从Paddle3D开始3D目标检测-测试版
2024-04-17 03:36:20
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。
P = ( f u 0 c u − f u b 0 f v c v 0 0 0 1 0 ) P =\begin{pmatrix} f_{u}&0 &c_{u} &-f_{u}b \\ 0& f_{v} &c_{v} &0 \\ 0&0 &1 &0 \end{pmatrix} P= fu000fv0cucv1−fub00
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。
K = ( f u 0 c u 0 f v c v 0 0 1 ) K =\begin{pmatrix} f_{u}&0 &c_{u} \\ 0& f_{v} &c_{v} \\ 0&0 &1 \end{pmatrix} K= fu000fv0cucv1
我们拿P0来说，P0相机的内参矩阵为：
K = ( 7.188560000000 e + 02 0 6.071928000000 e + 02 0 7.188560000000 e + 02 1.852157000000 e + 02 0 0 1 ) K =\begin{pmatrix} 7.188560000000e+02&0 &6.071928000000e+02 \\ 0& 7.188560000000e+02 &1.852157000000e+02 \\ 0&0 &1 \end{pmatrix} K= 7.188560000000e+020007.188560000000e+0206.071928000000e+021.852157000000e+021
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
43a712729c96b4c6a10458001b3079040cb6486ecreating: /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: 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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
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Building wheels for collected packages: pycocotools, filterpy, lapBuilding 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=249f1dbdbedcfa81ffa31e209e258623409d19969bd2c0e5592f9bdb19aa8822Stored in directory: /home/aistudio/.cache/pip/wheels/f8/94/70/046149e666bd5812b7de6b87a28dcef238f7162f4108e0b3d8Building wheel for filterpy (setup.py) ... [?25ldone
[?25h Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110451 sha256=f61580fd168bb3a38fc2443751458cebba699a77672f12b3b7a0d38998f49a7aStored in directory: /home/aistudio/.cache/pip/wheels/60/79/e4/a6df5e482863f893b95d3e95627d744cb2e57b26b44ec20b22Building 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=168259ed8d93d76612f05ae12ca6aa36856c6361d28f04a847301a0a8d2bcddfStored 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
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#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 workingfrom 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 workingfrom 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 workingfrom collections import Sized
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[36m2022-12-06 16:52:37,575[0m - INFO - The database generation has been done.[0m
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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 workingfrom 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 workingfrom 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 workingfrom 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-sidgcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0Python - 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0]Science Toolkits:cv2 - 4.6.0numpy - 1.19.5numba - 0.48.0pandas - 1.1.5pillow - 8.2.0skimage - 0.19.3PaddlePaddle:paddle(gpu) - 2.3.2paddle3d - 0.5.0paddleseg - 2.7.0FLAGS_cudnn_deterministic - Not set.FLAGS_cudnn_exhaustive_search - Not set.CUDA:cudnn - 8200nvcc - Build cuda_11.2.r11.2/compiler.29618528_0GPUs:
------------------------------------------------[0m
[36m2022-12-06 16:53:12,537[0m - INFO -
---------------Config Information---------------
batch_size: 2
iters: 1200
lr_scheduler:gamma: 0.8learning_rate: 0.0002step_size: 27840type: StepDecay
model:anchor_area_threshold: 1anchor_configs:- anchor_offsets:- 0.16- -39.52- -1.78anchor_strides:- 0.32- 0.32- 0.0matched_threshold: 0.6rotations:- 0- 1.57sizes:- 1.6- 3.9- 1.56unmatched_threshold: 0.45backbone:downsample_strides:- 2- 2- 2in_channels: 64layer_nums:- 3- 5- 5out_channels:- 64- 128- 256type: SecondBackbonehead:box_code_size: 7encode_background_as_zeros: truefeature_channels: 384nms_iou_threshold: 0.5nms_post_max_size: 300nms_pre_max_size: 1000nms_score_threshold: 0.05num_anchor_per_loc: 2num_classes: 1prediction_center_limit_range:- 0- -39.68- -5- 69.12- 39.68- 