论文地址：https://arxiv.org/pdf/1812.05784.pdf
代码地址：https://github.com/nutonomy/second.pytorch
https://github.com/open-mmlab/OpenPCDet
一、论文动机
1.将点云投影到鸟瞰图，往往会丧失大部分空间信息，导致特征比较稀疏，直接用卷积神经网络效果不是很好。
2.为了解决这个问题，在PointNet基础上提出了VoxelNet，算是真正意义上的端对端的3D检测方法。尽管性能很好，但是他的推理速度只有4.4Hz，无法实时部署，second对它进行改进，但3D卷积还是实时的瓶颈。
二、论文方法
1.提出了一种新颖的点云编码器和检测网络
2.由于去掉了3D卷积，所以他的速度非常快，可以达到60Hz
3.直接操作柱体，而不是voxel，可以直接使用2D卷积操作，在GPU上十分高效
三、网络结构
第一块：将点云划分为柱体块，然后扩充点的特征到9个(xyz,xc,yc,zc,xp,yp),用PointNet简化版进行特征升维处理，maxpooling得到每个柱体的特征，再放到伪图像里。
第二块：使用2D CNN对伪图像特征进行处理，同时使用RPN网络，获得更好的定位精度和语义特征。
第三块：根据得到的特征图在先验框的基础上进行回归和分类
四、损失函数和其他创新
4.1损失函数
和VoxelNet里一样，每个类锚点都由宽度，长度，高度和z中心描述，并在两个方向上应用：0度和90度。（x，y，z，w，l，h，θ）使用smoothL1损失，角度使用正弦损失，朝向使用softmax分类损失，类别使用Focal Loss损失。pointpillar里正负样本的定义，每个类别的GT和相应的每个类别的anchor单独计算正负样本，流程是：对于每个GT，找到与其IOU最大的anchor，直接赋为正样本，然后每个anchor找iou最大的GT，筛选大于阈值正样本，小于阈值负样本。这么做是为了防止有些GT分不到anchor。可能GT与所有anchor的最大iou为0.3，防止不满足阈值导致匹配不上。
分类损失：将正负样本用scatter_转换为独热向量(batch_size, 321408, 4)，4表示背景+三个类，然后模型预测的(batch_size, 248, 216, 18) --> (batch_size, 321408, 3)，然后只计算三个类的focalloss损失。
4.2数据增强
数据增强对于性能的提升非常明显。
1.仿照SECOND建立真实框库，每次向点云里随机插入
2.对真实框进行旋转平移增强
3.全局点云增强，随机镜像翻转，全局缩放旋转，全局平移模拟定位噪声
4.3巧妙设计
VoxelNet的编码器是两个PointNet，这里瘦身就使用了一个， 这使我们的运行时在PyTorch runtime中减少了2.5ms。通过将上采样特征图层的输出尺寸减少一半至128，我们又节省了3.9ms。 这些变化均不会影响检测性能。
4.4推理部分
特征图上每个点都有6个anchor(3个尺度*2个角度)。对每个anchor都会预测三个类别概率，七个检测框参数，对于xyz的偏移量，要先乘以缩放比例系数，xy的是,z的系数是高度h，角度是argsin。每个anchor预测三个类别，分别sigmoid，得到三个分数，然后求max，得到最大的。根据阈值卡掉大部分anchor，然后进行无类别NMS，无类别NMS时，首先要选取topk概率，然后再NMS。
五、代码阅读
5.1Pillar Feature Net
将输入的点云进行pillar划分，每个pillar长宽为0.16m，得到网格平面(432,496),选取非空pillar，组成(M,32,4)和(M,3)pillar在网格平面坐标，然后进行点云特征扩充，每个点云增加其相对于该pillar内选取点平均xyz的偏移量和相对于pillar几何中心的xyz偏移量，得到(M,32,10)，经过一个简化的PointNet对点云特征进行升维（M,32,64）再maxpooling得到（M，64），再将M个pillar放回到（432，496）的网格里，得到伪图像数据。
点云生成pillar代码pcdet/datasets/processor/data_processor.py
def transform_points_to_voxels(self, data_dict=None, config=None):
"""
将点云转换为pillar,使用spconv的VoxelGeneratorV2
因为pillar可是认为是一个z轴上所有voxel的集合，所以在设置的时候，
只需要将每个voxel的高度设置成kitti中点云的最大高度即可
"""
#初始化点云转换成pillar需要的参数
if data_dict is None:
# kitti截取的点云范围是[0, -39.68, -3, 69.12, 39.68, 1]
# 得到[69.12, 79.36, 4]/[0.16, 0.16, 4] = [432, 496, 1]
grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
self.grid_size = np.round(grid_size).astype(np.int64)
self.voxel_size = config.VOXEL_SIZE
