def regress_by_class(self, rois, label, bbox_pred, img_meta):
"""Regress the bbox for the predicted class. Used in Cascade R-CNN.
Args:
rois (Tensor): shape (n, 5) or (n, 6)
label (Tensor): shape (n, )
bbox_pred (Tensor): shape (n, 5*(#class+1)) or (n, 5)
img_meta (dict): Image meta info.
Returns:
Tensor: Regressed bboxes, the same shape as input rois.
"""
assert rois.size(1) == 5 or rois.size(1) == 6
if not self.reg_class_agnostic:
label = label * 5
inds = torch.stack(
(label, label + 1, label + 2, label + 3, label + 4), 1)
bbox_pred = torch.gather(bbox_pred, 1, inds)
assert bbox_pred.size(1) == 5
if rois.size(1) == 5:
new_rois = delta2bbox_rotated(rois, bbox_pred, self.target_means,
self.target_stds,
img_meta['img_shape'])
else:
bboxes = delta2bbox_rotated(rois[:, 1:], bbox_pred,
self.target_means, self.target_stds,
img_meta['img_shape'])
new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
return new_rois
def bbox_decode(bbox_preds,
anchors,
means=[0, 0, 0, 0, 0],
stds=[1, 1, 1, 1, 1],
num_anchors=1):
"""
Decode bboxes from deltas
:param bbox_preds: [N,5,H,W]
:param anchors: [H*W,5]
:param means: mean value to decode bbox
:param stds: std value to decode bbox
:return: [N,H,W,5]
"""
num_imgs, _, H, W = bbox_preds.shape
bboxes_list = []
for img_id in range(num_imgs):
bbox_pred = bbox_preds[img_id]
# bbox_pred.shape=[5,H,W]
bbox_delta = bbox_pred.permute(1, 2, 0).reshape(-1, 5)
bboxes = delta2bbox_rotated(anchors,
bbox_delta,
means,
stds,
wh_ratio_clip=1e-6)
bboxes = bboxes.reshape(num_anchors, H, W, 5)
bboxes_list.append(bboxes)
return torch.stack(bboxes_list, dim=0)
def get_bboxes_single(self,
cls_score_list,
bbox_pred_list,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
"""
Transform outputs for a single batch item into labeled boxes.
"""
assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
for cls_score, bbox_pred, anchors in zip(cls_score_list,
bbox_pred_list, mlvl_anchors):
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, 5)
# anchors = rect2rbox(anchors)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
# Get maximum scores for foreground classes.
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_rotated(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[..., :4] /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
if self.use_sigmoid_cls:
# Add a dummy background class to the front when using sigmoid
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
det_bboxes, det_labels = multiclass_nms_rotated(mlvl_bboxes,
mlvl_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
def bbox_decode(bbox_preds,
anchors,
means=[0, 0, 0, 0, 0],
stds=[1, 1, 1, 1, 1]):
num_imgs, _, H, W = bbox_preds.shape
bboxes_list = []
for img_id in range(num_imgs):
bbox_pred = bbox_preds[img_id]
anchor = anchors[img_id]
# bbox_pred.shape=[5,H,W]
bbox_delta = bbox_pred.permute(1, 2, 0).reshape(-1, 5)
bboxes = delta2bbox_rotated(anchor,
bbox_delta,
means,
stds,
wh_ratio_clip=1e-6)
bboxes = bboxes.reshape(H, W, 5)
bboxes_list.append(bboxes)
return torch.stack(bboxes_list, dim=0)
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
rotated_rois = bbox_to_rotated_box(rois[:, 1:])
if bbox_pred is not None:
bboxes = delta2bbox_rotated(rotated_rois, bbox_pred,
self.target_means, self.target_stds,
img_shape)
else:
bboxes = rotated_rois.clone()
polys = rotated_box_to_poly(bboxes)
if img_shape is not None:
polys[:, 0::2].clamp_(min=0, max=img_shape[1] - 1)
polys[:, 1::2].clamp_(min=0, max=img_shape[0] - 1)
bboxes = poly_to_rotated_box(polys)
if rescale:
if isinstance(scale_factor, float):
bboxes[..., :4] /= scale_factor
else:
bboxes[..., :4] /= torch.from_numpy(scale_factor).to(
bboxes.device)
if cfg is None:
return bboxes, scores
else:
det_bboxes, det_labels = multiclass_nms_rotated(
bboxes, scores, cfg.score_thr, cfg.nms, cfg.max_per_img)
return det_bboxes, det_labels
def get_refine_anchors(self,
bbox_preds,
init_anchors,
featmap_sizes,
img_metas,
device='cuda'):
num_levels = len(featmap_sizes)
anchor_list = []
for img_id, img_meta in enumerate(img_metas):
mlvl_anchors_list = []
for i in range(num_levels):
# generate refined anchors
bbox_pred = bbox_preds[i].detach()
bbox_pred = bbox_pred[img_id].permute(1, 2, 0).reshape(-1, 5)
refined_anchor = delta2bbox_rotated(init_anchors[img_id][i],
bbox_pred,
self.target_means,
self.target_stds,
wh_ratio_clip=1e-6)
mlvl_anchors_list.append(refined_anchor)
anchor_list.append(mlvl_anchors_list)
valid_flag_list = []
for img_id, img_meta in enumerate(img_metas):
multi_level_flags = []
for i in range(num_levels):
anchor_stride = self.anchor_strides[i]
feat_h, feat_w = featmap_sizes[i]
h, w, _ = img_meta['pad_shape']
valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
flags = self.anchor_generators[i].valid_flags(
(feat_h, feat_w), (valid_feat_h, valid_feat_w),
device=device)
multi_level_flags.append(flags)
valid_flag_list.append(multi_level_flags)
return anchor_list, valid_flag_list
