RTM3D-PyTorch

The PyTorch Implementation of the paper: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving (ECCV 2020)

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Demonstration

Features

• Realtime 3D object detection based on a monocular RGB image
• Support distributed data parallel training
• Tensorboard
• ResNet-based Keypoint Feature Pyramid Network (KFPN) (Using by setting --arch fpn_resnet_18)
• Use images from both left and right cameras (Control by setting the use_left_cam_prob argument)
• Release pre-trained models

Some modifications from the paper

• Formula (3):

  • A negative value can't be an input of the log operator, so please don't normalize dim as mentioned in the paper because the normalized dim values maybe less than 0. Hence I've directly regressed to absolute dimension values in meters.
  • Use L1 loss for depth estimation (applying the sigmoid activation to the depth output first).

• Formula (5): I haven't taken the absolute values of the ground-truth, I have used the relative values instead. The code is here

• Formula (7): argmin instead of argmax

• Generate heatmap for the center and vertexes of objects as the CenterNet paper. If you want to use the strategy from RTM3D paper, you can pass the dynamic-sigma argument to the train.py script.

2. Getting Started

2.1. Requirement

pip install -U -r requirements.txt

2.2. Data Preparation

Download the 3D KITTI detection dataset from here.

The downloaded data includes:

• Training labels of object data set (5 MB)
• Camera calibration matrices of object data set (16 MB)
• Left color images of object data set (12 GB)
• Right color images of object data set (12 GB)

Please make sure that you construct the source code & dataset directories structure as below.

2.3. RTM3D architecture

The model takes only the RGB images as the input and outputs the main center heatmap, vertexes heatmap, and vertexes coordinate as the base module to estimate 3D bounding box.

2.4. How to run

2.4.1. Visualize the dataset

cd src/data_process

• To visualize camera images with 3D boxes, let's execute:

python kitti_dataset.py

Then Press n to see the next sample >>> Press Esc to quit...

2.4.2. Inference

Download the trained model from here (will be released), then put it to ${ROOT}/checkpoints/ and execute:

python test.py --gpu_idx 0 --arch resnet_18 --pretrained_path ../checkpoints/rtm3d_resnet_18.pth

2.4.3. Evaluation

python evaluate.py --gpu_idx 0 --arch resnet_18 --pretrained_path <PATH>

2.4.4. Training

2.4.4.1. Single machine, single gpu

python train.py --gpu_idx 0 --arch <ARCH> --batch_size <N> --num_workers <N>...

2.4.4.2. Multi-processing Distributed Data Parallel Training

We should always use the nccl backend for multi-processing distributed training since it currently provides the best distributed training performance.

• Single machine (node), multiple GPUs

python train.py --dist-url 'tcp://127.0.0.1:29500' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0

• Two machines (two nodes), multiple GPUs

First machine

python train.py --dist-url 'tcp://IP_OF_NODE1:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 0

Second machine

python train.py --dist-url 'tcp://IP_OF_NODE2:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 1

To reproduce the results, you can run the bash shell script

./train.sh

Tensorboard

• To track the training progress, go to the logs/ folder and

cd logs/<saved_fn>/tensorboard/
tensorboard --logdir=./

• Then go to http://localhost:6006/:

Contact

If you think this work is useful, please give me a star!
If you find any errors or have any suggestions, please contact me (Email: [email protected]).
Thank you!

Citation

@article{RTM3D,
  author = {Peixuan Li,  Huaici Zhao, Pengfei Liu, Feidao Cao},
  title = {RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving},
  year = {2020},
  conference = {ECCV 2020},
}
@misc{RTM3D-PyTorch,
  author =       {Nguyen Mau Dung},
  title =        {{RTM3D-PyTorch: PyTorch Implementation of the RTM3D paper}},
  howpublished = {\url{https://github.com/maudzung/RTM3D-PyTorch}},
  year =         {2020}
}