# just bind the config, we will create the VoxelGeneratorWrapper later,
# to avoid pickling issues in multiprocess spawn
return partial(self.transform_points_to_voxels, config=config)
if self.voxel_generator is None:
self.voxel_generator = VoxelGeneratorWrapper(
#给定每个pillar的大小 [0.16, 0.16, 4]
vsize_xyz=config.VOXEL_SIZE,
#给定点云的范围 [0, -39.68, -3, 69.12, 39.68, 1]
coors_range_xyz=self.point_cloud_range,
#给定每个点云的特征维度，这里是x，y，z，r 其中r是激光雷达反射强度
num_point_features=self.num_point_features,
#给定每个pillar中最多能有多少个点 32
max_num_points_per_voxel=config.MAX_POINTS_PER_VOXEL,
#最多选取多少个pillar，因为生成的pillar中，很多都是没有点在里面的
# 可以重上面的可视化图像中查看到，所以这里只需要得到那些非空的pillar就行
max_num_voxels=config.MAX_NUMBER_OF_VOXELS[self.mode], # 16000
)
points = data_dict['points']
# 生成pillar输出
voxel_output = self.voxel_generator.generate(points)
# 假设一份点云数据是N*4，那么经过pillar生成后会得到三份数据
# voxels代表了每个生成的pillar数据，维度是[M,32,4]
# coordinates代表了每个生成的pillar所在的zyx轴坐标，维度是[M,3],其中z恒为0
# num_points代表了每个生成的pillar中有多少个有效的点维度是[m,]，因为不满32会被0填充
voxels, coordinates, num_points = voxel_output
if not data_dict['use_lead_xyz']:
voxels = voxels[..., 3:] # remove xyz in voxels(N, 3)
data_dict['voxels'] = voxels
data_dict['voxel_coords'] = coordinates
data_dict['voxel_num_points'] = num_points
return data_dict
# 下面是使用spconv生成pillar的代码
class VoxelGeneratorWrapper():
def __init__(self, vsize_xyz, coors_range_xyz, num_point_features, max_num_points_per_voxel, max_num_voxels):
try:
from spconv.utils import VoxelGeneratorV2 as VoxelGenerator
self.spconv_ver = 1
except:
try:
from spconv.utils import VoxelGenerator
self.spconv_ver = 1
except:
from spconv.utils import Point2VoxelCPU3d as VoxelGenerator
self.spconv_ver = 2
if self.spconv_ver == 1:
self._voxel_generator = VoxelGenerator(
voxel_size=vsize_xyz,
point_cloud_range=coors_range_xyz,
max_num_points=max_num_points_per_voxel,
max_voxels=max_num_voxels
)
else:
self._voxel_generator = VoxelGenerator(
vsize_xyz=vsize_xyz,
coors_range_xyz=coors_range_xyz,
num_point_features=num_point_features,
max_num_points_per_voxel=max_num_points_per_voxel,
max_num_voxels=max_num_voxels
)
def generate(self, points):
if self.spconv_ver == 1:
voxel_output = self._voxel_generator.generate(points)
if isinstance(voxel_output, dict):
voxels, coordinates, num_points = \
voxel_output['voxels'], voxel_output['coordinates'], voxel_output['num_points_per_voxel']
else:
voxels, coordinates, num_points = voxel_output
else:
assert tv is not None, f"Unexpected error, library: 'cumm' wasn't imported properly."
