Simple node deletion tool for onnx.

Related tags

Deep Learningpythonmodelsonnxonnxruntime
Overview

snd4onnx

Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs. Pull requests are welcome.

https://github.com/PINTO0309/simple-onnx-processing-tools

Downloads GitHub PyPI CodeQL

1. Setup

1-1. HostPC

### option
$ echo export PATH="~/.local/bin:$PATH" >> ~/.bashrc \
&& source ~/.bashrc

### run
$ pip install -U onnx \
&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \
&& pip install -U snd4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/snd4onnx:latest

### docker build
$ docker build -t pinto0309/snd4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/snd4onnx:latest
$ cd /workdir

2. CLI Usage

$ snd4onnx -h

usage:
  snd4onnx [-h]
    --remove_node_names REMOVE_NODE_NAMES [REMOVE_NODE_NAMES ...]
    --input_onnx_file_path INPUT_ONNX_FILE_PATH
    --output_onnx_file_path OUTPUT_ONNX_FILE_PATH

optional arguments:
  -h, --help
        show this help message and exit

  --remove_node_names REMOVE_NODE_NAMES [REMOVE_NODE_NAMES ...]
        ONNX node name to be deleted.

  --input_onnx_file_path INPUT_ONNX_FILE_PATH
        Input onnx file path.

  --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
        Output onnx file path.

3. In-script Usage

>>> from snd4onnx import remove
>>> help(remove)

Help on function remove in module snd4onnx.onnx_remove_node:

remove(
    remove_node_names: List[str],
    input_onnx_file_path: Union[str, NoneType] = '',
    output_onnx_file_path: Union[str, NoneType] = '',
    onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None
) -> onnx.onnx_ml_pb2.ModelProto

    Parameters
    ----------
    remove_node_names: List[str]
        List of OP names to be deleted.
        e.g. remove_node_names = ['op_name1', 'op_name2', 'op_name3', ...]

    input_onnx_file_path: Optional[str]
        Input onnx file path.
        Either input_onnx_file_path or onnx_graph must be specified.

    output_onnx_file_path: Optional[str]
        Output onnx file path.
        If output_onnx_file_path is not specified, no .onnx file is output.

    onnx_graph: Optional[onnx.ModelProto]
        onnx.ModelProto.
        Either input_onnx_file_path or onnx_graph must be specified.
        onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.

    Returns
    -------
    removed_graph: onnx.ModelProto
        OP removed onnx ModelProto.

4. CLI Execution

$ snd4onnx \
--remove_node_names node_name_a node_name_b
--input_onnx_file_path input.onnx \
--output_onnx_file_path output.onnx

5. In-script Execution

from snd4onnx import remove

onnx_graph = remove(
    remove_node_names=['node_name_a', 'node_name_b'],
    input_onnx_file_path='input.onnx',
)

# or

onnx_graph = remove(
    remove_node_names=['node_name_a', 'node_name_b'],
    onnx_graph=graph,
)

6. Sample

6-1. sample.1

Before After
test1 onnx test1_removed onnx

6-2. sample.2

Before After
test3 onnx test3_removed onnx

6-3. sample.3

Before After
test5 onnx test5_removed onnx

6-4. sample.4

Before After
test7 onnx test7_removed onnx

6-5. sample.5

Before After
test8 onnx test8_removed onnx

7. Reference

  1. https://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.html
  2. https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon
  3. https://github.com/PINTO0309/scs4onnx
  4. https://github.com/PINTO0309/sne4onnx
  5. https://github.com/PINTO0309/snc4onnx
  6. https://github.com/PINTO0309/sog4onnx
  7. https://github.com/PINTO0309/PINTO_model_zoo

8. Issues

https://github.com/PINTO0309/simple-onnx-processing-tools/issues

You might also like...
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage

Microsoft 5.7k Jan 9, 2023
PyTorch ,ONNX and TensorRT implementation of YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

null 4.2k Jan 1, 2023
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
YOLOv5 in PyTorch ONNX CoreML TFLite

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. All code and models are under active development, and are subject to modification or deletion without notice.

Ultralytics 34.1k Dec 31, 2022
A code generator from ONNX to PyTorch code

onnx-pytorch Generating pytorch code from ONNX. Currently support onnx==1.9.0 and torch==1.8.1. Installation From PyPI pip install onnx-pytorch From

Wenhao Hu 94 Jan 6, 2023
tf2onnx - Convert TensorFlow, Keras and Tflite models to ONNX.

tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or python api.

