【代码解读】OpenCOOD框架之model模块（以PointPillarFCooper为例）

point_pillar_fcooper

PointPillarFCooper
PointPillars
- PillarVFE
- PFNLayer
- PointPillarScatter
- BaseBEVBackbone
- DownsampleConv
- - DoubleConv
SpatialFusion
检测头

（紧扣PointPillarFCooper的框架结构，一点一点看代码）

PointPillarFCooper

# -*- coding: utf-8 -*-
# Author: Runsheng Xu <rxx3386@ucla.edu>
# License: TDG-Attribution-NonCommercial-NoDistrib
import pprint

import torch.nn as nn

from opencood.models.sub_modules.pillar_vfe import PillarVFE
from opencood.models.sub_modules.point_pillar_scatter import PointPillarScatter
from opencood.models.sub_modules.base_bev_backbone import BaseBEVBackbone
from opencood.models.sub_modules.downsample_conv import DownsampleConv
from opencood.models.sub_modules.naive_compress import NaiveCompressor
from opencood.models.fuse_modules.f_cooper_fuse import SpatialFusion


class PointPillarFCooper(nn.Module):
    """
    F-Cooper implementation with point pillar backbone.
    """
    def __init__(self, args):
        super(PointPillarFCooper, self).__init__()
        print("args: ")
        pprint.pprint(args)
        self.max_cav = args['max_cav']
        # PIllar VFE Voxel Feature Encoding
        self.pillar_vfe = PillarVFE(args['pillar_vfe'],
                                    num_point_features=4,
                                    voxel_size=args['voxel_size'],
                                    point_cloud_range=args['lidar_range'])
        self.scatter = PointPillarScatter(args['point_pillar_scatter'])
        self.backbone = BaseBEVBackbone(args['base_bev_backbone'], 64)
        # used to downsample the feature map for efficient computation
        self.shrink_flag = False
        if 'shrink_header' in args:
            self.shrink_flag = True
            self.shrink_conv = DownsampleConv(args['shrink_header'])
        self.compression = False

        if args['compression'] > 0:
            self.compression = True
            self.naive_compressor = NaiveCompressor(256, args['compression'])

        self.fusion_net = SpatialFusion()

        self.cls_head = nn.Conv2d(128 * 2, args['anchor_number'],
                                  kernel_size=1)
        self.reg_head = nn.Conv2d(128 * 2, 7 * args['anchor_number'],
                                  kernel_size=1)

        if args['backbone_fix']:
            self.backbone_fix()

args: 其实就是从hypes_yaml配置文件里传来的参数

args:
{'anchor_number': 2,
 'backbone_fix': False,
 'base_bev_backbone': {'layer_nums': [3, 5, 8],
                       'layer_strides': [2, 2, 2],
                       'num_filters': [64, 128, 256],
                       'num_upsample_filter': [128, 128, 128],
                       'upsample_strides': [1, 2, 4]},
 'compression': 0,
 'lidar_range': [-140.8, -40, -3, 140.8, 40, 1],
 'max_cav': 5,
 'pillar_vfe': {'num_filters': [64],
                'use_absolute_xyz': True,
                'use_norm': True,
                'with_distance': False},
 'point_pillar_scatter': {'grid_size': array([704, 200,   1], dtype=int64),
                          'num_features': 64},
 'shrink_header': {'dim': [256],
                   'input_dim': 384,
                   'kernal_size': [1],
                   'padding': [0],
                   'stride': [1]},
 'voxel_size': [0.4, 0.4, 4]}

PointPillarsFcooper结构

PointPillarFCooper(
  (pillar_vfe): PillarVFE(
    (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)
      )
    )
  )
  (scatter): PointPillarScatter()
  (backbone): BaseBEVBackbone(
    (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()
        (19): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (20): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (21): ReLU()
        (22): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (23): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (24): ReLU()
        (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (26): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (27): 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()
      )
    )
  )
  (shrink_conv): DownsampleConv(
    (layers): ModuleList(
      (0): DoubleConv(
        (double_conv): Sequential(
          (0): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1))
          (1): ReLU(inplace=True)
          (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (3): ReLU(inplace=True)
        )
      )
    )
  )
  (fusion_net): SpatialFusion()
  (cls_head): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))
  (reg_head): Conv2d(256, 14, kernel_size=(1, 1), stride=(1, 1))
)