5type: SSDHeaduse_direction_classifier: trueloss:bg_cls_weight: 1.0box_code_size: 7classification_loss:alpha: 0.25gamma: 2.0type: SigmoidFocalClassificationLossclassification_loss_weight: 1.0direction_loss:type: WeightedSoftmaxClassificationLossdirection_loss_weight: 0.2encode_background_as_zeros: trueencode_rot_error_by_sin: truefg_cls_weight: 1.0num_classes: 1regression_loss:code_weights:- 1.0- 1.0- 1.0- 1.0- 1.0- 1.0- 1.0sigma: 3.0type: WeightedSmoothL1RegressionLossregression_loss_weight: 2.0type: PointPillarsLossuse_direction_classifier: truemiddle_encoder:in_channels: 64point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: PointPillarsScattervoxel_size:- 0.16- 0.16- 4neck:in_channels:- 64- 128- 256out_channels:- 128- 128- 128type: SecondFPNupsample_strides:- 1- 2- 4use_conv_for_no_stride: falsepillar_encoder:feat_channels:- 64in_channels: 4legacy: falsemax_num_points_in_voxel: 32point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: PillarFeatureNetvoxel_size:- 0.16- 0.16- 4with_distance: falsetype: PointPillarsvoxelizer:max_num_points_in_voxel: 32max_num_voxels:- 16000- 40000point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: HardVoxelizervoxel_size:- 0.16- 0.16- 4
optimizer:grad_clip:clip_norm: 10.0type: ClipGradByGlobalNormtype: Adamweight_decay: 0.0001
train_dataset:class_names:- Cardataset_root: datasets/KITTImode: traintransforms:- dim: 4type: LoadPointClouduse_dim: 4- type: RemoveCameraInvisiblePointsKITTI- class_names:- Cardatabase_anno_path: datasets/KITTI/kitti_train_gt_database/anno_info_train.pkldatabase_root: datasets/KITTI/ignored_difficulty:- -1max_num_samples_per_class:Car: 15min_num_points_in_box_per_class:Car: 5type: SamplingDatabase- max_num_attempts: 100rotation_range:- -0.15707963267- 0.15707963267translation_std:- 0.25- 0.25- 0.25type: RandomObjectPerturb- type: RandomVerticalFlip- max_rot: 0.78539816min_rot: -0.78539816type: GlobalRotate- max_scale: 1.05min_scale: 0.95type: GlobalScale- translation_std:- 0.2- 0.2- 0.2type: GlobalTranslate- point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: FilterBBoxOutsideRange- type: ShufflePoint- max_points_in_voxel: 32max_voxel_num: 16000point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: HardVoxelizevoxel_size:- 0.16- 0.16- 4- anchor_area_threshold: 1anchor_configs:- anchor_offsets:- 0.16- -39.52- -1.78anchor_strides:- 0.32- 0.32- 0.0matched_threshold: 0.6rotations:- 0- 1.57sizes:- 1.6- 3.9- 1.56unmatched_threshold: 0.45output_stride_factor: 2point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: GenerateAnchorsvoxel_size:- 0.16- 0.16- 4- rpn_batch_size: 512type: Gt2PointPillarsTargettype: KittiPCDataset
val_dataset:class_names:- Cardataset_root: datasets/KITTImode: valtransforms:- dim: 4type: LoadPointClouduse_dim: 4- type: RemoveCameraInvisiblePointsKITTI- max_points_in_voxel: 32max_voxel_num: 40000point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: HardVoxelizevoxel_size:- 0.16- 0.16- 4- anchor_area_threshold: 1anchor_configs:- anchor_offsets:- 0.16- -39.52- -1.78anchor_strides:- 0.32- 0.32- 0.0matched_threshold: 0.6rotations:- 0- 1.57sizes:- 1.6- 3.9- 1.56unmatched_threshold: 0.45output_stride_factor: 2point_cloud_range:- 0- -39.68- -3- 69.12- 39.68- 1type: GenerateAnchorsvoxel_size:- 0.16- 0.16- 4type: KittiPCDataset
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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.")
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[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 workingfrom 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 workingfrom 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 workingfrom 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
- 官方demo
- Paddle3D仓库
- kitti数据集calib.txt文件
- KITTI手册
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
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