voxel_output = self._voxel_generator.point_to_voxel(tv.from_numpy(points))
tv_voxels, tv_coordinates, tv_num_points = voxel_output
# make copy with numpy(), since numpy_view() will disappear as soon as the generator is deleted
voxels = tv_voxels.numpy()
coordinates = tv_coordinates.numpy()
num_points = tv_num_points.numpy()
return voxels, coordinates, num_points点云特征扩充和简化版pointnet处理pcdet/models/backbones_3d/vfe/pillar_vfe.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from .vfe_template import VFETemplate
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:
# 根据论文中，这是是简化版pointnet网络层的初始化
# 论文中使用的是 1x1 的卷积层完成这里的升维操作（理论上使用卷积的计算速度会更快）
# 输入的通道数是刚刚经过数据增强过后的点云特征，每个点云有10个特征，
# 输出的通道数是64
self.linear = nn.Linear(in_channels, out_channels, bias=False)
# 一维BN层
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
def forward(self, inputs):
if inputs.shape[0] > self.part:
# nn.Linear performs randomly when batch size is too large
num_parts = inputs.shape[0] // self.part
part_linear_out = [self.linear(inputs[num_part * self.part:(num_part + 1) * self.part])
for num_part in range(num_parts + 1)]
x = torch.cat(part_linear_out, dim=0)
else:
# x的维度由（M, 32, 10）升维成了（M, 32, 64）
x = self.linear(inputs)
torch.backends.cudnn.enabled = False
# BatchNorm1d层:(M, 64, 32) --> (M, 32, 64)
# （pillars,num_point,channel）->(pillars,channel,num_points)
# 这里之所以变换维度，是因为BatchNorm1d在通道维度上进行,对于图像来说默认模式为[N,C,H*W],通道在第二个维度上
x = self.norm(x.permute(0, 2, 1)).permute(0, 2, 1) if self.use_norm else x
torch.backends.cudnn.enabled = True
x = F.relu(x)
# 完成pointnet的最大池化操作，找出每个pillar中最能代表该pillar的点
# x_max shape ：（M, 1, 64）
x_max = torch.max(x, dim=1, keepdim=True)[0]
if self.last_vfe:
# 返回经过简化版pointnet处理pillar的结果
return x_max
else:
x_repeat = x_max.repeat(1, inputs.shape[1], 1)
x_concatenated = torch.cat([x, x_repeat], dim=2)
return x_concatenated
class PillarVFE(VFETemplate):
"""
model_cfg:NAME: PillarVFE
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_NORM: True
NUM_FILTERS: [64]
num_point_features:4
voxel_size:[0.16 0.16 4]
POINT_CLOUD_RANGE: [0, -39.68, -3, 69.12, 39.68, 1]
"""
def __init__(self, model_cfg, num_point_features, voxel_size, point_cloud_range, **kwargs):
super().__init__(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_ABSLOTE_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))
)
# 加入线性层，将10维特征变为64维特征
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]
def get_output_feature_dim(self):
return self.num_filters[-1]
def get_paddings_indicator(self, actual_num, max_num, axis=0):
"""
计算padding的指示
Args:
actual_num:每个voxel实际点的数量（M，）
max_num:voxel最大点的数量（32，）
Returns:
paddings_indicator:表明一个pillar中哪些是真实数据，哪些是填充的0数据
"""
# 扩展一个维度，使变为（M，1）
actual_num = torch.unsqueeze(actual_num, axis + 1)
# [1, 1]
max_num_shape = [1] * len(actual_num.shape)
# [1, -1]
max_num_shape[axis + 1] = -1
# (1,32)
max_num = torch.arange(max_num, dtype=torch.int, device=actual_num.device).view(max_num_shape)
# (M, 32)
paddings_indicator = actual_num.int() > max_num
