从Paddle3D开始3D目标检测-测试版

经过了上一篇，我们已经体验过了Paddle3D的模型训练。这一篇来介绍一下数据集格式，方便我们自定义数据集进行训练。算法改为使用Pointpillars，通过本个项目，可以体现出P3D简单、高效的特点。通过本文你将学会1.一些传感器的基本知识2.如何组织KITTI格式的数据集3.使用Paddle3D的其他算法进行模型训练与导出

AI Studio

1095人浏览 · 2022-12-14 14:55:38

AI Studio · 2022-12-14 14:55:38 发布

0)简介

经过了上一篇，我们已经体验过了Paddle3D的模型训练。这一篇来介绍一下数据集格式，方便我们自定义数据集进行训练。算法改为使用Pointpillars，通过本个项目，可以体现出P3D简单、高效的特点。

通过本文你将学会

一些传感器的基本知识
如何组织KITTI格式的数据集
使用Paddle3D的其他算法进行模型训练与导出

0.1 KITTI数据集介绍

KITTI数据集是德国卡尔斯鲁厄理工学院主办的数据集，主要是面向自动驾驶的各种任务。因此数据中包含的不仅仅是点云，还有图像、IMU、GNSS等。比如做SLAM的里程计数据集，用于检测的object数据集等等。KITTI也是公认的用以验证算法有效性的数据集。

这是KITTI的采集车。可以直观看到车上的传感器组成。顶部是64线激光雷达。雷达前面一点，是黑白、彩色两组相机。每组相机又分为左目与右目两个。下面是GNSS与IMU，在自动驾驶场景中，这两个东西经常会放到一块。一般做融合也就是这几个东西，比如在VSLAM里最流行的是VIO就是IMU与相机，像是VINS-MONO、ORBSLAM3。本次项目的内容依然不涉及到融合，从点云来做检测可是说是基础。

0.2 KITTI数据集格式

拿上次的数据集做实例，我们看看文件结构。

ImagesSets存放了训练集与验证集的索引文件，training是我们数据集，也就是主要内容。我们要关注的是，如何组织一个3D检测数据集。要注意的是，KITTI任务中不止是目标检测，还有SLAM、追踪等等格式，这仅仅是其中的一种。

calib 标定文件
image_2 图像
label_2 物体标签
velodyne 雷达点云

我们挨着说。首先图像就是普通的彩色或者灰白图像这个没什么好说的。本次教程里的数据集属于单目相机，只有1个图片。KITTI中有多种不同的场景，城市、乡村、公路等等。场景很全的序列保证我们算法有很好的鲁棒性。

雷达点云是激光雷达采集到的数据。我们来说一说这个东西。现阶段雷达有多种型号，有2D的也有3D的。自动驾驶主要用的是机械、固态3D雷达。

在KITTI中所用的是机械式雷达。机械雷达是通过不断的在扫描，进而感知外界。每扫完1圈就构成一帧点云。这样造成的问题是，当我们的车在移动中，返回的雷达点并不是完全准确的。会存在一些畸变。点云去畸变也是标定很重要的一个工作。直接拿任乾老师的图解释。如果单纯学习做检测，你可以把雷达当个黑箱来用，不考虑雷达原理。考虑自运动也是一个比较重要的方向。

点云保存有多种格式，可以是pcd、bin。这里KITTI中使用的是bin。里面存放的就是各个雷达点的三维坐标。

讲了前面两种传感器，我们就可以来聊一聊什么是标定文件。里面是这样的。

P0: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 0.000000000000e+00 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
P1: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 -3.875744000000e+02 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
P2: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 4.485728000000e+01 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 2.163791000000e-01 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 2.745884000000e-03
P3: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 -3.395242000000e+02 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 2.199936000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 2.729905000000e-03
R0_rect: 9.999239000000e-01 9.837760000000e-03 -7.445048000000e-03 -9.869795000000e-03 9.999421000000e-01 -4.278459000000e-03 7.402527000000e-03 4.351614000000e-03 9.999631000000e-01
Tr_velo_to_cam: 7.533745000000e-03 -9.999714000000e-01 -6.166020000000e-04 -4.069766000000e-03 1.480249000000e-02 7.280733000000e-04 -9.998902000000e-01 -7.631618000000e-02 9.998621000000e-01 7.523790000000e-03 1.480755000000e-02 -2.717806000000e-01
Tr_imu_to_velo: 9.999976000000e-01 7.553071000000e-04 -2.035826000000e-03 -8.086759000000e-01 -7.854027000000e-04 9.998898000000e-01 -1.482298000000e-02 3.195559000000e-01 2.024406000000e-03 1.482454000000e-02 9.998881000000e-01 -7.997231000000e-01

里面内容显然没有点云文件多。可以看到，其实内容是几个矩阵。要想解释这些矩阵的含义，就要结合下面这张图。

这依然是数据集官方手册的一张图。这里矩阵分为两组，第一组是P0-P3。第二组是Tr。先解释第一组。

P0代表0号相机左边灰度相机
P1代表1号相机右边灰度相机
P2代表2号相机左边彩色相机
P3代表3号相机右边彩色相机
也就是我们前面提到的4个相机。里面内容的含义是什么？这就要引入相机投影矩阵这个概念。

$=\begin{pmatrix} f_{u}&0 &c_{u} &-f_{u}b \\ 0& f_{v} &c_{v} &0 \\ 0&0 &1 &0 \end{pmatrix}$

整个P0构成一个3x4的矩阵。（后面几个也一样。）

我们知道，这个3x4,左边3x3这部分，是相机的内参矩阵。

$=\begin{pmatrix} f_{u}&0 &c_{u} \\ 0& f_{v} &c_{v} \\ 0&0 &1 \end{pmatrix}$

我们拿P0来说，P0相机的内参矩阵为：

$=\begin{pmatrix} 7.188560000000e+02&0 &6.071928000000e+02 \\ 0& 7.188560000000e+02 &1.852157000000e+02 \\ 0&0 &1 \end{pmatrix}$

最右边那个1x3的向量，只有1个元素。b的含义是，相对于P0相机的偏移量。所以可以看到，P0的这一项直接为0。其他3个相机都会有一点偏移，不为0。这就是个坐标变换的矩阵。

第二组，Tr表示将velodyne坐标系转换到P0。与此同时还有IMU。雷达和相机各自采集的是自己坐标系下的数据，如果要融合，必然要考虑传感器之间的相对位置。

最后是label标签。

Car 0.50 1 -2.20 1073.46 148.45 1241.00 275.34 2.04 1.99 4.82 11.18 1.66 13.61 -1.52
Car 0.94 0 -2.28 0.00 191.06 78.97 374.00 1.48 1.56 4.12 -6.37 1.64 4.98 3.13
Car 0.58 1 -2.47 0.00 183.47 199.83 341.81 1.51 1.59 3.50 -6.66 1.65 7.73 3.12
Car 0.19 3 -2.57 0.00 159.99 335.24 300.09 1.74 1.75 4.57 -6.49 1.61 10.03 -3.14
Car 0.00 2 0.45 233.91 166.08 440.99 248.04 1.72 1.68 4.02 -6.09 1.61 16.23 0.10
Car 0.00 2 -2.85 274.55 177.54 461.58 237.25 1.41 1.42 4.27 -6.09 1.55 18.32 3.12
Car 0.00 2 -2.67 95.14 175.56 371.18 266.66 1.50 1.65 4.14 -6.73 1.58 13.08 -3.14
Car 0.00 1 0.20 451.55 174.37 549.93 205.09 1.38 1.66 4.38 -5.18 1.49 34.33 0.05
Car 0.00 1 0.99 998.35 161.75 1152.66 225.39 1.69 1.73 4.30 13.62 1.42 21.52 1.55
Car 0.00 2 -0.09 668.46 174.80 774.00 212.31 1.48 1.86 4.18 4.53 1.59 30.11 0.06
DontCare -1 -1 -10 610.60 166.69 623.14 193.81 -1 -1 -1 -1000 -1000 -1000 -10

标签表示的是在对应坐标系下，各个检测框的位置。

1)上机实验

1.1数据集准备与环境

按照上述内容准备数据集，就可以开始训练了。注意模型没有使用centerpoint，换成了pointpillars。基本流程和上次一样，在Paddle3D中仅仅通过修改config就可以更换算法。

#解压Paddle3D套件。
#git访问不稳定，这里我直接打包上传了套件
!unzip data/data181469/Paddle3D-0.5.zip -d ~

Archive:  data/data181469/Paddle3D-0.5.zip
43a712729c96b4c6a10458001b3079040cb6486e
   creating: /home/aistudio/Paddle3D-0.5/
  inflating: /home/aistudio/Paddle3D-0.5/.gitignore  
  inflating: /home/aistudio/Paddle3D-0.5/.pre-commit-config.yaml  
  inflating: /home/aistudio/Paddle3D-0.5/.style.yapf  
  inflating: /home/aistudio/Paddle3D-0.5/LICENSE  
  inflating: /home/aistudio/Paddle3D-0.5/README.md  
   creating: /home/aistudio/Paddle3D-0.5/configs/
   creating: /home/aistudio/Paddle3D-0.5/configs/_base_/
  inflating: /home/aistudio/Paddle3D-0.5/configs/_base_/kitti_mono.yml  
  inflating: /home/aistudio/Paddle3D-0.5/configs/_base_/semantickitti.yml  
   creating: /home/aistudio/Paddle3D-0.5/configs/caddn/
    linking: /home/aistudio/Paddle3D-0.5/configs/caddn/README.md  -> ../../docs/models/caddn/README.md 
  inflating: /home/aistudio/Paddle3D-0.5/configs/caddn/caddn_deeplabv3p_resnet101_os8_kitti.yml  
  inflating: /home/aistudio/Paddle3D-0.5/configs/caddn/caddn_ocrnet_hrnet_w18_kitti.yml  
   creating: /home/aistudio/Paddle3D-0.5/configs/centerpoint/
    linking: /home/aistudio/Paddle3D-0.5/configs/centerpoint/README.md  -> ../../docs/models/centerpoint/README.md 
  inflating: /home/aistudio/Paddle3D-0.5/configs/centerpoint/centerpoint_pillars_016voxel_kitti.yml  
  inflating: /home/aistudio/Paddle3D-0.5/configs/centerpoint/centerpoint_pillars_02voxel_nuscenes_10sweep.yml  
   creating: /home/aistudio/Paddle3D-0.5/configs/pointpillars/
    linking: /home/aistudio/Paddle3D-0.5/configs/pointpillars/README.md  -> ../../docs/models/pointpillars/README.md 
  inflating: /home/aistudio/Paddle3D-0.5/configs/pointpillars/pointpillars_xyres16_kitti_car.yml  
  inflating: /home/aistudio/Paddle3D-0.5/configs/pointpillars/pointpillars_xyres16_kitti_cyclist_pedestrian.yml  
   creating: /home/aistudio/Paddle3D-0.5/configs/smoke/
    linking: /home/aistudio/Paddle3D-0.5/configs/smoke/README.md  -> ../../docs/models/smoke/README.md 
  inflating: /home/aistudio/Paddle3D-0.5/configs/smoke/smoke_dla34_no_dcn_kitti.yml  
  inflating: /home/aistudio/Paddle3D-0.5/configs/smoke/smoke_hrnet18_no_dcn_kitti.yml  
   creating: /home/aistudio/Paddle3D-0.5/configs/squeezesegv3/
    linking: /home/aistudio/Paddle3D-0.5/configs/squeezesegv3/README.md  -> ../../docs/models/squeezesegv3/README.md 
  inflating: /home/aistudio/Paddle3D-0.5/configs/squeezesegv3/squeezesegv3_rangenet21_semantickitti.yml  
  inflating: /home/aistudio/Paddle3D-0.5/configs/squeezesegv3/squeezesegv3_rangenet53_semantickitti.yml  
   creating: /home/aistudio/Paddle3D-0.5/deploy/
   creating: /home/aistudio/Paddle3D-0.5/deploy/caddn/
   creating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/CMakeLists.txt  
   creating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/cmake/
   creating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/cmake/external/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/cmake/external/boost.cmake  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/compile.sh  
   creating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/grid_sample_3d.cc  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/grid_sample_3d.cu  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/grid_sample_3d.h  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/iou3d_cpu.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/iou3d_cpu.h  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/iou3d_nms.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/iou3d_nms.h  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/iou3d_nms_api.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/custom_ops/iou3d_nms_kernel.cu  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/cpp/main.cc  
   creating: /home/aistudio/Paddle3D-0.5/deploy/caddn/python/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/caddn/python/infer.py  
   creating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/
   creating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/CMakeLists.txt  
   creating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/cmake/
   creating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/cmake/external/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/cmake/external/boost.cmake  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/compile.sh  
   creating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/custom_ops/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/custom_ops/iou3d_nms_kernel.cu  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/custom_ops/postprocess.cc  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/custom_ops/postprocess.cu  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/custom_ops/voxelize_op.cc  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/custom_ops/voxelize_op.cu  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/cpp/main.cc  
   creating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/python/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/centerpoint/python/infer.py  
   creating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/
   creating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/CMakeLists.txt  
   creating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/cmake/
   creating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/cmake/external/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/cmake/external/boost.cmake  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/compile.sh  
   creating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/iou3d_cpu.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/iou3d_cpu.h  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/iou3d_nms.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/iou3d_nms.h  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/iou3d_nms_api.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/custom_ops/iou3d_nms_kernel.cu  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/cpp/main.cc  
   creating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/python/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/pointpillars/python/infer.py  
   creating: /home/aistudio/Paddle3D-0.5/deploy/smoke/
   creating: /home/aistudio/Paddle3D-0.5/deploy/smoke/cpp/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/smoke/cpp/CMakeLists.txt  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/smoke/cpp/compile.sh  
  inflating: /home/aistudio/Paddle3D-0.5/deploy/smoke/cpp/infer.cpp  
   creating: /home/aistudio/Paddle3D-0.5/deploy/smoke/python/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/smoke/python/infer.py  
   creating: /home/aistudio/Paddle3D-0.5/deploy/squeezesegv3/
   creating: /home/aistudio/Paddle3D-0.5/deploy/squeezesegv3/python/
  inflating: /home/aistudio/Paddle3D-0.5/deploy/squeezesegv3/python/infer.py  
   creating: /home/aistudio/Paddle3D-0.5/docs/
  inflating: /home/aistudio/Paddle3D-0.5/docs/api.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/apis/
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/checkpoint.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/config.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/apis/datasets/
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/datasets/kitti_mono_dataset.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/datasets/kitti_pointcloud_dataset.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/datasets/nuscenes_pointcloud_dataset.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/datasets/semantickitti_seg_dataset.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/apis/models/
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/models/smoke.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/scheduler.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/apis/trainer.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/configuration.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/datasets/
  inflating: /home/aistudio/Paddle3D-0.5/docs/datasets/custom.md  
 extracting: /home/aistudio/Paddle3D-0.5/docs/faq.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/installation.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/models/
   creating: /home/aistudio/Paddle3D-0.5/docs/models/caddn/
  inflating: /home/aistudio/Paddle3D-0.5/docs/models/caddn/README.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/models/centerpoint/
  inflating: /home/aistudio/Paddle3D-0.5/docs/models/centerpoint/README.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/models/pointpillars/
  inflating: /home/aistudio/Paddle3D-0.5/docs/models/pointpillars/README.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/models/smoke/
  inflating: /home/aistudio/Paddle3D-0.5/docs/models/smoke/README.md  
   creating: /home/aistudio/Paddle3D-0.5/docs/models/squeezesegv3/
  inflating: /home/aistudio/Paddle3D-0.5/docs/models/squeezesegv3/README.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/quickstart.md  
  inflating: /home/aistudio/Paddle3D-0.5/docs/release_note.md  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/checkpoint.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/config.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/manager.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/pipeline.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/scheduler.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/apis/trainer.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/base.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/box_utils.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/cadnn_kitti.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/calibration_kitti.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/common_utils.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/data_processor.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/image_augmentor_utils.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/kitti_object_eval_python/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/kitti_object_eval_python/eval.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/kitti_object_eval_python/evaluate.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/kitti_object_eval_python/kitti_common.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/kitti_object_eval_python/rotate_iou.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/cadnn_kitti/object3d_kitti.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/generate_gt_database.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/kitti_det.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/kitti_metric.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/kitti_mono_det.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/kitti_pointcloud_det.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/kitti/kitti_utils.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/metrics.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/nuscenes_det.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/nuscenes_manager.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/nuscenes_metric.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/nuscenes_pointcloud_det.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/nuscenes/nuscenes_utils.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/semantic_kitti/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/semantic_kitti/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/semantic_kitti/semantic_kitti.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/semantic_kitti/semantic_kitti_metric.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/datasets/semantic_kitti/semantic_kitti_seg.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/env.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/geometries/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/geometries/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/geometries/bbox.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/geometries/pointcloud.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/geometries/structure.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/dla.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/hrnet.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/resnet.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/sac.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/backbones/second_backbone.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/common/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/common/voxelize.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/bev.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/caddn.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/f2v/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/f2v/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/f2v/frustum_grid_generator.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/f2v/frustum_to_voxel.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/f2v/sampler.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/ddn_loss/
 extracting: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/ddn_loss/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/ddn_loss/balancer.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/ddn_loss/ddn_loss.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/caddn/ffe/ffe.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/centerpoint/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/centerpoint/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/centerpoint/center_head.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/centerpoint/centerpoint.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/anchors_generator.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/pointpillars.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/pointpillars_coder.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/pointpillars_head.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/pointpillars/pointpillars_loss.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/processor.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/smoke.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/smoke_coder.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/smoke_loss.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/detection/smoke/smoke_predictor.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/class_heads/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/class_heads/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/class_heads/deeplabv3_head.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/class_heads/ocrnet_head.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/anchor_head.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/target_assigner/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/target_assigner/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/target_assigner/anchor_generator.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/heads/dense_heads/target_assigner/axis_aligned_target_assigner.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/layers/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/layers/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/layers/gn.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/layers/layer_libs.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/layers/param_init.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/layers/pyramid_pool.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/losses/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/losses/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/losses/focal_loss.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/losses/reg_loss.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/losses/weight_loss.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/middle_encoders/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/middle_encoders/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/middle_encoders/pillar_scatter.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/necks/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/necks/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/necks/second_fpn.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/optimizers/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/optimizers/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/optimizers/lr_schedulers.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/optimizers/momentum_schedulers.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/optimizers/optimizers.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/optimizers/utils.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/segmentation/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/segmentation/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/segmentation/squeezesegv3/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/segmentation/squeezesegv3/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/segmentation/squeezesegv3/squeezesegv3.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/segmentation/squeezesegv3/squeezesegv3_loss.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxel_encoders/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxel_encoders/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxel_encoders/pillar_encoder.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxel_encoders/voxel_encoder.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxelizers/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxelizers/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/models/voxelizers/voxelize.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/centerpoint_postprocess/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/centerpoint_postprocess/iou3d_nms_kernel.cu  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/centerpoint_postprocess/postprocess.cc  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/centerpoint_postprocess/postprocess.cu  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/grid_sample_3d/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/grid_sample_3d/grid_sample_3d.cc  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/grid_sample_3d/grid_sample_3d.cu  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/grid_sample_3d/grid_sample_3d.h  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/iou3d_cpu.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/iou3d_cpu.h  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/iou3d_nms.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/iou3d_nms.h  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/iou3d_nms_api.cpp  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/iou3d_nms/iou3d_nms_kernel.cu  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/setup.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/voxel/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/voxel/voxelize_op.cc  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/ops/voxel/voxelize_op.cu  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/sample.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/__init__.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/LICENSE  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/README.md  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/eval.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/evaluate.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/kitti_common.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/kitti_object_eval_python/rotate_iou.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/.gitignore  
 extracting: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/.pep8  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/Dockerfile  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/LICENSE  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/README.md  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/SSCDataset.py  
 extracting: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/camera.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/eval_np.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/filelist2files.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/glow.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/laserscan.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/laserscanvis.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/np_ioueval.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/check_uniforms.vert  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/draw_pose.geom  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/draw_voxels.frag  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/draw_voxels.vert  
 extracting: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/empty.frag  
 extracting: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/empty.vert  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/shaders/passthrough.frag  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/auxiliary/torch_ioueval.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/config/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/config/semantic-kitti-all.yaml  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/config/semantic-kitti-mos.yaml  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/config/semantic-kitti.yaml  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/content.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/count.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/docker.sh  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/evaluate_completion.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/evaluate_mos.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/evaluate_panoptic.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/evaluate_semantics.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/evaluate_semantics_by_distance.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/generate_sequential.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/remap_semantic_labels.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/requirements.txt  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/validate_submission.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/visualize.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/visualize_mos.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/thirdparty/semantic_kitti_api/visualize_voxels.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/anchor_generator.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/base.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/functional.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/normalize.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/reader.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/sampling.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/target_generator.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/transforms/transform.py  
   creating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/__init__.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/box.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/box_coder.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/checkpoint.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/common.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/depth.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/download.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/grid.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/logger.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/timer.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/transform.py  
  inflating: /home/aistudio/Paddle3D-0.5/paddle3d/utils/xarfile.py  
  inflating: /home/aistudio/Paddle3D-0.5/requirements.txt  
  inflating: /home/aistudio/Paddle3D-0.5/setup.py  
   creating: /home/aistudio/Paddle3D-0.5/tests/
   creating: /home/aistudio/Paddle3D-0.5/tests/apis/
  inflating: /home/aistudio/Paddle3D-0.5/tests/apis/test_scheduler.py  
   creating: /home/aistudio/Paddle3D-0.5/tests/datasets/
  inflating: /home/aistudio/Paddle3D-0.5/tests/datasets/test_kitti_dataset.py  
  inflating: /home/aistudio/Paddle3D-0.5/tests/datasets/test_nuscenes_dataset.py  
   creating: /home/aistudio/Paddle3D-0.5/tools/
  inflating: /home/aistudio/Paddle3D-0.5/tools/create_det_gt_database.py  
  inflating: /home/aistudio/Paddle3D-0.5/tools/evaluate.py  
  inflating: /home/aistudio/Paddle3D-0.5/tools/export.py  
  inflating: /home/aistudio/Paddle3D-0.5/tools/train.py  
finishing deferred symbolic links:
  /home/aistudio/Paddle3D-0.5/configs/caddn/README.md -> ../../docs/models/caddn/README.md
  /home/aistudio/Paddle3D-0.5/configs/centerpoint/README.md -> ../../docs/models/centerpoint/README.md
  /home/aistudio/Paddle3D-0.5/configs/pointpillars/README.md -> ../../docs/models/pointpillars/README.md
  /home/aistudio/Paddle3D-0.5/configs/smoke/README.md -> ../../docs/models/smoke/README.md
  /home/aistudio/Paddle3D-0.5/configs/squeezesegv3/README.md -> ../../docs/models/squeezesegv3/README.md

