69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import torch
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from . import iou3d_cuda
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def boxes_iou_bev(boxes_a, boxes_b):
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"""Calculate boxes IoU in the bird view.
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Args:
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boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
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boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
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Returns:
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ans_iou (torch.Tensor): IoU result with shape (M, N).
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"""
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ans_iou = boxes_a.new_zeros(torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
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iou3d_cuda.boxes_iou_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_iou)
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return ans_iou
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def nms_gpu(boxes, scores, thresh, pre_maxsize=None, post_max_size=None):
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"""Nms function with gpu implementation.
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Args:
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boxes (torch.Tensor): Input boxes with the shape of [N, 5]
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([x1, y1, x2, y2, ry]).
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scores (torch.Tensor): Scores of boxes with the shape of [N].
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thresh (int): Threshold.
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pre_maxsize (int): Max size of boxes before nms. Default: None.
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post_maxsize (int): Max size of boxes after nms. Default: None.
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Returns:
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torch.Tensor: Indexes after nms.
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"""
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order = scores.sort(0, descending=True)[1]
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if pre_maxsize is not None:
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order = order[:pre_maxsize]
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boxes = boxes[order].contiguous()
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keep = torch.zeros(boxes.size(0), dtype=torch.long)
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num_out = iou3d_cuda.nms_gpu(boxes, keep, thresh, boxes.device.index)
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keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
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if post_max_size is not None:
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keep = keep[:post_max_size]
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return keep
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def nms_normal_gpu(boxes, scores, thresh):
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"""Normal non maximum suppression on GPU.
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Args:
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boxes (torch.Tensor): Input boxes with shape (N, 5).
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scores (torch.Tensor): Scores of predicted boxes with shape (N).
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thresh (torch.Tensor): Threshold of non maximum suppression.
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Returns:
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torch.Tensor: Remaining indices with scores in descending order.
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"""
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order = scores.sort(0, descending=True)[1]
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boxes = boxes[order].contiguous()
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keep = torch.zeros(boxes.size(0), dtype=torch.long)
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num_out = iou3d_cuda.nms_normal_gpu(boxes, keep, thresh, boxes.device.index)
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return order[keep[:num_out].cuda(boxes.device)].contiguous()
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