Einops is a Python library that provides a uniform, readable, and reliable API for tensor manipulation across multiple deep learning frameworks (NumPy, PyTorch, TensorFlow, JAX, and others). It uses Einstein-like notation (e.g., 'b c h w -> b h w c') to express axis reordering, composition, decomposition, reduction, and repetition in a single, explicit string. The library is built around a core transformation engine that parses axis patterns into a cached 'TransformRecipe', which is then applied via a backend abstraction layer. This architecture allows einops to support 10+ frameworks with minimal code duplication, while also providing framework-specific layer wrappers (Rearrange, Reduce, EinMix) for seamless integration into model definitions. The library is designed for production and research use, with a focus on reducing errors from manual view/permute/transpose operations.
Elevation Mapping CuPy is a GPU-accelerated elevation mapping system for legged robots, integrating point cloud registration, ray casting, multi-modal semantic fusion (geometry, RGB, semantics, deep features), and convex plane decomposition. It is built as a ROS Noetic package (ROS1) with a mixture of C++ (using pybind11) and Python (CuPy) nodes. The system ingests point clouds and images, processes them through configurable fusion algorithms, and outputs elevation maps with multiple layers (elevation, variance, traversability, semantic classes). It also includes a learning-based traversability filter supporting both Chainer and PyTorch backends, and a plane segmentation pipeline (CGAL/RANSAC) that converts elevation maps into planar terrain regions for motion planning. The architecture is modular with a plugin system for post-processing filters and a fusion manager for multiple data sources. While the core is tightly integrated with ROS1 and CUDA, several components (plugin manager, fusion framework, CuPy kernel compilation pattern, traversability filter) exhibit high craftsmanship and potential for reuse in other GPU-accelerated robotics projects. The codebase is well-tested and documented, but its dependency on ROS Noetic and specific CUDA versions limits portability and creates medium technical drift.