!tar xvzf data/data165771/kitti300frame.tar.gz

kitti300frame/ImageSets/train.txt

cd Paddle3D-0.5/

/home/aistudio/Paddle3D-0.5

#更换数据集位置
!mkdir datasets
!mv /home/aistudio/kitti300frame datasets/KITTI

!pwd

/home/aistudio/Paddle3D-0.5

#安装依赖
!python -m pip install -r requirements.txt

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: colorlog in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (4.1.0)
Requirement already satisfied: easydict in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 2)) (1.9)
Requirement already satisfied: filelock in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (3.0.12)
Requirement already satisfied: numba in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (0.48.0)
Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (1.19.5)
Collecting nuscenes-devkit
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c6/53/460bf754677b3b247fb99a447e3575490dbc5f42ec94d528bc0137176f6a/nuscenes_devkit-1.1.9-py3-none-any.whl (312 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m312.6/312.6 kB[0m [31m79.4 kB/s[0m eta [36m0:00:00[0m00:01[0m00:01[0m
[?25hRequirement already satisfied: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 7)) (4.6.0.66)
Requirement already satisfied: pandas in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (1.1.5)
Collecting paddledet
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a1/0a/0baf7328fcb50450cdc1fcf03459c2d75361e473a02b3ac2eea272827bef/paddledet-2.5.0-py3-none-any.whl (679 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m679.4/679.4 kB[0m [31m1.1 MB/s[0m eta [36m0:00:00[0m00:01[0m00:01[0m
[?25hCollecting paddleseg
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c1/30/c13ae53b180443edc4c88921b6a3f7999e7a3b831ca3b6990e966667148d/paddleseg-2.7.0-py3-none-any.whl (349 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m349.4/349.4 kB[0m [31m3.5 MB/s[0m eta [36m0:00:00[0ma [36m0:00:01[0m
[?25hCollecting pyquaternion
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/49/b3/d8482e8cacc8ea15a356efea13d22ce1c5914a9ee36622ba250523240bf2/pyquaternion-0.9.9-py3-none-any.whl (14 kB)
Requirement already satisfied: pyyaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 12)) (5.1.2)
Requirement already satisfied: pillow<=8.3.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 13)) (8.2.0)
Requirement already satisfied: rarfile in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 14)) (3.1)
Collecting scikit-image
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2d/ba/63ce953b7d593bd493e80be158f2d9f82936582380aee0998315510633aa/scikit_image-0.19.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (13.5 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m13.5/13.5 MB[0m [31m1.2 MB/s[0m eta [36m0:00:00[0m00:01[0m00:01[0m
[?25hRequirement already satisfied: visualdl in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 16)) (2.4.0)
Requirement already satisfied: sklearn in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 17)) (0.0)
Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from numba->-r requirements.txt (line 4)) (0.31.0)
Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from numba->-r requirements.txt (line 4)) (56.2.0)
Collecting jupyter
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/83/df/0f5dd132200728a86190397e1ea87cd76244e42d39ec5e88efd25b2abd7e/jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)
Collecting pycocotools>=2.0.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ef/c6/90220be3b39fbc4cbd203775ca47dd8dc97fae06fbd2b500637395621b7c/pycocotools-2.0.6.tar.gz (24 kB)
  Installing build dependencies ... [?25ldone
[?25h  Getting requirements to build wheel ... [?25ldone
[?25h  Preparing metadata (pyproject.toml) ... [?25ldone
[?25hRequirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (4.64.1)
Requirement already satisfied: cachetools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (4.0.0)
Requirement already satisfied: scikit-learn in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (0.24.2)
Requirement already satisfied: fire in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (0.4.0)
Requirement already satisfied: Shapely in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (1.8.5.post1)
Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (2.2.3)
Collecting descartes
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e5/b6/1ed2eb03989ae574584664985367ba70cd9cf8b32ee8cad0e8aaeac819f3/descartes-1.1.0-py3-none-any.whl (5.8 kB)
Requirement already satisfied: scipy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nuscenes-devkit->-r requirements.txt (line 6)) (1.6.3)
Requirement already satisfied: pytz>=2017.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pandas->-r requirements.txt (line 8)) (2019.3)
Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pandas->-r requirements.txt (line 8)) (2.8.2)
Collecting filterpy
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f6/1d/ac8914360460fafa1990890259b7fa5ef7ba4cd59014e782e4ab3ab144d8/filterpy-1.4.5.zip (177 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m178.0/178.0 kB[0m [31m1.4 MB/s[0m eta [36m0:00:00[0ma [36m0:00:01[0m
[?25h  Preparing metadata (setup.py) ... [?25ldone
[?25hRequirement already satisfied: terminaltables in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddledet->-r requirements.txt (line 9)) (3.1.10)
Collecting lap
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz (1.5 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.5/1.5 MB[0m [31m971.0 kB/s[0m eta [36m0:00:00[0m00:01[0m:01[0m0m
[?25h  Preparing metadata (setup.py) ... [?25ldone
[?25hCollecting typeguard
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9a/bb/d43e5c75054e53efce310e79d63df0ac3f25e34c926be5dffb7d283fb2a8/typeguard-2.13.3-py3-none-any.whl (17 kB)
Collecting pyclipper
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/21/b9/f8bd7bb8b04906ac2f93518ae22040c99db9dfc9faf2a29d444c6469b6a3/pyclipper-1.3.0.post4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (604 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m604.2/604.2 kB[0m [31m442.4 kB/s[0m eta [36m0:00:00[0m00:01[0m00:01[0m
[?25hCollecting motmetrics
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/45/41/b019fe934eb811b9aba9b335f852305b804b9c66f098d7e35c2bdb09d1c8/motmetrics-1.2.5-py3-none-any.whl (161 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m161.1/161.1 kB[0m [31m992.6 kB/s[0m eta [36m0:00:00[0ma [36m0:00:01[0m
[?25hRequirement already satisfied: Cython in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddledet->-r requirements.txt (line 9)) (0.29)
Requirement already satisfied: prettytable in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddleseg->-r requirements.txt (line 10)) (0.7.2)
Requirement already satisfied: networkx>=2.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image->-r requirements.txt (line 15)) (2.4)
Collecting tifffile>=2019.7.26
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d8/38/85ae5ed77598ca90558c17a2f79ddaba33173b31cf8d8f545d34d9134f0d/tifffile-2021.11.2-py3-none-any.whl (178 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m178.9/178.9 kB[0m [31m1.0 MB/s[0m eta [36m0:00:00[0ma [36m0:00:01[0m
[?25hRequirement already satisfied: packaging>=20.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image->-r requirements.txt (line 15)) (21.3)
Collecting PyWavelets>=1.1.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ae/56/4441877073d8a5266dbf7b04c7f3dc66f1149c8efb9323e0ef987a9bb1ce/PyWavelets-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.4 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m6.4/6.4 MB[0m [31m820.6 kB/s[0m eta [36m0:00:00[0m00:01[0m00:01[0m
[?25hRequirement already satisfied: imageio>=2.4.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image->-r requirements.txt (line 15)) (2.6.1)
Requirement already satisfied: Flask-Babel>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl->-r requirements.txt (line 16)) (1.0.0)
Requirement already satisfied: flask>=1.1.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl->-r requirements.txt (line 16)) (1.1.1)
Requirement already satisfied: six>=1.14.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl->-r requirements.txt (line 16)) (1.16.0)
Requirement already satisfied: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl->-r requirements.txt (line 16)) (2.24.0)
Requirement already satisfied: protobuf>=3.11.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl->-r requirements.txt (line 16)) (3.20.0)
Requirement already satisfied: bce-python-sdk in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl->-r requirements.txt (line 16)) (0.8.53)
Requirement already satisfied: itsdangerous>=0.24 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (1.1.0)
Requirement already satisfied: Jinja2>=2.10.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (3.0.0)
Requirement already satisfied: Werkzeug>=0.15 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (0.16.0)
Requirement already satisfied: click>=5.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (8.0.4)
Requirement already satisfied: Babel>=2.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Flask-Babel>=1.0.0->visualdl->-r requirements.txt (line 16)) (2.8.0)
Requirement already satisfied: decorator>=4.3.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from networkx>=2.2->scikit-image->-r requirements.txt (line 15)) (4.4.2)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from packaging>=20.0->scikit-image->-r requirements.txt (line 15)) (3.0.9)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->nuscenes-devkit->-r requirements.txt (line 6)) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->nuscenes-devkit->-r requirements.txt (line 6)) (0.10.0)
Requirement already satisfied: future>=0.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl->-r requirements.txt (line 16)) (0.18.0)
Requirement already satisfied: pycryptodome>=3.8.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl->-r requirements.txt (line 16)) (3.9.9)
Requirement already satisfied: termcolor in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from fire->nuscenes-devkit->-r requirements.txt (line 6)) (1.1.0)
Collecting qtconsole
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cc/00/4133199dc738e7f497385af86e619f5c29592aaa4c1731fbbc3ec7bb7080/qtconsole-5.4.0-py3-none-any.whl (121 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m121.0/121.0 kB[0m [31m89.5 kB/s[0m eta [36m0:00:00[0ma [36m0:00:01[0m
[?25hRequirement already satisfied: ipykernel in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (6.9.1)
Requirement already satisfied: notebook in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (5.7.0)
Collecting jupyter-console
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8b/0c/f9382ca7b7499c8594a5158817a72c95b4c09a6c6f2de10553bfe8905924/jupyter_console-6.4.4-py3-none-any.whl (22 kB)
Requirement already satisfied: ipywidgets in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (7.6.5)
Requirement already satisfied: nbconvert in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (6.4.4)
Collecting xmltodict>=0.12.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/94/db/fd0326e331726f07ff7f40675cd86aa804bfd2e5016c727fa761c934990e/xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 16)) (2.8)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 16)) (2019.9.11)
Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 16)) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 16)) (1.25.6)
Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->nuscenes-devkit->-r requirements.txt (line 6)) (0.14.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->nuscenes-devkit->-r requirements.txt (line 6)) (2.1.0)
Requirement already satisfied: importlib-metadata in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from click>=5.1->flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (4.2.0)
Requirement already satisfied: MarkupSafe>=2.0.0rc2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.10.1->flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (2.0.1)
Requirement already satisfied: ipython>=7.23.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (7.34.0)
Requirement already satisfied: debugpy<2.0,>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (1.6.0)
Requirement already satisfied: tornado<7.0,>=4.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (6.2)
Requirement already satisfied: traitlets<6.0,>=5.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (5.4.0)
Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.1.6)
Requirement already satisfied: nest-asyncio in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (1.5.5)
Requirement already satisfied: jupyter-client<8.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (7.3.5)
Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (5.5.0)
Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (3.0.3)
Requirement already satisfied: widgetsnbextension~=3.5.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (3.5.2)
Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.2.0)
Requirement already satisfied: pygments in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jupyter-console->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (2.13.0)
Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jupyter-console->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (2.0.10)
Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (1.5.0)
Requirement already satisfied: jupyter-core in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (4.11.1)
Requirement already satisfied: bleach in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (5.0.1)
Requirement already satisfied: jupyterlab-pygments in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.2.2)
Requirement already satisfied: defusedxml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.7.1)
Requirement already satisfied: beautifulsoup4 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (4.11.1)
Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.5.13)
Requirement already satisfied: entrypoints>=0.2.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.4)
Requirement already satisfied: testpath in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.4.2)
Requirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.8.4)
Requirement already satisfied: pyzmq>=17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from notebook->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (23.2.1)
Requirement already satisfied: Send2Trash in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from notebook->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (1.8.0)
Requirement already satisfied: terminado>=0.8.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from notebook->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.15.0)
Requirement already satisfied: prometheus_client in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from notebook->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.14.1)
Collecting qtpy>=2.0.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ca/56/3dfbcf8a6808d2b3566b75759c48a281bcdc2b9547760e5d044e6ec7e33b/QtPy-2.3.0-py3-none-any.whl (83 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m83.6/83.6 kB[0m [31m99.1 kB/s[0m eta [36m0:00:00[0m0:01[0m00:01[0m
[?25hRequirement already satisfied: pickleshare in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipython>=7.23.1->ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.7.5)
Requirement already satisfied: jedi>=0.16 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipython>=7.23.1->ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.17.2)
Requirement already satisfied: pexpect>4.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipython>=7.23.1->ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (4.7.0)
Requirement already satisfied: backcall in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ipython>=7.23.1->ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.1.0)
Requirement already satisfied: fastjsonschema in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (2.16.1)
Requirement already satisfied: jsonschema>=2.6 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (4.16.0)
Requirement already satisfied: wcwidth in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->jupyter-console->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.1.7)
Requirement already satisfied: ptyprocess in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from terminado>=0.8.1->notebook->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.7.0)
Requirement already satisfied: soupsieve>1.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from beautifulsoup4->nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (2.3.2.post1)
Requirement already satisfied: webencodings in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bleach->nbconvert->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.5.1)
Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata->click>=5.1->flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (4.3.0)
Requirement already satisfied: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata->click>=5.1->flask>=1.1.1->visualdl->-r requirements.txt (line 16)) (3.8.1)
Requirement already satisfied: parso<0.8.0,>=0.7.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jedi>=0.16->ipython>=7.23.1->ipykernel->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.7.1)
Requirement already satisfied: attrs>=17.4.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jsonschema>=2.6->nbformat>=4.2.0->ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (22.1.0)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jsonschema>=2.6->nbformat>=4.2.0->ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (0.18.1)
Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jsonschema>=2.6->nbformat>=4.2.0->ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (1.3.10)
Requirement already satisfied: importlib-resources>=1.4.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from jsonschema>=2.6->nbformat>=4.2.0->ipywidgets->jupyter->nuscenes-devkit->-r requirements.txt (line 6)) (5.9.0)
Building wheels for collected packages: pycocotools, filterpy, lap
  Building wheel for pycocotools (pyproject.toml) ... [?25ldone
[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.6-cp37-cp37m-linux_x86_64.whl size=275098 sha256=249f1dbdbedcfa81ffa31e209e258623409d19969bd2c0e5592f9bdb19aa8822
  Stored in directory: /home/aistudio/.cache/pip/wheels/f8/94/70/046149e666bd5812b7de6b87a28dcef238f7162f4108e0b3d8
  Building wheel for filterpy (setup.py) ... [?25ldone
[?25h  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110451 sha256=f61580fd168bb3a38fc2443751458cebba699a77672f12b3b7a0d38998f49a7a
  Stored in directory: /home/aistudio/.cache/pip/wheels/60/79/e4/a6df5e482863f893b95d3e95627d744cb2e57b26b44ec20b22
  Building wheel for lap (setup.py) ... [?25ldone
[?25h  Created wheel for lap: filename=lap-0.4.0-cp37-cp37m-linux_x86_64.whl size=1593877 sha256=168259ed8d93d76612f05ae12ca6aa36856c6361d28f04a847301a0a8d2bcddf
  Stored in directory: /home/aistudio/.cache/pip/wheels/5c/d0/d2/e331d17a999666b1e2eb99743cfa1742629f9d26c55c657001
Successfully built pycocotools filterpy lap
Installing collected packages: pyclipper, lap, xmltodict, typeguard, tifffile, PyWavelets, pyquaternion, scikit-image, qtpy, pycocotools, motmetrics, filterpy, descartes, qtconsole, jupyter-console, paddleseg, paddledet, jupyter, nuscenes-devkit
Successfully installed PyWavelets-1.3.0 descartes-1.1.0 filterpy-1.4.5 jupyter-1.0.0 jupyter-console-6.4.4 lap-0.4.0 motmetrics-1.2.5 nuscenes-devkit-1.1.9 paddledet-2.5.0 paddleseg-2.7.0 pyclipper-1.3.0.post4 pycocotools-2.0.6 pyquaternion-0.9.9 qtconsole-5.4.0 qtpy-2.3.0 scikit-image-0.19.3 tifffile-2021.11.2 typeguard-2.13.3 xmltodict-0.13.0

