Skip to content

Latest commit

 

History

History
244 lines (193 loc) · 10.7 KB

README.MD

File metadata and controls

244 lines (193 loc) · 10.7 KB
Raw

A template repository for NA565 final project

This repo is accommodated from MonoCon for the NA565 final project.

After setting up the envionment and converting the final project dataset into the KITTI format, the training code should work directly.

Submission Format Generation

The repo does not have an inference script to generate detection files in the test set, where ground truth labels are not available. You need to implement this by yourself to generate the submission file.

We provide utility functions kitti_3d_to_file() and kitti_file_to_3d() in utils/kitti_convert_utils.py, to generate detection files in kitti format. We also provide merger.py to merge detections files for all frames into a single file. Use them to write your own inference script to generate the submission file on the test set.

Hint: you may look into test.py and MonoconEngine.evaluate() and modify from there to output detection files.

Below are the original readme of MonoCon.

MonoCon (AAAI, 2022)

This repository contains unofficial pytorch implementation for MonoCon: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection (AAAI, 2022).
We have removed the dependency on mmdetection3d from the original author's code.

Features

Unchecked items are currently being prepared.
(Currently, we only have a single GPU, so implementation for Multi-GPU is limited for now.)

  • Single-GPU Training
  • KITTI Evaluation
  • KITTI Submission Format Generation
  • Multi-GPU Training
  • Visualization (2D Bounding Box + Projected 3D Bounding Box)
  • Visualization (Bird Eye's View)
  • Video Inference using KITTI Raw Data Sequences

Preparations

Setup

# [Step 1]: Create new conda environment and activate.
#           Set [ENV_NAME] freely to any name you want. (Please exclude the brackets.)
conda create --name [ENV_NAME] python=3.8
conda activate [ENV_NAME]

# [Step 2]: Clone this repository and change directory.
git clone https://github.com/2gunsu/monocon-pytorch
cd monocon-pytorch

# [Step 3]: See https://pytorch.org/get-started/locally/ and install pytorch for your environment.
#           We have tested on version 1.11.0.
#           It is recommended to install version 1.7.0 or higher.

# [Step 4]: Install some packages using 'requirements.txt' in the repository.
#           The version of numpy must be 1.22.4.
pip install -r requirements.txt

# [Step 5]
conda install cudatoolkit

Environments

We have tested our code in the following 3 environments.
Since the RTX A6000 and RTX 4090 does not support CUDA 10 version, we could not confirm the operation in CUDA 10 or lower environments.

[Environment 1]

OS Python Pytorch CUDA GPU NVIDIA Driver
Ubuntu 18.04.5 LTS 3.8.13 1.11.0 11.4 NVIDIA RTX A6000 470.129.06

[Environment 2]

OS Python Pytorch CUDA GPU NVIDIA Driver
Ubuntu 20.04.6 LTS 3.8.16 1.13.1 11.7 NVIDIA RTX 4090 530.41.03

[Environment 3]

OS Python Pytorch CUDA GPU NVIDIA Driver
Ubuntu 20.04.6 LTS 3.8.16 2.0.1 11.8 NVIDIA RTX 4090 530.41.03

Dataset

KITTI 3D Object Detection Dataset

Please download KITTI dataset from here.
Click the three items below from the link to download it and unzip it into the same folder.

  • Download left color images of object data set (12 GB)
  • Download camera calibration matrices of object data set (16 MB)
  • Download training labels of object data set (5 MB)

The structure of the data files should be as below.

