jackzhousz / lzu

Code for Learning to Zoom and Unzoom (CVPR 2023)

Learning to Zoom and Unzoom

Official repository for the CVPR 2023 paper Learning to Zoom and Unzoom [paper] [website].

In a nutshell, LZU is a highly flexible method to apply spatial attention to neural nets. The extremely simple source code (zoom and unzoom) can be applied to any model that uses spatial processing (e.g. convolutions).

Setup (Code + Data + Models)

1) Set up the coding environment

First, clone the repository (including the mmdet3d submodule):

git clone https://github.com/tchittesh/lzu.git --recursive && cd lzu

Then, you'll need to install the MMDetection3D (v1.0.0rc6) submodule and the lzu package. To do this, you can either:

• replicate our exact setup by installing miniconda and running

conda env create -f environment.yml

• OR install it from scratch according to getting_started.md and then install our lzu package with

pip install -e .

The first option should be more reliable, but not as flexible if you want to run specific versions of Python/PyTorch/MMCV.

2) Download the dataset

You'll need to set up the nuScenes dataset according to data_preparation.md. Your final data folder should look like this:

 data/nuscenes/
 ├── maps/
 ├── samples/
 ├── sweeps/
 ├── v1.0-trainval/
 ├── nuscenes_infos_train_mono3d.coco.json
 ├── nuscenes_infos_train.pkl
 ├── nuscenes_infos_val_mono3d.coco.json
 └── nuscenes_infos_val.pkl

3) [Optional] Download our pretrained checkpoints

Download our pretrained checkpoints from Google Drive and place them in this directory, using symbolic links if necessary.

Scripts

This should be super easy! Simply run

sh run.sh [experiment_name]

for any valid experiment name in the configs/ directory. Examples include fcos3d_0.50, which is the uniform downsampling baseline at 0.50x scale, and lzu_fcos3d_0.75, which is LZU at 0.75x scale.

This script will first run inference using the pretrained checkpoint, then train the model from scratch, and finally run inference using the trained model.

Results

Our pretrained models (from the paper) achieve the following NDS scores.

Scale	Baseline Experiment	NDS	LZU Experiment	NDS
0.25x	fcos3d_0.25	0.2177	lzu_fcos3d_0.25	0.2341
0.50x	fcos3d_0.50	0.2752	lzu_fcos3d_0.50	0.2926
0.75x	fcos3d_0.75	0.3053	lzu_fcos3d_0.75	0.3175
1.00x	fcos3d_1.00	0.3122	lzu_fcos3d_1.00	0.3258

As can be seen, LZU achieves a superior accuracy-latency tradeoff compared to uniform downsampling. For more details, please refer to our paper.

Citation

If you find our code useful, please consider citing us!

@misc{thavamani2023learning,
      title={Learning to Zoom and Unzoom}, 
      author={Chittesh Thavamani and Mengtian Li and Francesco Ferroni and Deva Ramanan},
      year={2023},
      eprint={2303.15390},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}