Simple ONNX operation generator. Simple Operation Generator for ONNX.

Overview

sog4onnx

Simple ONNX operation generator. Simple Operation Generator for ONNX.

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

Downloads GitHub PyPI CodeQL

Key concept

  • Variable, Constant, Operation and Attribute can be generated externally.
  • Allow Opset to be specified externally.
  • No check for consistency of Operations within the tool, as new OPs are added frequently and the definitions of existing OPs change with each new version of ONNX's Opset.
  • Only one OP can be defined at a time, and the goal is to generate free ONNX graphs using a combination of snc4onnx, sne4onnx, snd4onnx and scs4onnx.
  • List of parameters that can be specified: https://github.com/onnx/onnx/blob/main/docs/Operators.md

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 sog4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/sog4onnx:latest

### docker build
$ docker build -t pinto0309/sog4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/sog4onnx:latest
$ cd /workdir

2. CLI Usage

$ sog4onnx -h

usage: sog4onnx [-h]
  --op_type OP_TYPE
  --opset OPSET
  --op_name OP_NAME
  [--input_variables NAME TYPE VALUE]
  [--output_variables NAME TYPE VALUE]
  [--attributes NAME DTYPE VALUE]
  [--output_onnx_file_path OUTPUT_ONNX_FILE_PATH]
  [--non_verbose]

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

  --op_type OP_TYPE
        ONNX OP type.
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

  --opset OPSET
        ONNX opset number.

  --op_name OP_NAME
        OP name.

  --input_variables NAME DTYPE VALUE
        input_variables can be specified multiple times.
        --input_variables variable_name numpy.dtype shape
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

        e.g.
        --input_variables i1 float32 [1,3,5,5] \
        --input_variables i2 int32 [1] \
        --input_variables i3 float64 [1,3,224,224]

  --output_variables NAME DTYPE VALUE
        output_variables can be specified multiple times.
        --output_variables variable_name numpy.dtype shape
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

        e.g.
        --output_variables o1 float32 [1,3,5,5] \
        --output_variables o2 int32 [1] \
        --output_variables o3 float64 [1,3,224,224]

  --attributes NAME DTYPE VALUE
        attributes can be specified multiple times.
        dtype is one of "float32" or "float64" or "int32" or "int64" or "str".
        --attributes name dtype value
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

        e.g.
        --attributes alpha float32 1.0 \
        --attributes beta float32 1.0 \
        --attributes transA int32 0 \
        --attributes transB int32 0

  --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
        Output onnx file path.
        If not specified, a file with the OP type name is generated.

        e.g. op_type="Gemm" -> Gemm.onnx

  --non_verbose
        Do not show all information logs. Only error logs are displayed.

3. In-script Usage

$ python
>>> from sog4onnx import generate
>>> help(generate)
Help on function generate in module sog4onnx.onnx_operation_generator:

generate(
  op_type: str,
  opset: int,
  op_name: str,
  input_variables: dict,
  output_variables: dict,
  attributes: Union[dict, NoneType] = None,
  output_onnx_file_path: Union[str, NoneType] = '',
  non_verbose: Union[bool, NoneType] = False
) -> onnx.onnx_ml_pb2.ModelProto

    Parameters
    ----------
    op_type: str
        ONNX op type.
        See below for the types of OPs that can be specified.
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

        e.g. "Add", "Div", "Gemm", ...

    opset: int
        ONNX opset number.

        e.g. 11

    op_name: str
        OP name.

    input_variables: Optional[dict]
        Specify input variables for the OP to be generated.
        See below for the variables that can be specified.
        https://github.com/onnx/onnx/blob/main/docs/Operators.md
        {"input_var_name1": [numpy.dtype, shape], "input_var_name2": [dtype, shape], ...}

        e.g.
        input_variables = {
          "name1": [np.float32, [1,224,224,3]],
          "name2": [np.bool_, [0]],
          ...
        }

    output_variables: Optional[dict]
        Specify output variables for the OP to be generated.
        See below for the variables that can be specified.
        https://github.com/onnx/onnx/blob/main/docs/Operators.md
        {"output_var_name1": [numpy.dtype, shape], "output_var_name2": [dtype, shape], ...}

        e.g.
        output_variables = {
          "name1": [np.float32, [1,224,224,3]],
          "name2": [np.bool_, [0]],
          ...
        }

    attributes: Optional[dict]
        Specify output attributes for the OP to be generated.
        See below for the attributes that can be specified.
        When specifying Tensor format values, specify an array converted to np.ndarray.
        https://github.com/onnx/onnx/blob/main/docs/Operators.md
        {"attr_name1": value1, "attr_name2": value2, "attr_name3": value3, ...}

        e.g.
        attributes = {
          "alpha": 1.0,
          "beta": 1.0,
          "transA": 0,
          "transB": 0
        }
        Default: None

