Struct OnnxModel 

pub struct OnnxModel { /* private fields */ }

An ONNX model loaded from a protobuf file.

Provides methods for inspecting, extracting weights, saving quantized models, and validating graph connectivity.

Implementations

impl OnnxModel

pub fn load(path: impl AsRef<Path>) -> Result<Self>

Load an ONNX model from a file path.

Errors

Returns QuantizeError::ModelLoad if the file cannot be opened, is too large (>10 GB), or contains invalid protobuf data.

pub fn info(&self) -> ModelInfo

Return a summary of the model’s structure.

pub fn input_shapes(&self) -> Vec<Vec<i64>>

Return the shapes of each graph input from the protobuf type info.

Each inner Vec<i64> contains the dimension values. Dynamic dims (symbolic or missing) are returned as -1. Returns one entry per graph.input that has tensor type information.

pub fn extract_weights(&self) -> Vec<WeightTensor>

Extract all FP32 weight tensors from the model’s initializers.

pub fn total_size_bytes(&self) -> usize

Total size of all weight tensors in bytes (float32).

Prefer computing this from already-extracted weights when available: weights.iter().map(|w| w.size_bytes()).sum() avoids reparsing.

impl OnnxModel

pub fn save_quantized( &mut self, quantized_data: &[QdqWeightInput], path: impl AsRef<Path>, ) -> Result<()>

Save a quantized model using the QDQ (DequantizeLinear) pattern.

Signature is identical to v0.2.0 — existing callers (CLI, calibration pipeline, examples) compile without changes.

What changed internally

v0.2.0 appended metadata to initializer names (e.g. conv1.weight → conv1.weight__qINT8_s0.001_z-3_len9408) without updating the nodes that reference them. ONNX Runtime rejected these models on load.

v0.3.0 inserts a DequantizeLinear node per weight. The node’s output carries the original name, so every downstream node is unchanged. Graph connectivity is preserved by construction, and the resulting model loads and runs in ONNX Runtime.

INT4 storage note

DequantizeLinear requires INT8 input (opset < 21). INT4-quantized values ([-8, 7]) are stored as INT8 bytes. Quantization accuracy is still INT4-level; only the on-disk size is 4× instead of the 8× that bit-packing would give. True INT4 packing is a v0.4.0 target.

impl OnnxModel

pub fn validate_connectivity(&self) -> ConnectivityReport

Check that every node input in the graph resolves to a known tensor.

A “known tensor” is one of:

• a declared graph input
• an initializer
• the output of a node appearing earlier in the node list

This is the exact check ONNX Runtime performs on load. It’s the check that v0.2.0’s validate command skipped, which is why the rename bug went undetected. Integrate report.summary() into the CLI validate output alongside the existing structure / weight checks.

impl OnnxModel

pub fn load_quantized_info(&self) -> Vec<QuantizedWeightInfo>

Extract metadata about quantized weights from a QDQ-format model.

Looks for initializer triples: {base}_quantized, {base}_scale, {base}_zp

Scale and zero-point values are read directly from the tensors. Bit-width comes from metadata_props (written by save_quantized); defaults to 8 if the metadata entry is missing.

Trait Implementations

impl Debug for OnnxModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.