I have to emphasize that the precision is low and you'd better refer to official code for good performance.
Python3.6 torch 0.4.1 following the centernet. Link-->env prepare
First,you need to prepare data like the centernet do. Link-->kitti data prepare
In order to compile src/lib/utils/energy.cpp,you need to install pybind11. Link-->pybind11
cd ~/Project/RTM/src/lib/utils
make
~/PScanning dependencies of target energy
[ 50%] Building CXX object CMakeFiles/energy.dir/energy.cpp.o
[100%] Linking CXX shared module energy.cpython-36m-x86_64-linux-gnu.so
[100%] Built target energyroject/RTM/src/lib/utils$ make
Then test if you have install energy module successfully.
(CenterNet) kaixin1@213c6db174e2:~/Project/RTM/src/lib/utils$ python
Python 3.6.10 |Anaconda, Inc.| (default, Jan 7 2020, 21:14:29)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import energy
>>>
##Train
python main.py ddd --exp_id 3dop --dataset kitti --kitti_split 3dop --batch_size 4 --num_epochs 70 --lr_step 45,60 --arch resFP_18
##Test
CUDA_VISIBLE_DEVICES=0 python test.py ddd --exp_id 3dop --dataset kitti --kitti_split 3dop --load_model ../models/model_180.pth --arch resFP_18 --gpus 3