How to Implement RT-X Models using PyTorch

In this guide, we will explore the implementation of two innovative models, RTX-1 and RTX-2, as proposed in the paper titled “Open X-Embodiment: Robotic Learning Datasets and RT-X Models.” These models leverage multi-modal data to enhance robotic learning capabilities. This tutorial is designed to help you understand how to set up, use, and troubleshoot these models effectively.

Prerequisites

• You should have Python installed on your system.
• Ensure that PyTorch is set up correctly. You can find installation instructions at PyTorch Official Site.
• Familiarity with command-line interfaces (CLI) will help you navigate installation and execution more smoothly.

Installation

To install the RTX models, run the following command in your terminal:

pip install rtx-torch

Usage

Before diving into the different models, you can get detailed usage instructions by running:

python run.py --help

Understanding RTX-1 and RTX-2

Imagine you’re organizing a library. Each book has a title and content. If you want a librarian to find specific books based on both their titles (text) and genres (videos), you’d use RTX-1. On the other hand, RTX-2 is like a librarian capable of crafting multi-modal summaries by intermixing book covers (images) and synopses (text) to provide richer responses.

RTX-1 Implementation

RTX-1 takes in text instructions and videos. Currently, it does not utilize EfficientNet, but integration is planned for the future. Below is a demonstration of how to use RTX-1:

import torch
from rtx.rtx1 import RTX1, FilmViTConfig

# Use a pre-trained MaxVit model from pytorch
model = RTX1(film_vit_config=FilmViTConfig(pretrained=True))
video = torch.randn(2, 3, 6, 224, 224)
instructions = ["bring me that apple sitting on the table", "please pass the butter"]

# Compute training logits
train_logits = model.train(video, instructions)

# Set the model to evaluation mode
model.model.eval()

# Compute the evaluation logits with a conditional scale of 3
eval_logits = model.run(video, instructions, cond_scale=3.0)
print(eval_logits.shape)

RTX-2 Implementation

RTX-2 accepts both images and text, interleaving them to form comprehensive multi-modal sentences. It outputs text tokens rather than a 7-dimensional vector representing positions and rotations (x, y, z, roll, pitch, yaw, and gripper).

import torch
from rtx import RTX2

# Example usage
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))
model = RTX2()
output = model(img, text)
print(output)

EfficientNet Feature Extraction

EfficientNet can be utilized to extract features from images before they are fed into the RTX models. Here’s how to implement this:

from rtx import EfficientNetFilm

model = EfficientNetFilm('efficientnet-b0', 10)
out = model(img.jpeg)

Running Tests

To ensure that everything is working well, you can run tests on the modules using pytest. First, clone the repository and navigate into it:

git clone 
cd 
pip install -r requirements.txt
python -m pytest tests/tests.py

Troubleshooting

If you encounter issues, consider the following:

• Ensure that all dependencies are installed and are compatible with your current versions of Python and PyTorch.
• Check your input dimensions for both images and text, as mismatched dimensions can lead to runtime errors.
• Consult the detailed help documentation by running python run.py --help to ensure you are using the correct parameters.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.