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip available: [0m[31;49m22.1.2[0m[39;49m -> [0m[32;49m22.3.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

!python setup.py install

[0m

#会在KITTI文件夹下生成/kitti_train_gt_database文件，也会应用于train过程中。
!python tools/create_det_gt_database.py --dataset_name kitti --dataset_root ./datasets/KITTI --save_dir ./datasets/KITTI

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
[36m2022-12-06 16:52:29,018[0m -     INFO - Begin to generate a database for the KITTI dataset.[0m
[36m2022-12-06 16:52:37,559[0m -     INFO - [0m###############################################] 100.00%[0m
[36m2022-12-06 16:52:37,575[0m -     INFO - The database generation has been done.[0m
[0m

1.2套件Paddle3D一键训练

#开始训练

!python tools/train.py --config  configs/pointpillars/pointpillars_xyres16_kitti_car.yml \
--save_dir ./output_kitti \
--num_workers 3 --save_interval 5

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
[36m2022-12-06 16:53:12,421[0m -     INFO - Load 994 Car database infos[0m
[36m2022-12-06 16:53:12,421[0m -     INFO - After filtering min_num_points_in_box:[0m
[36m2022-12-06 16:53:12,422[0m -     INFO - Load 938 Car database infos[0m
[36m2022-12-06 16:53:12,423[0m -     INFO - After filtering ignored difficulty:[0m
[36m2022-12-06 16:53:12,423[0m -     INFO - Load 725 Car database infos[0m
[36m2022-12-06 16:53:12,524[0m -     INFO - 
------------Environment Information-------------
platform:
    Linux-4.15.0-140-generic-x86_64-with-debian-stretch-sid
    gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
    Python - 3.7.4 (default, Aug 13 2019, 20:35:49)  [GCC 7.3.0]

Science Toolkits:
    cv2 - 4.6.0
    numpy - 1.19.5
    numba - 0.48.0
    pandas - 1.1.5
    pillow - 8.2.0
    skimage - 0.19.3

PaddlePaddle:
    paddle(gpu) - 2.3.2
    paddle3d - 0.5.0
    paddleseg - 2.7.0
    FLAGS_cudnn_deterministic - Not set.
    FLAGS_cudnn_exhaustive_search - Not set.

