Simple node deletion tool for onnx.

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

PyTorch ,ONNX and TensorRT implementation of YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

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.

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

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.

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

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.

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

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

Releases(1.1.6)
Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
Code of paper Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification.

Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification We provide the codes for repr

12 Dec 12, 2022
code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation (CVPR 2021) Introduction PBR is a conceptually simple yet effective

H.Chen 143 Jan 05, 2023
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
Models, datasets and tools for Facial keypoints detection

Template for Data Science Project This repo aims to give a robust starting point to any Data Science related project. It contains readymade tools setu

girafe.ai 1 Feb 11, 2022
Official code repository for the EMNLP 2021 paper

Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization PyTorch code for the EMNLP 2021 paper "Integrating Visuospatia

Adyasha Maharana 23 Dec 19, 2022
Implementation of the method proposed in the paper "Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation"

Neural Descriptor Fields (NDF) PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and u

167 Jan 06, 2023
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance This is the codebase for video-based human motion reconstruction in human-mot

Jiachen Xu 5 Jul 14, 2022
Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

CoTuning Official implementation for NeurIPS 2020 paper Co-Tuning for Transfer Learning. [News] 2021/01/13 The COCO 70 dataset used in the paper is av

THUML @ Tsinghua University 35 Sep 23, 2022
Official PyTorch implementation of RobustNet (CVPR 2021 Oral)

RobustNet (CVPR 2021 Oral): Official Project Webpage Codes and pretrained models will be released soon. This repository provides the official PyTorch

Sungha Choi 173 Dec 21, 2022
Connecting Java/ImgLib2 + Python/NumPy

imglyb imglyb aims at connecting two worlds that have been seperated for too long: Python with numpy Java with ImgLib2 imglyb uses jpype to access num

ImgLib2 29 Dec 21, 2022
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
This is an official source code for implementation on Extensive Deep Temporal Point Process

Extensive Deep Temporal Point Process This is an official source code for implementation on Extensive Deep Temporal Point Process, which is composed o

Haitao Lin 8 Aug 15, 2022
Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking."

Expert-Linking Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking." This is

BoChen 12 Jan 01, 2023
PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

ALiBi PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation. Quickstart Clone this reposit

Jake Tae 4 Jul 27, 2022
This repository contains the official implementation code of the paper Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis

This repository contains the official implementation code of the paper Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis, accepted at ACMMM 2021.

Ziqi Yuan 10 Sep 30, 2022
Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN", accepted to ACM MM 2021 BNI Track.

RecycleD Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN

Yunan Zhu 23 Nov 05, 2022
Code & Data for Enhancing Photorealism Enhancement

Code & Data for Enhancing Photorealism Enhancement

Intel ISL (Intel Intelligent Systems Lab) 1.1k Jan 08, 2023
Crawl & visualize ICLR papers and reviews

Crawl and Visualize ICLR 2022 OpenReview Data Descriptions This Jupyter Notebook contains the data crawled from ICLR 2022 OpenReview webpages and thei

Federico Berto 75 Dec 05, 2022
YOLOv5 + ROS2 object detection package

YOLOv5-ROS YOLOv5 + ROS2 object detection package This program changes the input of detect.py (ultralytics/yolov5) to sensor_msgs/Image of ROS2. Requi

Ar-Ray 23 Dec 19, 2022
Camview - A CLI-tool used to stream CCTV online footage based on URL params

CamView A CLI-tool used to stream CCTV online footage based on URL params Get St

Finn Lancaster 54 Dec 09, 2022