Principle:Kornia Kornia ONNX Model Loading
| Knowledge Sources | |
|---|---|
| Domains | ONNX, Deployment, Model_Management |
| Last Updated | 2026-02-09 15:00 GMT |
Overview
Technique of loading pre-trained ONNX model files from local paths, URLs, or HuggingFace Hub for inference.
Description
ONNX (Open Neural Network Exchange) provides a standardized format for representing trained neural network models. Model loading involves fetching the serialized model graph and weights, deserializing to an onnx.ModelProto object, and optionally caching locally. Sources include:
- Local file paths — direct filesystem access.
- HTTP/HTTPS URLs — remote model downloads.
- HuggingFace Hub identifiers — prefixed with
"hf://".
Cached downloads avoid redundant network transfers for repeated use.
Usage
Use when you need to load ONNX models for inference pipelines, model chaining, or deployment. Required before creating ONNXSequential pipelines or running standalone inference.
Theoretical Basis
ONNX models are serialized as Protocol Buffers containing a computation graph (nodes, edges) and weight tensors. The graph defines operations (ops) from a versioned ONNX opset. Loading involves:
- Resolve source — determine whether the identifier is a local path, URL, or HF Hub reference.
- Download if needed — fetch remote models and cache locally.
- Deserialize protobuf — parse the binary file into a
ModelProtoobject. - Validate graph structure — ensure the model conforms to the ONNX specification.