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

    non_verbose: Optional[bool]
        Do not show all information logs. Only error logs are displayed.
        Default: False

    Returns
    -------
    single_op_graph: onnx.ModelProto
        Single op onnx ModelProto

4. CLI Execution

$ sog4onnx \
--op_type Gemm \
--opset 1 \
--op_name gemm_custom1 \
--input_variables i1 float32 [1,2,3] \
--input_variables i2 float32 [1,1] \
--input_variables i3 int32 [0] \
--output_variables o1 float32 [1,2,3] \
--attributes alpha float32 1.0 \
--attributes beta float32 1.0 \
--attributes transA int32 0 \
--attributes transB int32 0

5. In-script Execution

import numpy as np
from sog4onnx import generate

single_op_graph = generate(
    op_type = 'Gemm',
    opset = 1,
    op_name = "gemm_custom1",
    input_variables = {
      "i1": [np.float32, [1,2,3]],
      "i2": [np.float32, [1,1]],
      "i3": [np.int32, [0]],
    },
    output_variables = {
      "o1": [np.float32, [1,2,3]],
    },
    attributes = {
      "alpha": 1.0,
      "beta": 1.0,
      "broadcast": 0,
      "transA": 0,
      "transB": 0,
    },
    non_verbose = True,
)

6. Sample

6-1. opset=1, Gemm

$ sog4onnx \
--op_type Gemm \
--opset 1 \
--op_name gemm_custom1 \
--input_variables i1 float32 [1,2,3] \
--input_variables i2 float32 [1,1] \
--input_variables i3 int32 [0] \
--output_variables o1 float32 [1,2,3] \
--attributes alpha float32 1.0 \
--attributes beta float32 1.0 \
--attributes transA int32 0 \
--attributes transB int32 0
--non_verbose

image image

6-2. opset=11, Add

$ sog4onnx \
--op_type Add \
--opset 11 \
--op_name add_custom1 \
--input_variables i1 float32 [1,2,3] \
--input_variables i2 float32 [1,2,3] \
--output_variables o1 float32 [1,2,3] \
--non_verbose

image image

6-3. opset=11, NonMaxSuppression

$ sog4onnx \
--op_type NonMaxSuppression \
--opset 11 \
--op_name nms_custom1 \
--input_variables boxes float32 [1,6,4] \
--input_variables scores float32 [1,1,6] \
--input_variables max_output_boxes_per_class int64 [1] \
--input_variables iou_threshold float32 [1] \
--input_variables score_threshold float32 [1] \
--output_variables selected_indices int64 [3,3] \
--attributes center_point_box int64 1

image image

6-4. opset=11, Constant

$ sog4onnx \
--op_type Constant \
--opset 11 \
--op_name const_custom1 \
--output_variables boxes float32 [1,6,4] \
--attributes value float32 \
[[\
[0.5,0.5,1.0,1.0],\
[0.5,0.6,1.0,1.0],\
[0.5,0.4,1.0,1.0],\
[0.5,10.5,1.0,1.0],\
[0.5,10.6,1.0,1.0],\
[0.5,100.5,1.0,1.0]\
]]

image

7. Reference

  1. https://github.com/onnx/onnx/blob/main/docs/Operators.md
  2. https://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.html
  3. https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon
  4. https://github.com/PINTO0309/sne4onnx
  5. https://github.com/PINTO0309/snd4onnx
  6. https://github.com/PINTO0309/snc4onnx
  7. https://github.com/PINTO0309/scs4onnx
  8. https://github.com/PINTO0309/PINTO_model_zoo

8. Issues

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

You might also like...
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.

Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

Accelerated SMPL operation, commonly used in generate 3D human mesh, STAR included.

