add docker file

Dockerfile

@@ -0,0 +1,98 @@
+# From https://github.com/ufoym/deepo/blob/master/docker/Dockerfile.pytorch-py36-cu90
+
+# ==================================================================
+# module list
+# ------------------------------------------------------------------
+# python        3.6    (apt)
+# pytorch       latest (pip)
+# ==================================================================
+
+FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+RUN APT_INSTALL="apt-get install -y --no-install-recommends" && \
+    PIP_INSTALL="python -m pip --no-cache-dir install --upgrade" && \
+    GIT_CLONE="git clone --depth 10" && \
+    rm -rf /var/lib/apt/lists/* \
+           /etc/apt/sources.list.d/cuda.list \
+           /etc/apt/sources.list.d/nvidia-ml.list && \
+    apt-get update && \
+# ==================================================================
+# tools
+# ------------------------------------------------------------------
+    DEBIAN_FRONTEND=noninteractive $APT_INSTALL \
+        build-essential \
+        ca-certificates \
+        cmake \
+        wget \
+        git \
+        vim \
+        fish \
+        libsparsehash-dev \
+        && \
+# ==================================================================
+# python
+# ------------------------------------------------------------------
+    DEBIAN_FRONTEND=noninteractive $APT_INSTALL \
+        software-properties-common \
+        && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update && \
+    DEBIAN_FRONTEND=noninteractive $APT_INSTALL \
+        python3.6 \
+        python3.6-dev \
+        && \
+    wget -O ~/get-pip.py \
+        https://bootstrap.pypa.io/get-pip.py && \
+    python3.6 ~/get-pip.py && \
+    ln -s /usr/bin/python3.6 /usr/local/bin/python3 && \
+    ln -s /usr/bin/python3.6 /usr/local/bin/python && \
+    $PIP_INSTALL \
+        setuptools \
+        && \
+    $PIP_INSTALL \
+        numpy \
+        scipy \
+        matplotlib \
+        Cython \
+        && \
+# ==================================================================
+# pytorch
+# ------------------------------------------------------------------
+    $PIP_INSTALL \
+        torch_nightly -f \
+        https://download.pytorch.org/whl/nightly/cu90/torch_nightly.html \
+        && \
+    $PIP_INSTALL \
+        torchvision_nightly \
+        && \
+# ==================================================================
+# config & cleanup
+# ------------------------------------------------------------------
+    ldconfig && \
+    apt-get clean && \
+    apt-get autoremove && \
+    rm -rf /var/lib/apt/lists/* /tmp/* ~/*
+
+RUN PIP_INSTALL="python -m pip --no-cache-dir install --upgrade" && \
+    $PIP_INSTALL \
+        shapely fire pybind11 pyqtgraph tensorboardX protobuf \
+        pyopengl pyqt5 matplotlib scikit-image numba pillow
+
+WORKDIR /root
+RUN wget https://dl.bintray.com/boostorg/release/1.68.0/source/boost_1_68_0.tar.gz
+RUN tar xzvf boost_1_68_0.tar.gz
+RUN cp -r ./boost_1_68_0/boost /usr/include
+RUN rm -rf ./boost_1_68_0
+RUN rm -rf ./boost_1_68_0.tar.gz
+RUN git clone https://github.com/traveller59/second.pytorch.git --depth 10
+RUN git clone https://github.com/traveller59/SparseConvNet.git --depth 10
+RUN cd ./SparseConvNet && python setup.py install && cd .. && rm -rf SparseConvNet
+ENV NUMBAPRO_CUDA_DRIVER=/usr/lib/x86_64-linux-gnu/libcuda.so
+ENV NUMBAPRO_NVVM=/usr/local/cuda/nvvm/lib64/libnvvm.so
+ENV NUMBAPRO_LIBDEVICE=/usr/local/cuda/nvvm/libdevice
+ENV PYTHONPATH=/root/second.pytorch
+
+VOLUME ["/root/data"]
+VOLUME ["/root/model"]
+WORKDIR /root/second.pytorch/second
+
+ENTRYPOINT ["fish"]

README.md

@@ -3,8 +3,6 @@ SECOND detector. Based on my unofficial implementation of VoxelNet with some imp
 
 ONLY support python 3.6+, pytorch 0.4.1+. Don't support pytorch 0.4.0. Tested in Ubuntu 16.04/18.04.
 
-Feel free to contact me by issue or email if encounter any problems. I don't know whether this project is runnable in other computer.
-
 ### Performance in KITTI validation set (50/50 split, people have problems, need to be tuned.)
 
 ```
@@ -191,19 +189,28 @@ output.features = SubmanifoldConvolutionFunction.apply(
 
 You can download pretrained models in [google drive](https://drive.google.com/open?id=1eblyuILwbxkJXfIP5QlALW5N_x5xJZhL). The car model is corresponding to car.config, the car_tiny model is corresponding to car.tiny.config and the people model is corresponding to people.config.
 
+## Docker
+
+You can use a prebuilt docker for testing:
+```
+docker pull scrin/second-pytorch 
+```
+Then run:
+```
+nvidia-docker run -it --rm -v /media/yy/960evo/datasets/:/root/data -v $HOME/pretrained_models:/root/model --ipc=host second-pytorch:latest
+python ./pytorch/train.py evaluate --config_path=./configs/car.config --model_dir=/root/model/car
+...
+```
+
+Currently there is a problem that training and evaluating in docker is very slow.
+
 ## Try Kitti Viewer (Unstable)
 
 You should use kitti viewer based on pyqt and pyqtgraph to check data before training.
 
 run ```python ./kittiviewer/viewer.py```, check following picture to use kitti viewer:
 ![GuidePic](https://raw.githubusercontent.com/traveller59/second.pytorch/master/images/simpleguide.png)
 
-## TODO
-
-- [ ] add unit tests (2018.10~2018.11).
-- [ ] add a self-hosted CI (2018.10~2018.12).
-- [ ] replace pyqtgraph with modernGL or other draw tools (2018.11~2019.1).
-
 ## Concepts