Chen Yulin's Blog

Posted 2025-04-22Updated 2025-05-03Note4 minutes read (About 563 words)

Official repo:
https://github.com/liuhengyue/fcsgg
Our repo:
https://github.com/PSGBOT/KAF-Generation

Installation

Environment Preparation

git clone git@github.com:liuhengyue/fcsgg.git
cd fcsgg
git submodule init
git submodule update
conda create --name fcsgg
conda create -n fcsgg python=3.10
conda install nvidia/label/cuda-11.8.0::cuda-toolkit -c nvidia/label/cuda-11.8.0
conda install cudatoolkit
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# for building detectron
conda install -c conda-forge gcc=11.2.0
conda install -c conda-forge gxx=11.2.0

conda env config vars set LD_LIBRARY_PATH="/home/cyl/miniconda3/envs/fcsgg/lib/"
conda env config vars set CPATH="/home/cyl/miniconda3/envs/fcsgg/include/"
conda env config vars set CUDA_HOME="/home/cyl/miniconda3/envs/fcsgg/"

conda deactivate
conda activate fcsgg

export CC=$CONDA_PREFIX/bin/gcc
export CXX=$CONDA_PREFIX/bin/g++

pip install -r requirements.txt
python -m pip install -e detectron2

Downloads

Datasets:

cd ~/Reconst
wget https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip -P ./Data/vg/
wget https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip -P ./Data/vg/
unzip -j ./Data/vg/images.zip -d ./Data/vg/VG_100K
unzip -j ./Data/vg/images2.zip -d ./Data/vg/VG_100K

Download the scene graphs and extract them to datasets/vg/VG-SGG-with-attri.h5.

Issues

1

1	AttributeError: module 'PIL.Image' has no attribute 'LINEAR'. Did you mean: 'BILINEAR'?

LINEAR-> BILINEAR: commit

2

在尝试训练的过程中报错：

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  File "/home/cyl/Reconst/fcsgg/fcsgg/data/detection_utils.py", line 432, in generate_score_map
    masked_fmap = torch.max(masked_fmap, gaussian_mask * k)
RuntimeError: The size of tensor a (55) must match the size of tensor b (56) at non-singleton dimension 1

modify detection_utils.py: commit

Training

首先更改训练的配置文件./config/quick_schedules/Quick-FCSGG-HRNet-W32.yaml, (原文件使用预训练的参数)

MODEL:
  META_ARCHITECTURE: "CenterNet"
  HRNET:
    WEIGHTS: "output/FasterR-CNN-HR32-3x.pth"

更改为train from scratch

MODEL:
  META_ARCHITECTURE: "CenterNet"
  HRNET:
    WEIGHTS: ""  # Empty string to train from scratch

再运行：

1	python tools/train_net.py --num-gpus 1 --config-file configs/quick_schedules/Quick-FCSGG-HRNet-W32.yaml

成功训练✌

...
[04/23 10:21:01] d2.utils.events INFO:  eta: 0:05:37  iter: 1159  total_loss: 1.042  loss_cls: 0.7593  loss_box_wh: 0.08827  loss_center_reg: 0.02485  loss_raf: 0.1754    time: 0.4042  last_time: 0.4519  data_time: 0.0043  last_data_time: 0.0044   lr: 0.001  max_mem: 4141M
[04/23 10:21:09] d2.utils.events INFO:  eta: 0:05:29  iter: 1179  total_loss: 1.028  loss_cls: 0.7208  loss_box_wh: 0.09246  loss_center_reg: 0.02669  loss_raf: 0.1625    time: 0.4042  last_time: 0.4035  data_time: 0.0041  last_data_time: 0.0044   lr: 0.001  max_mem: 4141M
[04/23 10:21:17] d2.utils.events INFO:  eta: 0:05:21  iter: 1199  total_loss: 1.01  loss_cls: 0.671  loss_box_wh: 0.1038  loss_center_reg: 0.02432  loss_raf: 0.1635    time: 0.4042  last_time: 0.3943  data_time: 0.0042  last_data_time: 0.0043   lr: 0.001  max_mem: 4141M
[04/23 10:21:25] d2.utils.events INFO:  eta: 0:05:13  iter: 1219  total_loss: 0.9737  loss_cls: 0.6887  loss_box_wh: 0.0929  loss_center_reg: 0.02574  loss_raf: 0.1749    time: 0.4041  last_time: 0.4101  data_time: 0.0041  last_data_time: 0.0042   lr: 0.001  max_mem: 4141M
...