return paddings_indicator
def forward(self, batch_dict, **kwargs):
"""
batch_dict:
points:(N,5) --> (batch_index,x,y,z,r) batch_index代表了该点云数据在当前batch中的index
frame_id:(4,) --> (003877,001908,006616,005355) 帧ID
gt_boxes:(4,40,8)--> (x,y,z,dx,dy,dz,ry,class)
use_lead_xyz:(4,) --> (1,1,1,1)
voxels:(M,32,4) --> (x,y,z,r)
voxel_coords:(M,4) --> (batch_index,z,y,x) batch_index代表了该点云数据在当前batch中的index
voxel_num_points:(M,)
image_shape:(4,2) 每份点云数据对应的2号相机图片分辨率
batch_size:4 batch_size大小
"""
voxel_features, voxel_num_points, coords = batch_dict['voxels'], batch_dict['voxel_num_points'], batch_dict[
'voxel_coords']
# 求每个pillar中所有点云的和 (M, 32, 3)->(M, 1, 3) 设置keepdim=True的，则保留原来的维度信息
# 然后在使用求和信息除以每个点云中有多少个点来求每个pillar中所有点云的平均值 points_mean shape：(M, 1, 3)
points_mean = voxel_features[:, :, :3].sum(dim=1, keepdim=True) / voxel_num_points.type_as(voxel_features).view(
-1, 1, 1)
# 每个点云数据减去该点对应pillar的平均值得到差值 xc,yc,zc
f_cluster = voxel_features[:, :, :3] - points_mean
# 创建每个点云到该pillar的坐标中心点偏移量空数据 xp,yp,zp
f_center = torch.zeros_like(voxel_features[:, :, :3])
# coords是每个网格点的坐标，即[432, 496, 1]，需要乘以每个pillar的长宽得到点云数据中实际的长宽（单位米）
# 同时为了获得每个pillar的中心点坐标，还需要加上每个pillar长宽的一半得到中心点坐标
# 每个点的x、y、z减去对应pillar的坐标中心点，得到每个点到该点中心点的偏移量
f_center[:, :, 0] = voxel_features[:, :, 0] - (
coords[:, 3].to(voxel_features.dtype).unsqueeze(1) * self.voxel_x + self.x_offset)
f_center[:, :, 1] = voxel_features[:, :, 1] - (
coords[:, 2].to(voxel_features.dtype).unsqueeze(1) * self.voxel_y + self.y_offset)
# 此处偏移多了z轴偏移 论文中没有z轴偏移
f_center[:, :, 2] = voxel_features[:, :, 2] - (
coords[:, 1].to(voxel_features.dtype).unsqueeze(1) * self.voxel_z + self.z_offset)
# 如果使用绝对坐标，直接组合
if self.use_absolute_xyz:
features = [voxel_features, f_cluster, f_center]
# 否则，取voxel_features的3维之后，在组合
else:
features = [voxel_features[..., 3:], f_cluster, f_center]
# 如果使用距离信息
if self.with_distance:
# torch.norm的第一个2指的是求2范数，第二个2是在第三维度求范数
points_dist = torch.norm(voxel_features[:, :, :3], 2, 2, keepdim=True)
features.append(points_dist)
# 将特征在最后一维度拼接 得到维度为（M，32,10）的张量
features = torch.cat(features, dim=-1)
# 每个pillar中点云的最大数量
voxel_count = features.shape[1]
"""
由于在生成每个pillar中，不满足最大32个点的pillar会存在由0填充的数据，
而刚才上面的计算中，会导致这些
由0填充的数据在计算出现xc,yc,zc和xp,yp,zp出现数值，
所以需要将这个被填充的数据的这些数值清0,
因此使用get_paddings_indicator计算features中哪些是需要被保留真实数据和需要被置0的填充数据
"""
# 得到mask维度是（M， 32）
# mask中指名了每个pillar中哪些是需要被保留的数据
mask = self.get_paddings_indicator(voxel_num_points, voxel_count, axis=0)
# （M， 32）->(M, 32, 1)
mask = torch.unsqueeze(mask, -1).type_as(voxel_features)
# 将feature中被填充数据的所有特征置0
features *= mask
for pfn in self.pfn_layers:
features = pfn(features)
# (M, 64), 每个pillar抽象出一个64维特征
features = features.squeeze()
batch_dict['pillar_features'] = features
return batch_dict将M个pillar放回到原来坐标分布中形成伪图像pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py
import torch
import torch.nn as nn
class PointPillarScatter(nn.Module):
"""
对应到论文中就是stacked pillars，将生成的pillar按照坐标索引还原到原空间中
"""
