点云3d检测SA-SSD

C. He, H. Zeng, J. Huang, X. -S. Hua and L. Zhang, “Structure Aware Single-Stage 3D Object Detection From Point Cloud,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11870-11879, doi: 10.1109/CVPR42600.2020.01189.

源码：https://github.com/skyhehe123/SA-SSD

论文解读

创新点：辅助网络auxiliary net和检测头部分PS Warp(part-sensitive warping)

auxiliary net

点级监督辅助网络，引导骨干 CNN 不同阶段的中间特征学习点云细粒度结构特征。仅应用于训练阶段，不会额外增加推理成本。
○ 目的：协助 backbone提取的特征了解三维点云的结构信息。
○ 提出原因：backbone 从点云提取的下采样卷积特征丢失了结构细节，而这些细节对生成准确的对象定位至关重要。

• 前景分割
  引导 backbone CNN 在对象边界学习更多的判别模式表示。特别地，利用 sigmoid 函数预测每个点前景/背景概率。focal loss 优化分割任务。从而更精确检测对象边界.

  然而，即使边界点被正确检测出，由于特征图的稀疏性，边界框的尺度和形状仍存在歧义。故而，为进一步提高定位精度，作者采用另一个辅助任务学习每个对象点相对对象中心的相对位置。

• 中心点预测
  对象点和对象中心的相对位置关系。帮助尺度和形状确定，实现更精确的定位。

PSWarp

通过特征映射上的空间转换操作，解决预测边界框和置信度映射之间的非对齐，减少NMS后处理中预测边界框和相应置信度不一致问题。

mmdet

mmdetection是面向目标检测的代码集成库，也是一个工程性很强的开源框架，对调参和搭建网络都非常友好。SA-SSD代码是在mmdetection平台上开发。

mmdetection初认识：https://www.dazhuanlan.com/disen123/topics/1078289
github主页

编译mmdet某些库

points_op、 iou3d、pointnet2 编译C++/CUDA模块链接库points_op_cpu.so、iou3d_cuda.so、pointnet2_cuda.so：

python setup.py build_ext --inplace

遇见错误：

• FAILED: /home/hf/program/SA-SSD-master/mmdet/ops/points_op/build/temp.linux-x86_64-3.7/src/points_op.o

• /home/hf/program/SA-SSD-master/mmdet/ops/points_op/src/points_op.cpp:15:29: error: ‘AT_CHECK’ was not declared in this scope; did you mean ‘CHECK’?

  参考方法：修改源文件points_op.cpp、interpolate.cpp、iou3d.cpp.

• 编译iou3d。Cuda version和GPU compute capability冲突。==》修改 cuda、torch 版本

• 编译pointnet2。
  fatal error: THC/THC.h，error: ‘THCState’ does not name a type ==》注释对应语句
  error: ‘getCurrentCUDAStream’ is not a member of ‘at::cuda’ ==》添加#include

数据准备

python tools/create_data.py

生成.pkl文件、gt_databse目录、velodyne_reduced目录。

数据解读

• .pkl 文件
  pickle.load()读取。list类型存放，每个元素以字典形式存放，内容如下：
  

• image_2文件
  mmcv.imread读取，颜色类型color格式、通道bgr，返回数据类型array。
  transform：(mean, std)标准化，toRGB，size padding to [384, 1248] (32的倍数)

• label_2文件：
  .txt 文件，open读取。每行内容如下：
  mmdet.datasets.kitti_utils.Object3d 存取label，格式如下：
  

• calib文件：
  mmdet.datasets.kitti_utils.Calibration处理:

  • open 读取.txt文件，内容如下：
    

  • 校准矩阵、相机参数

    ‘’’ Calibration matrices and utils
    3d XYZ in label.txt are in rect camera coord.
    2d box xy are in image2 coord
    Points in lidar.bin are in Velodyne coord.
    y_image2 = P^2_rect * x_rect
    y_image2 = P^2_rect * R0_rect * Tr_velo_to_cam * x_velo
    x_ref = Tr_velo_to_cam * x_velo
    x_rect = R0_rect * x_ref
    P^2_rect = [f^2_u, 0, c^2_u, -f^2_u b^2_x;
    0, f^2_v, c^2_v, -f^2_v b^2_y;
    0, 0, 1, 0]
    = K * [1|t]
    image2 coord:
    ----> x-axis (u)
    |
    |
    v y-axis (v)
    velodyne coord:
    front x, left y, up z
    rect/ref camera coord:
    right x, down y, front z
    Ref (KITTI paper): http://www.cvlibs.net/publications/Geiger2013IJRR.pdf
    TODO(rqi): do matrix multiplication only once for each projection.
    ‘’’

