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
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model The task of age transformation illustrates the change of an individual

444 Dec 30, 2022
PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

IIM - Crowd Localization This repo is the official implementation of paper: Learning Independent Instance Maps for Crowd Localization. The code is dev

tao han 91 Nov 10, 2022
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift (ICCV 2021)

Π-NAS This repository provides the evaluation code of our submitted paper: Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training

Jiqi Zhang 18 Aug 18, 2022
The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.

Object-Placement-Assessment-Dataset-OPA Object-Placement-Assessment (OPA) is to verify whether a composite image is plausible in terms of the object p

BCMI 53 Nov 15, 2022
A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models with machine learning (NeurIPS 2021 Datasets and Benchmarks Track)

ClimART - A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models Official PyTorch Implementation Using deep le

21 Dec 31, 2022
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
A collection of educational notebooks on multi-view geometry and computer vision.

Multiview notebooks This is a collection of educational notebooks on multi-view geometry and computer vision. Subjects covered in these notebooks incl

Max 65 Dec 09, 2022
Empower Sequence Labeling with Task-Aware Language Model

LM-LSTM-CRF Check Our New NER Toolkit 🚀 🚀 🚀 Inference: LightNER: inference w. models pre-trained / trained w. any following tools, efficiently. Tra

Liyuan Liu 838 Jan 05, 2023
Pyramid Pooling Transformer for Scene Understanding

Pyramid Pooling Transformer for Scene Understanding Requirements: torch 1.6+ torchvision 0.7.0 timm==0.3.2 Validated on torch 1.6.0, torchvision 0.7.0

Yu-Huan Wu 119 Dec 29, 2022
A sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes

CFD Python Please cite as: Barba, Lorena A., and Forsyth, Gilbert F. (2018). CFD Python: the 12 steps to Navier-Stokes equations. Journal of Open Sour

Barba group 2.6k Dec 30, 2022
[ECCV 2020] Reimplementation of 3DDFAv2, including face mesh, head pose, landmarks, and more.

Stable Head Pose Estimation and Landmark Regression via 3D Dense Face Reconstruction Reimplementation of (ECCV 2020) Towards Fast, Accurate and Stable

Remilia Scarlet 221 Dec 30, 2022
A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Requirements pytorch 1.1+ torchvision 0.3+ pyclipper opencv3 gcc

zhoujun 400 Dec 26, 2022
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

BUPT GAMMA Lab 519 Jan 02, 2023
Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Swin Unet V2 Swin Unet V2 is a modified version of Swin Unet arxiv based on Swin

Chenxu Peng 26 Dec 03, 2022
Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)

Learning-Action-Completeness-from-Points Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal A

Pilhyeon Lee 67 Jan 03, 2023
PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

PyGAD: Genetic Algorithm in Python PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine lear

Ahmed Gad 1.1k Dec 26, 2022
Continuous Conditional Random Field Convolution for Point Cloud Segmentation

CRFConv This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation" 1. Setup 1) Building c

Fei Yang 8 Dec 08, 2022
Main Results on ImageNet with Pretrained Models

This repository contains Pytorch evaluation code, training code and pretrained models for the following projects: SPACH (A Battle of Network Structure

Microsoft 151 Dec 14, 2022
Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

Semi Hand-Object Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time (CVPR 2021).

96 Dec 27, 2022
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022