Merge branch 'jwdinius-master'

build_ceres_for_docker.sh

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+#!/bin/bash
+export ROOT_PATH=$1
+mkdir -p $ROOT_PATH/ceres
+cd $ROOT_PATH/ceres
+wget http:https://ceres-solver.org/ceres-solver-1.14.0.tar.gz
+tar xvzf ceres-solver-1.14.0.tar.gz
+cd ceres-solver-1.14.0
+sed -i 's/\(^option(BUILD_SHARED_LIBS.*\)OFF/\1ON/' CMakeLists.txt
+rm -rf build
+mkdir build
+cd build
+cmake ..
+make -j8
+sudo make install

docker/Dockerfile

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+FROM nvidia/cuda:10.2-devel-ubuntu16.04
+# NOTE: build this image from dir above Dockerfile location
+# USE BASH
+SHELL ["/bin/bash", "-c"]
+
+## modify below
+ARG username=pvnet
+ARG groupid=1000
+ARG userid=1000
+## end modify
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ cmake \
+ git \
+ curl \
+ wget \
+ vim \
+ ca-certificates \
+ python-qt4 \
+ libjpeg-dev \
+ zip \
+ unzip \
+ sudo \
+ libpng-dev \
+ libeigen3-dev \
+ libgoogle-glog-dev=0.3.4-0.1 \
+ libsuitesparse-dev=1:4.4.6-1 \
+ libatlas-base-dev=3.10.2-9 &&\
+ rm -rf /var/lib/apt/lists/*
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64
+ENV PYTHON_VERSION=3.6.7
+
+RUN curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
+ chmod +x ~/miniconda.sh && \
+ ~/miniconda.sh -b -p /opt/conda && \
+ rm ~/miniconda.sh && \
+ /opt/conda/bin/conda install conda-build
+
+ENV PATH=$PATH:/opt/conda/bin/
+
+RUN adduser --disabled-password --gecos '' $username \
+ && adduser $username sudo \
+ && echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+USER $username
+
+# clone the repo and build ceres from source
+RUN cd /home/$username \
+ && git clone https://github.com/jwdinius/pvnet.git \
+ && cd pvnet \
+ && chmod a+rwx build_ceres_for_docker.sh \
+ && ./build_ceres_for_docker.sh /home/$username
+
+# Create Enviroment
+RUN cd /home/$username/pvnet \
+ && conda env create -f environment.yml
+
+# follow remainder of steps in README
+RUN source activate pvnet \
+ && cd /home/$username/pvnet/lib/ransac_voting_gpu_layer \
+ && python setup.py build_ext --inplace
+RUN source activate pvnet \
+ && cd /home/$username/pvnet/lib/utils/extend_utils \
+ && python build_extend_utils_cffi_for_docker.py
+ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/$username/pvnet/lib/utils/extend_utils/lib
+
+WORKDIR /home/$username

docker/daemon.json

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+{
+ "runtimes": {
+ "nvidia": {
+ "path": "nvidia-container-runtime",
+ "runtimeArgs": []
+ }
+ },
+ "default-runtime": "nvidia",
+ "bridge": "none",
+ "storage-driver": "devicemapper",
+ "storage-opts": [
+ "dm.basesize=20G"
+ ]
+}

docker/how-to-docker.md

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+## How to run `tools/demo.py` from docker
+
+_Steps verified on Ubuntu 18.04, Cuda 10.2 with a GTX1080 Ti card, and docker 19.03.2 installed on the host machine_
+
+### Install docker
+
+[From the docker site](https://docs.docker.com/install/linux/docker-ce/ubuntu/)
+
+### Install nvidia-docker2
+
+[Good How-To on medium](https://medium.com/@sh.tsang/docker-tutorial-5-nvidia-docker-2-0-installation-in-ubuntu-18-04-cb80f17cac65)
+
+### Expand image size and set default runtime
+
+The docker image created by `Dockerfile` is quite large, ~10GB, which means you may need to up the max image size allowed for docker. Check [this link](https://guide.blazemeter.com/hc/en-us/articles/115003812129-Overcoming-Container-Storage-Limitation-Overcoming-Container-Storage-Limitation) for more info. I have tried to streamline the insights from the article and to give the user what they need from it. 
+
+Copy [daemon.json](./daemon.json) to your `/etc/docker/daemon.json`. Stop/start docker:
+
+```
+sudo systemctl stop docker
+# sudo rm -rf /var/lib/docker/* ## IF YOU HAVE IMAGES, SAVE THEM IN A TARBALL ARCHIVE AND RESTORE AFTER DOING THIS
+sudo systemctl start docker
+```
+
+_Note: you may or may not need to do the middle step. If you have previously-built images, you will need to remove them before restarting docker for the updated max image size to take effect._
+
+You can run `docker info` to verify the desired change took place: Look for the line in the output:
+
+```
+...
+Storage Driver: devicemapper
+... some lines here ...
+ Base Device Size: 21.47GB
+... more lines follow ...
+```
+
+### Build the `pvnet` image
+
+```
+$ cd {path-to-pvnet}/docker
+$ docker build --network=host -t pvnet-nvidia .
+```
+
+This will take awhile...
+
+### Run the demo
+
+#### Download the pre-trained model
+
+Follow the steps from the [README](../README.md). Note that the [`launch-docker.sh`](./launch-docker.sh) script handles mounting the pre-trained model's location on the host _inside_ of the running container instance that the script launches.
+
+#### Launch the container
+
+```
+$[host] cd {path-to-pvnet}
+$[host] ./docker/launch-docker.sh
+$[container] source activate pvnet
+$[container] cd /home/pvnet/pvnet
+$[container] python tools/demo.py
+```
+
+You should see the desired figure pop-up:
+
+<p align="center"> 
+<img src="./demo_success.png">
+</p>
+
+And that's it! Once you exit the container, it will automatically be deleted and it's resources released.