SMPL2 An enchanced and accelerated SMPL operation which commonly used in 3D human mesh generation. It takes a poses, shapes, cam_trans as inputs, outp

Liecasadi - liecasadi implements Lie groups operation written in CasADi

liecasadi liecasadi implements Lie groups operation written in CasADi, mainly di

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

Simple node deletion tool for onnx.
Simple node deletion tool for onnx.

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

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.

Comments
  • Small fixes to README

    Small fixes to README

    Thank you for the tool. There are small fixes needed in the README: the attributes of one example missing the type, and the numpy import in another one.

    Otherwise, it works perfectly.

    opened by ibaiGorordo 1
Releases(1.0.15)
  • 1.0.15(Nov 20, 2022)

    • Fixed a bug where Constant and ConstantOfShape opsets were not set

    Full Changelog: https://github.com/PINTO0309/sog4onnx/compare/1.0.14...1.0.15

    Source code(tar.gz)
    Source code(zip)
  • 1.0.14(Sep 8, 2022)

    • Add short form parameter
      $ sog4onnx -h
      
      usage: sog4onnx [-h]
        --ot OP_TYPE
        --os OPSET
        --on OP_NAME
        [-iv NAME TYPE VALUE]
        [-ov NAME TYPE VALUE]
        [-a NAME DTYPE VALUE]
        [-of OUTPUT_ONNX_FILE_PATH]
        [-n]
      
      optional arguments:
        -h, --help
          show this help message and exit
      
        -ot OP_TYPE, --op_type OP_TYPE
          ONNX OP type.
          https://github.com/onnx/onnx/blob/main/docs/Operators.md
      
        -os OPSET, --opset OPSET
          ONNX opset number.
      
        -on OP_NAME, --op_name OP_NAME
          OP name.
      
        -iv INPUT_VARIABLES INPUT_VARIABLES INPUT_VARIABLES, --input_variables INPUT_VARIABLES INPUT_VARIABLES INPUT_VARIABLES
          input_variables can be specified multiple times.
          --input_variables variable_name numpy.dtype shape
          https://github.com/onnx/onnx/blob/main/docs/Operators.md
      
          e.g.
          --input_variables i1 float32 [1,3,5,5] \
          --input_variables i2 int32 [1] \
          --input_variables i3 float64 [1,3,224,224]
      
        -ov OUTPUT_VARIABLES OUTPUT_VARIABLES OUTPUT_VARIABLES, --output_variables OUTPUT_VARIABLES OUTPUT_VARIABLES OUTPUT_VARIABLES
          output_variables can be specified multiple times.
          --output_variables variable_name numpy.dtype shape
          https://github.com/onnx/onnx/blob/main/docs/Operators.md
      
          e.g.
          --output_variables o1 float32 [1,3,5,5] \
          --output_variables o2 int32 [1] \
          --output_variables o3 float64 [1,3,224,224]
      
        -a ATTRIBUTES ATTRIBUTES ATTRIBUTES, --attributes ATTRIBUTES ATTRIBUTES ATTRIBUTES
          attributes can be specified multiple times.
          dtype is one of "float32" or "float64" or "int32" or "int64" or "str".
          --attributes name dtype value
          https://github.com/onnx/onnx/blob/main/docs/Operators.md
      
          e.g.
          --attributes alpha float32 1.0 \
          --attributes beta float32 1.0 \
          --attributes transA int32 0 \
          --attributes transB int32 0
      
        -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
          Output onnx file path.
          If not specified, a file with the OP type name is generated.
      
          e.g. op_type="Gemm" -> Gemm.onnx
      
        -n, --non_verbose
          Do not show all information logs. Only error logs are displayed.
      
    Source code(tar.gz)
    Source code(zip)
  • 1.0.13(Jun 10, 2022)

  • 1.0.12(Jun 7, 2022)

  • 1.0.11(May 25, 2022)

  • 1.0.10(May 15, 2022)

  • 1.0.9(Apr 26, 2022)

    • Added op_name as an input parameter, allowing OPs to be named.
      • CLI
        sog4onnx [-h]
          --op_type OP_TYPE
          --opset OPSET
          --op_name OP_NAME
          [--input_variables NAME TYPE VALUE]
          [--output_variables NAME TYPE VALUE]
          [--attributes NAME DTYPE VALUE]
          [--output_onnx_file_path OUTPUT_ONNX_FILE_PATH]
          [--non_verbose]
        
