shaderobotics/vision-perceiver repository overview

⁠Vision Perceiver-ROS2 Wrapper

This is a ROS2 wrapper for the Vision Perceiver w/ Iterative Attention model with an image classification head, Vision Perciever I/O⁠. We utilize huggingface and the transformers for the source of the algorithm⁠. The main idea is for this container to act as a standalone interface and node, removing the necessity to integrate separate packages and solve numerous dependency issues. We have built all different versions of the algorithm (i.e. learned, fourier, etc.) for each ROS2 distribution.

The authors introduce the Perceiver - a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to hundreds of thousands of inputs, like ConvNets. The model leverages an asymmetric attention mechanism to iteratively distill inputs into a tight latent bottleneck, allowing it to scale to handle very large inputs. The paper shows that this architecture is competitive with or outperforms strong, specialized models on classification tasks across various modalities: images, point clouds, audio, video, and video+audio.

⁠Installation Guide

⁠Using Docker Pull

1. Install Docker⁠ and ensure the Docker daemon is running in the background.
2. Run docker pull shaderobotics/deit:${ROS2_DISTRO}-${MODEL_VERSION} we support all ROS2 distributions along with all model versions found in the model version section below.
3. Follow the run commands in the usage section below

⁠Build Docker Image Natively

1. Install Docker⁠ and ensure the Docker daemon is running in the background.
2. Clone this repo with git pull https://github.com/open-shade/vision_perceiver.git
3. Enter the repo with cd vision_perceiver
4. To pick a specific model version, edit the ALGO_VERSION constant in /vision_perceiver/vision_perceiver.py
5. Build the container with docker build . -t [name]. This will take a while. We have also provided associated cloudbuild.sh scripts to build on GCP all of the associated versions.
6. Follow the run commands in the usage section below.

⁠Model Versions

• fourier
• learned
• conv

More information about these versions can be found in the paper⁠. fourier, conv, learned, are changes in the inner working layers of the model (weights).

⁠Example Docker Command

docker pull shaderobotics/vision-perceiver:foxy-fourier

⁠Usage

⁠Run the Vision Perceier Node

Run docker run -t --net=host shaderobotics/vision-perceiver:${ROS_DISTRO}-${MODEL_VERSION}. Your node should be running now. Then, by running ros2 topic list, you should see all the possible pub and sub routes.

For more details explaining how to run Docker images, visit the official Docker documentation here⁠. Also, additional information as to how ROS2 communicates between external environment or multiple docker containers, visit the official ROS2 (foxy) docs here⁠.

⁠Topics

⁠Testing / Demo

To test and ensure that this package is properly installed, replace the Dockerfile in the root of this repo with what exists in the demo folder. Installed in the demo image contains a camera stream emulator⁠ by klintan⁠ which directly pubs images to the Vision Perceiver node and processes it for you to observe the outputs.

To run this, run docker build . -t [name], then docker run --net=host -t [name]. Observing the logs for this will show you what is occurring within the container. If you wish to enter the running container and preform other activities, run docker ps, find the id of the running container, then run docker exec -it [containerId] /bin/bash