def __init__(self, model_cfg, grid_size, **kwargs):
super().__init__()
self.model_cfg = model_cfg
self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES # 64
self.nx, self.ny, self.nz = grid_size # [432,496,1]
assert self.nz == 1
def forward(self, batch_dict, **kwargs):
"""
Args:
pillar_features:(M,64)
coords:(M, 4) 第一维是batch_index 其余维度为xyz
Returns:
batch_spatial_features:(batch_size, 64, 496, 432)
"""
# 拿到经过前面pointnet处理过后的pillar数据和每个pillar所在点云中的坐标位置
# pillar_features 维度 （M， 64）
# coords 维度 （M， 4）
pillar_features, coords = batch_dict['pillar_features'], batch_dict['voxel_coords']
# 将转换成为伪图像的数据存在到该列表中
batch_spatial_features = []
batch_size = coords[:, 0].max().int().item() + 1
# batch中的每个数据独立处理
for batch_idx in range(batch_size):
# 创建一个空间坐标所有用来接受pillar中的数据
# self.num_bev_features是64
# self.nz * self.nx * self.ny是生成的空间坐标索引 [496, 432, 1]的乘积
# spatial_feature 维度 (64,214272)
spatial_feature = torch.zeros(
self.num_bev_features,
self.nz * self.nx * self.ny,
dtype=pillar_features.dtype,
device=pillar_features.device) # (64,214272)-->1x432x496=214272
# 从coords[:, 0]取出该batch_idx的数据mask
batch_mask = coords[:, 0] == batch_idx
# 根据mask提取坐标
this_coords = coords[batch_mask, :]
# this_coords中存储的坐标是z,y和x的形式,且只有一层，因此计算索引的方式如下
# 平铺后需要计算前面有多少个pillar 一直到当前pillar的索引
"""
因为前面是将所有数据flatten成一维的了，相当于一个图片宽高为[496, 432]的图片
被flatten成一维的图片数据了，变成了496*432=214272;
而this_coords中存储的是平面（不需要考虑Z轴）中一个点的信息，所以要
将这个点的位置放回被flatten的一位数据时，需要计算在该点之前所有行的点总和加上
该点所在的列即可
"""
# 这里得到所有非空pillar在伪图像的对应索引位置
indices = this_coords[:, 1] + this_coords[:, 2] * self.nx + this_coords[:, 3]
# 转换数据类型
indices = indices.type(torch.long)
# 根据mask提取pillar_features
pillars = pillar_features[batch_mask, :]
pillars = pillars.t()
# 在索引位置填充pillars
spatial_feature[:, indices] = pillars
# 将空间特征加入list,每个元素为(64, 214272)
batch_spatial_features.append(spatial_feature)
# 在第0个维度将所有的数据堆叠在一起
batch_spatial_features = torch.stack(batch_spatial_features, 0)
# reshape回原空间(伪图像) （4, 64, 214272）--> (4, 64, 496, 432)
batch_spatial_features = batch_spatial_features.view(batch_size, self.num_bev_features * self.nz, self.ny,
self.nx)
# 将结果加入batch_dict
batch_dict['spatial_features'] = batch_spatial_features
return batch_dict5.2 2D CNN
得到伪图像特征（batch_size,64,432,496）,使用FPN网络，进行多尺度特征提取和融合，三次上采样后得到（batch_size,128,248,216）,拼接得到（batch_size,384,248,216）
pcdet/models/backbones_2d/base_bev_backbone.py
import numpy as np
import torch
import torch.nn as nn
class BaseBEVBackbone(nn.Module):
def __init__(self, model_cfg, input_channels):
super().__init__()
self.model_cfg = model_cfg
# 读取下采样层参数
if self.model_cfg.get('LAYER_NUMS', None) is not None:
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 = []
# 读取上采样层参数
if self.model_cfg.get('UPSAMPLE_STRIDES', None) is not None:
assert len(self.model_cfg.UPSAMPLE_STRIDES) == len(self.model_cfg.NUM_UPSAMPLE_FILTERS)
num_upsample_filters = self.model_cfg.NUM_UPSAMPLE_FILTERS
upsample_strides = self.model_cfg.UPSAMPLE_STRIDES
else:
upsample_strides = num_upsample_filters = []
