SECOND for KITTI/NuScenes object detection (1.6.0 Alpha)

SECOND detector.

"Alpha" means there may be many bugs, config format may change, spconv API may change.

ONLY support python 3.6+, pytorch 1.0.0+. Tested in Ubuntu 16.04/18.04/Windows 10.

If you want to train nuscenes dataset, see this.

News

2019-4-1: SECOND V1.6.0alpha released: New Data API, NuScenes support, PointPillars support, fp16 and multi-gpu support.

2019-3-21: SECOND V1.5.1 (minor improvement and bug fix) released!

2019-1-20: SECOND V1.5 released! Sparse convolution-based network.

See release notes for more details.

WARNING: you should rerun info generation after every code update.

Performance in KITTI validation set (50/50 split)

car.fhd.config + 160 epochs (25 fps in 1080Ti):

Car [email protected], 0.70, 0.70:
bbox AP:90.77, 89.50, 80.80
bev  AP:90.28, 87.73, 79.67
3d   AP:88.84, 78.43, 76.88

car.fhd.config + 50 epochs + super converge (6.5 hours) + (25 fps in 1080Ti):

Car [email protected], 0.70, 0.70:
bbox AP:90.78, 89.59, 88.42
bev  AP:90.12, 87.87, 86.77
3d   AP:88.62, 78.31, 76.62

Performance in NuScenes validation set (all.pp.config, NuScenes mini train set, 3517 samples, not v1.0-mini)

car Nusc dist [email protected], 1.0, 2.0, 4.0
62.90, 73.07, 76.77, 78.79
bicycle Nusc dist [email protected], 1.0, 2.0, 4.0
0.00, 0.00, 0.00, 0.00
bus Nusc dist [email protected], 1.0, 2.0, 4.0
9.53, 26.17, 38.01, 40.60
construction_vehicle Nusc dist [email protected], 1.0, 2.0, 4.0
0.00, 0.00, 0.44, 1.43
motorcycle Nusc dist [email protected], 1.0, 2.0, 4.0
9.25, 12.90, 13.69, 14.11
pedestrian Nusc dist [email protected], 1.0, 2.0, 4.0
61.44, 62.61, 64.09, 66.35
traffic_cone Nusc dist [email protected], 1.0, 2.0, 4.0
11.63, 13.14, 15.81, 21.22
trailer Nusc dist [email protected], 1.0, 2.0, 4.0
0.80, 9.90, 17.61, 23.26
truck Nusc dist [email protected], 1.0, 2.0, 4.0
9.81, 21.40, 27.55, 30.34

Install

1. Clone code

git clone https://github.com/guochengqian/second.pytorch.git
cd ./second.pytorch/second

2. Install dependence python packages

It is recommend to use Anaconda package manager.

source env_install.sh

Follow instructions in spconv to install spconv.

If you want to train with fp16 mixed precision (train faster in RTX series, Titan V/RTX and Tesla V100, but I only have 1080Ti), you need to install apex.

If you want to use NuScenes dataset, you need to install nuscenes-devkit.

Prepare dataset

• KITTI Dataset preparation

Download KITTI dataset and create some directories first:

└── KITTI_DATASET_ROOT
       ├── training    <-- 7481 train data
       |   ├── image_2 <-- for visualization
       |   ├── calib
       |   ├── label_2
       |   ├── velodyne
       |   └── velodyne_reduced <-- empty directory
       └── testing     <-- 7580 test data
           ├── image_2 <-- for visualization
           ├── calib
           ├── velodyne
           └── velodyne_reduced <-- empty directory

Then run

python create_data.py kitti_data_prep KITTI_DATASET_ROOT

• Modify config file

There is some path need to be configured in config file:

train_input_reader: {
  ...
  database_sampler {
    database_info_path: "/path/to/dataset_dbinfos_train.pkl"
    ...
  }
  dataset: {
    dataset_class_name: "DATASET_NAME"
    kitti_info_path: "/path/to/dataset_infos_train.pkl"
    kitti_root_path: "DATASET_ROOT"
  }
}
...
eval_input_reader: {
  ...
  dataset: {
    dataset_class_name: "DATASET_NAME"
    kitti_info_path: "/path/to/dataset_infos_val.pkl"
    kitti_root_path: "DATASET_ROOT"
  }
}

Usage

train

I recommend to use script.py to train and eval. see script.py for more details.

train with single GPU

python ./pytorch/train.py train --config_path=./configs/car.fhd.config --model_dir=/path/to/model_dir

train with multiple GPU (need test, I only have one GPU)

Assume you have 4 GPUs and want to train with 3 GPUs:

CUDA_VISIBLE_DEVICES=0,1,3 python ./pytorch/train.py train --config_path=./configs/car.fhd.config --model_dir=/path/to/model_dir --multi_gpu=True

Note: The batch_size and num_workers in config file is per-GPU, if you use multi-gpu, they will be multiplied by number of GPUs. Don't modify them manually.

You need to modify total step in config file. For example, 50 epochs = 15500 steps for car.lite.config and single GPU, if you use 4 GPUs, you need to divide steps and steps_per_eval by 4.

train with fp16 (mixed precision)

Modify config file, set enable_mixed_precision to true.

• Make sure "/path/to/model_dir" doesn't exist if you want to train new model. A new directory will be created if the model_dir doesn't exist, otherwise will read checkpoints in it.

• training process use batchsize=6 as default for 1080Ti, you need to reduce batchsize if your GPU has less memory.

• Currently only support single GPU training, but train a model only needs 20 hours (165 epoch) in a single 1080Ti and only needs 50 epoch to reach 78.3 AP with super converge in car moderate 3D in Kitti validation dateset.

evaluate

python ./pytorch/train.py evaluate --config_path=./configs/car.fhd.config --model_dir=/path/to/model_dir --measure_time=True --batch_size=1

• detection result will saved as a result.pkl file in model_dir/eval_results/step_xxx or save as official KITTI label format if you use --pickle_result=False.

pretrained model

You can download pretrained models in google drive. The car_fhd model is corresponding to car.fhd.config.

Note that this pretrained model is trained before a bug of sparse convolution fixed, so the eval result may slightly worse.

Try Kitti Viewer Web

Major step

1. run python ./kittiviewer/backend/main.py main --port=16666 in your server/local.

2. run cd ./kittiviewer/frontend && python -m http.server to launch a local web server.

3. open your browser and enter your frontend url (e.g. http://127.0.0.1:8000, default]).

4. input backend url (e.g. http://127.0.0.1:16666)

5. input root path (eg. /data/KITTI/object), info path (eg. /data/KITTI/object/kitti_infos_train.pkl)and det path (optional)

6. click load, loadDet (optional), input image index in center bottom of screen and press Enter.

Inference step

Firstly the load button must be clicked and load successfully.

1. input checkpointPath (eg. /home/qiang/Documents/codefiles/3D/detection/our-second/second/ckpts/voxelnet-13920.tckpt)and configPath (eg. /home/qiang/Documents/codefiles/3D/detection/our-second/second/configs/car.fhd.config).

2. click buildNet.

3. click inference.

Try Kitti Viewer (Deprecated)

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:

Concepts

• Kitti lidar box

A kitti lidar box is consist of 7 elements: [x, y, z, w, l, h, rz], see figure.

All training and inference code use kitti box format. So we need to convert other format to KITTI format before training.

• Kitti camera box

A kitti camera box is consist of 7 elements: [x, y, z, l, h, w, ry].