## Environment Setup
step 1. Install environment for pytorch training
```
conda create --name FlashOcc python=3.8.5
conda activate FlashOcc
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.5.3
pip install mmdet==2.25.1
pip install mmsegmentation==0.25.0

sudo apt-get install python3-dev 
sudo apt-get install libevent-dev
sudo apt-get groupinstall 'development tools'
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_ROOT=/usr/local/cuda
pip install pycuda

pip install lyft_dataset_sdk
pip install networkx==2.2
pip install numba==0.53.0
pip install numpy==1.23.5
pip install nuscenes-devkit
pip install plyfile
pip install scikit-image
pip install tensorboard
pip install trimesh==2.35.39
pip install setuptools==59.5.0
pip install yapf==0.40.1

cd Path_to_FlashOcc
git clone git@github.com:Yzichen/FlashOCC.git

cd Path_to_FlashOcc/FlashOcc
git clone https://github.com/open-mmlab/mmdetection3d.git

cd Path_to_FlashOcc/FlashOcc/mmdetection3d
git checkout v1.0.0rc4
pip install -v -e . 

cd Path_to_FlashOcc/FlashOcc/projects
pip install -v -e . 
```

step 3. Prepare nuScenes dataset as introduced in [nuscenes_det.md](nuscenes_det.md) and create the pkl for FlashOCC by running:
```shell
python tools/create_data_bevdet.py
```
thus, the folder will be ranged as following:
```shell script
└── Path_to_FlashOcc/
    └── data
        └── nuscenes
            ├── v1.0-trainval (existing)
            ├── sweeps  (existing)
            ├── samples (existing)
            ├── bevdetv2-nuscenes_infos_train.pkl (new)
            └── bevdetv2-nuscenes_infos_val.pkl (new)
```

step 4. For Occupancy Prediction task, download (only) the 'gts' from [CVPR2023-3D-Occupancy-Prediction](https://github.com/CVPR2023-3D-Occupancy-Prediction/CVPR2023-3D-Occupancy-Prediction) and arrange the folder as:
```shell script
└── Path_to_FlashOcc/
    └── data
        └── nuscenes
            ├── v1.0-trainval (existing)
            ├── sweeps  (existing)
            ├── samples (existing)
            ├── gts (new)
            ├── bevdetv2-nuscenes_infos_train.pkl (new)
            └── bevdetv2-nuscenes_infos_val.pkl (new)
```
(for panoptic occupancy), we follow the data setting in SparseOcc:

(1) Download Occ3D-nuScenes occupancy GT from [gdrive](https://drive.google.com/file/d/1kiXVNSEi3UrNERPMz_CfiJXKkgts_5dY/view?usp=drive_link), unzip it, and save it to `data/nuscenes/occ3d`.

(2) Generate the panoptic occupancy ground truth with `gen_instance_info.py`. The panoptic version of Occ3D will be saved to `data/nuscenes/occ3d_panoptic`.


step 5. CKPTS Preparation
(1) Download flashocc-r50-256x704.pth[https://drive.google.com/file/d/1k9BzXB2nRyvXhqf7GQx3XNSej6Oq6I-B/view] to Path_to_FlashOcc/FlashOcc/ckpts/, then run:
```shell script
bash tools/dist_test.sh projects/configs/flashocc/flashocc-r50.py  ckpts/flashocc-r50-256x704.pth 4 --eval map
```

step 6. (Optional) Install mmdeploy for tensorrt testing
```shell script
conda activate FlashOcc
pip install Cython==0.29.24

### get tensorrt
wget https://developer.download.nvidia.com/compute/machine-learning/tensorrt/secure/8.4.0/tars/TensorRT-8.4.0.6.Linux.x86_64-gnu.cuda-11.6.cudnn8.3.tar.gz
export TENSORRT_DIR=Path_to_TensorRT-8.4.0.6

### get onnxruntime
ONNXRUNTIME_VERSION=1.8.1
pip install onnxruntime-gpu==${ONNXRUNTIME_VERSION}
cd Path_to_your_onnxruntime
wget https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz \
     && tar -zxvf onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz
# export ONNXRUNTIME_DIR=/data01/shuchangyong/pkgs/onnxruntime-linux-x64-1.8.1
export ONNXRUNTIME_DIR=Path_to_your_onnxruntime/onnxruntime-linux-x64-1.8.1
cd Path_to_FlashOcc/FlashOcc/
git clone git@github.com:drilistbox/mmdeploy.git
cd Path_to_FlashOcc/FlashOcc/mmdeploy
git submodule update --init --recursive
mkdir -p build
cd Path_to_FlashOcc/FlashOcc/mmdeploy/build
cmake -DMMDEPLOY_TARGET_BACKENDS="ort;trt" ..
make -j 16
cd Path_to_FlashOcc/FlashOcc/mmdeploy
pip install -e .

### build sdk
cd Path_to_pplcv/
git clone https://github.com/openppl-public/ppl.cv.git
cd Path_to_pplcv/ppl.cv
export PPLCV_VERSION=0.7.0
git checkout tags/v${PPLCV_VERSION} -b v${PPLCV_VERSION}
./build.sh cuda

#pip install nvidia-tensorrt==8.4.0.6
pip install nvidia-tensorrt==8.4.1.5
pip install tensorrt
#pip install h5py
pip install spconv==2.3.6

export PATH=Path_to_TensorRT-8.4.0.6/bin:$PATH
export LD_LIBRARY_PATH=Path_to_TensorRT-8.4.0.6/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=Path_to_TensorRT-8.4.0.6/lib:$LIBRARY_PATH
```

## The finally overall rangement
1. Tensort
```shell script
└── Path_to_TensorRT-8.4.0.6
    └── TensorRT-8.4.0.6
```
2. FlashOcc
```shell script
└── Path_to_FlashOcc/
    └── data
        └── nuscenes
            ├── v1.0-trainval (existing)
            ├── sweeps  (existing)
            ├── samples (existing)
            ├── gts (new)
            ├── bevdetv2-nuscenes_infos_train.pkl (new)
            └── bevdetv2-nuscenes_infos_val.pkl (new)
    └── doc
        ├── install.md
        └── trt_test.md
    ├── figs
    ├── mmdeploy (new)
    ├── mmdetection3d (new)
    ├── projects
    ├── requirements
    ├── tools
    └── README.md
```
3. ppl.cv
```shell script
└── Path_to_pplcv
    └── ppl.cv
```