CUDA:
    cudnn - 8200
    nvcc - Build cuda_11.2.r11.2/compiler.29618528_0

GPUs:
------------------------------------------------[0m
[36m2022-12-06 16:53:12,537[0m -     INFO - 
---------------Config Information---------------
batch_size: 2
iters: 1200
lr_scheduler:
  gamma: 0.8
  learning_rate: 0.0002
  step_size: 27840
  type: StepDecay
model:
  anchor_area_threshold: 1
  anchor_configs:
  - anchor_offsets:
    - 0.16
    - -39.52
    - -1.78
    anchor_strides:
    - 0.32
    - 0.32
    - 0.0
    matched_threshold: 0.6
    rotations:
    - 0
    - 1.57
    sizes:
    - 1.6
    - 3.9
    - 1.56
    unmatched_threshold: 0.45
  backbone:
    downsample_strides:
    - 2
    - 2
    - 2
    in_channels: 64
    layer_nums:
    - 3
    - 5
    - 5
    out_channels:
    - 64
    - 128
    - 256
    type: SecondBackbone
  head:
    box_code_size: 7
    encode_background_as_zeros: true
    feature_channels: 384
    nms_iou_threshold: 0.5
    nms_post_max_size: 300
    nms_pre_max_size: 1000
    nms_score_threshold: 0.05
    num_anchor_per_loc: 2
    num_classes: 1
    prediction_center_limit_range:
    - 0
    - -39.68
    - -5
    - 69.12
    - 39.68
    - 5
    type: SSDHead
    use_direction_classifier: true
  loss:
    bg_cls_weight: 1.0
    box_code_size: 7
    classification_loss:
      alpha: 0.25
      gamma: 2.0
      type: SigmoidFocalClassificationLoss
    classification_loss_weight: 1.0
    direction_loss:
      type: WeightedSoftmaxClassificationLoss
    direction_loss_weight: 0.2
    encode_background_as_zeros: true
    encode_rot_error_by_sin: true
    fg_cls_weight: 1.0
    num_classes: 1
    regression_loss:
      code_weights:
      - 1.0
      - 1.0
      - 1.0
      - 1.0
      - 1.0
      - 1.0
      - 1.0
      sigma: 3.0
      type: WeightedSmoothL1RegressionLoss
    regression_loss_weight: 2.0
    type: PointPillarsLoss
    use_direction_classifier: true
  middle_encoder:
    in_channels: 64
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: PointPillarsScatter
    voxel_size:
    - 0.16
    - 0.16
    - 4
  neck:
    in_channels:
    - 64
    - 128
    - 256
    out_channels:
    - 128
    - 128
    - 128
    type: SecondFPN
    upsample_strides:
    - 1
    - 2
    - 4
    use_conv_for_no_stride: false
  pillar_encoder:
    feat_channels:
    - 64
    in_channels: 4
    legacy: false
    max_num_points_in_voxel: 32
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: PillarFeatureNet
    voxel_size:
    - 0.16
    - 0.16
    - 4
    with_distance: false
  type: PointPillars
  voxelizer:
    max_num_points_in_voxel: 32
    max_num_voxels:
    - 16000
    - 40000
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: HardVoxelizer
    voxel_size:
    - 0.16
    - 0.16
    - 4
optimizer:
  grad_clip:
    clip_norm: 10.0
    type: ClipGradByGlobalNorm
  type: Adam
  weight_decay: 0.0001
train_dataset:
  class_names:
  - Car
  dataset_root: datasets/KITTI
  mode: train
  transforms:
  - dim: 4
    type: LoadPointCloud
    use_dim: 4
  - type: RemoveCameraInvisiblePointsKITTI
  - class_names:
    - Car
    database_anno_path: datasets/KITTI/kitti_train_gt_database/anno_info_train.pkl
    database_root: datasets/KITTI/
    ignored_difficulty:
    - -1
    max_num_samples_per_class:
      Car: 15
    min_num_points_in_box_per_class:
      Car: 5
    type: SamplingDatabase
  - max_num_attempts: 100
    rotation_range:
    - -0.15707963267
    - 0.15707963267
    translation_std:
    - 0.25
    - 0.25
    - 0.25
    type: RandomObjectPerturb
  - type: RandomVerticalFlip
  - max_rot: 0.78539816
    min_rot: -0.78539816
    type: GlobalRotate
  - max_scale: 1.05
    min_scale: 0.95
    type: GlobalScale
  - translation_std:
    - 0.2
    - 0.2
    - 0.2
    type: GlobalTranslate
  - point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: FilterBBoxOutsideRange
  - type: ShufflePoint
  - max_points_in_voxel: 32
    max_voxel_num: 16000
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: HardVoxelize
    voxel_size:
    - 0.16
    - 0.16
    - 4
  - anchor_area_threshold: 1
    anchor_configs:
    - anchor_offsets:
      - 0.16
      - -39.52
      - -1.78
      anchor_strides:
      - 0.32
      - 0.32
      - 0.0
      matched_threshold: 0.6
      rotations:
      - 0
      - 1.57
      sizes:
      - 1.6
      - 3.9
      - 1.56
      unmatched_threshold: 0.45
    output_stride_factor: 2
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: GenerateAnchors
    voxel_size:
    - 0.16
    - 0.16
    - 4
  - rpn_batch_size: 512
    type: Gt2PointPillarsTarget
  type: KittiPCDataset
val_dataset:
  class_names:
  - Car
  dataset_root: datasets/KITTI
  mode: val
  transforms:
  - dim: 4
    type: LoadPointCloud
    use_dim: 4
  - type: RemoveCameraInvisiblePointsKITTI
  - max_points_in_voxel: 32
    max_voxel_num: 40000
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: HardVoxelize
    voxel_size:
    - 0.16
    - 0.16
    - 4
  - anchor_area_threshold: 1
    anchor_configs:
    - anchor_offsets:
      - 0.16
      - -39.52
      - -1.78
      anchor_strides:
      - 0.32
      - 0.32
      - 0.0
      matched_threshold: 0.6
      rotations:
      - 0
      - 1.57
      sizes:
      - 1.6
      - 3.9
      - 1.56
      unmatched_threshold: 0.45
    output_stride_factor: 2
    point_cloud_range:
    - 0
    - -39.68
    - -3
    - 69.12
    - 39.68
    - 1
    type: GenerateAnchors
    voxel_size:
    - 0.16
    - 0.16
    - 4
  type: KittiPCDataset
------------------------------------------------[0m
W1206 16:53:12.540575  1487 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2
W1206 16:53:12.540611  1487 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.
  "When training, we now always track global mean and variance.")
[36m2022-12-06 16:53:29,975[0m -     INFO - Push model to checkpoint ./output_kitti/iter_5[0m
[36m2022-12-06 16:53:30,576[0m -     INFO - [TRAIN] epoch=1/10, iter=10/1200, loss=43.086683, lr=0.000200 | ETA 00:02:35[0m
[36m2022-12-06 16:53:30,726[0m -     INFO - Push model to checkpoint ./output_kitti/iter_10[0m
[36m2022-12-06 16:53:31,456[0m -     INFO - Push model to checkpoint ./output_kitti/iter_15[0m
[36m2022-12-06 16:53:31,998[0m -     INFO - [TRAIN] epoch=1/10, iter=20/1200, loss=10.813367, lr=0.000200 | ETA 00:02:23[0m
[36m2022-12-06 16:53:32,167[0m -     INFO - Push model to checkpoint ./output_kitti/iter_20[0m
[36m2022-12-06 16:53:32,888[0m -     INFO - Push model to checkpoint ./output_kitti/iter_25[0m
[36m2022-12-06 16:53:33,463[0m -     INFO - [TRAIN] epoch=1/10, iter=30/1200, loss=7.056719, lr=0.000200 | ETA 00:02:52[0m
[36m2022-12-06 16:53:33,475[0m -     INFO - Pop model from ./output_kitti/iter_5[0m
[36m2022-12-06 16:53:33,603[0m -     INFO - Push model to checkpoint ./output_kitti/iter_30[0m
[36m2022-12-06 16:53:34,186[0m -     INFO - Pop model from ./output_kitti/iter_10[0m
[36m2022-12-06 16:53:34,338[0m -     INFO - Push model to checkpoint ./output_kitti/iter_35[0m
[36m2022-12-06 16:53:34,898[0m -     INFO - [TRAIN] epoch=1/10, iter=40/1200, loss=5.753927, lr=0.000200 | ETA 00:02:19[0m
[36m2022-12-06 16:53:34,910[0m -     INFO - Pop model from ./output_kitti/iter_15[0m
[36m2022-12-06 16:53:35,085[0m -     INFO - Push model to checkpoint ./output_kitti/iter_40[0m
[36m2022-12-06 16:53:35,681[0m -     INFO - Pop model from ./output_kitti/iter_20[0m
[36m2022-12-06 16:53:35,814[0m -     INFO - Push model to checkpoint ./output_kitti/iter_45[0m
[36m2022-12-06 16:53:36,492[0m -     INFO - [TRAIN] epoch=1/10, iter=50/1200, loss=4.593835, lr=0.000200 | ETA 00:03:14[0m
[36m2022-12-06 16:53:36,507[0m -     INFO - Pop model from ./output_kitti/iter_25[0m
[36m2022-12-06 16:53:36,699[0m -     INFO - Push model to checkpoint ./output_kitti/iter_50[0m
[36m2022-12-06 16:53:37,303[0m -     INFO - Pop model from ./output_kitti/iter_30[0m
[36m2022-12-06 16:53:37,471[0m -     INFO - Push model to checkpoint ./output_kitti/iter_55[0m
[36m2022-12-06 16:53:38,155[0m -     INFO - [TRAIN] epoch=1/10, iter=60/1200, loss=3.576567, lr=0.000200 | ETA 00:03:03[0m
[36m2022-12-06 16:53:38,167[0m -     INFO - Pop model from ./output_kitti/iter_35[0m
[36m2022-12-06 16:53:38,283[0m -     INFO - Push model to checkpoint ./output_kitti/iter_60[0m
[36m2022-12-06 16:53:38,876[0m -     INFO - Pop model from ./output_kitti/iter_40[0m
[36m2022-12-06 16:53:39,051[0m -     INFO - Push model to checkpoint ./output_kitti/iter_65[0m
[36m2022-12-06 16:53:39,659[0m -     INFO - [TRAIN] epoch=1/10, iter=70/1200, loss=3.254735, lr=0.000200 | ETA 00:02:43[0m
[36m2022-12-06 16:53:39,671[0m -     INFO - Pop model from ./output_kitti/iter_45[0m
[36m2022-12-06 16:53:39,801[0m -     INFO - Push model to checkpoint ./output_kitti/iter_70[0m
[36m2022-12-06 16:53:40,387[0m -     INFO - Pop model from ./output_kitti/iter_50[0m
[36m2022-12-06 16:53:40,554[0m -     INFO - Push model to checkpoint ./output_kitti/iter_75[0m
[36m2022-12-06 16:53:41,152[0m -     INFO - [TRAIN] epoch=1/10, iter=80/1200, loss=2.806425, lr=0.000200 | ETA 00:02:41[0m
[36m2022-12-06 16:53:41,163[0m -     INFO - Pop model from ./output_kitti/iter_55[0m
[36m2022-12-06 16:53:41,277[0m -     INFO - Push model to checkpoint ./output_kitti/iter_80[0m
[36m2022-12-06 16:53:41,858[0m -     INFO - Pop model from ./output_kitti/iter_60[0m
[36m2022-12-06 16:53:41,975[0m -     INFO - Push model to checkpoint ./output_kitti/iter_85[0m
[36m2022-12-06 16:53:42,489[0m -     INFO - [TRAIN] epoch=1/10, iter=90/1200, loss=2.616933, lr=0.000200 | ETA 00:02:09[0m
[36m2022-12-06 16:53:42,500[0m -     INFO - Pop model from ./output_kitti/iter_65[0m
[36m2022-12-06 16:53:42,662[0m -     INFO - Push model to checkpoint ./output_kitti/iter_90[0m
[36m2022-12-06 16:53:43,198[0m -     INFO - Pop model from ./output_kitti/iter_70[0m
[36m2022-12-06 16:53:43,372[0m -     INFO - Push model to checkpoint ./output_kitti/iter_95[0m
[36m2022-12-06 16:53:43,956[0m -     INFO - [TRAIN] epoch=1/10, iter=100/1200, loss=2.618091, lr=0.000200 | ETA 00:02:37[0m
[36m2022-12-06 16:53:43,968[0m -     INFO - Pop model from ./output_kitti/iter_75[0m
[36m2022-12-06 16:53:44,115[0m -     INFO - Push model to checkpoint ./output_kitti/iter_100[0m
[36m2022-12-06 16:53:44,695[0m -     INFO - Pop model from ./output_kitti/iter_80[0m
[36m2022-12-06 16:53:44,878[0m -     INFO - Push model to checkpoint ./output_kitti/iter_105[0m
[36m2022-12-06 16:53:45,477[0m -     INFO - [TRAIN] epoch=1/10, iter=110/1200, loss=2.588927, lr=0.000200 | ETA 00:02:16[0m
[36m2022-12-06 16:53:45,489[0m -     INFO - Pop model from ./output_kitti/iter_85[0m
[36m2022-12-06 16:53:45,663[0m -     INFO - Push model to checkpoint ./output_kitti/iter_110[0m
[36m2022-12-06 16:53:46,216[0m -     INFO - Pop model from ./output_kitti/iter_90[0m
[36m2022-12-06 16:53:46,335[0m -     INFO - Push model to checkpoint ./output_kitti/iter_115[0m
[36m2022-12-06 16:53:46,878[0m -     INFO - [TRAIN] epoch=1/10, iter=120/1200, loss=2.439421, lr=0.000200 | ETA 00:02:07[0m
[36m2022-12-06 16:53:46,889[0m -     INFO - Pop model from ./output_kitti/iter_95[0m
[36m2022-12-06 16:53:47,184[0m -     INFO - Push model to checkpoint ./output_kitti/iter_120[0m
[36m2022-12-06 16:53:47,522[0m -     INFO - Pop model from ./output_kitti/iter_100[0m
[36m2022-12-06 16:53:47,646[0m -     INFO - Push model to checkpoint ./output_kitti/iter_125[0m
[36m2022-12-06 16:54:02,053[0m -     INFO - [TRAIN] epoch=2/10, iter=130/1200, loss=2.356736, lr=0.000200 | ETA 00:11:01[0m
[36m2022-12-06 16:54:02,068[0m -     INFO - Pop model from ./output_kitti/iter_105[0m
[36m2022-12-06 16:54:02,248[0m -     INFO - Push model to checkpoint ./output_kitti/iter_130[0m
[36m2022-12-06 16:54:02,886[0m -     INFO - Pop model from ./output_kitti/iter_110[0m
[36m2022-12-06 16:54:03,032[0m -     INFO - Push model to checkpoint ./output_kitti/iter_135[0m
[36m2022-12-06 16:54:03,677[0m -     INFO - [TRAIN] epoch=2/10, iter=140/1200, loss=2.477083, lr=0.000200 | ETA 00:02:31[0m
[36m2022-12-06 16:54:03,927[0m -     INFO - Pop model from ./output_kitti/iter_115[0m
[36m2022-12-06 16:54:04,067[0m -     INFO - Push model to checkpoint ./output_kitti/iter_140[0m
[36m2022-12-06 16:54:04,859[0m -     INFO - Pop model from ./output_kitti/iter_120[0m
[36m2022-12-06 16:54:04,998[0m -     INFO - Push model to checkpoint ./output_kitti/iter_145[0m
[36m2022-12-06 16:54:05,582[0m -     INFO - [TRAIN] epoch=2/10, iter=150/1200, loss=2.139329, lr=0.000200 | ETA 00:02:10[0m
[36m2022-12-06 16:54:05,596[0m -     INFO - Pop model from ./output_kitti/iter_125[0m
[36m2022-12-06 16:54:05,763[0m -     INFO - Push model to checkpoint ./output_kitti/iter_150[0m
[36m2022-12-06 16:54:06,400[0m -     INFO - Pop model from ./output_kitti/iter_130[0m
[36m2022-12-06 16:54:06,551[0m -     INFO - Push model to checkpoint ./output_kitti/iter_155[0m
[36m2022-12-06 16:54:07,190[0m -     INFO - [TRAIN] epoch=2/10, iter=160/1200, loss=2.087846, lr=0.000200 | ETA 00:02:28[0m
[36m2022-12-06 16:54:07,201[0m -     INFO - Pop model from ./output_kitti/iter_135[0m
[36m2022-12-06 16:54:07,363[0m -     INFO - Push model to checkpoint ./output_kitti/iter_160[0m
[36m2022-12-06 16:54:07,990[0m -     INFO - Pop model from ./output_kitti/iter_140[0m
[36m2022-12-06 16:54:08,144[0m -     INFO - Push model to checkpoint ./output_kitti/iter_165[0m
[36m2022-12-06 16:54:08,765[0m -     INFO - [TRAIN] epoch=2/10, iter=170/1200, loss=2.059088, lr=0.000200 | ETA 00:02:34[0m
[36m2022-12-06 16:54:08,782[0m -     INFO - Pop model from ./output_kitti/iter_145[0m
[36m2022-12-06 16:54:08,931[0m -     INFO - Push model to checkpoint ./output_kitti/iter_170[0m
[36m2022-12-06 16:54:09,568[0m -     INFO - Pop model from ./output_kitti/iter_150[0m
[36m2022-12-06 16:54:09,707[0m -     INFO - Push model to checkpoint ./output_kitti/iter_175[0m
[36m2022-12-06 16:54:10,367[0m -     INFO - [TRAIN] epoch=2/10, iter=180/1200, loss=2.114183, lr=0.000200 | ETA 00:02:42[0m
[36m2022-12-06 16:54:10,380[0m -     INFO - Pop model from ./output_kitti/iter_155[0m
[36m2022-12-06 16:54:10,517[0m -     INFO - Push model to checkpoint ./output_kitti/iter_180[0m
[36m2022-12-06 16:54:11,102[0m -     INFO - Pop model from ./output_kitti/iter_160[0m
[36m2022-12-06 16:54:11,273[0m -     INFO - Push model to checkpoint ./output_kitti/iter_185[0m
[36m2022-12-06 16:54:11,858[0m -     INFO - [TRAIN] epoch=2/10, iter=190/1200, loss=1.853890, lr=0.000200 | ETA 00:02:26[0m
[36m2022-12-06 16:54:11,874[0m -     INFO - Pop model from ./output_kitti/iter_165[0m
[36m2022-12-06 16:54:12,013[0m -     INFO - Push model to checkpoint ./output_kitti/iter_190[0m
[36m2022-12-06 16:54:12,658[0m -     INFO - Pop model from ./output_kitti/iter_170[0m
[36m2022-12-06 16:54:12,787[0m -     INFO - Push model to checkpoint ./output_kitti/iter_195[0m
[36m2022-12-06 16:54:13,383[0m -     INFO - [TRAIN] epoch=2/10, iter=200/1200, loss=1.987085, lr=0.000200 | ETA 00:02:30[0m
[36m2022-12-06 16:54:13,454[0m -     INFO - Pop model from ./output_kitti/iter_175[0m
[36m2022-12-06 16:54:13,580[0m -     INFO - Push model to checkpoint ./output_kitti/iter_200[0m
[36m2022-12-06 16:54:14,190[0m -     INFO - Pop model from ./output_kitti/iter_180[0m
[36m2022-12-06 16:54:14,345[0m -     INFO - Push model to checkpoint ./output_kitti/iter_205[0m
[36m2022-12-06 16:54:14,970[0m -     INFO - [TRAIN] epoch=2/10, iter=210/1200, loss=2.702900, lr=0.000200 | ETA 00:02:15[0m
[36m2022-12-06 16:54:14,983[0m -     INFO - Pop model from ./output_kitti/iter_185[0m
[36m2022-12-06 16:54:15,145[0m -     INFO - Push model to checkpoint ./output_kitti/iter_210[0m
[36m2022-12-06 16:54:15,790[0m -     INFO - Pop model from ./output_kitti/iter_190[0m
[36m2022-12-06 16:54:15,945[0m -     INFO - Push model to checkpoint ./output_kitti/iter_215[0m
[36m2022-12-06 16:54:16,552[0m -     INFO - [TRAIN] epoch=2/10, iter=220/1200, loss=1.926959, lr=0.000200 | ETA 00:02:21[0m
[36m2022-12-06 16:54:16,569[0m -     INFO - Pop model from ./output_kitti/iter_195[0m
