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https://github.com/uzh-rpg/rpg_asynet
Code for the paper "Event-based Asynchronous Sparse Convolutional Networks" (ECCV, 2020).
https://github.com/uzh-rpg/rpg_asynet
Last synced: about 5 hours ago
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Code for the paper "Event-based Asynchronous Sparse Convolutional Networks" (ECCV, 2020).
- Host: GitHub
- URL: https://github.com/uzh-rpg/rpg_asynet
- Owner: uzh-rpg
- License: gpl-3.0
- Created: 2020-07-20T07:32:48.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-04-07T08:49:39.000Z (over 3 years ago)
- Last Synced: 2024-07-31T02:34:54.543Z (3 months ago)
- Language: Python
- Homepage:
- Size: 648 KB
- Stars: 125
- Watchers: 12
- Forks: 22
- Open Issues: 8
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# Event-based Asynchronous Sparse Convolutional Networks
This is the code for the paper **Event-based Asynchronous Sparse Convolutional Networks**
([PDF](http://rpg.ifi.uzh.ch/docs/ECCV20_Messikommer.pdf)) by Nico Messikommer*, [Daniel Gehrig*](https://danielgehrig18.github.io),
[Antonio Loquercio](https://antonilo.github.io/), and [Davide Scaramuzza](http://rpg.ifi.uzh.ch/people_scaramuzza.html).
If you use any of this code, please cite the following publication:
```bibtex
@InProceedings{Messikommer20eccv,
author = {Nico Messikommer and Daniel Gehrig and Antonio Loquercio and Davide Scaramuzza},
title = {Event-based Asynchronous Sparse Convolutional Networks},
journal = {European Conference on Computer Vision. (ECCV)},
url = {http://rpg.ifi.uzh.ch/docs/ECCV20_Messikommer.pdf},
year = 2020
}
```
## Installation
First set up an [Anaconda](https://www.anaconda.com/) environment:
conda create -n asynet python=3.7
conda activate asynet
Then clone the repository and install the dependencies with pip
git clone [email protected]:uzh-rpg/rpg_asynet.git
cd rpg_asynet/
pip install -r requirements.txt
In addition `sparseconvnet 0.2` needs to be installed from [here](https://github.com/facebookresearch/SparseConvNet).
### CPP Bindings
To build the cpp bindings for the event representation tool, you can follow the instructions below:
pip install event_representation_tool/
For the bindings for asynchronous sparse convolutions, we first need to clone the 3.4.0-rc version of [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) into the `include` folder. In addition, pybind11 is required.
cd async_sparse_py/include
git clone https://gitlab.com/libeigen/eigen.git --branch 3.4.0-rc1
conda install -c conda-forge pybind11
Finally, the bindings can be installed
pip install async_sparse_py/
## Sparse CNN Training
The training parameters can be adjusted in the `config/settings.yaml` file.
The following training tasks and datasets are supported:
* Classification on NCars and NCaltech101.
* Object Detection on Prophesee Gen1 Automotive and NCaltech101
Download location of the datasets:
* [Classification NCaltech101](http://rpg.ifi.uzh.ch/datasets/gehrig_et_al_iccv19/N-Caltech101.zip)
* [NCars](http://www.prophesee.ai/dataset-n-cars/)
* [Object Detection NCaltech101](https://www.garrickorchard.com/datasets/n-caltech101)
* [Prophesee Gen1 Automotive](https://www.prophesee.ai/2020/01/24/prophesee-gen1-automotive-detection-dataset/):
To test the code, make a directory `data/` in the root of the repository and download one of the datasets:
mkdir data
cd data
wget http://rpg.ifi.uzh.ch/datasets/gehrig_et_al_iccv19/N-Caltech101.zip
unzip N-Caltech101.zip
rm N-Caltech101.zip
The dataset can be configured in the `dataset/name` tag in `config/settings.yaml`. Different model types can be chosen based on the task and whether or not sparse convolutions should be used.
* Sparse VGG for Classification and Object Detection
* Standard VGG for Classification and Object Detection
These can be configured in the `model` tag in `config/settings.yaml`.
The following command starts the training:
```bash
CUDA_VISIBLE_DEVICES=, python train.py --settings_file config/settings.yaml
```
By default, a folder with the current date and time is created in `log/` containing the corresponding tensorboard files.
## Unit Tests
To test the different asynchronous and sparse layers, multiple unit tests are implemented in `unittests/`:
* Asynchronous Sparse Convolution Layer 2D: `sparse_conv2D_test.py`
* Asynchronous Sparse Convolution Layer 2D CPP Implementation: `sparse_conv2D_cpp_test.py`
* Asynchronous Sparse Max Pooling Layer: `sparse_max_pooling_test.py`
* Asynchronous Sparse VGG: `sparse_VGG_test.py`.
There are three paths in the script `sparse_VGG_test.py` specified with `'PATH_TO_MODEL'` and `'PATH_TO_DATA'`,
which need to be replaced with the NCaltech Classification dataset and the sparse classification model trained on N-Caltech.
To run the unittests call
python -m unittest discover -s unittests/ -p '*_test.py'
## Evaluation
There is one script in the `evaluation/sliding_window_flops.py` folder for computing the number of FLOPs.
The command to execute the script is:
python -m evaluation.sliding_window_flops --setting config/settings.yaml
--save_dir --num_events 1 --num_samples 500
--representation histogram --use_multiprocessing
The output of the script are the numbers of FLOPs for the four processing modes (Asyn Sparse Conv, Asyn Conv, Sparse Conv, Standard Conv).