
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
super(MaskScoringRoIHead, self).init_weights(pretrained)
self.mask_iou_head.init_weights()
def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
img_metas):
"""Run forward function and calculate loss for Mask head in
training."""
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
mask_results = super(MaskScoringRoIHead,
self)._mask_forward_train(x, sampling_results,
bbox_feats, gt_masks,
img_metas)
if mask_results['loss_mask'] is None:
return mask_results
# mask iou head forward and loss
pos_mask_pred = mask_results['mask_pred'][
range(mask_results['mask_pred'].size(0)), pos_labels]
mask_iou_pred = self.mask_iou_head(mask_results['mask_feats'],
pos_mask_pred)
pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)),
pos_labels]
mask_iou_targets = self.mask_iou_head.get_targets(
sampling_results, gt_masks, pos_mask_pred,
mask_results['mask_targets'], self.train_cfg)
loss_mask_iou = self.mask_iou_head.loss(pos_mask_iou_pred,
mask_iou_targets)
mask_results['loss_mask'].update(loss_mask_iou)
return mask_results
def simple_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False):
"""Obtain mask prediction without augmentation."""
# image shapes of images in the batch
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
num_imgs = len(det_bboxes)
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
num_classes = self.mask_head.num_classes
segm_results = [[[] for _ in range(num_classes)]
for _ in range(num_imgs)]
mask_scores = [[[] for _ in range(num_classes)]
for _ in range(num_imgs)]
else:
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i]
for i in range(num_imgs)
]
mask_rois = bbox2roi(_bboxes)
mask_results = self._mask_forward(x, mask_rois)
concat_det_labels = torch.cat(det_labels)
# get mask scores with mask iou head
mask_feats = mask_results['mask_feats']
mask_pred = mask_results['mask_pred']
mask_iou_pred = self.mask_iou_head(
mask_feats, mask_pred[range(concat_det_labels.size(0)),
concat_det_labels])
# split batch mask prediction back to each image
num_bboxes_per_img = tuple(len(_bbox) for _bbox in _bboxes)
mask_preds = mask_pred.split(num_bboxes_per_img, 0)
mask_iou_preds = mask_iou_pred.split(num_bboxes_per_img, 0)
# apply mask post-processing to each image individually
segm_results = []
mask_scores = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[] for _ in range(self.mask_head.num_classes)])
mask_scores.append(
[[] for _ in range(self.mask_head.num_classes)])
else:
segm_result = self.mask_head.get_seg_masks(
mask_preds[i], _bboxes[i], det_labels[i],
self.test_cfg, ori_shapes[i], scale_factors[i],
rescale)
# get mask scores with mask iou head
mask_score = self.mask_iou_head.get_mask_scores(
mask_iou_preds[i], det_bboxes[i], det_labels[i])
segm_results.append(segm_result)
mask_scores.append(mask_score)
return list(zip(segm_results, mask_scores))
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