num_levels = len(layer_nums) # 2
c_in_list = [input_channels, *num_filters[:-1]] # (256, 128) input_channels:256, num_filters[:-1]：64,128
self.blocks = nn.ModuleList()
self.deblocks = nn.ModuleList()
for idx in range(num_levels): # (64,64)-->(64,128)-->(128,256) # 这里为cur_layers的第一层且stride=2
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]): # 根据layer_nums堆叠卷积层
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()
])
# 在block中添加该层
# *作用是：将列表解开成几个独立的参数，传入函数 # 类似的运算符还有两个星号(**)，是将字典解开成独立的元素作为形参
self.blocks.append(nn.Sequential(*cur_layers))
if len(upsample_strides) > 0: # 构造上采样层 # (1, 2, 4)
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) # 512
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
def forward(self, data_dict):
"""
Args:
data_dict:
spatial_features : (4, 64, 496, 432)
Returns:
"""
spatial_features = data_dict['spatial_features']
ups = []
ret_dict = {}
x = spatial_features
for i in range(len(self.blocks)):
x = self.blocks[i](x)
stride = int(spatial_features.shape[2] / x.shape[2])
ret_dict['spatial_features_%dx' % stride] = x
if len(self.deblocks) > 0: # (4,64,248,216)-->(4,128,124,108)-->(4,256,62,54)
ups.append(self.deblocks[i](x))
else:
ups.append(x)
# 如果存在上采样层，将上采样结果连接
if len(ups) > 1:
"""
最终经过所有上采样层得到的3个尺度的的信息
每个尺度的 shape 都是 （batch_size, 128, 248, 216）
在第一个维度上进行拼接得到x 维度是 （batch_size, 384, 248, 216）
"""
x = torch.cat(ups, dim=1)
elif len(ups) == 1:
x = ups[0]
# Fasle
if len(self.deblocks) > len(self.blocks):
x = self.deblocks[-1](x)
# 将结果存储在spatial_features_2d中并返回
data_dict['spatial_features_2d'] = x
return data_dict5.3 SSD检测头
先验框的设计上，一共有三个类别的先验框，每个类别有一个尺度两个角度的先验框。
pcdet/models/dense_heads/anchor_head_single.py文章来源：https://www.toymoban.com/news/detail-716275.html
import numpy as np
import torch.nn as nn
from .anchor_head_template import AnchorHeadTemplate
class AnchorHeadSingle(AnchorHeadTemplate):
"""
Args:
model_cfg: AnchorHeadSingle的配置
input_channels: 384 输入通道数
num_class: 3
class_names: ['Car','Pedestrian','Cyclist']
grid_size: (432, 496, 1)
point_cloud_range: (0, -39.68, -3, 69.12, 39.68, 1)
predict_boxes_when_training: False
"""
def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range,
predict_boxes_when_training=True, **kwargs):
super().__init__(
model_cfg=model_cfg, num_class=num_class, class_names=class_names, grid_size=grid_size,
point_cloud_range=point_cloud_range,
predict_boxes_when_training=predict_boxes_when_training
)
# 每个点有3个尺度的个先验框 每个先验框都有两个方向（0度，90度） num_anchors_per_location:[2, 2, 2]
self.num_anchors_per_location = sum(self.num_anchors_per_location) # sum([2, 2, 2])
# Conv2d(512,18,kernel_size=(1,1),stride=(1,1))
self.conv_cls = nn.Conv2d(
input_channels, self.num_anchors_per_location * self.num_class,
kernel_size=1
)
# Conv2d(512,42,kernel_size=(1,1),stride=(1,1))
self.conv_box = nn.Conv2d(
input_channels, self.num_anchors_per_location * self.box_coder.code_size,
kernel_size=1
)
