infur
onnx-simplifier
| infur | onnx-simplifier | |
|---|---|---|
| 2 | 3 | |
| 42 | 3,550 | |
| - | - | |
| 3.5 | 6.5 | |
| 7 months ago | 24 days ago | |
| Rust | C++ | |
| MIT License | Apache License 2.0 |
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infur
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[Help Needed] Deployment of torchscript using rust
Here's a cool example that was posted last month: https://github.com/ahirner/infur. Different front end goal, but the onnxruntime makes it easy. Tch-rs is nice as others have said as well.
- Show: Cross-platform Image segmentation on video using eGUI, onnxruntime and ffmpeg
onnx-simplifier
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Show: Cross-platform Image segmentation on video using eGUI, onnxruntime and ffmpeg
onnx-simplifier can shed some of incompatibilities in widespread use, but is itself bug ridden and lagging behind the standard. For any serious model, or when you don't get lucky simplifying the model upstream, you'd generally want good support of opset 11.
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[Technical Article] OCR Upgrade
ONNX Simplifier：https://github.com/daquexian/onnx-simplifier
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PyTorch 1.10
As far as I know, the ONNX format won't give you a performance boost on its own. However, there are ONNX optimizers for the ONNX runtime which will speed up your inference.
But if you are using Nvidia Hardware, then TensorRT should give you the best performance possible, especially if you change the precision level. Don't forget to simplify your ONNX model before you converting it to TensorRT though: https://github.com/daquexian/onnx-simplifier
What are some alternatives?
tch-rs - Rust bindings for the C++ api of PyTorch.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
torch2trt - An easy to use PyTorch to TensorRT converter
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
functorch - functorch is JAX-like composable function transforms for PyTorch.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
opencv-mobile - The minimal opencv for Android, iOS, ARM Linux, Windows, Linux, MacOS, WebAssembly
Paddle2ONNX - ONNX Model Exporter for PaddlePaddle
vision - Datasets, Transforms and Models specific to Computer Vision
PaddleOCR2Pytorch - PaddleOCR inference in PyTorch. Converted from [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)