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mmdet.core.post_processing.bbox_nms 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops.nms import batched_nms

from mmdet.core.bbox.iou_calculators import bbox_overlaps


[文档]def multiclass_nms(multi_bboxes, multi_scores, score_thr, nms_cfg, max_num=-1, score_factors=None, return_inds=False): """NMS for multi-class bboxes. Args: multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) multi_scores (Tensor): shape (n, #class), where the last column contains scores of the background class, but this will be ignored. score_thr (float): bbox threshold, bboxes with scores lower than it will not be considered. nms_thr (float): NMS IoU threshold max_num (int, optional): if there are more than max_num bboxes after NMS, only top max_num will be kept. Default to -1. score_factors (Tensor, optional): The factors multiplied to scores before applying NMS. Default to None. return_inds (bool, optional): Whether return the indices of kept bboxes. Default to False. Returns: tuple: (dets, labels, indices (optional)), tensors of shape (k, 5), (k), and (k). Dets are boxes with scores. Labels are 0-based. """ num_classes = multi_scores.size(1) - 1 # exclude background category if multi_bboxes.shape[1] > 4: bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4) else: bboxes = multi_bboxes[:, None].expand( multi_scores.size(0), num_classes, 4) scores = multi_scores[:, :-1] labels = torch.arange(num_classes, dtype=torch.long, device=scores.device) labels = labels.view(1, -1).expand_as(scores) bboxes = bboxes.reshape(-1, 4) scores = scores.reshape(-1) labels = labels.reshape(-1) if not torch.onnx.is_in_onnx_export(): # NonZero not supported in TensorRT # remove low scoring boxes valid_mask = scores > score_thr # multiply score_factor after threshold to preserve more bboxes, improve # mAP by 1% for YOLOv3 if score_factors is not None: # expand the shape to match original shape of score score_factors = score_factors.view(-1, 1).expand( multi_scores.size(0), num_classes) score_factors = score_factors.reshape(-1) scores = scores * score_factors if not torch.onnx.is_in_onnx_export(): # NonZero not supported in TensorRT inds = valid_mask.nonzero(as_tuple=False).squeeze(1) bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds] else: # TensorRT NMS plugin has invalid output filled with -1 # add dummy data to make detection output correct. bboxes = torch.cat([bboxes, bboxes.new_zeros(1, 4)], dim=0) scores = torch.cat([scores, scores.new_zeros(1)], dim=0) labels = torch.cat([labels, labels.new_zeros(1)], dim=0) if bboxes.numel() == 0: if torch.onnx.is_in_onnx_export(): raise RuntimeError('[ONNX Error] Can not record NMS ' 'as it has not been executed this time') dets = torch.cat([bboxes, scores[:, None]], -1) if return_inds: return dets, labels, inds else: return dets, labels dets, keep = batched_nms(bboxes, scores, labels, nms_cfg) if max_num > 0: dets = dets[:max_num] keep = keep[:max_num] if return_inds: return dets, labels[keep], inds[keep] else: return dets, labels[keep]
[文档]def fast_nms(multi_bboxes, multi_scores, multi_coeffs, score_thr, iou_thr, top_k, max_num=-1): """Fast NMS in `YOLACT <https://arxiv.org/abs/1904.02689>`_. Fast NMS allows already-removed detections to suppress other detections so that every instance can be decided to be kept or discarded in parallel, which is not possible in traditional NMS. This relaxation allows us to implement Fast NMS entirely in standard GPU-accelerated matrix operations. Args: multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) multi_scores (Tensor): shape (n, #class+1), where the last column contains scores of the background class, but this will be ignored. multi_coeffs (Tensor): shape (n, #class*coeffs_dim). score_thr (float): bbox threshold, bboxes with scores lower than it will not be considered. iou_thr (float): IoU threshold to be considered as conflicted. top_k (int): if there are more than top_k bboxes before NMS, only top top_k will be kept. max_num (int): if there are more than max_num bboxes after NMS, only top max_num will be kept. If -1, keep all the bboxes. Default: -1. Returns: tuple: (dets, labels, coefficients), tensors of shape (k, 5), (k, 1), and (k, coeffs_dim). Dets are boxes with scores. Labels are 0-based. """ scores = multi_scores[:, :-1].t() # [#class, n] scores, idx = scores.sort(1, descending=True) idx = idx[:, :top_k].contiguous() scores = scores[:, :top_k] # [#class, topk] num_classes, num_dets = idx.size() boxes = multi_bboxes[idx.view(-1), :].view(num_classes, num_dets, 4) coeffs = multi_coeffs[idx.view(-1), :].view(num_classes, num_dets, -1) iou = bbox_overlaps(boxes, boxes) # [#class, topk, topk] iou.triu_(diagonal=1) iou_max, _ = iou.max(dim=1) # Now just filter out the ones higher than the threshold keep = iou_max <= iou_thr # Second thresholding introduces 0.2 mAP gain at negligible time cost keep *= scores > score_thr # Assign each kept detection to its corresponding class classes = torch.arange( num_classes, device=boxes.device)[:, None].expand_as(keep) classes = classes[keep] boxes = boxes[keep] coeffs = coeffs[keep] scores = scores[keep] # Only keep the top max_num highest scores across all classes scores, idx = scores.sort(0, descending=True) if max_num > 0: idx = idx[:max_num] scores = scores[:max_num] classes = classes[idx] boxes = boxes[idx] coeffs = coeffs[idx] cls_dets = torch.cat([boxes, scores[:, None]], dim=1) return cls_dets, classes, coeffs
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