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, 4) or (n, 5) label (Tensor): shape (n, ) bbox_pred (Tensor): shape (n, 4*(#class+1)) or (n, 4) img_meta (dict): Image meta info. Returns: Tensor: Regressed bboxes, the same shape as input rois. """ assert rois.size(1) == 4 or rois.size(1) == 5 if not self.reg_class_agnostic: label = label * 4 inds = torch.stack((label, label + 1, label + 2, label + 3), 1) bbox_pred = torch.gather(bbox_pred, 1, inds) assert bbox_pred.size(1) == 4 if rois.size(1) == 4: new_rois = delta2bbox(rois, bbox_pred, self.target_means, self.target_stds, img_meta['img_shape']) else: bboxes = delta2bbox(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 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: bboxes /= scale_factor 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, img_shape, scale_factor, cfg, rescale=False): mlvl_proposals = [] for idx in range(len(cls_scores)): rpn_cls_score = cls_scores[idx] rpn_bbox_pred = bbox_preds[idx] assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] anchors = mlvl_anchors[idx] rpn_cls_score = rpn_cls_score.permute(1, 2, 0) if self.use_sigmoid_cls: rpn_cls_score = rpn_cls_score.reshape(-1) scores = rpn_cls_score.sigmoid() else: rpn_cls_score = rpn_cls_score.reshape(-1, 2) scores = rpn_cls_score.softmax(dim=1)[:, 1] rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4) if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: _, topk_inds = scores.topk(cfg.nms_pre) rpn_bbox_pred = rpn_bbox_pred[topk_inds, :] anchors = anchors[topk_inds, :] scores = scores[topk_inds] proposals = delta2bbox(anchors, rpn_bbox_pred, self.target_means, self.target_stds, img_shape) if cfg.min_bbox_size > 0: w = proposals[:, 2] - proposals[:, 0] + 1 h = proposals[:, 3] - proposals[:, 1] + 1 valid_inds = torch.nonzero((w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size)).squeeze() proposals = proposals[valid_inds, :] scores = scores[valid_inds] proposals = torch.cat([proposals, scores.unsqueeze(-1)], dim=-1) proposals, _ = nms(proposals, cfg.nms_thr) proposals = proposals[:cfg.nms_post, :] mlvl_proposals.append(proposals) proposals = torch.cat(mlvl_proposals, 0) if cfg.nms_across_levels: proposals, _ = nms(proposals, cfg.nms_thr) proposals = proposals[:cfg.max_num, :] else: scores = proposals[:, 4] num = min(cfg.max_num, proposals.shape[0]) _, topk_inds = scores.topk(num) proposals = proposals[topk_inds, :] return proposals
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: bboxes /= scale_factor if cfg is None: return bboxes, scores else: values, indices = torch.max(scores, dim=1) bboxes[:, 8 + 3] = bboxes[:, 8 + 1] + (bboxes[:, 8 + 3] - bboxes[:, 8 + 1]) / 0.4 # print(bboxes[indices==3, 1]) # print(bboxes[indices==3, 3] - bboxes[indices==3, 1]) bboxes[:, 12 + 1] = bboxes[:, 12 + 3] - (bboxes[:, 12 + 3] - bboxes[:, 12 + 1]) / 0.6 # print(bboxes[indices==3, 1]) bboxes[indices == 2, 4:8] = bboxes[indices == 2, 8:12] bboxes[indices == 3, 4:8] = bboxes[indices == 3, 12:16] scores[:, 1] = torch.max(scores[:, 1:], dim=1)[0] scores[:, 2] = 0 scores[:, 3] = 0 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, 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 in zip(cls_scores, bbox_preds, 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, 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) 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) 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 get_bboxes_single(self, cls_scores, bbox_preds, mlvl_anchors, mlvl_masks, img_shape, scale_factor, cfg, rescale=False): mlvl_proposals = [] for idx in range(len(cls_scores)): rpn_cls_score = cls_scores[idx] rpn_bbox_pred = bbox_preds[idx] anchors = mlvl_anchors[idx] mask = mlvl_masks[idx] assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] # if no location is kept, end. if mask.sum() == 0: continue rpn_cls_score = rpn_cls_score.permute(1, 2, 0) if self.use_sigmoid_cls: rpn_cls_score = rpn_cls_score.reshape(-1) scores = rpn_cls_score.sigmoid() else: rpn_cls_score = rpn_cls_score.reshape(-1, 2) scores = rpn_cls_score.softmax(dim=1)[:, 1] # filter scores, bbox_pred w.r.t. mask. # anchors are filtered in get_anchors() beforehand. scores = scores[mask] rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)[mask, :] if scores.dim() == 0: rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0) anchors = anchors.unsqueeze(0) scores = scores.unsqueeze(0) # filter anchors, bbox_pred, scores w.r.t. scores if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: _, topk_inds = scores.topk(cfg.nms_pre) rpn_bbox_pred = rpn_bbox_pred[topk_inds, :] anchors = anchors[topk_inds, :] scores = scores[topk_inds] # get proposals w.r.t. anchors and rpn_bbox_pred proposals = delta2bbox(anchors, rpn_bbox_pred, self.target_means, self.target_stds, img_shape) # filter out too small bboxes if cfg.min_bbox_size > 0: w = proposals[:, 2] - proposals[:, 0] + 1 h = proposals[:, 3] - proposals[:, 1] + 1 valid_inds = torch.nonzero((w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size)).squeeze() proposals = proposals[valid_inds, :] scores = scores[valid_inds] proposals = torch.cat([proposals, scores.unsqueeze(-1)], dim=-1) # NMS in current level proposals, _ = nms(proposals, cfg.nms_thr) proposals = proposals[:cfg.nms_post, :] mlvl_proposals.append(proposals) proposals = torch.cat(mlvl_proposals, 0) if cfg.nms_across_levels: # NMS across multi levels proposals, _ = nms(proposals, cfg.nms_thr) proposals = proposals[:cfg.max_num, :] else: scores = proposals[:, 4] num = min(cfg.max_num, proposals.shape[0]) _, topk_inds = scores.topk(num) proposals = proposals[topk_inds, :] return proposals