0.0.3

Changelog:

1. Encapsulation:

   • Enclosed both MaxViT and RT2 functionalities within a single class named RT2 to streamline the initialization and usage process.

2. Default Parameters:

   • Set default values for various parameters in the RT2 class. This allows users to instantiate the class without having to provide every single parameter, unless they need a non-default configuration.

3. Training and Evaluation Modes:

   • Introduced train() and eval() methods to easily toggle between training and evaluation modes for the RT2 model, reflecting standard practice in PyTorch.

4. Unified Forward Method:

   • Created a __call__ method that wraps around the forward method of the RT2 model. This provides an intuitive way to process videos and instructions by directly invoking the instance of the RoboticTransformer class.

5. Conditional Execution:

   • Modified the forward process (via the __call__ method) to conditionally use the cond_scale argument if provided, ensuring that it's used only during evaluation as hinted in the provided code.

6. Example Usage:

   • Added an example at the end to demonstrate how to use the new RT2 class for training and evaluation.

Overall, these changes are geared towards making the code more user-friendly and modular, encapsulating intricacies, and providing a more Pythonic interface to users.