[36m2022-12-06 16:54:16,707[0m -     INFO - Push model to checkpoint ./output_kitti/iter_220[0m
[36m2022-12-06 16:54:17,300[0m -     INFO - Pop model from ./output_kitti/iter_200[0m
[36m2022-12-06 16:54:17,471[0m -     INFO - Push model to checkpoint ./output_kitti/iter_225[0m
[36m2022-12-06 16:54:18,068[0m -     INFO - [TRAIN] epoch=2/10, iter=230/1200, loss=1.922943, lr=0.000200 | ETA 00:02:27[0m
[36m2022-12-06 16:54:18,083[0m -     INFO - Pop model from ./output_kitti/iter_205[0m
[36m2022-12-06 16:54:18,232[0m -     INFO - Push model to checkpoint ./output_kitti/iter_230[0m
[36m2022-12-06 16:54:18,855[0m -     INFO - Pop model from ./output_kitti/iter_210[0m
[36m2022-12-06 16:54:18,978[0m -     INFO - Push model to checkpoint ./output_kitti/iter_235[0m
[36m2022-12-06 16:54:19,554[0m -     INFO - [TRAIN] epoch=2/10, iter=240/1200, loss=1.903518, lr=0.000200 | ETA 00:02:18[0m
[36m2022-12-06 16:54:19,568[0m -     INFO - Pop model from ./output_kitti/iter_215[0m
[36m2022-12-06 16:54:19,712[0m -     INFO - Push model to checkpoint ./output_kitti/iter_240[0m
[36m2022-12-06 16:54:20,272[0m -     INFO - Pop model from ./output_kitti/iter_220[0m
[36m2022-12-06 16:54:20,391[0m -     INFO - Push model to checkpoint ./output_kitti/iter_245[0m
[36m2022-12-06 16:54:20,719[0m -     INFO - [TRAIN] epoch=2/10, iter=250/1200, loss=1.890805, lr=0.000200 | ETA 00:01:06[0m
[36m2022-12-06 16:54:20,731[0m -     INFO - Pop model from ./output_kitti/iter_225[0m
[36m2022-12-06 16:54:20,850[0m -     INFO - Push model to checkpoint ./output_kitti/iter_250[0m
[36m2022-12-06 16:54:35,170[0m -     INFO - Pop model from ./output_kitti/iter_230[0m
[36m2022-12-06 16:54:35,317[0m -     INFO - Push model to checkpoint ./output_kitti/iter_255[0m
[36m2022-12-06 16:54:35,978[0m -     INFO - [TRAIN] epoch=3/10, iter=260/1200, loss=1.852238, lr=0.000200 | ETA 00:02:39[0m
[36m2022-12-06 16:54:35,988[0m -     INFO - Pop model from ./output_kitti/iter_235[0m
[36m2022-12-06 16:54:36,147[0m -     INFO - Push model to checkpoint ./output_kitti/iter_260[0m
[36m2022-12-06 16:54:36,864[0m -     INFO - Pop model from ./output_kitti/iter_240[0m
[36m2022-12-06 16:54:36,986[0m -     INFO - Push model to checkpoint ./output_kitti/iter_265[0m
[36m2022-12-06 16:54:37,657[0m -     INFO - [TRAIN] epoch=3/10, iter=270/1200, loss=1.840273, lr=0.000200 | ETA 00:02:39[0m
[36m2022-12-06 16:54:37,672[0m -     INFO - Pop model from ./output_kitti/iter_245[0m
[36m2022-12-06 16:54:37,914[0m -     INFO - Push model to checkpoint ./output_kitti/iter_270[0m
[36m2022-12-06 16:54:38,562[0m -     INFO - Pop model from ./output_kitti/iter_250[0m
[36m2022-12-06 16:54:38,686[0m -     INFO - Push model to checkpoint ./output_kitti/iter_275[0m
[36m2022-12-06 16:54:39,277[0m -     INFO - [TRAIN] epoch=3/10, iter=280/1200, loss=1.819519, lr=0.000200 | ETA 00:02:00[0m
[36m2022-12-06 16:54:39,288[0m -     INFO - Pop model from ./output_kitti/iter_255[0m
[36m2022-12-06 16:54:39,450[0m -     INFO - Push model to checkpoint ./output_kitti/iter_280[0m
[36m2022-12-06 16:54:40,068[0m -     INFO - Pop model from ./output_kitti/iter_260[0m
[36m2022-12-06 16:54:40,206[0m -     INFO - Push model to checkpoint ./output_kitti/iter_285[0m
[36m2022-12-06 16:54:40,781[0m -     INFO - [TRAIN] epoch=3/10, iter=290/1200, loss=1.819388, lr=0.000200 | ETA 00:01:51[0m
[36m2022-12-06 16:54:40,793[0m -     INFO - Pop model from ./output_kitti/iter_265[0m
[36m2022-12-06 16:54:40,949[0m -     INFO - Push model to checkpoint ./output_kitti/iter_290[0m
[36m2022-12-06 16:54:41,578[0m -     INFO - Pop model from ./output_kitti/iter_270[0m
[36m2022-12-06 16:54:41,709[0m -     INFO - Push model to checkpoint ./output_kitti/iter_295[0m
[36m2022-12-06 16:54:42,293[0m -     INFO - [TRAIN] epoch=3/10, iter=300/1200, loss=1.746375, lr=0.000200 | ETA 00:01:55[0m
[36m2022-12-06 16:54:42,305[0m -     INFO - Pop model from ./output_kitti/iter_275[0m
[36m2022-12-06 16:54:42,474[0m -     INFO - Push model to checkpoint ./output_kitti/iter_300[0m
[36m2022-12-06 16:54:43,067[0m -     INFO - Pop model from ./output_kitti/iter_280[0m
[36m2022-12-06 16:54:43,191[0m -     INFO - Push model to checkpoint ./output_kitti/iter_305[0m
[36m2022-12-06 16:54:43,787[0m -     INFO - [TRAIN] epoch=3/10, iter=310/1200, loss=1.721080, lr=0.000200 | ETA 00:02:00[0m
[36m2022-12-06 16:54:43,799[0m -     INFO - Pop model from ./output_kitti/iter_285[0m
[36m2022-12-06 16:54:43,973[0m -     INFO - Push model to checkpoint ./output_kitti/iter_310[0m
[36m2022-12-06 16:54:44,578[0m -     INFO - Pop model from ./output_kitti/iter_290[0m
[36m2022-12-06 16:54:44,715[0m -     INFO - Push model to checkpoint ./output_kitti/iter_315[0m
[36m2022-12-06 16:54:45,284[0m -     INFO - [TRAIN] epoch=3/10, iter=320/1200, loss=1.665512, lr=0.000200 | ETA 00:01:46[0m
[36m2022-12-06 16:54:45,296[0m -     INFO - Pop model from ./output_kitti/iter_295[0m
[36m2022-12-06 16:54:45,460[0m -     INFO - Push model to checkpoint ./output_kitti/iter_320[0m
[36m2022-12-06 16:54:46,071[0m -     INFO - Pop model from ./output_kitti/iter_300[0m
[36m2022-12-06 16:54:46,206[0m -     INFO - Push model to checkpoint ./output_kitti/iter_325[0m
[36m2022-12-06 16:54:46,790[0m -     INFO - [TRAIN] epoch=3/10, iter=330/1200, loss=1.734788, lr=0.000200 | ETA 00:01:56[0m
[36m2022-12-06 16:54:46,802[0m -     INFO - Pop model from ./output_kitti/iter_305[0m
[36m2022-12-06 16:54:46,975[0m -     INFO - Push model to checkpoint ./output_kitti/iter_330[0m
[36m2022-12-06 16:54:47,567[0m -     INFO - Pop model from ./output_kitti/iter_310[0m
[36m2022-12-06 16:54:47,701[0m -     INFO - Push model to checkpoint ./output_kitti/iter_335[0m
[36m2022-12-06 16:54:48,307[0m -     INFO - [TRAIN] epoch=3/10, iter=340/1200, loss=1.703260, lr=0.000200 | ETA 00:02:07[0m
[36m2022-12-06 16:54:48,371[0m -     INFO - Pop model from ./output_kitti/iter_315[0m
[36m2022-12-06 16:54:48,501[0m -     INFO - Push model to checkpoint ./output_kitti/iter_340[0m
[36m2022-12-06 16:54:49,089[0m -     INFO - Pop model from ./output_kitti/iter_320[0m
[36m2022-12-06 16:54:49,241[0m -     INFO - Push model to checkpoint ./output_kitti/iter_345[0m
[36m2022-12-06 16:54:49,868[0m -     INFO - [TRAIN] epoch=3/10, iter=350/1200, loss=1.667587, lr=0.000200 | ETA 00:02:06[0m
[36m2022-12-06 16:54:49,880[0m -     INFO - Pop model from ./output_kitti/iter_325[0m
[36m2022-12-06 16:54:50,023[0m -     INFO - Push model to checkpoint ./output_kitti/iter_350[0m
[36m2022-12-06 16:54:50,664[0m -     INFO - Pop model from ./output_kitti/iter_330[0m
[36m2022-12-06 16:54:50,806[0m -     INFO - Push model to checkpoint ./output_kitti/iter_355[0m
[36m2022-12-06 16:54:51,379[0m -     INFO - [TRAIN] epoch=3/10, iter=360/1200, loss=1.701627, lr=0.000200 | ETA 00:01:51[0m
[36m2022-12-06 16:54:51,390[0m -     INFO - Pop model from ./output_kitti/iter_335[0m
[36m2022-12-06 16:54:51,557[0m -     INFO - Push model to checkpoint ./output_kitti/iter_360[0m
[36m2022-12-06 16:54:52,182[0m -     INFO - Pop model from ./output_kitti/iter_340[0m
[36m2022-12-06 16:54:52,329[0m -     INFO - Push model to checkpoint ./output_kitti/iter_365[0m
[36m2022-12-06 16:54:52,916[0m -     INFO - [TRAIN] epoch=3/10, iter=370/1200, loss=1.712149, lr=0.000200 | ETA 00:01:48[0m
[36m2022-12-06 16:54:52,927[0m -     INFO - Pop model from ./output_kitti/iter_345[0m
[36m2022-12-06 16:54:53,051[0m -     INFO - Push model to checkpoint ./output_kitti/iter_370[0m
[36m2022-12-06 16:54:53,399[0m -     INFO - Pop model from ./output_kitti/iter_350[0m
[36m2022-12-06 16:54:53,517[0m -     INFO - Push model to checkpoint ./output_kitti/iter_375[0m
[36m2022-12-06 16:55:07,754[0m -     INFO - [TRAIN] epoch=4/10, iter=380/1200, loss=1.703503, lr=0.000200 | ETA 00:07:53[0m
[36m2022-12-06 16:55:07,765[0m -     INFO - Pop model from ./output_kitti/iter_355[0m
[36m2022-12-06 16:55:07,890[0m -     INFO - Push model to checkpoint ./output_kitti/iter_380[0m
[36m2022-12-06 16:55:08,505[0m -     INFO - Pop model from ./output_kitti/iter_360[0m
[36m2022-12-06 16:55:08,692[0m -     INFO - Push model to checkpoint ./output_kitti/iter_385[0m
[36m2022-12-06 16:55:09,354[0m -     INFO - [TRAIN] epoch=4/10, iter=390/1200, loss=1.639393, lr=0.000200 | ETA 00:02:09[0m
[36m2022-12-06 16:55:09,369[0m -     INFO - Pop model from ./output_kitti/iter_365[0m
[36m2022-12-06 16:55:09,499[0m -     INFO - Push model to checkpoint ./output_kitti/iter_390[0m
[36m2022-12-06 16:55:10,110[0m -     INFO - Pop model from ./output_kitti/iter_370[0m
[36m2022-12-06 16:55:10,290[0m -     INFO - Push model to checkpoint ./output_kitti/iter_395[0m
[36m2022-12-06 16:55:10,865[0m -     INFO - [TRAIN] epoch=4/10, iter=400/1200, loss=1.782376, lr=0.000200 | ETA 00:01:54[0m
[36m2022-12-06 16:55:10,876[0m -     INFO - Pop model from ./output_kitti/iter_375[0m
[36m2022-12-06 16:55:11,037[0m -     INFO - Push model to checkpoint ./output_kitti/iter_400[0m
[36m2022-12-06 16:55:11,689[0m -     INFO - Pop model from ./output_kitti/iter_380[0m
[36m2022-12-06 16:55:11,843[0m -     INFO - Push model to checkpoint ./output_kitti/iter_405[0m
[36m2022-12-06 16:55:12,586[0m -     INFO - [TRAIN] epoch=4/10, iter=410/1200, loss=1.649922, lr=0.000200 | ETA 00:02:05[0m
[36m2022-12-06 16:55:12,597[0m -     INFO - Pop model from ./output_kitti/iter_385[0m
[36m2022-12-06 16:55:12,777[0m -     INFO - Push model to checkpoint ./output_kitti/iter_410[0m
[36m2022-12-06 16:55:13,480[0m -     INFO - Pop model from ./output_kitti/iter_390[0m
[36m2022-12-06 16:55:13,634[0m -     INFO - Push model to checkpoint ./output_kitti/iter_415[0m
[36m2022-12-06 16:55:14,252[0m -     INFO - [TRAIN] epoch=4/10, iter=420/1200, loss=1.697619, lr=0.000200 | ETA 00:01:54[0m
[36m2022-12-06 16:55:14,269[0m -     INFO - Pop model from ./output_kitti/iter_395[0m
[36m2022-12-06 16:55:14,400[0m -     INFO - Push model to checkpoint ./output_kitti/iter_420[0m
[36m2022-12-06 16:55:14,989[0m -     INFO - Pop model from ./output_kitti/iter_400[0m
[36m2022-12-06 16:55:15,146[0m -     INFO - Push model to checkpoint ./output_kitti/iter_425[0m
[36m2022-12-06 16:55:15,769[0m -     INFO - [TRAIN] epoch=4/10, iter=430/1200, loss=1.713560, lr=0.000200 | ETA 00:01:56[0m
[36m2022-12-06 16:55:15,782[0m -     INFO - Pop model from ./output_kitti/iter_405[0m
[36m2022-12-06 16:55:15,918[0m -     INFO - Push model to checkpoint ./output_kitti/iter_430[0m
[36m2022-12-06 16:55:16,577[0m -     INFO - Pop model from ./output_kitti/iter_410[0m
[36m2022-12-06 16:55:16,713[0m -     INFO - Push model to checkpoint ./output_kitti/iter_435[0m
[36m2022-12-06 16:55:17,292[0m -     INFO - [TRAIN] epoch=4/10, iter=440/1200, loss=1.606159, lr=0.000200 | ETA 00:01:35[0m
[36m2022-12-06 16:55:17,303[0m -     INFO - Pop model from ./output_kitti/iter_415[0m
[36m2022-12-06 16:55:17,456[0m -     INFO - Push model to checkpoint ./output_kitti/iter_440[0m
[36m2022-12-06 16:55:18,068[0m -     INFO - Pop model from ./output_kitti/iter_420[0m
[36m2022-12-06 16:55:18,205[0m -     INFO - Push model to checkpoint ./output_kitti/iter_445[0m
[36m2022-12-06 16:55:18,969[0m -     INFO - [TRAIN] epoch=4/10, iter=450/1200, loss=1.666688, lr=0.000200 | ETA 00:01:40[0m
[36m2022-12-06 16:55:18,981[0m -     INFO - Pop model from ./output_kitti/iter_425[0m
[36m2022-12-06 16:55:19,154[0m -     INFO - Push model to checkpoint ./output_kitti/iter_450[0m
[36m2022-12-06 16:55:19,976[0m -     INFO - Pop model from ./output_kitti/iter_430[0m
[36m2022-12-06 16:55:20,130[0m -     INFO - Push model to checkpoint ./output_kitti/iter_455[0m
[36m2022-12-06 16:55:20,857[0m -     INFO - [TRAIN] epoch=4/10, iter=460/1200, loss=1.691741, lr=0.000200 | ETA 00:01:59[0m
[36m2022-12-06 16:55:20,868[0m -     INFO - Pop model from ./output_kitti/iter_435[0m
[36m2022-12-06 16:55:21,027[0m -     INFO - Push model to checkpoint ./output_kitti/iter_460[0m
[36m2022-12-06 16:55:21,688[0m -     INFO - Pop model from ./output_kitti/iter_440[0m
[36m2022-12-06 16:55:21,834[0m -     INFO - Push model to checkpoint ./output_kitti/iter_465[0m
[36m2022-12-06 16:55:22,473[0m -     INFO - [TRAIN] epoch=4/10, iter=470/1200, loss=1.685890, lr=0.000200 | ETA 00:01:50[0m
[36m2022-12-06 16:55:22,483[0m -     INFO - Pop model from ./output_kitti/iter_445[0m
[36m2022-12-06 16:55:22,626[0m -     INFO - Push model to checkpoint ./output_kitti/iter_470[0m
[36m2022-12-06 16:55:23,203[0m -     INFO - Pop model from ./output_kitti/iter_450[0m
[36m2022-12-06 16:55:23,351[0m -     INFO - Push model to checkpoint ./output_kitti/iter_475[0m
[36m2022-12-06 16:55:23,952[0m -     INFO - [TRAIN] epoch=4/10, iter=480/1200, loss=1.774031, lr=0.000200 | ETA 00:01:43[0m
[36m2022-12-06 16:55:23,965[0m -     INFO - Pop model from ./output_kitti/iter_455[0m
[36m2022-12-06 16:55:24,111[0m -     INFO - Push model to checkpoint ./output_kitti/iter_480[0m
[36m2022-12-06 16:55:24,686[0m -     INFO - Pop model from ./output_kitti/iter_460[0m
[36m2022-12-06 16:55:24,859[0m -     INFO - Push model to checkpoint ./output_kitti/iter_485[0m
[36m2022-12-06 16:55:25,455[0m -     INFO - [TRAIN] epoch=4/10, iter=490/1200, loss=1.613240, lr=0.000200 | ETA 00:01:43[0m
[36m2022-12-06 16:55:25,467[0m -     INFO - Pop model from ./output_kitti/iter_465[0m
[36m2022-12-06 16:55:25,600[0m -     INFO - Push model to checkpoint ./output_kitti/iter_490[0m
[36m2022-12-06 16:55:26,123[0m -     INFO - Pop model from ./output_kitti/iter_470[0m
[36m2022-12-06 16:55:26,239[0m -     INFO - Push model to checkpoint ./output_kitti/iter_495[0m
[36m2022-12-06 16:55:26,567[0m -     INFO - [TRAIN] epoch=4/10, iter=500/1200, loss=1.614279, lr=0.000200 | ETA 00:00:49[0m
[36m2022-12-06 16:55:26,579[0m -     INFO - Pop model from ./output_kitti/iter_475[0m
[36m2022-12-06 16:55:26,695[0m -     INFO - Push model to checkpoint ./output_kitti/iter_500[0m
[36m2022-12-06 16:55:41,090[0m -     INFO - Pop model from ./output_kitti/iter_480[0m
[36m2022-12-06 16:55:41,211[0m -     INFO - Push model to checkpoint ./output_kitti/iter_505[0m
[36m2022-12-06 16:55:41,790[0m -     INFO - [TRAIN] epoch=5/10, iter=510/1200, loss=1.692122, lr=0.000200 | ETA 00:01:37[0m
[36m2022-12-06 16:55:41,801[0m -     INFO - Pop model from ./output_kitti/iter_485[0m
[36m2022-12-06 16:55:41,957[0m -     INFO - Push model to checkpoint ./output_kitti/iter_510[0m
[36m2022-12-06 16:55:42,574[0m -     INFO - Pop model from ./output_kitti/iter_490[0m
[36m2022-12-06 16:55:42,708[0m -     INFO - Push model to checkpoint ./output_kitti/iter_515[0m
[36m2022-12-06 16:55:43,320[0m -     INFO - [TRAIN] epoch=5/10, iter=520/1200, loss=1.664066, lr=0.000200 | ETA 00:01:36[0m
[36m2022-12-06 16:55:43,372[0m -     INFO - Pop model from ./output_kitti/iter_495[0m
[36m2022-12-06 16:55:43,488[0m -     INFO - Push model to checkpoint ./output_kitti/iter_520[0m
[36m2022-12-06 16:55:44,063[0m -     INFO - Pop model from ./output_kitti/iter_500[0m
[36m2022-12-06 16:55:44,194[0m -     INFO - Push model to checkpoint ./output_kitti/iter_525[0m
[36m2022-12-06 16:55:44,754[0m -     INFO - [TRAIN] epoch=5/10, iter=530/1200, loss=1.489274, lr=0.000200 | ETA 00:01:33[0m
[36m2022-12-06 16:55:44,766[0m -     INFO - Pop model from ./output_kitti/iter_505[0m
[36m2022-12-06 16:55:44,905[0m -     INFO - Push model to checkpoint ./output_kitti/iter_530[0m
[36m2022-12-06 16:55:45,491[0m -     INFO - Pop model from ./output_kitti/iter_510[0m
[36m2022-12-06 16:55:45,669[0m -     INFO - Push model to checkpoint ./output_kitti/iter_535[0m
[36m2022-12-06 16:55:46,282[0m -     INFO - [TRAIN] epoch=5/10, iter=540/1200, loss=1.745534, lr=0.000200 | ETA 00:01:32[0m
[36m2022-12-06 16:55:46,293[0m -     INFO - Pop model from ./output_kitti/iter_515[0m
[36m2022-12-06 16:55:46,444[0m -     INFO - Push model to checkpoint ./output_kitti/iter_540[0m
[36m2022-12-06 16:55:47,080[0m -     INFO - Pop model from ./output_kitti/iter_520[0m
[36m2022-12-06 16:55:47,219[0m -     INFO - Push model to checkpoint ./output_kitti/iter_545[0m