# 如果存在方向损失，则添加方向卷积层Conv2d(512,12,kernel_size=(1,1),stride=(1,1))
if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', None) is not None:
self.conv_dir_cls = nn.Conv2d(
input_channels,
self.num_anchors_per_location * self.model_cfg.NUM_DIR_BINS,
kernel_size=1
)
else:
self.conv_dir_cls = None
self.init_weights()
# 初始化参数
def init_weights(self):
pi = 0.01
# 初始化分类卷积偏置
nn.init.constant_(self.conv_cls.bias, -np.log((1 - pi) / pi))
# 初始化分类卷积权重
nn.init.normal_(self.conv_box.weight, mean=0, std=0.001)
def forward(self, data_dict):
# 从字典中取出经过backbone处理过的信息
# spatial_features_2d 维度 （batch_size, 384, 248, 216）
spatial_features_2d = data_dict['spatial_features_2d']
# 每个坐标点上面6个先验框的类别预测 --> (batch_size, 18, 200, 176)
cls_preds = self.conv_cls(spatial_features_2d)
# 每个坐标点上面6个先验框的参数预测 --> (batch_size, 42, 200, 176) 其中每个先验框需要预测7个参数，分别是（x, y, z, w, l, h, θ）
box_preds = self.conv_box(spatial_features_2d)
# 维度调整，将类别放置在最后一维度 [N, H, W, C] --> (batch_size, 200, 176, 18)
cls_preds = cls_preds.permute(0, 2, 3, 1).contiguous()
# 维度调整，将先验框调整参数放置在最后一维度 [N, H, W, C] --> (batch_size ,200, 176, 42)
box_preds = box_preds.permute(0, 2, 3, 1).contiguous()
# 将类别和先验框调整预测结果放入前向传播字典中
self.forward_ret_dict['cls_preds'] = cls_preds
self.forward_ret_dict['box_preds'] = box_preds
# 进行方向分类预测
if self.conv_dir_cls is not None:
# # 每个先验框都要预测为两个方向中的其中一个方向 --> (batch_size, 12, 200, 176)
dir_cls_preds = self.conv_dir_cls(spatial_features_2d)
# 将类别和先验框方向预测结果放到最后一个维度中 [N, H, W, C] --> (batch_size, 248, 216, 12)
dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).contiguous()
# 将方向预测结果放入前向传播字典中
self.forward_ret_dict['dir_cls_preds'] = dir_cls_preds
else:
dir_cls_preds = None
"""
如果是在训练模式的时候，需要对每个先验框分配GT来计算loss
"""
if self.training:
# targets_dict = {
# 'box_cls_labels': cls_labels, # (4，211200）
# 'box_reg_targets': bbox_targets, # (4，211200, 7）
# 'reg_weights': reg_weights # (4，211200）
# }
targets_dict = self.assign_targets(
gt_boxes=data_dict['gt_boxes'] # （4，39，8）
)
# 将GT分配结果放入前向传播字典中
self.forward_ret_dict.update(targets_dict)
# 如果不是训练模式，则直接生成进行box的预测
if not self.training or self.predict_boxes_when_training:
# 根据预测结果解码生成最终结果
batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
batch_size=data_dict['batch_size'],
cls_preds=cls_preds, box_preds=box_preds, dir_cls_preds=dir_cls_preds
)
data_dict['batch_cls_preds'] = batch_cls_preds # (1, 211200, 3) 70400*3=211200
data_dict['batch_box_preds'] = batch_box_preds # (1, 211200, 7)
data_dict['cls_preds_normalized'] = False
return data_dict六、Reference
https://blog.csdn.net/qq_41366026/article/details/123006401?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522166692373016800182114331%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=166692373016800182114331&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~top_positive~default-1-123006401-null-null.142^v62^control_1,201^v3^control_1,213^v1^t3_control1&utm_term=pointpillars&spm=1018.2226.3001.4187文章来源地址https://www.toymoban.com/news/detail-716275.html
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