def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(feats, img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
# TODO more flexible
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip)
rois = bbox2roi([proposals])
# recompute feature maps to save GPU memory
roi_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
roi_feats = self.shared_head(roi_feats)
cls_score, bbox_pred = self.bbox_head(roi_feats)
bboxes, scores = self.bbox_head.get_det_bboxes(rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
return det_bboxes, det_labels
def get_bboxes_single(self,
cls_scores,
bbox_preds,
mlvl_points,
img_shape,
scale_factor,
cfg,
rescale=False,
nms=True):
assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
mlvl_bboxes = []
mlvl_scores = []
for i_lvl, (cls_score, bbox_pred, points) in enumerate(
zip(cls_scores, bbox_preds, mlvl_points)):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
max_scores, _ = scores[:, 1:].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
points = points[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1)
bboxes = bbox_pred * self.point_strides[i_lvl] + bbox_pos_center
x1 = bboxes[:, 0].clamp(min=0, max=img_shape[1])
y1 = bboxes[:, 1].clamp(min=0, max=img_shape[0])
x2 = bboxes[:, 2].clamp(min=0, max=img_shape[1])
y2 = bboxes[:, 3].clamp(min=0, max=img_shape[0])
bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
if self.use_sigmoid_cls:
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
if nms:
det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
else:
return mlvl_bboxes, mlvl_scores
def get_bboxes_single(self,
cls_scores,
bbox_preds,
featmap_sizes,
point_list,
img_shape,
scale_factor,
cfg,
rescale=False):
assert len(cls_scores) == len(bbox_preds) == len(point_list)
det_bboxes = []
det_scores = []
for cls_score, bbox_pred, featmap_size, stride, base_len, (y, x) \
in zip(cls_scores, bbox_preds, featmap_sizes, self.strides,
self.base_edge_list, point_list):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).exp()
nms_pre = cfg.get('nms_pre', -1)
if (nms_pre > 0) and (scores.shape[0] > nms_pre):
max_scores, _ = scores.max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
y = y[topk_inds]
x = x[topk_inds]
x1 = (stride * x - base_len * bbox_pred[:, 0]).\
clamp(min=0, max=img_shape[1] - 1)
y1 = (stride * y - base_len * bbox_pred[:, 1]).\
clamp(min=0, max=img_shape[0] - 1)
x2 = (stride * x + base_len * bbox_pred[:, 2]).\
clamp(min=0, max=img_shape[1] - 1)
y2 = (stride * y + base_len * bbox_pred[:, 3]).\
clamp(min=0, max=img_shape[0] - 1)
bboxes = torch.stack([x1, y1, x2, y2], -1)
det_bboxes.append(bboxes)
det_scores.append(scores)
det_bboxes = torch.cat(det_bboxes)
if rescale:
det_bboxes /= det_bboxes.new_tensor(scale_factor)
det_scores = torch.cat(det_scores)
padding = det_scores.new_zeros(det_scores.shape[0], 1)
det_scores = torch.cat([padding, det_scores], dim=1)
det_bboxes, det_labels = multiclass_nms(det_bboxes, det_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
def get_bboxes_single(self,
cls_scores,
bbox_preds,
centernesses,
mlvl_points,
img_shape,
scale_factor,
cfg,
rescale=False):
assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
mlvl_bboxes = []
mlvl_scores = []
mlvl_centerness = []
for cls_score, bbox_pred, centerness, points in zip(
cls_scores, bbox_preds, centernesses, mlvl_points):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
centerness = centerness.permute(1, 2, 0).reshape(-1).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
max_scores, _ = (scores * centerness[:, None]).max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
points = points[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
centerness = centerness[topk_inds]
bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_centerness.append(centerness)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
mlvl_centerness = torch.cat(mlvl_centerness)
det_bboxes, det_labels = multiclass_nms(mlvl_bboxes,
mlvl_scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=mlvl_centerness)
return det_bboxes, det_labels
def get_det_bboxes(self,
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None):
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
if bbox_pred is not None:
bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
self.target_stds, img_shape)
else:
bboxes = rois[:, 1:].clone()
if img_shape is not None:
bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
if rescale:
if isinstance(scale_factor, float):
bboxes /= scale_factor
else:
scale_factor = torch.from_numpy(scale_factor).to(bboxes.device)
bboxes = (bboxes.view(bboxes.size(0), -1, 4) /
scale_factor).view(bboxes.size()[0], -1)
if cfg is None:
return bboxes, scores
else:
det_bboxes, det_labels = multiclass_nms(bboxes, scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
def get_bboxes_single(self,
cls_scores,
bbox_preds,
mlvl_anchors,
mlvl_masks,
img_shape,
scale_factor,
cfg,
rescale=False):
assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
for cls_score, bbox_pred, anchors, mask in zip(cls_scores, bbox_preds,
mlvl_anchors,
mlvl_masks):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
# if no location is kept, end.
if mask.sum() == 0:
continue
# reshape scores and bbox_pred
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
# filter scores, bbox_pred w.r.t. mask.
# anchors are filtered in get_anchors() beforehand.
scores = scores[mask, :]
bbox_pred = bbox_pred[mask, :]
if scores.dim() == 0:
anchors = anchors.unsqueeze(0)
scores = scores.unsqueeze(0)
bbox_pred = bbox_pred.unsqueeze(0)
# filter anchors, bbox_pred, scores w.r.t. scores
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
max_scores, _ = scores[:, 1:].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
bboxes = delta2bbox(anchors, bbox_pred, self.target_means,
self.target_stds, img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
if self.use_sigmoid_cls:
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
# multi class NMS
det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
def aug_test(self, imgs, img_metas, proposals=None, rescale=False):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
# recompute feats to save memory
proposal_list = self.aug_test_rpn(self.extract_feats(imgs), img_metas,
self.test_cfg.rpn)
rcnn_test_cfg = self.test_cfg.rcnn
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(self.extract_feats(imgs), img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip)
# "ms" in variable names means multi-stage
ms_scores = []
rois = bbox2roi([proposals])
for i in range(self.num_stages):
bbox_roi_extractor = self.bbox_roi_extractor[i]
bbox_head = self.bbox_head[i]
bbox_feats = bbox_roi_extractor(
x[:len(bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = bbox_head(bbox_feats)
ms_scores.append(cls_score)
if i < self.num_stages - 1:
bbox_label = cls_score.argmax(dim=1)
rois = bbox_head.regress_by_class(rois, bbox_label,
bbox_pred, img_meta[0])
cls_score = sum(ms_scores) / float(len(ms_scores))
bboxes, scores = self.bbox_head[-1].get_det_bboxes(rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
bbox_result = bbox2result(det_bboxes, det_labels,
self.bbox_head[-1].num_classes)
if self.with_mask:
if det_bboxes.shape[0] == 0:
segm_result = [[]
for _ in range(self.mask_head[-1].num_classes -
1)]
else:
aug_masks = []
aug_img_metas = []
for x, img_meta in zip(self.extract_feats(imgs), img_metas):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip)
mask_rois = bbox2roi([_bboxes])
for i in range(self.num_stages):
mask_feats = self.mask_roi_extractor[i](
x[:len(self.mask_roi_extractor[i].featmap_strides
)], mask_rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
mask_pred = self.mask_head[i](mask_feats)
aug_masks.append(mask_pred.sigmoid().cpu().numpy())
aug_img_metas.append(img_meta)
merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
self.test_cfg.rcnn)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
rcnn_test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return bbox_result, segm_result
else:
return bbox_result