One of the core features of Polygraphy is comparison of model outputs across multiple different backends. This makes it possible to check the accuracy of one backend with respect to another.

In this example, we'll look at how you can use the Polygraphy API to run inference with synthetic input data using ONNX-Runtime and TensorRT, and then compare the results using two different comparison methods:

A simple comparison using absolute tolerance
A more comprehensive comparison using distance metrics (L2 distance, cosine similarity, and PSNR)

Running The Example

Install prerequisites
- Ensure that TensorRT is installed
- Install other dependencies with python3 -m pip install -r requirements.txt
Run the example
```
python3 example.py
```
[Optional] Inspect the inference outputs from the example:
```
polygraphy inspect data inference_results.json
```

Comparison Methods

The example demonstrates two approaches for comparing outputs:

Simple Comparison: Uses absolute tolerance to determine if outputs match within a specified threshold.
Distance Metrics: Performs a more comprehensive comparison using multiple metrics including:
- L2 distance (Euclidean distance)
- Cosine similarity (measures the angle between vectors)
- PSNR (Peak Signal-to-Noise Ratio, useful for comparing image-like data)

These comparison methods help validate that frameworks produce equivalent results within acceptable margins.