    
    从P2获取rect camera coord到image_2 coord投影矩阵3X4
    从P3获取rect camera coord到image_3 coord投影矩阵3X4
    从Tr_velo_to_cam获取velodyne coord到 reference camera coord刚性变换3X4
    从R0_rect获取reference camera coord到 rect camera coord旋转3X3

  gt_bboxes：label文件获取的box3d, [x, y, z, w, l, h, ry]。前三项为相机坐标location，中间三项分别为3d bbox的width、length、height，最后一项为相机坐标系中绕Y轴的角度[-pi, pi]。

• 3d bbox，rect相机到点云坐标系转换
  更新gt_bboxes[:,:3]：
  rect相机坐标系转reference相机坐标系：pts_3d_rect @ np.linalg.inv(calib.R0).T
  reference坐标系转点云坐标系：pts_3d_ref @ calib.C2V.T

• velodyne_reduced文件：
  .bin文件，存储点云（正视图）。numpy加载文件，并reshape(-1, 4). 数据如下：
  

• PointAugmentor
  gt_database文件.bin 中读取box3d_lidar。

  训练集：

self.augmentor.noise_per_object_(gt_bboxes, points, num_try=100) # 随机噪声，gt_bboxes增强
gt_bboxes, points = self.augmentor.random_flip(gt_bboxes, points) # 点随机翻转
gt_bboxes, points = self.augmentor.global_rotation(gt_bboxes, points) # 点随机旋转
gt_bboxes, points = self.augmentor.global_scaling(gt_bboxes, points) # 点随机缩放

• Point2Voxel
  点转体素。
  voxels, coordinates, num_points = self.generator.generate(points)

训练数据元素：

训练train

def forward_train(self, img, img_meta, **kwargs):
        batch_size = len(img_meta)
        ret = self.merge_second_batch(kwargs)
        vx = self.backbone(ret['voxels'], ret['num_points'])
        x, conv6, point_misc = self.neck(vx, ret['coordinates'], batch_size, is_test=False)

        losses = dict()
        aux_loss = self.neck.aux_loss(*point_misc, gt_bboxes=ret['gt_bboxes'])
        losses.update(aux_loss)

        # RPN forward and loss
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            rpn_loss_inputs = rpn_outs + (ret['gt_bboxes'], ret['gt_labels'], ret['gt_types'],\
                            ret['anchors'], ret['anchors_mask'], self.train_cfg.rpn)
            rpn_losses = self.rpn_head.loss(*rpn_loss_inputs)
            losses.update(rpn_losses)
            guided_anchors, _ = self.rpn_head.get_guided_anchors(*rpn_outs, ret['anchors'],\
                        ret['anchors_mask'], ret['gt_bboxes'], ret['gt_labels'], thr=self.train_cfg.rpn.anchor_thr)
        else:
            raise NotImplementedError

        # bbox head forward and loss
        if self.extra_head:
            bbox_score = self.extra_head(conv6, guided_anchors)
            refine_loss_inputs = (bbox_score, ret['gt_bboxes'], ret['gt_labels'], guided_anchors, self.train_cfg.extra)
            refine_losses = self.extra_head.loss(*refine_loss_inputs)
            losses.update(refine_losses)

        return losses

输入数据kwargs：
批次单个数据合并批数据ret：

backbone

处理体素数据ret[‘voxels’]，size：[n, 5, 4]，调用SimpleVoxel简化体素，返回points_mean，size：[n，4]。sum+mean:

points_mean = features[:, :, :self.num_input_features].sum(dim=1, keepdim=False) 
								/ num_voxels.type_as(features).view(-1, 1)

neck

处理backbone体素（均值）、坐标点，调用SpMiddleFHD。

其中主干网络为VxNet，体素特征维度变化：[70142, 4]–>[70142, 16]–>[106542,32]–>[63797,64]–>[27599,64]，提取深层特征out、和中间层多尺度特征middle(3种尺度）;
全卷积网络部分采用BEVNet（8个conv2d+bn+relu）处理深层特征out，其维度变化为：[4, 320, 200, 176]–>[4, 256, 200, 176]，返回处理后特征x、和第7层特征conv6。

辅助网络：依次处理中间多尺度层middle，并返回每个尺度在backbone体素上的特征$p_0$、$p_1$、$p_2$。
其大小如下：

1. tensor2points 结合offset和voxel_size分别处理中间层多尺度特征middle[i]的坐标，返回特征vx_feat和坐标vx_nxyz.