Open Neural Network Exchange 1.8k Jan 8, 2023
Export CenterPoint PonintPillars ONNX Model For TensorRT
Export CenterPoint PonintPillars ONNX Model For TensorRT

CenterPoint-PonintPillars Pytroch model convert to ONNX and TensorRT Welcome to CenterPoint! This project is fork from tianweiy/CenterPoint. I impleme

CarkusL 149 Dec 13, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.

null 7.7k Jan 6, 2023
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
YOLOv3 in PyTorch ONNX CoreML TFLite

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices

Ultralytics 9.3k Jan 7, 2023
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

null 7.7k Jan 3, 2023
Releases(1.1.6)
Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
GitHub Repository
Unsupervised Representation Learning by Invariance Propagation

Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre

FengWang 15 Jul 06, 2022
Code for "Retrieving Black-box Optimal Images from External Databases" (WSDM 2022)

Retrieving Black-box Optimal Images from External Databases (WSDM 2022) We propose how a user retreives an optimal image from external databases of we

joisino 5 Apr 13, 2022
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

HOW TO USE THIS PROJECT A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets Based on DeepLabCut toolbox, we run wit

1 Jan 10, 2022
A parametric soroban written with CADQuery.

A parametric soroban written in CADQuery The purpose of this project is to demonstrate how "code CAD" can be intuitive to learn. See soroban.py for a

Lee 4 Aug 13, 2022
Learning Continuous Image Representation with Local Implicit Image Function

LIIF This repository contains the official implementation for LIIF introduced in the following paper: Learning Continuous Image Representation with Lo

Yinbo Chen 1k Dec 25, 2022
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

SPLADE 🍴 + 🥄 = 🔎 This repository contains the weights for four models as well as the code for running inference for our two papers: [v1]: SPLADE: S

NAVER 170 Dec 28, 2022
An end-to-end project on customer segmentation

End-to-end Customer Segmentation Project Note: This project is in progress. Tools Used in This Project Prefect: Orchestrate workflows hydra: Manage co

Ocelot Consulting 8 Oct 06, 2022
Evaluating Cross-lingual Sentence Representations

XNLI: The Cross-Lingual NLI Corpus XNLI is an evaluation corpus for language transfer and cross-lingual sentence classification in 15 languages. New:

Meta Research 395 Dec 19, 2022
TorchIO is a Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

Fernando Pérez-García 1.6k Jan 06, 2023
Evaluating deep transfer learning for whole-brain cognitive decoding

Evaluating deep transfer learning for whole-brain cognitive decoding This README file contains the following sections: Project description Repository

Armin Thomas 5 Oct 31, 2022
This project aims to segment 4 common retinal lesions from Fundus Images.

This project aims to segment 4 common retinal lesions from Fundus Images.

Husam Nujaim 1 Oct 10, 2021
RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection

🚀 RetinaNet Horizontal Detector Based PyTorch This is a horizontal detector Ret

Fang Zhonghao 13 Nov 19, 2022
One-line your code easily but still with the fun of doing so!

One-liner-iser One-line your code easily but still with the fun of doing so! Have YOU ever wanted to write one-line Python code, but don't have the sa

5 May 04, 2022
Byzantine-robust decentralized learning via self-centered clipping

Byzantine-robust decentralized learning via self-centered clipping In this paper, we study the challenging task of Byzantine-robust decentralized trai

EPFL Machine Learning and Optimization Laboratory 4 Aug 27, 2022
MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network

MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network This repository is the official implementation of MatchGAN: A S

Justin Sun 12 Dec 27, 2022
A no-BS, dead-simple training visualizer for tf-keras

A no-BS, dead-simple training visualizer for tf-keras TrainingDashboard Plot inter-epoch and intra-epoch loss and metrics within a jupyter notebook wi

Vibhu Agrawal 3 May 28, 2021
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 03, 2023
Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Graph Transformer - Pytorch Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by bot

Phil Wang 97 Dec 28, 2022
一个免费开源一键搭建的通用验证码识别平台，大部分常见的中英数验证码识别都没啥问题。

captcha_server 一个免费开源一键搭建的通用验证码识别平台，大部分常见的中英数验证码识别都没啥问题。 使用方法 python = 3.8 以上环境 pip install -r requirements.txt -i https://pypi.douban.com/simple gun

Sml2h3 189 Dec 02, 2022
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo

chenxin 1 Dec 19, 2021