PointPillars

在这里插入图片描述
网络overview：网络的主要组成部分是PFN、Backbone和 SSD 检测头。原始点云被转换为堆叠的柱子张量和柱子索引张量。编码器使用堆叠的柱子来学习一组特征，这些特征可以分散回卷积神经网络的 2D 伪图像。检测头使用来自主干的特征来预测对象的 3D 边界框。请注意：在这里，我们展示了汽车网络的骨干维度。

PillarVFE

就是 voxel feature encoder：先对点云进行特征提取
VFE由PFNLayer(Pillar Feature Net)组成

model_cfg

{'num_filters': [64],
 'use_absolute_xyz': True,
  'use_norm': True,
  'with_distance': False},

class PillarVFE(nn.Module):
    def __init__(self, model_cfg, num_point_features, voxel_size,
                 point_cloud_range):
        super().__init__()
        self.model_cfg = model_cfg

        self.use_norm = self.model_cfg['use_norm']
        self.with_distance = self.model_cfg['with_distance']

        self.use_absolute_xyz = self.model_cfg['use_absolute_xyz']
        num_point_features += 6 if self.use_absolute_xyz else 3
        if self.with_distance:
            num_point_features += 1

        self.num_filters = self.model_cfg['num_filters']
        assert len(self.num_filters) > 0
        num_filters = [num_point_features] + list(self.num_filters)

        pfn_layers = []
        for i in range(len(num_filters) - 1):
            in_filters = num_filters[i]
            out_filters = num_filters[i + 1]
            pfn_layers.append(
                PFNLayer(in_filters, out_filters, self.use_norm,
                         last_layer=(i >= len(num_filters) - 2))
            )
        self.pfn_layers = nn.ModuleList(pfn_layers)

        self.voxel_x = voxel_size[0]
        self.voxel_y = voxel_size[1]
        self.voxel_z = voxel_size[2]
        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]

PFNLayer

这里只是一个全连接+归一化（好像和原来的算法有出入）

class PFNLayer(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 use_norm=True,
                 last_layer=False):
        super().__init__()

        self.last_vfe = last_layer
        self.use_norm = use_norm
        if not self.last_vfe:
            out_channels = out_channels // 2

        if self.use_norm:
            self.linear = nn.Linear(in_channels, out_channels, bias=False)
            self.norm = nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01)
        else:
            self.linear = nn.Linear(in_channels, out_channels, bias=True)

        self.part = 50000

PointPillarScatter

主要作用就是三维点云压缩成bev（鸟瞰图）

class PointPillarScatter(nn.Module):
    def __init__(self, model_cfg):
        super().__init__()

        self.model_cfg = model_cfg
        self.num_bev_features = self.model_cfg['num_features']
        self.nx, self.ny, self.nz = model_cfg['grid_size']
        assert self.nz == 1

model_cfg:

{'grid_size': array([704, 200,   1], dtype=int64),
 'num_features': 64}

BaseBEVBackbone

参考这个图
在这里插入图片描述
3 * Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
↓
5 * Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
↓
8 * Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)

3、5、8对应着layer_nums

model_cfg

{'layer_nums': [3, 5, 8],
 'layer_strides': [2, 2, 2],
 'num_filters': [64, 128, 256],
 'num_upsample_filter': [128, 128, 128],
 'upsample_strides': [1, 2, 4]},

class BaseBEVBackbone(nn.Module):
    def __init__(self, model_cfg, input_channels):
        super().__init__()
        self.model_cfg = model_cfg

        if 'layer_nums' in self.model_cfg:

            assert len(self.model_cfg['layer_nums']) == \
                   len(self.model_cfg['layer_strides']) == \
                   len(self.model_cfg['num_filters'])

            layer_nums = self.model_cfg['layer_nums']
            layer_strides = self.model_cfg['layer_strides']
            num_filters = self.model_cfg['num_filters']
        else:
            layer_nums = layer_strides = num_filters = []

        if 'upsample_strides' in self.model_cfg:
            assert len(self.model_cfg['upsample_strides']) \
                   == len(self.model_cfg['num_upsample_filter'])

            num_upsample_filters = self.model_cfg['num_upsample_filter']
            upsample_strides = self.model_cfg['upsample_strides']

        else:
            upsample_strides = num_upsample_filters = []

        num_levels = len(layer_nums)   # len(layer_nums)个Sequential
        c_in_list = [input_channels, *num_filters[:-1]]

        self.blocks = nn.ModuleList()
        self.deblocks = nn.ModuleList()