def tensor2points(tensor, offset=(0., -40., -3.), voxel_size=(.05, .05, .1)):
    indices = tensor.indices.float()
    offset = torch.Tensor(offset).to(indices.device)
    voxel_size = torch.Tensor(voxel_size).to(indices.device)
    indices[:, 1:] = indices[:, [3, 2, 1]] * voxel_size + offset + .5 * voxel_size
    return tensor.features, indices

2. nearest_neighbor_interpolate 处理backbone简化的体素points_mean(unknown)和上一步的vx_nxyz(known)、vx_feat(known_feats)，返回输入体素3邻域的特征p。

def nearest_neighbor_interpolate(unknown, known, known_feats):
    """
    :param pts: (n, 4) tensor of the bxyz positions of the unknown features
    :param ctr: (m, 4) tensor of the bxyz positions of the known features
    :param ctr_feats: (m, C) tensor of features to be propigated
    :return:
        new_features: (n, C) tensor of the features of the unknown features
    """
    dist, idx = pointnet2_utils.three_nn(unknown, known) # find the three nearest neighbors of unknown in known,compute l2-distance, and return idx
    dist_recip = 1.0 / (dist + 1e-8)
    norm = torch.sum(dist_recip, dim=1, keepdim=True) # [n,3], do sum(col), return [n,1]
    weight = dist_recip / norm
    interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight) # [unknown.shape[0], known_feats.shape[1]]

    return interpolated_feats

3. Linear(in_features=160, out_features=64, bias=False)全连接点云体素3邻域多尺度特征torch.cat([p0, p1, p2], dim=-1)，返回pointwise
4. Linear(in_features=64, out_features=1, bias=False)分类，返回point_cls
5. Linear(in_features=64, out_features=3, bias=False)回归，返回point_reg

neck 部分返回：x, conv6, point_misc。分别表示BEVNet深层特征x和第7层卷积特征conv6，(points_mean, point_cls, point_reg).

loss

gt_bboxes: (M, 7) , [x, y, z, h, w, l, ry]。从label_2文件获取，依次表示相机坐标(x, y, z)，目标维度(h, w, l)，目标在相机坐标系中绕Y轴角度ry。

测试test

1. data
VoxelGenerator:
point_cloud_range：x：[0, 70.4]，y：[-40, 40]，z:[-3, 1] .
voxel_size：[0.05, 0.05, 0.1].
grid_size：[1408.0, 1600.0, 40.0]

grid_size = (point_cloud_range[3:] - point_cloud_range[:3]) / voxel_size

image–transform:

scale=1，不翻转，padding to [384, 1248]

点云points_to_voxel:

def points_to_voxel(points,voxel_size,coors_range,
                     max_points=35,
                     reverse_index=True,
                     max_voxels=20000):
    """convert kitti points(N, >=3) to voxels. This version calculate
    everything in one loop. now it takes only 4.2ms(complete point cloud)
    with jit and 3.2ghz cpu.(don't calculate other features)
    Note: this function in ubuntu seems faster than windows 10.

    Args:
        points: [N, ndim] float tensor. points[:, :3] contain xyz points and
            points[:, 3:] contain other information such as reflectivity.
        voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size
        coors_range: [6] list/tuple or array, float. indicate voxel range.
            format: xyzxyz, minmax
        max_points: int. indicate maximum points contained in a voxel.
        reverse_index: boolean. indicate whether return reversed coordinates.
            if points has xyz format and reverse_index is True, output
            coordinates will be zyx format, but points in features always
            xyz format.
        max_voxels: int. indicate maximum voxels this function create.
            for second, 20000 is a good choice. you should shuffle points
            before call this function because max_voxels may drop some points.

    Returns:
        voxels: [M, max_points, ndim] float tensor. only contain points.
        coordinates: [M, 3] int32 tensor.
        num_points_per_voxel: [M] int32 tensor.
    """
    if not isinstance(voxel_size, np.ndarray):
        voxel_size = np.array(voxel_size, dtype=points.dtype)
    if not isinstance(coors_range, np.ndarray):
        coors_range = np.array(coors_range, dtype=points.dtype)
    voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size
    voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist())
    if reverse_index:
        voxelmap_shape = voxelmap_shape[::-1]
    # don't create large array in jit(nopython=True) code.
    num_points_per_voxel = np.zeros(shape=(max_voxels, ), dtype=np.int32)
    coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32)
    voxels = np.zeros(
        shape=(max_voxels, max_points, points.shape[-1]), dtype=points.dtype)
    coors = np.zeros(shape=(max_voxels, 3), dtype=np.int32)
    if reverse_index:
        voxel_num = _points_to_voxel_reverse_kernel(
            points, voxel_size, coors_range, num_points_per_voxel,
            coor_to_voxelidx, voxels, coors, max_points, max_voxels)
    else:
        voxel_num = _points_to_voxel_kernel(
            points, voxel_size, coors_range, num_points_per_voxel,
            coor_to_voxelidx, voxels, coors, max_points, max_voxels)

    coors = coors[:voxel_num]
    voxels = voxels[:voxel_num]
    num_points_per_voxel = num_points_per_voxel[:voxel_num]
    return voxels, coors, num_points_per_voxel