Explanation

Posted 2025-03-31Updated 2025-05-03Note2 minutes read (About 236 words)

DINOv2 Repository Application

Repository:

Installation

official:

1	conda env create -f conda.yaml

不建议使用official的conda.yaml, 使用更改后的conda_cyl.yaml。

pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1

conda install nvidia/label/cuda-11.7.0::cuda-toolkit -c nvidia/label/cuda-11.7.0
conda install cudatoolkit
conda install -c conda-forge gcc=11.2.0
conda install -c conda-forge gxx=11.2.0

conda env config vars set LD_LIBRARY_PATH="/home/cyl/miniconda3/envs/dinov2/lib/"
conda env config vars set CPATH="/home/cyl/miniconda3/envs/dinov2/include/"
conda env config vars set CUDA_HOME="/home/cyl/miniconda3/envs/dinov2/"

export CC=$CONDA_PREFIX/bin/gcc
export CXX=$CONDA_PREFIX/bin/g++

# check with `which g++`

conda env update -f conda_cyl.yaml

pip3 install -U xformers==0.0.18

conda env config vars set PYTHONPATH="/home/cyl/Reconst/dinov2/"

Demo 🐱

官方提供了 depth estimation 和 segmentation 的 notebook，可以找时间理解一下

Train

使用的数据集为Imagenet-mini

imagenet-mini
├── labels.txt
├── train
└── val

Note: 需要额外添加一个label.txt

使用脚本生产数据集的meta data:

Posted 2025-03-24Updated 2025-05-03Note3 minutes read (About 444 words)

Semantic-SAM Repository Application

My repository: https://github.com/Chen-Yulin/Semantic-SAM

Installation

官方步骤：

pip3 install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113
python -m pip install 'git+https://github.com/MaureenZOU/detectron2-xyz.git'
pip install git+https://github.com/cocodataset/panopticapi.git
git clone https://github.com/UX-Decoder/Semantic-SAM
cd Semantic-SAM
python -m pip install -r requirements.txt

export DATASET=/pth/to/dataset  # path to your coco data

一些绊脚石 ^ ^

1

根据[[Cuda+Torch]]，需要先安装cudatoolkit和cuda-toolkit

conda install nvidia/label/cuda-11.7.0::cuda-toolkit -c nvidia/label/cuda-11.7.0 
conda install cudatoolkit # no need to specify version
conda env config vars set LD_LIBRARY_PATH="/home/cyl/miniconda3/envs/<name>/lib/"
conda env config vars set CPATH="/home/cyl/miniconda3/envs/<name>/include/"
conda env config vars set CUDA_HOME="/home/cyl/miniconda3/envs/<name>/"

然后按照torch官网的安装指令：

1	conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

2

第二行直接运行可能会报错，提示系统gcc版本过高，安装gcc=11.2.0

conda install -c conda-forge gcc=11.2.0
conda install -c conda-forge gxx=11.2.0

# 指定编译器路径
export CC=$CONDA_PREFIX/bin/gcc
export CXX=$CONDA_PREFIX/bin/g++

如果编译时出现ld: cannot find -lcudart: No such file or directory collect2: error: ld returned 1 exit status 报错，只是因为没有安装cudatoolkit ^ ^

3

安装完成后直接import semantic_sam会报错ModuleNotFoundError: No module named 'MultiScaleDeformableAttention' ^ ^
提示：

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Please compile MultiScaleDeformableAttention CUDA op with the following commands:
	`cd mask2former[/modeling/pixel_decoder/ops](http://127.0.0.1:8888/modeling/pixel_decoder/ops)`
	`sh make.sh`

需要手动make一下 Mask2Former:

1 2	cd Mask2Former/mask2former/modeling/pixel_decoder/ops/ sh make.sh

4

一些版本问题

1	pip install gradio==3.37.0

Demo 🐱

Generate multi-granularity Mask on CLICK

1	python demo.py --ckpt ./weights/swinl_only_sam_many2many.pth

Comment: 效果相较于SAM更多体现了语义的一致性，而不是基于texture进行分割。

Automatically Generate Mask on Different Granularity

1	python demo_auto_generation.py --ckpt ./weights/swinl_only_sam_many2many.pth

Posted 2023-05-10Updated 2025-05-03a few seconds read (About 86 words)

Git Repository

Add remote repository
Branch

Add remote repository

1	git remote add origin git@github.com:...

origin is the name of the repository
Check remote repository settings:

1	git remote -v

Before push to remote repository, make sure the branch name is the same.

Branch

find all the branch

1	git branch -a

find all the branch on remote repository

1	git remote -r

Rename

1	git branch -m oldBranchName newBranchName

Move to branch:

1	git checkout [branchname/SHA-1]

Posted 2023-05-10Updated 2025-05-03a few seconds read (About 63 words)

Environmen generation

Generator file

File Path: catkin_ws/src/test/script/env_pkl_generator.py

Launch Ros

1 2	cd ~ roslaunch test demo_gazebo.launch

It will open Gazebo and RViz and display the robotic arm on both sofware.

Load Scene

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3

cd ~
rosrun test moveit_setscene.py
rosrun test gazebo_setscene.py

远程链接

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Installation

Environment Preparation

Downloads

Issues

1

2

Training

Explanation

Installation

Demo 🐱

Train

Installation

一些绊脚石 ^ ^

1

2

3

4

Demo 🐱

Generate multi-granularity Mask on CLICK

Automatically Generate Mask on Different Granularity

Add remote repository

Branch

Generator file

Launch Ros

Load Scene

远程链接

Archives

Recents

Tags