从零实现一个3D目标检测算法（1）：（持续更新中）

本文是根据github上的项目：https://github.com/open-mmlab/OpenPCDet整理而来，使用的预训练模型权重也为此项目权重，不过此项目已更新为0.2版，文中代码可能略有不同。本文实现的3D目标检测算法是PointPillars，论文地址为：https://arxiv.org/abs/1812.05784，使用的点云数据是KITTI激光雷达数据。

1.3D目标检测算法

2.PointPillars网络

PointPillar(
  (vfe): PillarFeatureNetOld2(
    (pfn_layers): ModuleList(
      (0): PFNLayer(
        (linear): Linear(in_features=10, out_features=64, bias=False)
        (norm): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (rpn_net): PointPillarsScatter()
  (rpn_head): RPNV2(
    (blocks): ModuleList(
      (0): Sequential(
        (0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
        (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), bias=False)
        (2): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (3): ReLU()
        (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (5): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (6): ReLU()
        (7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (8): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (9): ReLU()
        (10): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (11): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (12): ReLU()
      )
      (1): Sequential(
        (0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
        (1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), bias=False)
        (2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (3): ReLU()
        (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (5): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (6): ReLU()
        (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (8): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (9): ReLU()
        (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (11): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (12): ReLU()
        (13): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (14): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (15): ReLU()
        (16): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (17): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (18): ReLU()
      )
      (2): Sequential(
        (0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
        (1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)
        (2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (3): ReLU()
        (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (5): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (6): ReLU()
        (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (8): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (9): ReLU()
        (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (11): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (12): ReLU()
        (13): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (14): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (15): ReLU()
        (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (17): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (18): ReLU()
      )
    )
    (deblocks): ModuleList(
      (0): Sequential(
        (0): ConvTranspose2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (1): Sequential(
        (0): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): Sequential(
        (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(4, 4), bias=False)
        (1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU()
      )
    )
    (conv_cls): Conv2d(384, 18, kernel_size=(1, 1), stride=(1, 1))
    (conv_box): Conv2d(384, 42, kernel_size=(1, 1), stride=(1, 1))
    (conv_dir_cls): Conv2d(384, 12, kernel_size=(1, 1), stride=(1, 1))
  )
)