BiRefNet TensorRT Inference

Introduction

This project provides code for performing inference with BiRefNet using TensorRT. The aim is to accelerate the inference process by leveraging the high-performance capabilities of TensorRT.

Inference Time Comparison

1. First Inference Time

2. Average Inference Time (excluding the first)

Note:

1. Both the PyTorch and ONNX models are from the official BiRefNet GitHub.
2. The TensorRT model was converted using Convert-ONNX-Model-to-TensorRT-Engine.
3. All tests were conducted on a Win10 system with an RTX 4080 Super.
4. Refer to model_compare.py for the conversion code.

Features

• [x] Efficient inference with BiRefNet using TensorRT
• [x] foreground estimate
• [x] colab example
• [x] Performance comparison between PyTorch, ONNX, and TensorRT inference
• [ ] Inference using Docker for an isolated and reproducible environment

Prerequisites

• NVIDIA GPU with CUDA(>=11.X) and Cudnn(>=8.X)
• Python 3.9

Installation

pip install -r requirements.txt

Usage

1. download onnx model

First, download onnx model from Google Drive

2. Convert ONNX Model to TensorRT Engine

second, convert your ONNX model to a TensorRT engine using the provided conversion script:

from utils import convert_onnx_to_engine

onnx_file_path = "birefnet.onnx"
engine_file_path = "engine.trt"

convert_onnx_to_engine(onnx_file_path, engine_file_path)

3. Run Inference

Now, you can run inference using the TensorRT engine with the following command:

3.1 single infer

 python .\infer.py --image-path image_path --output-path result.png --output-alpha-path result_alpha.png --engine-path .\engine.trt

3.2 infer for directory

python .\infer.py --image-path image_dir --output-path output_dir --output-alpha-path alpha_dir --engine-path .\engine.trt --mode m

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue if you have any suggestions or find bugs.

Thanks

1. Birefnet
2. fast-foreground-estimation