        for idx in range(num_levels):
            cur_layers = [
                nn.ZeroPad2d(1),
                nn.Conv2d(
                    c_in_list[idx], num_filters[idx], kernel_size=3,
                    stride=layer_strides[idx], padding=0, bias=False
                ),
                nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
                nn.ReLU()
            ]
            for k in range(layer_nums[idx]):  # 每个Sequential里有多少个以下结构
                cur_layers.extend([
                    nn.Conv2d(num_filters[idx], num_filters[idx],
                              kernel_size=3, padding=1, bias=False),
                    nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
                    nn.ReLU()
                ])

            self.blocks.append(nn.Sequential(*cur_layers))
            # 以下是deblock模块
            if len(upsample_strides) > 0:
                stride = upsample_strides[idx]
                if stride >= 1:
                    self.deblocks.append(nn.Sequential(
                        nn.ConvTranspose2d(
                            num_filters[idx], num_upsample_filters[idx],
                            upsample_strides[idx],
                            stride=upsample_strides[idx], bias=False
                        ),
                        nn.BatchNorm2d(num_upsample_filters[idx],
                                       eps=1e-3, momentum=0.01),
                        nn.ReLU()
                    ))
                else:
                    stride = np.round(1 / stride).astype(np.int)
                    self.deblocks.append(nn.Sequential(
                        nn.Conv2d(
                            num_filters[idx], num_upsample_filters[idx],
                            stride,
                            stride=stride, bias=False
                        ),
                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3,
                                       momentum=0.01),
                        nn.ReLU()
                    ))

        c_in = sum(num_upsample_filters)
        if len(upsample_strides) > num_levels:
            self.deblocks.append(nn.Sequential(
                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1],
                                   stride=upsample_strides[-1], bias=False),
                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
                nn.ReLU(),
            ))

        self.num_bev_features = c_in

DownsampleConv

其实就是下采样（用了几个DoubleConv）
主要作用就是

降低计算成本: 在深度神经网络中，参数量和计算量通常会随着输入数据的尺寸增加而增加。通过下采样，可以降低每个层的输入数据的尺寸，从而降低网络的计算成本。
减少过拟合: 下采样可以通过减少输入数据的维度和数量来减少模型的复杂性，从而有助于降低过拟合的风险。过拟合是指模型在训练数据上表现良好，但在测试数据上表现较差的现象。
提高模型的泛化能力: 通过减少输入数据的空间分辨率，下采样有助于模型学习更加抽象和通用的特征，从而提高了模型对于不同数据的泛化能力。
加速训练和推理过程: 由于下采样可以降低网络的计算成本，因此可以加快模型的训练和推理过程。这对于处理大规模数据和实时应用特别有用。

class DownsampleConv(nn.Module):
    def __init__(self, config):
        super(DownsampleConv, self).__init__()
        self.layers = nn.ModuleList([])
        input_dim = config['input_dim']

        for (ksize, dim, stride, padding) in zip(config['kernal_size'],
                                                 config['dim'],
                                                 config['stride'],
                                                 config['padding']):
            self.layers.append(DoubleConv(input_dim,
                                          dim,
                                          kernel_size=ksize,
                                          stride=stride,
                                          padding=padding))
            input_dim = dim

config参数

{'dim': [256],
 'input_dim': 384,
 'kernal_size': [1],
 'padding': [0],
 'stride': [1]},

DoubleConv

其实就是两层卷积

class DoubleConv(nn.Module):
    """
    Double convoltuion
    Args:
        in_channels: input channel num
        out_channels: output channel num
    """

    def __init__(self, in_channels, out_channels, kernel_size,
                 stride, padding):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
                      stride=stride, padding=padding),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

SpatialFusion

其实就是取最大来进行融合特征
在这里插入图片描述

class SpatialFusion(nn.Module):
    def __init__(self):
        super(SpatialFusion, self).__init__()

    def regroup(self, x, record_len):
        cum_sum_len = torch.cumsum(record_len, dim=0)
        split_x = torch.tensor_split(x, cum_sum_len[:-1].cpu())
        return split_x

    def forward(self, x, record_len):
        # x: B, C, H, W, split x:[(B1, C, W, H), (B2, C, W, H)]
        split_x = self.regroup(x, record_len)
        out = []

        for xx in split_x:
            xx = torch.max(xx, dim=0, keepdim=True)[0]
            out.append(xx)
        return torch.cat(out, dim=0)

检测头

(cls_head): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))
(reg_head): Conv2d(256, 14, kernel_size=(1, 1), stride=(1, 1))