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EGDTrack

目录

并非完整版教程，需要根据pytracking的步骤做完

安装

本实验最终在3080GPU上训练测试
python3.7 cuda11.3.1 ubuntu20
参考了 pytracking系列跟踪算法的配置（LWL, KYS, PrDiMP, DiMP and ATOM Trackers）（Ubuntu版本）
pytracking框架 服务器端配置采坑（ubuntu18.04+cuda11.3）
遇到的一些问题可以看这个解决 pytracking安装及运行3090Ubuntu18+cuda11（1）
也可以直接看官方安装历程最重要就是选择一个很是的pytorch版本

我的历程

首先我直接虚拟环境安装了torch1.10版本的，但是有问题的。出现要设置CUDAHOME当时不明白 https://blog.csdn.net/qq_41166909/article/details/124243523 这里面说明了所以需要手动安装一下，我先尝试了cuda11.3 失败，也没有找方法。

  1. 目前的pytracking环境的配置 conda 环境安装mamba install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge 电脑上无法用好 我手动安装11.3 失败 错误： raise RuntimeError(message) from e RuntimeError: Error building extension '_prroi_pooling'

  2. tracking_de 配置 现在使用cuda10看看 mamba install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch 手动安装cuda10.0成功

  3. 后面还是出现了问题，但是看这个 pytracking安装及运行3090Ubuntu18+cuda11（1）
    export CUDA_HOME=/usr/local/cuda

测试运行

pytracking test

export CUDA_HOME=/usr/local/cuda
# 1. 终端不一定需要
python -m visdom.server
# 2. 另一个
python pytracking/run_tracker.py atom default --dataset_name otb --sequence Soccer --debug 1 --threads 0

测试

数据集下载DepthTrack测试集

数据集来于DeT

DepthTrack Test set (50 Sequences)

Download

DepthTrack Training set (152 Sequences)

Download (100 seqs), Download (52 seqs)

All videoes are 640x360, except 4 sequences in 640x320: painting_indoor_320, pine02_wild_320, toy07_indoor_320 (some gt missing), hat02_indoor_320

还可以测试CDTB。目前只做了两个
VOT-test

  1. vot initialize --workspace . --nodownload
  2. 最好直接把test命名为sequence然后下一级才是各类图片
   rm -rf sequences
   sudo ln -s /path/to/datasettest/sequences ./
  1. vot analysis --workapce . mycocoDeT_DiMP50_Mean_78 --format html or --format json

Monocular Depth Estimation

使用尝试的EG-BTS（coco2017数据集）

Train

Using the default DiMP50 or ATOM pretrained checkpoints can reduce the training time.

For example, move the default dimp50.pth into the checkpoints folder and rename as DiMPNet_Det_EP0050.pth.tar

cd ltr
python run_training.py bbreg DeT_ATOM_Max
python run_training.py bbreg DeT_ATOM_Mean
python run_training.py bbreg DeT_ATOM_MC

python run_training.py dimp DeT_DiMP50_Max
python run_training.py dimp DeT_DiMP50_Mean
python run_training.py dimp DeT_DiMP50_MC

Test

/pytracking中有两个脚本分别测试CDTB和DepthTrack，参考

cd pytracking
python run_tracker.py atom DeT_ATOM_Max --dataset_name depthtrack --input_dtype rgbcolormap
python run_tracker.py atom DeT_ATOM_Mean --dataset_name depthtrack --input_dtype rgbcolormap
python run_tracker.py atom DeT_ATOM_MC --dataset_name depthtrack --input_dtype rgbcolormap

python run_tracker.py dimp DeT_DiMP50_Max --dataset_name depthtrack --input_dtype rgbcolormap
python run_tracker.py dimp DeT_DiMP50_Mean --dataset_name depthtrack --input_dtype rgbcolormap
python run_tracker.py dimp DeT_DiMP50_MC --dataset_name depthtrack --input_dtype rgbcolormap
python run_tracker.py dimp DeT_DiMP50_DO --dataset_name depthtrack --input_dtype colormap


python run_tracker.py dimp dimp50 --dataset_name depthtrack --input_dtype color
python run_tracker.py atom default --dataset_name depthtrack --input_dtype color

Architecture

The settings are same as that of Pytracking, please read the document of Pytracking for details.

Actually the network architecture is very simple, just adding one ResNet50 feature extractor for Depth input and then merging the RGB and Depth feature maps. Below figures are

  1. the feature maps for RGB, D inputs and the merged RGBD ones,
  2. the network for RGBD DiMP50, and
  3. RGBD ATOM.

The feature maps for RGB, D and the merged RGBD The network for RGB+D DiMP50 The network for RGB+D ATOM

Download

  1. Download the training dataset and edit the path in local.py

  2. Download the checkpoints for DeT trackers (in install.sh)

The checkpoints (don't edit it :):

https://drive.google.com/drive/folders/1DHDVhGHYYhoI9mjmgVUoautQe11SIKHL?usp=sharing

These links do not work now !

gdown https://drive.google.com/uc\?id\=1djSx6YIRmuy3WFjt9k9ZfI8q343I7Y75 -O pytracking/networks/DeT_DiMP50_Max.pth
gdown https://drive.google.com/uc\?id\=1JW3NnmFhX3ZnEaS3naUA05UaxFz6DLFW -O pytracking/networks/DeT_DiMP50_Mean.pth
gdown https://drive.google.com/uc\?id\=1wcGJc1Xq_7d-y-1nWh6M7RaBC1AixRTu -O pytracking/networks/DeT_DiMP50_MC.pth
gdown https://drive.google.com/uc\?id\=17IIroLZ0M_ZVuxkGN6pVy4brTpicMrn8 -O pytracking/networks/DeT_DiMP50_DO.pth
gdown https://drive.google.com/uc\?id\=17aaOiQW-zRCCqPePLQ9u1s466qCtk7Lh -O pytracking/networks/DeT_ATOM_Max.pth
gdown https://drive.google.com/uc\?id\=15LqCjNelRx-pOXAwVd1xwiQsirmiSLmK -O pytracking/networks/DeT_ATOM_Mean.pth
gdown https://drive.google.com/uc\?id\=14wyUaG-pOUu4Y2MPzZZ6_vvtCuxjfYPg -O pytracking/networks/DeT_ATOM_MC.pth