[36m2022-12-06 16:55:47,794[0m -     INFO - [TRAIN] epoch=5/10, iter=550/1200, loss=1.511290, lr=0.000200 | ETA 00:01:21[0m
[36m2022-12-06 16:55:47,805[0m -     INFO - Pop model from ./output_kitti/iter_525[0m
[36m2022-12-06 16:55:47,972[0m -     INFO - Push model to checkpoint ./output_kitti/iter_550[0m
[36m2022-12-06 16:55:48,568[0m -     INFO - Pop model from ./output_kitti/iter_530[0m
[36m2022-12-06 16:55:48,714[0m -     INFO - Push model to checkpoint ./output_kitti/iter_555[0m
[36m2022-12-06 16:55:49,364[0m -     INFO - [TRAIN] epoch=5/10, iter=560/1200, loss=1.689239, lr=0.000200 | ETA 00:01:40[0m
[36m2022-12-06 16:55:49,376[0m -     INFO - Pop model from ./output_kitti/iter_535[0m
[36m2022-12-06 16:55:49,507[0m -     INFO - Push model to checkpoint ./output_kitti/iter_560[0m
[36m2022-12-06 16:55:50,217[0m -     INFO - Pop model from ./output_kitti/iter_540[0m
[36m2022-12-06 16:55:50,360[0m -     INFO - Push model to checkpoint ./output_kitti/iter_565[0m
[36m2022-12-06 16:55:50,977[0m -     INFO - [TRAIN] epoch=5/10, iter=570/1200, loss=1.588625, lr=0.000200 | ETA 00:01:24[0m
[36m2022-12-06 16:55:51,193[0m -     INFO - Pop model from ./output_kitti/iter_545[0m
[36m2022-12-06 16:55:51,314[0m -     INFO - Push model to checkpoint ./output_kitti/iter_570[0m
[36m2022-12-06 16:55:51,983[0m -     INFO - Pop model from ./output_kitti/iter_550[0m
[36m2022-12-06 16:55:52,131[0m -     INFO - Push model to checkpoint ./output_kitti/iter_575[0m
[36m2022-12-06 16:55:52,776[0m -     INFO - [TRAIN] epoch=5/10, iter=580/1200, loss=1.643620, lr=0.000200 | ETA 00:01:33[0m
[36m2022-12-06 16:55:52,789[0m -     INFO - Pop model from ./output_kitti/iter_555[0m
[36m2022-12-06 16:55:52,929[0m -     INFO - Push model to checkpoint ./output_kitti/iter_580[0m
[36m2022-12-06 16:55:53,583[0m -     INFO - Pop model from ./output_kitti/iter_560[0m
[36m2022-12-06 16:55:53,736[0m -     INFO - Push model to checkpoint ./output_kitti/iter_585[0m
[36m2022-12-06 16:55:54,377[0m -     INFO - [TRAIN] epoch=5/10, iter=590/1200, loss=1.533861, lr=0.000200 | ETA 00:01:35[0m
[36m2022-12-06 16:55:54,389[0m -     INFO - Pop model from ./output_kitti/iter_565[0m
[36m2022-12-06 16:55:54,563[0m -     INFO - Push model to checkpoint ./output_kitti/iter_590[0m
[36m2022-12-06 16:55:55,269[0m -     INFO - Pop model from ./output_kitti/iter_570[0m
[36m2022-12-06 16:55:55,415[0m -     INFO - Push model to checkpoint ./output_kitti/iter_595[0m
[36m2022-12-06 16:55:56,104[0m -     INFO - [TRAIN] epoch=5/10, iter=600/1200, loss=1.605315, lr=0.000200 | ETA 00:01:32[0m
[36m2022-12-06 16:55:56,116[0m -     INFO - Pop model from ./output_kitti/iter_575[0m
[36m2022-12-06 16:55:56,277[0m -     INFO - Push model to checkpoint ./output_kitti/iter_600[0m
[36m2022-12-06 16:55:56,984[0m -     INFO - Pop model from ./output_kitti/iter_580[0m
[36m2022-12-06 16:55:57,134[0m -     INFO - Push model to checkpoint ./output_kitti/iter_605[0m
[36m2022-12-06 16:55:57,766[0m -     INFO - [TRAIN] epoch=5/10, iter=610/1200, loss=1.460397, lr=0.000200 | ETA 00:01:28[0m
[36m2022-12-06 16:55:57,779[0m -     INFO - Pop model from ./output_kitti/iter_585[0m
[36m2022-12-06 16:55:57,921[0m -     INFO - Push model to checkpoint ./output_kitti/iter_610[0m
[36m2022-12-06 16:55:58,506[0m -     INFO - Pop model from ./output_kitti/iter_590[0m
[36m2022-12-06 16:55:58,671[0m -     INFO - Push model to checkpoint ./output_kitti/iter_615[0m
[36m2022-12-06 16:55:59,198[0m -     INFO - [TRAIN] epoch=5/10, iter=620/1200, loss=1.565339, lr=0.000200 | ETA 00:01:08[0m
[36m2022-12-06 16:55:59,209[0m -     INFO - Pop model from ./output_kitti/iter_595[0m
[36m2022-12-06 16:55:59,324[0m -     INFO - Push model to checkpoint ./output_kitti/iter_620[0m
[36m2022-12-06 16:55:59,664[0m -     INFO - Pop model from ./output_kitti/iter_600[0m
[36m2022-12-06 16:55:59,780[0m -     INFO - Push model to checkpoint ./output_kitti/iter_625[0m
[36m2022-12-06 16:56:14,089[0m -     INFO - [TRAIN] epoch=6/10, iter=630/1200, loss=1.522946, lr=0.000200 | ETA 00:05:39[0m
[36m2022-12-06 16:56:14,165[0m -     INFO - Pop model from ./output_kitti/iter_605[0m
[36m2022-12-06 16:56:14,324[0m -     INFO - Push model to checkpoint ./output_kitti/iter_630[0m
[36m2022-12-06 16:56:14,968[0m -     INFO - Pop model from ./output_kitti/iter_610[0m
[36m2022-12-06 16:56:15,090[0m -     INFO - Push model to checkpoint ./output_kitti/iter_635[0m
[36m2022-12-06 16:56:15,681[0m -     INFO - [TRAIN] epoch=6/10, iter=640/1200, loss=1.538381, lr=0.000200 | ETA 00:01:16[0m
[36m2022-12-06 16:56:15,693[0m -     INFO - Pop model from ./output_kitti/iter_615[0m
[36m2022-12-06 16:56:15,842[0m -     INFO - Push model to checkpoint ./output_kitti/iter_640[0m
[36m2022-12-06 16:56:16,484[0m -     INFO - Pop model from ./output_kitti/iter_620[0m
[36m2022-12-06 16:56:16,625[0m -     INFO - Push model to checkpoint ./output_kitti/iter_645[0m
[36m2022-12-06 16:56:17,294[0m -     INFO - [TRAIN] epoch=6/10, iter=650/1200, loss=1.531008, lr=0.000200 | ETA 00:01:32[0m
[36m2022-12-06 16:56:17,307[0m -     INFO - Pop model from ./output_kitti/iter_625[0m
[36m2022-12-06 16:56:17,421[0m -     INFO - Push model to checkpoint ./output_kitti/iter_650[0m
[36m2022-12-06 16:56:18,108[0m -     INFO - Pop model from ./output_kitti/iter_630[0m
[36m2022-12-06 16:56:18,285[0m -     INFO - Push model to checkpoint ./output_kitti/iter_655[0m
[36m2022-12-06 16:56:18,894[0m -     INFO - [TRAIN] epoch=6/10, iter=660/1200, loss=1.540601, lr=0.000200 | ETA 00:01:15[0m
[36m2022-12-06 16:56:18,955[0m -     INFO - Pop model from ./output_kitti/iter_635[0m
[36m2022-12-06 16:56:19,075[0m -     INFO - Push model to checkpoint ./output_kitti/iter_660[0m
[36m2022-12-06 16:56:19,686[0m -     INFO - Pop model from ./output_kitti/iter_640[0m
[36m2022-12-06 16:56:19,835[0m -     INFO - Push model to checkpoint ./output_kitti/iter_665[0m
[36m2022-12-06 16:56:20,452[0m -     INFO - [TRAIN] epoch=6/10, iter=670/1200, loss=1.466818, lr=0.000200 | ETA 00:01:19[0m
[36m2022-12-06 16:56:20,468[0m -     INFO - Pop model from ./output_kitti/iter_645[0m
[36m2022-12-06 16:56:20,590[0m -     INFO - Push model to checkpoint ./output_kitti/iter_670[0m
[36m2022-12-06 16:56:21,186[0m -     INFO - Pop model from ./output_kitti/iter_650[0m
[36m2022-12-06 16:56:21,327[0m -     INFO - Push model to checkpoint ./output_kitti/iter_675[0m
[36m2022-12-06 16:56:21,952[0m -     INFO - [TRAIN] epoch=6/10, iter=680/1200, loss=1.492952, lr=0.000200 | ETA 00:01:16[0m
[36m2022-12-06 16:56:21,964[0m -     INFO - Pop model from ./output_kitti/iter_655[0m
[36m2022-12-06 16:56:22,104[0m -     INFO - Push model to checkpoint ./output_kitti/iter_680[0m
[36m2022-12-06 16:56:22,774[0m -     INFO - Pop model from ./output_kitti/iter_660[0m
[36m2022-12-06 16:56:22,923[0m -     INFO - Push model to checkpoint ./output_kitti/iter_685[0m
[36m2022-12-06 16:56:23,778[0m -     INFO - [TRAIN] epoch=6/10, iter=690/1200, loss=1.535962, lr=0.000200 | ETA 00:01:22[0m
[36m2022-12-06 16:56:23,793[0m -     INFO - Pop model from ./output_kitti/iter_665[0m
[36m2022-12-06 16:56:23,936[0m -     INFO - Push model to checkpoint ./output_kitti/iter_690[0m
[36m2022-12-06 16:56:24,571[0m -     INFO - Pop model from ./output_kitti/iter_670[0m
[36m2022-12-06 16:56:24,714[0m -     INFO - Push model to checkpoint ./output_kitti/iter_695[0m
[36m2022-12-06 16:56:25,294[0m -     INFO - [TRAIN] epoch=6/10, iter=700/1200, loss=1.549930, lr=0.000200 | ETA 00:01:03[0m
[36m2022-12-06 16:56:25,306[0m -     INFO - Pop model from ./output_kitti/iter_675[0m
[36m2022-12-06 16:56:25,479[0m -     INFO - Push model to checkpoint ./output_kitti/iter_700[0m
[36m2022-12-06 16:56:26,083[0m -     INFO - Pop model from ./output_kitti/iter_680[0m
[36m2022-12-06 16:56:26,242[0m -     INFO - Push model to checkpoint ./output_kitti/iter_705[0m
[36m2022-12-06 16:56:26,877[0m -     INFO - [TRAIN] epoch=6/10, iter=710/1200, loss=1.500789, lr=0.000200 | ETA 00:01:15[0m
[36m2022-12-06 16:56:26,889[0m -     INFO - Pop model from ./output_kitti/iter_685[0m
[36m2022-12-06 16:56:27,028[0m -     INFO - Push model to checkpoint ./output_kitti/iter_710[0m
[36m2022-12-06 16:56:27,690[0m -     INFO - Pop model from ./output_kitti/iter_690[0m
[36m2022-12-06 16:56:27,828[0m -     INFO - Push model to checkpoint ./output_kitti/iter_715[0m
[36m2022-12-06 16:56:28,462[0m -     INFO - [TRAIN] epoch=6/10, iter=720/1200, loss=1.448351, lr=0.000200 | ETA 00:01:18[0m
[36m2022-12-06 16:56:28,477[0m -     INFO - Pop model from ./output_kitti/iter_695[0m
[36m2022-12-06 16:56:28,652[0m -     INFO - Push model to checkpoint ./output_kitti/iter_720[0m
[36m2022-12-06 16:56:29,289[0m -     INFO - Pop model from ./output_kitti/iter_700[0m
[36m2022-12-06 16:56:29,440[0m -     INFO - Push model to checkpoint ./output_kitti/iter_725[0m
[36m2022-12-06 16:56:30,072[0m -     INFO - [TRAIN] epoch=6/10, iter=730/1200, loss=1.551960, lr=0.000200 | ETA 00:01:13[0m
[36m2022-12-06 16:56:30,094[0m -     INFO - Pop model from ./output_kitti/iter_705[0m
[36m2022-12-06 16:56:30,237[0m -     INFO - Push model to checkpoint ./output_kitti/iter_730[0m
[36m2022-12-06 16:56:30,893[0m -     INFO - Pop model from ./output_kitti/iter_710[0m
[36m2022-12-06 16:56:31,081[0m -     INFO - Push model to checkpoint ./output_kitti/iter_735[0m
[36m2022-12-06 16:56:31,664[0m -     INFO - [TRAIN] epoch=6/10, iter=740/1200, loss=1.468403, lr=0.000200 | ETA 00:01:08[0m
[36m2022-12-06 16:56:31,687[0m -     INFO - Pop model from ./output_kitti/iter_715[0m
[36m2022-12-06 16:56:31,841[0m -     INFO - Push model to checkpoint ./output_kitti/iter_740[0m
[36m2022-12-06 16:56:32,433[0m -     INFO - Pop model from ./output_kitti/iter_720[0m
[36m2022-12-06 16:56:32,603[0m -     INFO - Push model to checkpoint ./output_kitti/iter_745[0m
[36m2022-12-06 16:56:33,182[0m -     INFO - [TRAIN] epoch=6/10, iter=750/1200, loss=1.521409, lr=0.000200 | ETA 00:00:36[0m
[36m2022-12-06 16:56:33,194[0m -     INFO - Pop model from ./output_kitti/iter_725[0m
[36m2022-12-06 16:56:33,344[0m -     INFO - Push model to checkpoint ./output_kitti/iter_750[0m
[36m2022-12-06 16:56:48,184[0m -     INFO - Pop model from ./output_kitti/iter_730[0m
[36m2022-12-06 16:56:48,347[0m -     INFO - Push model to checkpoint ./output_kitti/iter_755[0m
[36m2022-12-06 16:56:48,955[0m -     INFO - [TRAIN] epoch=7/10, iter=760/1200, loss=1.585845, lr=0.000200 | ETA 00:01:10[0m
[36m2022-12-06 16:56:48,969[0m -     INFO - Pop model from ./output_kitti/iter_735[0m
[36m2022-12-06 16:56:49,101[0m -     INFO - Push model to checkpoint ./output_kitti/iter_760[0m
[36m2022-12-06 16:56:49,702[0m -     INFO - Pop model from ./output_kitti/iter_740[0m
[36m2022-12-06 16:56:49,864[0m -     INFO - Push model to checkpoint ./output_kitti/iter_765[0m
[36m2022-12-06 16:56:50,478[0m -     INFO - [TRAIN] epoch=7/10, iter=770/1200, loss=1.376411, lr=0.000200 | ETA 00:00:59[0m
[36m2022-12-06 16:56:50,488[0m -     INFO - Pop model from ./output_kitti/iter_745[0m
[36m2022-12-06 16:56:50,631[0m -     INFO - Push model to checkpoint ./output_kitti/iter_770[0m
[36m2022-12-06 16:56:51,292[0m -     INFO - Pop model from ./output_kitti/iter_750[0m
[36m2022-12-06 16:56:51,438[0m -     INFO - Push model to checkpoint ./output_kitti/iter_775[0m
[36m2022-12-06 16:56:52,008[0m -     INFO - [TRAIN] epoch=7/10, iter=780/1200, loss=1.424098, lr=0.000200 | ETA 00:00:53[0m
[36m2022-12-06 16:56:52,065[0m -     INFO - Pop model from ./output_kitti/iter_755[0m
[36m2022-12-06 16:56:52,187[0m -     INFO - Push model to checkpoint ./output_kitti/iter_780[0m
[36m2022-12-06 16:56:52,798[0m -     INFO - Pop model from ./output_kitti/iter_760[0m
[36m2022-12-06 16:56:52,973[0m -     INFO - Push model to checkpoint ./output_kitti/iter_785[0m
[36m2022-12-06 16:56:53,565[0m -     INFO - [TRAIN] epoch=7/10, iter=790/1200, loss=1.486521, lr=0.000200 | ETA 00:00:53[0m
[36m2022-12-06 16:56:53,577[0m -     INFO - Pop model from ./output_kitti/iter_765[0m
[36m2022-12-06 16:56:53,718[0m -     INFO - Push model to checkpoint ./output_kitti/iter_790[0m
[36m2022-12-06 16:56:54,393[0m -     INFO - Pop model from ./output_kitti/iter_770[0m
[36m2022-12-06 16:56:54,545[0m -     INFO - Push model to checkpoint ./output_kitti/iter_795[0m
[36m2022-12-06 16:56:55,174[0m -     INFO - [TRAIN] epoch=7/10, iter=800/1200, loss=1.467646, lr=0.000200 | ETA 00:01:01[0m
[36m2022-12-06 16:56:55,186[0m -     INFO - Pop model from ./output_kitti/iter_775[0m
[36m2022-12-06 16:56:55,335[0m -     INFO - Push model to checkpoint ./output_kitti/iter_800[0m
[36m2022-12-06 16:56:56,007[0m -     INFO - Pop model from ./output_kitti/iter_780[0m
[36m2022-12-06 16:56:56,172[0m -     INFO - Push model to checkpoint ./output_kitti/iter_805[0m
[36m2022-12-06 16:56:56,779[0m -     INFO - [TRAIN] epoch=7/10, iter=810/1200, loss=1.448185, lr=0.000200 | ETA 00:00:52[0m
[36m2022-12-06 16:56:56,790[0m -     INFO - Pop model from ./output_kitti/iter_785[0m
[36m2022-12-06 16:56:56,962[0m -     INFO - Push model to checkpoint ./output_kitti/iter_810[0m
[36m2022-12-06 16:56:57,586[0m -     INFO - Pop model from ./output_kitti/iter_790[0m
[36m2022-12-06 16:56:57,722[0m -     INFO - Push model to checkpoint ./output_kitti/iter_815[0m
[36m2022-12-06 16:56:58,376[0m -     INFO - [TRAIN] epoch=7/10, iter=820/1200, loss=1.503476, lr=0.000200 | ETA 00:00:59[0m
[36m2022-12-06 16:56:58,388[0m -     INFO - Pop model from ./output_kitti/iter_795[0m
[36m2022-12-06 16:56:58,531[0m -     INFO - Push model to checkpoint ./output_kitti/iter_820[0m
[36m2022-12-06 16:56:59,168[0m -     INFO - Pop model from ./output_kitti/iter_800[0m
[36m2022-12-06 16:56:59,308[0m -     INFO - Push model to checkpoint ./output_kitti/iter_825[0m
[36m2022-12-06 16:56:59,896[0m -     INFO - [TRAIN] epoch=7/10, iter=830/1200, loss=1.534744, lr=0.000200 | ETA 00:00:45[0m
[36m2022-12-06 16:56:59,957[0m -     INFO - Pop model from ./output_kitti/iter_805[0m
[36m2022-12-06 16:57:00,098[0m -     INFO - Push model to checkpoint ./output_kitti/iter_830[0m
[36m2022-12-06 16:57:00,698[0m -     INFO - Pop model from ./output_kitti/iter_810[0m
[36m2022-12-06 16:57:00,861[0m -     INFO - Push model to checkpoint ./output_kitti/iter_835[0m
[36m2022-12-06 16:57:01,475[0m -     INFO - [TRAIN] epoch=7/10, iter=840/1200, loss=1.459582, lr=0.000200 | ETA 00:00:54[0m
[36m2022-12-06 16:57:01,487[0m -     INFO - Pop model from ./output_kitti/iter_815[0m
[36m2022-12-06 16:57:01,621[0m -     INFO - Push model to checkpoint ./output_kitti/iter_840[0m
[36m2022-12-06 16:57:02,263[0m -     INFO - Pop model from ./output_kitti/iter_820[0m
[36m2022-12-06 16:57:02,380[0m -     INFO - Push model to checkpoint ./output_kitti/iter_845[0m
[36m2022-12-06 16:57:02,952[0m -     INFO - [TRAIN] epoch=7/10, iter=850/1200, loss=1.455661, lr=0.000200 | ETA 00:00:50[0m
[36m2022-12-06 16:57:02,967[0m -     INFO - Pop model from ./output_kitti/iter_825[0m
[36m2022-12-06 16:57:03,096[0m -     INFO - Push model to checkpoint ./output_kitti/iter_850[0m
[36m2022-12-06 16:57:03,681[0m -     INFO - Pop model from ./output_kitti/iter_830[0m
[36m2022-12-06 16:57:03,816[0m -     INFO - Push model to checkpoint ./output_kitti/iter_855[0m
[36m2022-12-06 16:57:04,399[0m -     INFO - [TRAIN] epoch=7/10, iter=860/1200, loss=1.452714, lr=0.000200 | ETA 00:00:43[0m
[36m2022-12-06 16:57:04,455[0m -     INFO - Pop model from ./output_kitti/iter_835[0m
[36m2022-12-06 16:57:04,575[0m -     INFO - Push model to checkpoint ./output_kitti/iter_860[0m
[36m2022-12-06 16:57:05,172[0m -     INFO - Pop model from ./output_kitti/iter_840[0m
[36m2022-12-06 16:57:05,310[0m -     INFO - Push model to checkpoint ./output_kitti/iter_865[0m
[36m2022-12-06 16:57:05,857[0m -     INFO - [TRAIN] epoch=7/10, iter=870/1200, loss=1.465789, lr=0.000200 | ETA 00:00:39[0m
[36m2022-12-06 16:57:05,870[0m -     INFO - Pop model from ./output_kitti/iter_845[0m
[36m2022-12-06 16:57:05,987[0m -     INFO - Push model to checkpoint ./output_kitti/iter_870[0m