      • In-script
        generate(
          op_type: str,
          opset: int,
          op_name: str,
          input_variables: dict,
          output_variables: dict,
          attributes: Union[dict, NoneType] = None,
          output_onnx_file_path: Union[str, NoneType] = '',
          non_verbose: Union[bool, NoneType] = False
        ) -> onnx.onnx_ml_pb2.ModelProto
        
    Source code(tar.gz)
    Source code(zip)
  • 1.0.8(Apr 15, 2022)

  • 1.0.7(Apr 14, 2022)

  • 1.0.6(Apr 14, 2022)

  • 1.0.5(Apr 13, 2022)

  • 1.0.4(Apr 13, 2022)

  • 1.0.3(Apr 12, 2022)

  • 1.0.2(Apr 12, 2022)

  • 1.0.1(Apr 12, 2022)

  • 1.0.0(Apr 12, 2022)

  • 0.0.2(Apr 12, 2022)

  • 0.0.1(Apr 12, 2022)

Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
Standalone pre-training recipe with JAX+Flax

Sabertooth Sabertooth is standalone pre-training recipe based on JAX+Flax, with data pipelines implemented in Rust. It runs on CPU, GPU, and/or TPU, b

Nikita Kitaev 26 Nov 28, 2022
The implementation of DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 06, 2023
An addon uses SMPL's poses and global translation to drive cartoon character in Blender.

Blender addon for driving character The addon drives the cartoon character by passing SMPL's poses and global translation into model's armature in Ble

犹在镜中 153 Dec 14, 2022
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

Nonuniform-to-Uniform Quantization This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quanti

Zechun Liu 60 Dec 28, 2022
A PyTorch port of the Neural 3D Mesh Renderer

Neural 3D Mesh Renderer (CVPR 2018) This repo contains a PyTorch implementation of the paper Neural 3D Mesh Renderer by Hiroharu Kato, Yoshitaka Ushik

Daniilidis Group University of Pennsylvania 1k Jan 09, 2023
Code for "ATISS: Autoregressive Transformers for Indoor Scene Synthesis", NeurIPS 2021

ATISS: Autoregressive Transformers for Indoor Scene Synthesis This repository contains the code that accompanies our paper ATISS: Autoregressive Trans

138 Dec 22, 2022
This game was designed to encourage young people not to gamble on lotteries, as the probablity of correctly guessing the number is infinitesimal!

Lottery Simulator 2022 for Web Launch Application Developed by John Seong in Ontario. This game was designed to encourage young people not to gamble o

John Seong 2 Sep 02, 2022
discovering subdomains, hidden paths, extracting unique links

python-website-crawler discovering subdomains, hidden paths, extracting unique links pip install -r requirements.txt discover subdomain: You can give

merve 4 Sep 05, 2022
Use tensorflow to implement a Deep Neural Network for real time lane detection

LaneNet-Lane-Detection Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "To

MaybeShewill-CV 1.9k Jan 08, 2023
ML course - EPFL Machine Learning Course, Fall 2021

EPFL Machine Learning Course CS-433 Machine Learning Course, Fall 2021 Repository for all lecture notes, labs and projects - resources, code templates

EPFL Machine Learning and Optimization Laboratory 1k Jan 04, 2023
Lab Materials for MIT 6.S191: Introduction to Deep Learning

This repository contains all of the code and software labs for MIT 6.S191: Introduction to Deep Learning! All lecture slides and videos are available

Alexander Amini 5.6k Dec 26, 2022
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

Daniel Povey 41 Jan 07, 2023
Generative Models for Graph-Based Protein Design

Graph-Based Protein Design This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay

John Ingraham 159 Dec 15, 2022
《LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classification》(AAAI 2021) GitHub:

LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classification

76 Dec 05, 2022
SPEAR: Semi suPErvised dAta progRamming

Semi-Supervised Data Programming for Data Efficient Machine Learning SPEAR is a library for data programming with semi-supervision. The package implem

decile-team 91 Dec 06, 2022
Deep ViT Features as Dense Visual Descriptors

dino-vit-features [paper] [project page] Official implementation of the paper "Deep ViT Features as Dense Visual Descriptors". We demonstrate the effe

Shir Amir 113 Dec 24, 2022
[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Robot Action Primitives (RAPS) This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action

Murtaza Dalal 55 Dec 27, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022