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Usage

Prerequisites

The code is built with following libraries:

  • CUDA = 11.3

  • gcc version = 9.4 (versions that are too new may cause compatibility issues.)

  • Python >= 3.8, <3.9

    conda create -n {env_name} python=3.8 -y

  • PyTorch >= 1.9, <= 1.10.2 (I used 1.10.1)

    pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html

  • torchpack

    pip install torchpack==0.3.1

  • OpenMPI = 4.0.4 and mpi4py = 3.0.3 (Needed for torchpack)

    • download OpenMPI source code:
    wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.4.tar.gz
tar xf ./openmpi-4.0.4.tar.gz
    • set compilation parameters:
    cd openmpi-4.0.4
./configure --prefix=<path>

    --prefix is used to specify the installation path for OpenMPI, where the compiled files will be placed after completion.

    • compile:
    make
# or
make -j4 # indicates compiling with four threads.
    • installation:
    make install
    • environment variable settings:

    add these two lines to ~/.bashrc

    export PATH=/opt/openmpi/bin:$PATH
export LD_LIBRARY_PATH=/opt/openmpi/lib:$LD_LIBRARY_PATH

    Then you can start to install mpi4py:

    pip install mpi4py==3.0.3

  • mmcv = 1.4.0

    pip install mmcv-full==1.4.0

  • mmdetection = 2.20.0

    pip install mmdet==2.20.0

  • TorchEx

    cd ./mmdet3d/ops/TorchEx
pip install -v .

After installing these dependencies, please run this command to install the codebase:

python setup.py develop

Then, run this command to install the the remaining packages:

pip install -r requirements.txt

You can uninstall mmdetection3d to avoid conflicts with the IS_bevfusion/mmdet3d directory:

pip uninstall mmdet3d

Data Preparation

nuScenes

You can run this command to download the full NuScenes dataset:

bash ./data/dataset.sh

Then run this command to preprocess:

python ./tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes

After data preparation, you will be able to see the following directory structure:

IS_bevfusion
├── data
│   ├── nuscenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
|   |   ├── v1.0-trainval
│   │   ├── nuscenes_gt_database
│   │   ├── nuscenes_infos_train.pkl
│   │   ├── nuscenes_infos_val.pkl
│   │   ├── nuscenes_infos_test.pkl
│   │   ├── nuscenes_dbinfos_train.pkl

Training

Please download the checkpoints using the following script:

./tools/download_pretrained.sh

We provide instructions to reproduce our results on nuScenes. you will be able to run:

torchpack dist-run -np [number of gpus] python tools/train.py [config file path] --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/[checkpoint name].pth

For example, if you want to train the camera-only variant for object detection after modified, please run:

torchpack dist-run -np 2 python tools/train.py configs/nuscenes/det/centerhead/lssfpn/camera/256x704/swint/default_V2.yaml --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth

Note: please run tools/test.py separately after training to get the final evaluation metrics.

Evaluation

We also provide instructions for evaluating our pretrained models.

Then, you will be able to run:

torchpack dist-run -np [number of gpus] python tools/test.py [config file path] [checkpoint path] --eval [evaluation type]

For example, if you want to evaluate the camera-only variant for object detection after modified, you can try:

torchpack dist-run -np 2 python tools/test.py configs/nuscenes/det/centerhead/lssfpn/camera/256x704/swint/default_V2.yaml [checkpoint path] --eval bbox