[36m2022-12-06 16:57:06,334[0m -     INFO - Pop model from ./output_kitti/iter_850[0m
[36m2022-12-06 16:57:06,451[0m -     INFO - Push model to checkpoint ./output_kitti/iter_875[0m
[36m2022-12-06 16:57:21,272[0m -     INFO - [TRAIN] epoch=8/10, iter=880/1200, loss=1.450452, lr=0.000200 | ETA 00:03:06[0m
[36m2022-12-06 16:57:21,282[0m -     INFO - Pop model from ./output_kitti/iter_855[0m
[36m2022-12-06 16:57:21,438[0m -     INFO - Push model to checkpoint ./output_kitti/iter_880[0m
[36m2022-12-06 16:57:22,081[0m -     INFO - Pop model from ./output_kitti/iter_860[0m
[36m2022-12-06 16:57:22,218[0m -     INFO - Push model to checkpoint ./output_kitti/iter_885[0m
[36m2022-12-06 16:57:22,855[0m -     INFO - [TRAIN] epoch=8/10, iter=890/1200, loss=1.448031, lr=0.000200 | ETA 00:00:48[0m
[36m2022-12-06 16:57:22,869[0m -     INFO - Pop model from ./output_kitti/iter_865[0m
[36m2022-12-06 16:57:22,991[0m -     INFO - Push model to checkpoint ./output_kitti/iter_890[0m
[36m2022-12-06 16:57:23,588[0m -     INFO - Pop model from ./output_kitti/iter_870[0m
[36m2022-12-06 16:57:23,758[0m -     INFO - Push model to checkpoint ./output_kitti/iter_895[0m
[36m2022-12-06 16:57:24,354[0m -     INFO - [TRAIN] epoch=8/10, iter=900/1200, loss=1.415632, lr=0.000200 | ETA 00:00:43[0m
[36m2022-12-06 16:57:24,364[0m -     INFO - Pop model from ./output_kitti/iter_875[0m
[36m2022-12-06 16:57:24,498[0m -     INFO - Push model to checkpoint ./output_kitti/iter_900[0m
[36m2022-12-06 16:57:25,079[0m -     INFO - Pop model from ./output_kitti/iter_880[0m
[36m2022-12-06 16:57:25,219[0m -     INFO - Push model to checkpoint ./output_kitti/iter_905[0m
[36m2022-12-06 16:57:25,852[0m -     INFO - [TRAIN] epoch=8/10, iter=910/1200, loss=1.381511, lr=0.000200 | ETA 00:00:41[0m
[36m2022-12-06 16:57:25,864[0m -     INFO - Pop model from ./output_kitti/iter_885[0m
[36m2022-12-06 16:57:25,983[0m -     INFO - Push model to checkpoint ./output_kitti/iter_910[0m
[36m2022-12-06 16:57:26,584[0m -     INFO - Pop model from ./output_kitti/iter_890[0m
[36m2022-12-06 16:57:26,726[0m -     INFO - Push model to checkpoint ./output_kitti/iter_915[0m
[36m2022-12-06 16:57:27,352[0m -     INFO - [TRAIN] epoch=8/10, iter=920/1200, loss=1.407649, lr=0.000200 | ETA 00:00:42[0m
[36m2022-12-06 16:57:27,364[0m -     INFO - Pop model from ./output_kitti/iter_895[0m
[36m2022-12-06 16:57:27,483[0m -     INFO - Push model to checkpoint ./output_kitti/iter_920[0m
[36m2022-12-06 16:57:28,115[0m -     INFO - Pop model from ./output_kitti/iter_900[0m
[36m2022-12-06 16:57:28,273[0m -     INFO - Push model to checkpoint ./output_kitti/iter_925[0m
[36m2022-12-06 16:57:28,879[0m -     INFO - [TRAIN] epoch=8/10, iter=930/1200, loss=1.489723, lr=0.000200 | ETA 00:00:36[0m
[36m2022-12-06 16:57:28,891[0m -     INFO - Pop model from ./output_kitti/iter_905[0m
[36m2022-12-06 16:57:29,042[0m -     INFO - Push model to checkpoint ./output_kitti/iter_930[0m
[36m2022-12-06 16:57:29,683[0m -     INFO - Pop model from ./output_kitti/iter_910[0m
[36m2022-12-06 16:57:29,846[0m -     INFO - Push model to checkpoint ./output_kitti/iter_935[0m
[36m2022-12-06 16:57:30,483[0m -     INFO - [TRAIN] epoch=8/10, iter=940/1200, loss=1.345369, lr=0.000200 | ETA 00:00:37[0m
[36m2022-12-06 16:57:30,494[0m -     INFO - Pop model from ./output_kitti/iter_915[0m
[36m2022-12-06 16:57:30,645[0m -     INFO - Push model to checkpoint ./output_kitti/iter_940[0m
[36m2022-12-06 16:57:31,273[0m -     INFO - Pop model from ./output_kitti/iter_920[0m
[36m2022-12-06 16:57:31,407[0m -     INFO - Push model to checkpoint ./output_kitti/iter_945[0m
[36m2022-12-06 16:57:32,055[0m -     INFO - [TRAIN] epoch=8/10, iter=950/1200, loss=1.422478, lr=0.000200 | ETA 00:00:41[0m
[36m2022-12-06 16:57:32,066[0m -     INFO - Pop model from ./output_kitti/iter_925[0m
[36m2022-12-06 16:57:32,194[0m -     INFO - Push model to checkpoint ./output_kitti/iter_950[0m
[36m2022-12-06 16:57:32,857[0m -     INFO - Pop model from ./output_kitti/iter_930[0m
[36m2022-12-06 16:57:32,992[0m -     INFO - Push model to checkpoint ./output_kitti/iter_955[0m
[36m2022-12-06 16:57:33,587[0m -     INFO - [TRAIN] epoch=8/10, iter=960/1200, loss=1.337420, lr=0.000200 | ETA 00:00:32[0m
[36m2022-12-06 16:57:33,598[0m -     INFO - Pop model from ./output_kitti/iter_935[0m
[36m2022-12-06 16:57:33,744[0m -     INFO - Push model to checkpoint ./output_kitti/iter_960[0m
[36m2022-12-06 16:57:34,386[0m -     INFO - Pop model from ./output_kitti/iter_940[0m
[36m2022-12-06 16:57:34,541[0m -     INFO - Push model to checkpoint ./output_kitti/iter_965[0m
[36m2022-12-06 16:57:35,211[0m -     INFO - [TRAIN] epoch=8/10, iter=970/1200, loss=1.410908, lr=0.000200 | ETA 00:00:32[0m
[36m2022-12-06 16:57:35,221[0m -     INFO - Pop model from ./output_kitti/iter_945[0m
[36m2022-12-06 16:57:35,366[0m -     INFO - Push model to checkpoint ./output_kitti/iter_970[0m
[36m2022-12-06 16:57:35,963[0m -     INFO - Pop model from ./output_kitti/iter_950[0m
[36m2022-12-06 16:57:36,081[0m -     INFO - Push model to checkpoint ./output_kitti/iter_975[0m
[36m2022-12-06 16:57:36,656[0m -     INFO - [TRAIN] epoch=8/10, iter=980/1200, loss=1.357713, lr=0.000200 | ETA 00:00:31[0m
[36m2022-12-06 16:57:36,667[0m -     INFO - Pop model from ./output_kitti/iter_955[0m
[36m2022-12-06 16:57:36,802[0m -     INFO - Push model to checkpoint ./output_kitti/iter_980[0m
[36m2022-12-06 16:57:37,377[0m -     INFO - Pop model from ./output_kitti/iter_960[0m
[36m2022-12-06 16:57:37,520[0m -     INFO - Push model to checkpoint ./output_kitti/iter_985[0m
[36m2022-12-06 16:57:38,093[0m -     INFO - [TRAIN] epoch=8/10, iter=990/1200, loss=1.347160, lr=0.000200 | ETA 00:00:26[0m
[36m2022-12-06 16:57:38,104[0m -     INFO - Pop model from ./output_kitti/iter_965[0m
[36m2022-12-06 16:57:38,295[0m -     INFO - Push model to checkpoint ./output_kitti/iter_990[0m
[36m2022-12-06 16:57:38,979[0m -     INFO - Pop model from ./output_kitti/iter_970[0m
[36m2022-12-06 16:57:39,104[0m -     INFO - Push model to checkpoint ./output_kitti/iter_995[0m
[36m2022-12-06 16:57:39,444[0m -     INFO - [TRAIN] epoch=8/10, iter=1000/1200, loss=1.339190, lr=0.000200 | ETA 00:00:14[0m
[36m2022-12-06 16:57:39,456[0m -     INFO - Pop model from ./output_kitti/iter_975[0m
[36m2022-12-06 16:57:39,581[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1000[0m
[36m2022-12-06 16:57:53,293[0m -     INFO - Pop model from ./output_kitti/iter_980[0m
[36m2022-12-06 16:57:53,460[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1005[0m
[36m2022-12-06 16:57:54,169[0m -     INFO - [TRAIN] epoch=9/10, iter=1010/1200, loss=1.488680, lr=0.000200 | ETA 00:00:29[0m
[36m2022-12-06 16:57:54,184[0m -     INFO - Pop model from ./output_kitti/iter_985[0m
[36m2022-12-06 16:57:54,333[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1010[0m
[36m2022-12-06 16:57:54,999[0m -     INFO - Pop model from ./output_kitti/iter_990[0m
[36m2022-12-06 16:57:55,166[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1015[0m
[36m2022-12-06 16:57:55,785[0m -     INFO - [TRAIN] epoch=9/10, iter=1020/1200, loss=1.372268, lr=0.000200 | ETA 00:00:26[0m
[36m2022-12-06 16:57:55,855[0m -     INFO - Pop model from ./output_kitti/iter_995[0m
[36m2022-12-06 16:57:55,993[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1020[0m
[36m2022-12-06 16:57:56,763[0m -     INFO - Pop model from ./output_kitti/iter_1000[0m
[36m2022-12-06 16:57:56,909[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1025[0m
[36m2022-12-06 16:57:57,494[0m -     INFO - [TRAIN] epoch=9/10, iter=1030/1200, loss=1.308354, lr=0.000200 | ETA 00:00:21[0m
[36m2022-12-06 16:57:57,570[0m -     INFO - Pop model from ./output_kitti/iter_1005[0m
[36m2022-12-06 16:57:57,694[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1030[0m
[36m2022-12-06 16:57:58,529[0m -     INFO - Pop model from ./output_kitti/iter_1010[0m
[36m2022-12-06 16:57:58,662[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1035[0m
[36m2022-12-06 16:57:59,377[0m -     INFO - [TRAIN] epoch=9/10, iter=1040/1200, loss=1.236764, lr=0.000200 | ETA 00:00:21[0m
[36m2022-12-06 16:57:59,387[0m -     INFO - Pop model from ./output_kitti/iter_1015[0m
[36m2022-12-06 16:57:59,561[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1040[0m
[36m2022-12-06 16:58:00,355[0m -     INFO - Pop model from ./output_kitti/iter_1020[0m
[36m2022-12-06 16:58:00,483[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1045[0m
[36m2022-12-06 16:58:01,075[0m -     INFO - [TRAIN] epoch=9/10, iter=1050/1200, loss=1.324740, lr=0.000200 | ETA 00:00:22[0m
[36m2022-12-06 16:58:01,085[0m -     INFO - Pop model from ./output_kitti/iter_1025[0m
[36m2022-12-06 16:58:01,231[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1050[0m
[36m2022-12-06 16:58:01,869[0m -     INFO - Pop model from ./output_kitti/iter_1030[0m
[36m2022-12-06 16:58:02,006[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1055[0m
[36m2022-12-06 16:58:02,587[0m -     INFO - [TRAIN] epoch=9/10, iter=1060/1200, loss=1.268469, lr=0.000200 | ETA 00:00:17[0m
[36m2022-12-06 16:58:02,598[0m -     INFO - Pop model from ./output_kitti/iter_1035[0m
[36m2022-12-06 16:58:02,764[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1060[0m
[36m2022-12-06 16:58:03,363[0m -     INFO - Pop model from ./output_kitti/iter_1040[0m
[36m2022-12-06 16:58:03,494[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1065[0m
[36m2022-12-06 16:58:04,087[0m -     INFO - [TRAIN] epoch=9/10, iter=1070/1200, loss=1.367448, lr=0.000200 | ETA 00:00:16[0m
[36m2022-12-06 16:58:04,100[0m -     INFO - Pop model from ./output_kitti/iter_1045[0m
[36m2022-12-06 16:58:04,260[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1070[0m
[36m2022-12-06 16:58:04,880[0m -     INFO - Pop model from ./output_kitti/iter_1050[0m
[36m2022-12-06 16:58:05,027[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1075[0m
[36m2022-12-06 16:58:05,663[0m -     INFO - [TRAIN] epoch=9/10, iter=1080/1200, loss=1.203761, lr=0.000200 | ETA 00:00:17[0m
[36m2022-12-06 16:58:05,675[0m -     INFO - Pop model from ./output_kitti/iter_1055[0m
[36m2022-12-06 16:58:05,824[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1080[0m
[36m2022-12-06 16:58:06,480[0m -     INFO - Pop model from ./output_kitti/iter_1060[0m
[36m2022-12-06 16:58:06,618[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1085[0m
[36m2022-12-06 16:58:07,176[0m -     INFO - [TRAIN] epoch=9/10, iter=1090/1200, loss=1.234560, lr=0.000200 | ETA 00:00:13[0m
[36m2022-12-06 16:58:07,188[0m -     INFO - Pop model from ./output_kitti/iter_1065[0m
[36m2022-12-06 16:58:07,382[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1090[0m
[36m2022-12-06 16:58:08,271[0m -     INFO - Pop model from ./output_kitti/iter_1070[0m
[36m2022-12-06 16:58:08,398[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1095[0m
[36m2022-12-06 16:58:09,152[0m -     INFO - [TRAIN] epoch=9/10, iter=1100/1200, loss=1.377125, lr=0.000200 | ETA 00:00:15[0m
[36m2022-12-06 16:58:09,162[0m -     INFO - Pop model from ./output_kitti/iter_1075[0m
[36m2022-12-06 16:58:09,301[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1100[0m
[36m2022-12-06 16:58:10,075[0m -     INFO - Pop model from ./output_kitti/iter_1080[0m
[36m2022-12-06 16:58:10,206[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1105[0m
[36m2022-12-06 16:58:10,867[0m -     INFO - [TRAIN] epoch=9/10, iter=1110/1200, loss=1.315077, lr=0.000200 | ETA 00:00:14[0m
[36m2022-12-06 16:58:10,881[0m -     INFO - Pop model from ./output_kitti/iter_1085[0m
[36m2022-12-06 16:58:11,019[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1110[0m
[36m2022-12-06 16:58:11,587[0m -     INFO - Pop model from ./output_kitti/iter_1090[0m
[36m2022-12-06 16:58:11,776[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1115[0m
[36m2022-12-06 16:58:12,300[0m -     INFO - [TRAIN] epoch=9/10, iter=1120/1200, loss=1.305369, lr=0.000200 | ETA 00:00:09[0m
[36m2022-12-06 16:58:12,312[0m -     INFO - Pop model from ./output_kitti/iter_1095[0m
[36m2022-12-06 16:58:12,431[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1120[0m
[36m2022-12-06 16:58:12,771[0m -     INFO - Pop model from ./output_kitti/iter_1100[0m
[36m2022-12-06 16:58:12,889[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1125[0m
[36m2022-12-06 16:58:26,884[0m -     INFO - [TRAIN] epoch=10/10, iter=1130/1200, loss=1.425837, lr=0.000200 | ETA 00:00:42[0m
[36m2022-12-06 16:58:26,897[0m -     INFO - Pop model from ./output_kitti/iter_1105[0m
[36m2022-12-06 16:58:27,062[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1130[0m
[36m2022-12-06 16:58:27,664[0m -     INFO - Pop model from ./output_kitti/iter_1110[0m
[36m2022-12-06 16:58:27,801[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1135[0m
[36m2022-12-06 16:58:28,379[0m -     INFO - [TRAIN] epoch=10/10, iter=1140/1200, loss=1.277330, lr=0.000200 | ETA 00:00:08[0m
[36m2022-12-06 16:58:28,391[0m -     INFO - Pop model from ./output_kitti/iter_1115[0m
[36m2022-12-06 16:58:28,542[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1140[0m
[36m2022-12-06 16:58:29,181[0m -     INFO - Pop model from ./output_kitti/iter_1120[0m
[36m2022-12-06 16:58:29,323[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1145[0m
[36m2022-12-06 16:58:29,980[0m -     INFO - [TRAIN] epoch=10/10, iter=1150/1200, loss=1.213248, lr=0.000200 | ETA 00:00:07[0m
[36m2022-12-06 16:58:29,991[0m -     INFO - Pop model from ./output_kitti/iter_1125[0m
[36m2022-12-06 16:58:30,162[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1150[0m
[36m2022-12-06 16:58:30,788[0m -     INFO - Pop model from ./output_kitti/iter_1130[0m
[36m2022-12-06 16:58:30,946[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1155[0m
[36m2022-12-06 16:58:31,581[0m -     INFO - [TRAIN] epoch=10/10, iter=1160/1200, loss=1.304263, lr=0.000200 | ETA 00:00:05[0m
[36m2022-12-06 16:58:31,592[0m -     INFO - Pop model from ./output_kitti/iter_1135[0m
[36m2022-12-06 16:58:31,751[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1160[0m
[36m2022-12-06 16:58:32,376[0m -     INFO - Pop model from ./output_kitti/iter_1140[0m
[36m2022-12-06 16:58:32,520[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1165[0m
[36m2022-12-06 16:58:33,096[0m -     INFO - [TRAIN] epoch=10/10, iter=1170/1200, loss=1.314660, lr=0.000200 | ETA 00:00:03[0m
[36m2022-12-06 16:58:33,164[0m -     INFO - Pop model from ./output_kitti/iter_1145[0m
[36m2022-12-06 16:58:33,283[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1170[0m
[36m2022-12-06 16:58:33,896[0m -     INFO - Pop model from ./output_kitti/iter_1150[0m
[36m2022-12-06 16:58:34,055[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1175[0m
[36m2022-12-06 16:58:34,662[0m -     INFO - [TRAIN] epoch=10/10, iter=1180/1200, loss=1.279029, lr=0.000200 | ETA 00:00:02[0m
[36m2022-12-06 16:58:34,673[0m -     INFO - Pop model from ./output_kitti/iter_1155[0m
[36m2022-12-06 16:58:34,802[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1180[0m
[36m2022-12-06 16:58:35,483[0m -     INFO - Pop model from ./output_kitti/iter_1160[0m
[36m2022-12-06 16:58:35,622[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1185[0m
[36m2022-12-06 16:58:36,193[0m -     INFO - [TRAIN] epoch=10/10, iter=1190/1200, loss=1.237343, lr=0.000200 | ETA 00:00:01[0m
[36m2022-12-06 16:58:36,252[0m -     INFO - Pop model from ./output_kitti/iter_1165[0m
[36m2022-12-06 16:58:36,375[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1190[0m
[36m2022-12-06 16:58:36,976[0m -     INFO - Pop model from ./output_kitti/iter_1170[0m
[36m2022-12-06 16:58:37,124[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1195[0m
[36m2022-12-06 16:58:37,700[0m -     INFO - [TRAIN] epoch=10/10, iter=1200/1200, loss=1.228641, lr=0.000200 | ETA 00:00:00[0m
[36m2022-12-06 16:58:37,712[0m -     INFO - Pop model from ./output_kitti/iter_1175[0m
[36m2022-12-06 16:58:37,881[0m -     INFO - Push model to checkpoint ./output_kitti/iter_1200[0m
[36m2022-12-06 16:58:37,886[0m -     INFO - Training is complete.[0m
[0m

