creyone_model

creyone_model provides PyTorch layer-building utilities for the CREYONE framework. It ships a lightweight, dataclass-driven configuration system that lets you assemble convolutional building blocks — convolution, normalisation, and activation — without touching boilerplate code.

Key features

• Config-first API – every block is fully described by a CNNBlockCfg dataclass. Swap layer types (e.g. BatchNorm → LayerNorm) by changing a single field.

• Hierarchical config composition – BaseCfg supports nested configs; BaseCfg.instance() builds the whole tree from a flat **kwargs dict and returns any unconsumed keys.

• Dimension-agnostic – set tensor_dims=1/2/3 to target 1-D sequences, 2-D images, or 3-D volumes with the same config object.

Contents:

• Getting Started
  • Installation
  • Basic usage
  • Building configs from kwargs
• API Reference
  • Top-level exports
  • CNN blocks (creyone_model.cnn)
  • Utilities (creyone_model.utils)

Indices and tables

• Index

• Module Index