2. model预测

dimensions ---- 3d目标的长宽高
location ---- 3d框中心点位置
rotation_y ---- 3d框yaw角度

boxes3d转bev_boxes：

def boxes3d_to_bev_torch(boxes3d):
    """
    :param boxes3d: (N, 7) [x, y, z, h, w, l, ry]
    :return:
        boxes_bev: (N, 5) [x1, y1, x2, y2, ry]
    """
    boxes_bev = boxes3d.new(torch.Size((boxes3d.shape[0], 5)))

    cu, cv = boxes3d[:, 0], boxes3d[:, 1] # x,y
    half_l, half_w = boxes3d[:, 3] / 2, boxes3d[:, 4] / 2 # h/2, w/2
    boxes_bev[:, 0], boxes_bev[:, 1] = cu - half_l, cv - half_w
    boxes_bev[:, 2], boxes_bev[:, 3] = cu + half_l, cv + half_w
    boxes_bev[:, 4] = boxes3d[:, 6]
    return boxes_bev

• 部分模块解析
  
• 测试github提供的预训练模型【epoch_50.pth】，更改模型加载权参代码，其检测结果依然为none。？？？？

def load_params_from_file(model, filename, to_cpu=False):
    if not os.path.isfile(filename):
        raise FileNotFoundError

    print('==> Loading parameters from checkpoint %s to %s' % (filename, 'CPU' if to_cpu else 'GPU'))
    loc_type = torch.device('cpu') if to_cpu else None
    checkpoint = torch.load(filename, map_location=loc_type)
    model_state_disk = checkpoint['state_dict'] #  model_state  state_dict

    if 'version' in checkpoint:
        print('==> Checkpoint trained from version: %s' % checkpoint['version'])

    update_model_state = {}
    for key, val in model_state_disk.items():

        # changed key for pretrained model. @2022.8.2
        new_key = 'module.' + key  
    
        if new_key in model.state_dict() and model.state_dict()[new_key].shape == model_state_disk[key].shape:
            update_model_state[new_key] = val
        elif new_key in model.state_dict() and model.state_dict()[new_key].shape != val.shape:
            # with different spconv versions, we need to adapt weight shapes for spconv blocks.
            # adapt spconv weights from version 1.x to version 2.x if you used weights from spconv 1.x
            val_native = val.transpose(-1, -2)  # (k1, k2, k3, c_in, c_out) to (k1, k2, k3, c_out, c_in)
            if val_native.shape == model.state_dict()[new_key].shape:
                val = val_native.contiguous()
            else:
                assert val.shape.__len__() == 5, 'currently only spconv 3D is supported'
                val_implicit = val.permute(4, 0, 1, 2, 3)  # (k1, k2, k3, c_in, c_out) to (c_out, k1, k2, k3, c_in)
                if val_implicit.shape == model.state_dict()[new_key].shape:
                    val = val_implicit.contiguous()
            update_model_state[new_key] = val

        if key in model.state_dict() and model.state_dict()[key].shape == model_state_disk[key].shape:
            update_model_state[key] = val
            # logger.info('Update weight %s: %s' % (key, str(val.shape)))

    update_model_state['module.neck.point_fc.weight'] = model_state_disk['neck.backbone.point_fc.weight']  # [email protected]
    update_model_state['module.neck.point_cls.weight'] = model_state_disk['neck.backbone.point_cls.weight']  # [email protected]
    update_model_state['module.neck.point_reg.weight'] = model_state_disk['neck.backbone.point_reg.weight']  # [email protected]

    state_dict = model.state_dict()
    state_dict.update(update_model_state)
    model.load_state_dict(state_dict)

    for key in state_dict:
        if key not in update_model_state:
            print('Not updated weight %s: %s' % (key, str(state_dict[key].shape)))

    print('==> Done (loaded %d/%d)' % (len(update_model_state), len(model.state_dict())))

3. 评估
PR曲线定性分析，AP定量分析。从AP_bbox2d、AP_bev、AP_3d、AOS(平均方向相似度–检测目标旋转角度准确率，衡量检测结果与GT方向相似性)