1.3导出

由于推理部分的代码和centerpoint一模一样，不再额外重复，这里仅仅给出训练与导出。

!python tools/export.py --config configs/pointpillars/pointpillars_xyres16_kitti_car.yml \
--model ./output_kitti/iter_1180/model.pdparams \
el.pdparams \
--save_dir ./output_kitti_inference

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
W1206 17:03:07.710546  4312 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2
W1206 17:03:07.714442  4312 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
[36m2022-12-06 17:03:08,869[0m -     INFO - There are 106/106 variables loaded into PointPillars.[0m
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpn9yf7wb9.py:21
The behavior of expression A / B has been unified with elementwise_div(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_div(X, Y, axis=0) instead of A / B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpn9yf7wb9.py:22
The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpn9yf7wb9.py:32
The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpn9yf7wb9.py:35
The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/framework.py:2664: UserWarning: The Attr(force_cpu) of Op(fill_constant) will be deprecated in the future, please use 'device_guard' instead. 'device_guard' has higher priority when they are used at the same time.
  "used at the same time." % type)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpn9yf7wb9.py:68
The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpy2o0e4sp.py:80
The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/tensor.py:658: UserWarning: paddle.assign doesn't support float64 input now due to current platform protobuf data limitation, we convert it to float32
  "paddle.assign doesn't support float64 input now due "
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpjacqis9t.py:11
The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmpjacqis9t.py:18
The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.
  op_type, op_type, EXPRESSION_MAP[method_name]))
[33m2022-12-06 17:03:10,715[0m -  WARNING - No custom op iou3d_nms_cuda found, try JIT build[0m
Compiling user custom op, it will cost a few seconds.....
[01m[Kcc1plus:[m[K [01;35m[Kwarning: [m[Kcommand line option ‘[01m[K-Wstrict-prototypes[m[K’ is valid for C/ObjC but not for C++
[01m[Kcc1plus:[m[K [01;35m[Kwarning: [m[Kcommand line option ‘[01m[K-Wstrict-prototypes[m[K’ is valid for C/ObjC but not for C++
[01m[Kcc1plus:[m[K [01;35m[Kwarning: [m[Kcommand line option ‘[01m[K-Wstrict-prototypes[m[K’ is valid for C/ObjC but not for C++
[36m2022-12-06 17:03:21,349[0m -     INFO - iou3d_nms_cuda builded success![0m
[36m2022-12-06 17:03:22,657[0m -     INFO - Exported model is saved in ./output_kitti_inference[0m
[0m

Reference

2)写在最后

我们学会了如何使用自己的数据集。下一篇，我们将学习如何3D检测算法的理论知识。

笔者写本文时仓促，若有意见还请指正！

关于作者：

作者是在北京一个学校做自动驾驶SLAM方面RA的gap year学生。很高兴认识一起学习paddle的你。

此文章为搬运
原项目链接

百度飞桨AI Studio社区

学大模型，用大模型上飞桨星河社区！每天8点V100G算力免费领！免费领取ERNIE 4.0 100w Token >>>

更多推荐

Python调用文心一言的API

百度飞桨星河社区

文心一言API调用（python）

百度飞桨星河社区

[PaddleSpeech 原神] 音色克隆之胡桃

[PaddleSpeech] 音色克隆之原神角色，项目代码已完整放在aistudio,地址如下：快去体验吧，可以直接运行。

百度飞桨星河社区

所有评论(0)

查看更多评论

AI Studio

@m0_63642362

已为社区贡献1438条内容