This is a PyTorch attention mechanism library and a collection of deep learning model components. It provides reference implementations of research-based attention mechanisms and neural network layers used to verify and understand deep learning papers.
The project facilitates deep learning research implementation and attention mechanism prototyping to capture global and local dependencies within complex datasets. It includes tools for neural network architecture design, specifically for building custom model components.
The library covers the development of multi-layer perceptrons, convolution blocks, and re-parameterized layers. These modular components are designed for PyTorch model development, allowing for the construction of reusable deep learning architectures.
