quantize-rs 0.7.0

Neural network quantization toolkit for ONNX models
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42

# Examples

Runnable examples demonstrating quantize-rs library usage.

## Prerequisites

Place one or more ONNX model files in the project root before running:

```bash
# MNIST
curl -L -o mnist.onnx https://github.com/onnx/models/raw/main/validated/vision/classification/mnist/model/mnist-8.onnx

# ResNet-18
curl -L -o resnet18-v1-7.onnx https://github.com/onnx/models/raw/main/validated/vision/classification/resnet/model/resnet18-v1-7.onnx
```

## Running

```bash
cargo run --example basic_quantization
cargo run --example batch_quantize
cargo run --example activation_calibration
cargo run --example validate_real_model -- --bits 8 --per-channel model.onnx
```

## Descriptions

### `basic_quantization.rs`

Minimal INT8 quantization workflow: load model, extract weights, quantize, save, and print compression results.

### `batch_quantize.rs`

Iterates over multiple model files and quantizes each one, skipping any that are not found on disk.

### `activation_calibration.rs`

Full activation-based calibration pipeline. Loads or generates calibration data, runs inference through tract to collect per-layer activation statistics, quantizes using the observed ranges, and saves the result. Accepts `--model`, `--calibration-data`, `--output`, `--bits`, `--per-channel`, and `--shape` arguments.

### `validate_real_model.rs`

Loads any ONNX file, quantizes all weights, and reports per-tensor mean absolute error (MAE) and compression ratio. Optionally saves the quantized model and validates the resulting QDQ graph. Useful for quickly checking quantization quality on a real model without running inference. Accepts `--bits`, `--per-channel`, `--min-elements`, and `--output`.