Add caffe/docker and test aocnn_m40_5.prototxt

.gitignore

@@ -6,6 +6,8 @@ caffe-official
 *.vscode
 *.zip
 caffe/experiments/dataset
+caffe/experiments/models
+caffe/experiments/*.log
 tensorflow/script/dataset
 tensorflow/script/logs
 dist
@@ -15,3 +17,5 @@ __pycache__
 .vscode/
 *.pyd
 *.so
+*.png
+*.fig

caffe/docker/gpu/Dockerfile

@@ -0,0 +1,92 @@
+FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04
+
+# dir
+ENV WORKSPACE=/workspace
+ENV OCNN_ROOT=$WORKSPACE/ocnn
+ENV CAFFE_ROOT=$WORKSPACE/caffe
+
+
+# dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        curl \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libcgal-dev \
+        libeigen3-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-setuptools \
+        python-scipy \
+        rsync \
+        vim \
+        zip && \
+    rm -rf /var/lib/apt/lists/*
+
+
+# cmake
+WORKDIR $WORKSPACE
+RUN wget https://cmake.org/files/v3.16/cmake-3.16.2-Linux-x86_64.sh && \
+    mkdir cmake-3.16.2 && \
+    sh cmake-3.16.2-Linux-x86_64.sh --prefix=$WORKSPACE/cmake-3.16.2 --skip-license && \
+    ln -s $WORKSPACE/cmake-3.16.2/bin/cmake /usr/bin/cmake && \
+    rm cmake-3.16.2-Linux-x86_64.sh
+
+
+# nccl
+WORKDIR $WORKSPACE
+ARG NCCL_COMMIT=286916a1a37ca1fe8cd43e280f5c42ec29569fc5
+RUN git clone https://github.com/NVIDIA/nccl.git && \
+    cd nccl && \
+    git reset --hard $NCCL_COMMIT && \
+    make -j install && \
+    cd .. && \
+    rm -rf nccl
+
+
+# ocnn
+WORKDIR $OCNN_ROOT
+RUN git clone https://github.com/Microsoft/O-CNN.git .
+RUN cd octree/external && \
+    git clone --recursive https://github.com/wang-ps/octree-ext.git && \
+    cd .. && \
+    mkdir build && \
+    cd build && \
+    cmake ..  && \
+    cmake --build . --config Release
+
+
+# caffe
+WORKDIR $CAFFE_ROOT
+ARG CAFFE_COMMIT=6bfc5ca8f7c2a4b7de09dfe7a01cf9d3470d22b3
+RUN git clone https://github.com/BVLC/caffe.git . && \
+    git reset --hard $CAFFE_COMMIT && \
+    rsync -a $OCNN_ROOT/caffe/ ./ && \
+    pip install --upgrade pip && \
+    cd python && \
+    for req in $(cat requirements.txt) pydot; do pip install $req; done && \
+    cd .. && \
+    mkdir build && cd build && \
+    cmake -DUSE_CUDNN=1 -DUSE_NCCL=1 .. && \
+    make -j"$(nproc)"
+
+
+# path
+ENV PYCAFFE_ROOT=$CAFFE_ROOT/python
+ENV PYTHONPATH=$PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH=$CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$OCNN_ROOT/octree/build:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace

caffe/experiments/aocnn_m40_5.prototxt

@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   data_param {
-    source: "dataset/m40_adaptive_5_2_12_train_lmdb"
+    source: "dataset/m40_5_adaptive_2_12_train_lmdb"
     batch_size: 32
     backend: LMDB
   }
@@ -26,7 +26,7 @@ layer {
     phase: TEST
   }
   data_param {
-    source: "dataset/m40_adaptive_5_2_12_test_lmdb"
+    source: "dataset/m40_5_adaptive_2_12_test_lmdb"
     batch_size: 32
     backend: LMDB
   }

caffe/experiments/aocnn_m40_5_solver.prototxt

@@ -9,7 +9,7 @@ test_interval: 2000
 # The base learning rate, momentum and the weight decay of the network.
 base_lr: 0.1 
 momentum: 0.9 
-weight_decay: 0.0005
+weight_decay: 0.0008
 
 # The learning rate policy
 lr_policy: "step"

caffe/experiments/prepare_dataset.py

@@ -16,6 +16,8 @@ parser.add_argument('--octree', type=str, required=False,
                     help='The path of the octree')
 parser.add_argument('--simplify_points', type=str, required=False,
                     help='The path of the simplify_points')
+parser.add_argument('--depth', type=int, default=5,
+                    help='The octree depth')
 
 args = parser.parse_args()
 cmd  = args.run
@@ -177,7 +179,7 @@ def m40_generate_aocnn_lmdb(depth=5):
   # generate octree
   root_folder =  os.path.join(abs_path, 'dataset')
   points_folder = os.path.join(root_folder, 'ModelNet40.points')
-  m40_convert_points_to_octree(points_folder, depth, adaptive=1, node_dis=1)
+  m40_convert_points_to_octree(points_folder, depth, adaptive=1, node_dis=0)
 
   # generate lmdb
   octree_folder = os.path.join(root_folder, 'ModelNet40.octree.%d.adaptive' % depth)
@@ -243,7 +245,7 @@ if __name__ == '__main__':
   elif cmd == 'm40_generate_ocnn_lmdb':
     m40_generate_ocnn_lmdb(depth=5)
   elif cmd == 'm40_generate_aocnn_lmdb':
-    m40_generate_aocnn_lmdb(depth=5)
+    m40_generate_aocnn_lmdb(depth=args.depth)
   elif cmd == 'm40_generate_ocnn_points_tfrecords':
     m40_generate_ocnn_points_tfrecords()
   elif cmd == 'm40_generate_ocnn_octree_tfrecords':

docs/installation.md

@@ -51,6 +51,13 @@ If you want to try the original code or do some speed comparisons with our `O-CN
 feel free to drop me an email, we can share the original code with you. 
 
 
+<!-- 
+cd caffe/docker
+docker build --tag=ocnn:caffe gpu
+docker run --runtime=nvidia --name=ocnn-caffe -it --rm ocnn:caffe /bin/bash 
+docker pull wangps/ocnn:caffe
+-->
+
 ## Tensorflow
 
 The code has been tested with Ubuntu 16.04/18.04 and TensorFlow 1.14.0/1.12.0.