docker/launch-docker.sh

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+# NOTE: run this from pvnet root
+docker run -it --rm \
+ -v $(pwd)/data:/home/pvnet/pvnet/data \
+ -v /tmp/.X11-unix:/tmp/.X11-unix \
+ -e DISPLAY=$DISPLAY \
+ --name pvnet-nvidia-c \
+ --net host \
+ --privileged \
+ --runtime=nvidia \
+ pvnet-nvidia

environment.yml

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+name: pvnet
+channels:
+ - conda-forge
+ - pytorch
+ - fastai
+dependencies:
+ - python==3.6.7
+ - pytorch==1.1.0
+ - torchvision
+ - fastprogress
+ - fastai
+ - jupyter
+ - scipy==1.2.1
+ - matplotlib==3.0.2
+ - transforms3d==0.3.1
+ - setuptools==39.1.0
+ - cffi==1.11.5
+ - numpy==1.14.3
+ - cython==0.29.14
+ - tensorboardx
+ - pip
+ - pip:
+ - plyfile==0.6
+ - opencv_contrib_python==3.4.2.16
+ - lmdb==0.94
+ - glumpy==1.0.6
+ - scikit_image==0.13.1
+ - easydict==1.7

lib/utils/extend_utils/build_extend_utils_cffi_for_docker.py

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+import os
+
+ceres_include='/usr/local/include' # contains Ceres/ceres.h
+ceres_library='/usr/local/lib/libceres.so'
+eigen_include='/usr/include/eigen3' # contains Eigen/Core
+glog_library='/usr/lib/x86_64-linux-gnu/libglog.so'
+cuda_include='/usr/local/cuda-10.2/include'
+cudart = '/usr/local/cuda-10.2/lib64/libcudart.so'
+
+os.system('gcc -shared src/mesh_rasterization.cpp -c -o src/mesh_rasterization.cpp.o -fopenmp -fPIC -O2 -std=c++11')
+os.system('gcc -shared src/farthest_point_sampling.cpp -c -o src/farthest_point_sampling.cpp.o -fopenmp -fPIC -O2 -std=c++11')
+os.system('gcc -shared src/uncertainty_pnp.cpp -c -o src/uncertainty_pnp.cpp.o -fopenmp -fPIC -O2 -std=c++11 -I {} -I {}'.
+ format(ceres_include,eigen_include))
+os.system('nvcc src/nearest_neighborhood.cu -c -o src/nearest_neighborhood.cu.o -x cu -Xcompiler -fPIC -O2 -arch=sm_52 -I {} -D_FORCE_INLINES'.
+ format(cuda_include))
+
+from cffi import FFI
+ffibuilder = FFI()
+
+
+# cdef() expects a string listing the C types, functions and
+# globals needed from Python. The string follows the C syntax.
+with open(os.path.join(os.path.dirname(__file__), "src/utils_python_binding.h")) as f:
+ ffibuilder.cdef(f.read())
+
+ffibuilder.set_source("_extend_utils",
+ """
+ #include "src/utils_python_binding.h" // the C header of the library
+ """,
+ extra_objects=['src/mesh_rasterization.cpp.o','src/farthest_point_sampling.cpp.o',
+ 'src/uncertainty_pnp.cpp.o','src/nearest_neighborhood.cu.o',
+ # 'src/post_process.cpp.o',
+ ceres_library, glog_library,
+ cudart],
+ libraries=['stdc++']
+ )
+
+if __name__ == "__main__":
+ ffibuilder.compile(verbose=True)
+ os.system("rm src/*.o")
+ os.system("rm *.o")