[ROOT]
│
├── training
│   ├── calib
│   │   ├── 000000.txt
│   │   ├── 000001.txt
│   │   └── ...
│   ├── image_2
│   │   ├── 000000.png
│   │   ├── 000001.png
│   │   └── ...
│   └── label_2
│       ├── 000000.txt
│       ├── 000001.txt
│       └── ...
│
└── testing
    ├── calib
    └── image_2

Usage

Training

Just edit the items in config/monocon_configs.py before execution.
If your GPU memory is less than 16GB, please set _C.USE_BENCHMARK in config/monocon_configs.py to False.

python train.py

Evaluation

python test.py  --config_file       [FILL]      # Config file (.yaml file)
                --checkpoint_file   [FILL]      # Checkpoint file (.pth file)
                --gpu_id            [Optional]  # Index of GPU to use for testing (Default: 0)
                --evaluate                      # Perform evaluation (Quantitative Results)

Inference

python test.py  --config_file       [FILL]      # Config file (.yaml file)
                --checkpoint_file   [FILL]      # Checkpoint file (.pth file)
                --visualize                     # Perform visualization (Qualitative Results)
                --gpu_id            [Optional]  # Index of GPU to use for testing (Default: 0)
                --save_dir          [FILL]      # Path where visualization results will be saved to

Video Inference on KITTI Raw Dataset

The KITTI Raw Dataset can be downloaded by scene from here. (You will probably need to log in.)
There are 4 download links for each scene, of which, please download only "synced+rectified data" and "calibration".

python test_raw.py  --data_dir          [FILL]      # Path where sequence images are saved
                    --calib_file        [FILL]      # Calibration file ("calib_cam_to_cam.txt")
                    --checkpoint_file   [FILL]      # Checkpoint file (.pth file)
                    --gpu_id            [Optional]  # Index of GPU to use for testing (Default: 0)
                    --fps               [Optional]  # FPS of the result video (Default: 25)
                    --save_dir          [FILL]      # Path of the directory to save the result video

Quantitative Results

3D Metric on Car Class

AP40@Easy AP40@Mod. AP40@Hard
Official 26.33 19.03 16.00
This Repo 26.03 (-0.30) 19.02 (-0.01) 15.92 (-0.08)

BEV Metric on Car Class

AP40@Easy AP40@Mod. AP40@Hard
Official 34.65 25.39 21.93
This Repo 35.98 (+1.33) 26.01 (+0.62) 22.41 (+0.48)

You can download the weight file and config file for the above pretrained model here.
Change the value of DATA.ROOT in the config.yaml file to the KITTI data path.

Depending on the starting seed, the above performance may not be reproduced.
The tables below show the performance obtained through 5 independent random seed training, and it can be seen that there is some performance deviation.

[Results from Environment 1]

AP40@Easy AP40@Mod. AP40@Hard
Train #1 26.03 19.02 15.92
Train #2 25.50 18.40 15.47
Train #3 23.61 16.85 14.79
Train #4 23.40 17.36 14.69
Train #5 24.29 17.95 15.32
Mean 24.57 17.92 15.24
Std 1.16 0.85 0.51

[Results from Environment 2]

AP40@Easy AP40@Mod. AP40@Hard
Train #1 23.22 17.84 15.10
Train #2 25.19 17.58 15.40
Train #3 25.01 17.93 15.33
Train #4 24.31 17.33 15.20
Train #5 24.11 17.95 15.22
Mean 24.37 17.73 15.25
Std 0.79 0.27 0.12

Qualitative Results

Visualizations for 2D Boxes, 3D Boxes, and BEV, respectively, from top to bottom for each sample.

  • 000008.png (Validation Set)

  • 000134.png (Validation Set)

  • 000472.png (Validation Set)

  • 004122.png (Validation Set)

Known Issues

  • (2022.09.08) It has been found that reducing the batch size causes the target data corresponding to that mini-batch to be empty, resulting in errors. If possible, use a value greater than 4 as the batch size.

Change Log

This repository was last updated to v1.0.3 on 2022.09.10.
Check changelog.md for detailed update history.

Citation

@InProceedings{liu2022monocon,
    title={Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection},
    author={Xianpeng Liu, Nan Xue, Tianfu Wu},
    booktitle = {36th AAAI Conference on Artifical Intelligence (AAAI)},
    month = {Feburary},
    year = {2022}
}

References

The following repositories were referred.