orionvis 1.1.3

A Python package for computer vision tasks with integrated CUDA extensions.

OrionVis

A PyTorch‑friendly Computer Vision toolkit for unified backbone loading, feature extraction, and registered preprocessing.

Overview

OrionVis streamlines common CV workflows in PyTorch: load backbones with a single call, manage pretrained weights via YAML with lazy indexing, and apply registered transforms for classification inference. It consolidates model, weight, and preprocessing logic while staying lightweight and production‑ready.

Features

• Unified backbone loader: resnet*, dino*, mobilemamba_*.
• YAML‑based weights with cached index and lazy loading.
• Transform registry (e.g., ImageClassification) for ready‑to‑use preprocessing.
• Feature extraction helpers and utilities.

Installation

Prerequisites:

• NVIDIA GPU with CUDA (tested on CUDA 11.8+)
• PyTorch >= 2.0
• Microsoft Visual C++ Build Tools (for Windows)

Install from Pre-built Wheels (Recommended for Users)

If you do not want to compile from source, you can install the pre-built wheel package directly:

pip install orionvis

Development Installation (Recommended for Developers)

For Windows users, we provide a build.bat script that handles environment setup, cleaning, and editable installation:

git clone https://github.com/bluemoon-o2/orionvis
cd orionvis
# install in editable mode
build.bat

CUDA Extensions

OrionVis comes with integrated high-performance CUDA extensions to accelerate specific operations. These extensions are automatically compiled during installation.

1. Selective Scan (Mamba)

Optimized CUDA implementation of the Selective Scan mechanism used in Mamba architectures (mamba_mobile, etc.). It provides significant speedups compared to the pure PyTorch implementation.

• Backend: orionvis.extentions.selective_scan
• Usage: Automatically used by mobilemamba backbones when available.

2. InPlace Activated Batch Normalization (InPlace-ABN)

A memory-efficient implementation of Batch Normalization combined with activation functions. It performs the activation in-place, saving GPU memory during training.

• Backend: orionvis.extentions.inplace_abn
• Usage:

from orionvis.extentions.inplace_abn import InPlaceABN

# Use just like standard BatchNorm2d but with activation included
layer = InPlaceABN(num_features=64, activation="leaky_relu", activation_param=0.01)

Quickstart

import orionvis

# Load a backbone with pretrained weights
model = orionvis.load("mobilemamba_b1s")

# Retrieve weight metadata and preprocessing transform
weight = orionvis.get_weights("mobilemamba_b1s")
transform = orionvis.get_transform("mobilemamba_b1s")

# Example: preprocess an input PIL image
# img = transform(img)
# logits = model(img.unsqueeze(0))

OrionVis Hub

OrionVis Hub allows you to load models and entrypoints directly from GitHub repositories or local directories.

Load Model

Load a model or entrypoint from a GitHub repository:

import orionvis.hub as hub

# Load from GitHub
model = hub.load("pytorch/vision:v0.10.0", "resnet18")

# Load from a local directory
# model = hub.load("/path/to/local/repo", "custom_model", source="local")

List Entrypoints

List all available entrypoints in a repository:

import orionvis.hub as hub

entrypoints = hub.entrypoints("pytorch/vision:v0.10.0")
print(entrypoints)

View Documentation

View the docstring of a specific entrypoint:

import orionvis.hub as hub

doc = hub.docs("pytorch/vision:v0.10.0", "resnet18")
print(doc)

Load State Dict

Download and load a state dictionary from a URL:

import orionvis.hub as hub

state_dict = hub.load_state_dict_from_url(
    "https://download.pytorch.org/models/resnet18-5c106cde.pth",
    check_hash=True
)

Models

• Programmatic list: orionvis.list_models()
• Included backbones: resnext*, wide_resnet*, dinov2_*, dinov3_*, mobilemamba_*.

Weights

• Config path: orionvis/configs/weights/mobilemamba.yaml (multi‑model YAML; one class per document).
• Download path cache: ~/.cache/orionvis (configurable via ORIONVIS_CACHE).

License

Apache License 2.0

Citation

@article{mobilemamba,
  title={MobileMamba: Lightweight Multi-Receptive Visual Mamba Network},
  author={Haoyang He and Jiangning Zhang and Yuxuan Cai and Hongxu Chen and Xiaobin Hu and Zhenye Gan and Yabiao Wang and Chengjie Wang and Yunsheng Wu and Lei Xie},
  journal={arXiv preprint arXiv:2411.15941},
  year={2024}
}

References

• torchvision
• DINOv2
• DINOv3
• MobileMamba