References

[1] CenterNet: Objects as Points paper, PyTorch Implementation

Folder structure

${ROOT}
└── checkpoints/    
    ├── rtm3d_resnet_18.pth
    ├── rtm3d_fpn_resnet_18.pth
└── dataset/    
    └── kitti/
        ├──ImageSets/
        │   ├── test.txt
        │   ├── train.txt
        │   └── val.txt
        ├── training/
        │   ├── image_2/ (left color camera)
        │   ├── image_3/ (right color camera)
        │   ├── calib/
        │   ├── label_2/
        └── testing/  
        │   ├── image_2/ (left color camera)
        │   ├── image_3/ (right color camera)
        │   ├── calib/
        └── classes_names.txt
└── src/
    ├── config/
    │   ├── train_config.py
    │   └── kitti_config.py
    ├── data_process/
    │   ├── kitti_dataloader.py
    │   ├── kitti_dataset.py
    │   └── kitti_data_utils.py
    ├── models/
    │   ├── fpn_resnet.py
    │   ├── resnet.py
    │   ├── model_utils.py
    └── utils/
    │   ├── evaluation_utils.py
    │   ├── logger.py
    │   ├── misc.py
    │   ├── torch_utils.py
    │   ├── train_utils.py
    ├── evaluate.py
    ├── test.py
    ├── train.py
    └── train.sh
├── README.md 
└── requirements.txt

Usage

usage: train.py [-h] [--seed SEED] [--saved_fn FN] [--root-dir PATH]
                [--arch ARCH] [--pretrained_path PATH] [--head_conv HEAD_CONV]
                [--hflip_prob HFLIP_PROB]
                [--use_left_cam_prob USE_LEFT_CAM_PROB] [--dynamic-sigma]
                [--no-val] [--num_samples NUM_SAMPLES]
                [--num_workers NUM_WORKERS] [--batch_size BATCH_SIZE]
                [--print_freq N] [--tensorboard_freq N] [--checkpoint_freq N]
                [--start_epoch N] [--num_epochs N] [--lr_type LR_TYPE]
                [--lr LR] [--minimum_lr MIN_LR] [--momentum M] [-wd WD]
                [--optimizer_type OPTIMIZER] [--steps [STEPS [STEPS ...]]]
                [--world-size N] [--rank N] [--dist-url DIST_URL]
                [--dist-backend DIST_BACKEND] [--gpu_idx GPU_IDX] [--no_cuda]
                [--multiprocessing-distributed] [--evaluate]
                [--resume_path PATH] [--K K]

The Implementation of RTM3D using PyTorch

optional arguments:
  -h, --help            show this help message and exit
  --seed SEED           re-produce the results with seed random
  --saved_fn FN         The name using for saving logs, models,...
  --root-dir PATH       The ROOT working directory
  --arch ARCH           The name of the model architecture
  --pretrained_path PATH
                        the path of the pretrained checkpoint
  --head_conv HEAD_CONV
                        conv layer channels for output head0 for no conv
                        layer-1 for default setting: 64 for resnets and 256
                        for dla.
  --hflip_prob HFLIP_PROB
                        The probability of horizontal flip
  --use_left_cam_prob USE_LEFT_CAM_PROB
                        The probability of using the left camera
  --dynamic-sigma       If true, compute sigma based on Amax, Amin then
                        generate heamapIf false, compute radius as CenterNet
                        did
  --no-val              If true, dont evaluate the model on the val set
  --num_samples NUM_SAMPLES
                        Take a subset of the dataset to run and debug
  --num_workers NUM_WORKERS
                        Number of threads for loading data
  --batch_size BATCH_SIZE
                        mini-batch size (default: 16), this is the totalbatch
                        size of all GPUs on the current node when usingData
                        Parallel or Distributed Data Parallel
  --print_freq N        print frequency (default: 50)
  --tensorboard_freq N  frequency of saving tensorboard (default: 50)
  --checkpoint_freq N   frequency of saving checkpoints (default: 5)
  --start_epoch N       the starting epoch
  --num_epochs N        number of total epochs to run
  --lr_type LR_TYPE     the type of learning rate scheduler (cosin or
                        multi_step)
  --lr LR               initial learning rate
  --minimum_lr MIN_LR   minimum learning rate during training
  --momentum M          momentum
  -wd WD, --weight_decay WD
                        weight decay (default: 1e-6)
  --optimizer_type OPTIMIZER
                        the type of optimizer, it can be sgd or adam
  --steps [STEPS [STEPS ...]]
                        number of burn in step
  --world-size N        number of nodes for distributed training
  --rank N              node rank for distributed training
  --dist-url DIST_URL   url used to set up distributed training
  --dist-backend DIST_BACKEND
                        distributed backend
  --gpu_idx GPU_IDX     GPU index to use.
  --no_cuda             If true, cuda is not used.
  --multiprocessing-distributed
                        Use multi-processing distributed training to launch N
                        processes per node, which has N GPUs. This is the
                        fastest way to use PyTorch for either single node or
                        multi node data parallel training
  --evaluate            only evaluate the model, not training
  --resume_path PATH    the path of the resumed checkpoint
  --K K                 the number of top K