mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-04-02 20:30:40 +00:00
Merge 1acf7a3900 into 8a70a8cd6f
This commit is contained in:
Ammar Qammaz
GitHub
commit
70f89b5328
84
docker/Dockerfile
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84
docker/Dockerfile
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#FROM tensorflow/tensorflow:latest-gpu
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FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
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ENV DEBIAN_FRONTEND noninteractive
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ARG user_id
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ARG root_psw="12345678"
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ARG user_psw="ok"
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ARG user_name=user
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# Installs the necessary pkgs.
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RUN \
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echo "**** packages installation ****" \
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&& apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/3bf863cc.pub \
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&& apt-get update && apt-get dist-upgrade -y && apt-get install -y \
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vim \
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build-essential \
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cmake \
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ffmpeg \
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imagemagick \
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unzip \
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freeglut3-dev \
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libopencv-dev \
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libjpeg-dev \
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libpng-dev \
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libglew-dev \
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libpthread-stubs0-dev \
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git \
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virtualenv \
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sqlite3 \
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libsqlite3-dev \
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time \
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sudo \
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wget \
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nano \
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libboost-program-options-dev \
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libboost-filesystem-dev \
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libboost-graph-dev \
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libboost-regex-dev \
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libboost-system-dev \
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libboost-test-dev \
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libeigen3-dev \
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libsuitesparse-dev \
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libfreeimage-dev \
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libgoogle-glog-dev \
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libgflags-dev \
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libglew-dev \
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qtbase5-dev \
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libqt5opengl5-dev \
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libcgal-dev \
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libcgal-qt5-dev \
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libmetis-dev \
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libflann-dev \
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libatlas-base-dev \
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libsuitesparse-dev \
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libcufft10 \
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libcusparse11 \
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libcublas11 \
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libcublaslt11 \
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&& echo "**** python pip update ****" \
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&& /usr/bin/python3 -m pip install --upgrade pip \
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&& echo "**** aliases for l and ll commands creation ****" \
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&& echo -e 'ls --color=auto "$@"' > /usr/bin/l \
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&& echo -e 'ls -lah --color=auto "$@"' > /usr/bin/ll \
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&& chmod +x /usr/bin/ll /usr/bin/l \
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&& echo "**** history-search-backward by pressing F8 ****" \
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&& sed -i 's/# "\\e\[5~": history-search-backward/"\\e\[19~": history-search-backward/' /etc/inputrc \
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&& echo "**** root password creation ****" \
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&& echo "root:${root_psw}" | chpasswd \
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&& echo "**** user creation ****" \
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&& useradd -m -s /usr/bin/bash -u ${user_id} -G sudo ${user_name} \
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&& echo "${user_name}:${user_psw}" | chpasswd \
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&& chown -R ${user_name}:${user_name} /home/${user_name}/ \
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&& mkdir /home/${user_name}/workspace/ \
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&& chown -R user:user /home/${user_name}/workspace
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USER ${user_name}
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WORKDIR /home/${user_name}/
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RUN git config --global http.sslverify false
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RUN cd /home/${user_name}/workspace && git clone --recursive https://github.com/graphdeco-inria/gaussian-splatting && cd gaussian-splatting && docker/initialize.sh
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44
docker/build_and_deploy.sh
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44
docker/build_and_deploy.sh
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#!/usr/bin/env bash
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# This script builds and runs a docker image for local use.
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#Although I dislike the use of docker for a myriad of reasons, due needing it to deploy on a particular machine
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#I am adding a docker container builder for the repository to automate the process
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd "$DIR"
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cd ..
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REPOSITORY=`pwd`
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cd "$DIR"
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NAME="gaussian-splatting"
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dockerfile_pth="$DIR"
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mount_pth="$REPOSITORY"
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# update tensorflow image
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docker pull tensorflow/tensorflow:latest-gpu
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# build and run tensorflow
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docker build \
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-t $NAME \
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$dockerfile_pth \
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--build-arg user_id=$UID
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docker run -d \
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--gpus all \
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--shm-size 8G \
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-it \
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--name $NAME-container \
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-v $mount_pth:/home/user/workspace \
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$NAME
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docker ps -a
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OUR_DOCKER_ID=`docker ps -a | grep gaussian-splatting | cut -f1 -d' '`
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echo "Our docker ID is : $OUR_DOCKER_ID"
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echo "Attaching it using : docker attach $OUD_DOCKER_ID"
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docker attach $OUR_DOCKER_ID
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exit 0
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59
docker/initialize.sh
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59
docker/initialize.sh
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#!/bin/bash
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#git clone https://github.com/NVIDIA/cuda-samples #<- To Test docker Cuda image..
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#cd cuda-samples
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#also get add-ons
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git clone https://github.com/antimatter15/splat
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git clone https://github.com/ReshotAI/gaussian-splatting-blender-addon/
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git clone https://github.com/francescofugazzi/3dgsconverter #needs scikit-learn
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git clone https://ceres-solver.googlesource.com/ceres-solver
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cd ceres-solver
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#git checkout $(git describe --tags) # Checkout the latest release
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mkdir build
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cd build
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cmake .. -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF -DCMAKE_CUDA_ARCHITECTURES="60;70;80" #-DUSE_CUDA=OFF
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make -j8
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sudo make install
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cd ..
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cd ..
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git clone https://github.com/colmap/colmap
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cd colmap
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#git checkout dev
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mkdir build
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cd build
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cmake .. -DCMAKE_CUDA_ARCHITECTURES="60;70;80" #-DCUDA_ENABLED=OFF
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make -j8
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sudo make install
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cd ..
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cd ..
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python3.10 -m pip install plyfile tqdm scikit-learn
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python3.10 -m pip install https://huggingface.co/camenduru/gaussian-splatting/resolve/main/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl
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python3.10 -m pip install https://huggingface.co/camenduru/gaussian-splatting/resolve/main/simple_knn-0.0.0-cp310-cp310-linux_x86_64.whl
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python3.10 -m pip install torchvision
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ln -s docker/run.sh ./run.sh
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#Build viewer
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#sudo apt install -y libimgui-dev libglew-dev libassimp-dev libboost-all-dev libgtk-3-dev libopencv-dev libglfw3-dev libavdevice-dev libavcodec-dev libeigen3-dev libxxf86vm-dev libembree-dev
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#git clone https://github.com/JayFoxRox/SIBR_viewers
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#cd SIBR_viewers
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#cmake -Bbuild . -DCMAKE_BUILD_TYPE=Release -DASSIMP_LIBRARY=/usr/lib/x86_64-linux-gnu/libassimp.so
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#cmake --build build -j24 --target install
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#cd ..
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#sudo apt-get -y install cuda
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sudo apt -y install nvidia-cuda-toolkit
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exit 0
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33
docker/run.sh
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33
docker/run.sh
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#!/bin/bash
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd "$DIR"
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#cd ..
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mkdir -p $1-data/input
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mkdir -p $1-data/output
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FPS="5" #<-change this to change framerate
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if [ -f $1-data/input/0001.jpg ]
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then
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echo "File $1 appears to have already been split .."
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else
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ffmpeg -i $1 -qscale:v 1 -qmin 1 -vf fps=$FPS $1-data/input/%04d.jpg
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fi
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python3.10 convert.py -s $1-data/ --camera SIMPLE_RADIAL --no_gpu #GPU produces worse results (?)
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python3.10 train.py -s $1-data/ -r 1 --model_path=$1-data/output/ --position_lr_init 0.000016 --scaling_lr 0.001 --iterations 35000 #Test more training budget
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python3.10 3dgsconverter/3dgsconverter.py -i $1-data/output/point_cloud/iteration_30000/point_cloud.ply -o $1-data/output/point_cloud/iteration_30000/output_cc.ply -f cc --rgb --density_filter --remove_flyers
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python3.10 3dgsconverter/3dgsconverter.py -i $1-data/output/point_cloud/iteration_30000/output_cc.ply -o $1-data/output/point_cloud/iteration_30000/point_cloud_clean.ply -f 3dgs
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#pack it in
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#tar cvfjh "$1.tar.bz2" $1-data/
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exit 0
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40
docker/setup.sh
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40
docker/setup.sh
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#!/usr/bin/env bash
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# This script builds and runs a docker image for local use.
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd "$DIR"
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cd ..
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REPOSITORY=`pwd`
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cd "$DIR"
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#https://docs.nvidia.com/ai-enterprise/deployment-guide/dg-docker.html
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sudo apt-get update
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sudo apt-get install -y \
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apt-transport-https \
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ca-certificates \
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curl \
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gnupg-agent \
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software-properties-common
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curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
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sudo apt-key fingerprint 0EBFCD88
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sudo add-apt-repository \
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"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
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$(lsb_release -cs) \
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stable"
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sudo apt-get update
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sudo apt-get install -y docker-ce docker-ce-cli containerd.io
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sudo docker run hello-world
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#Make sure docker group is ok
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sudo groupadd docker
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sudo usermod -aG docker $USER
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newgrp docker
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exit 0
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14
train.py
14
train.py
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TENSORBOARD_FOUND = False
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def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
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bestLossEncountered = 1000000
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bestIterationEncountered = 0
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goodLossThreshold = 0.07
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first_iter = 0
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tb_writer = prepare_output_and_logger(dataset)
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gaussians = GaussianModel(dataset.sh_degree)
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@ -103,6 +107,16 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
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if iteration == opt.iterations:
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progress_bar.close()
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if (bestLossEncountered>ema_loss_for_log) and (goodLossThreshold > ema_loss_for_log):
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if (bestIterationEncountered!=0):
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print("\n[GOOD ITER {}] Erasing previous best iteration..".format(bestIterationEncountered))
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os.system("rm %s/point_cloud/iteration_%u/*.ply && rmdir %s/point_cloud/iteration_%u/"%(scene.model_path,bestIterationEncountered,scene.model_path,bestIterationEncountered))
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bestLossEncountered = ema_loss_for_log
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bestIterationEncountered = iteration
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print("\n[GOOD ITER {}] Now remembering this iteration..".format(iteration))
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saving_iterations.append(iteration)
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# Log and save
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training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))
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if (iteration in saving_iterations):
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