A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

Last update: May 15, 2022

Related tags

Overview

sam4onnx

A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

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

Key concept

Specify an arbitrary OP name and Constant type INPUT name or an arbitrary OP name and Attribute name, and pass the modified constants to rewrite the parameters of the relevant OP.
Two types of input are accepted: .onnx file input and onnx.ModelProto format objects.
To design the operation to be simple, only a single OP can be specified.
Attributes and constants are forcibly rewritten, so the integrity of the entire graph is not checked in detail.

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 sam4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/sam4onnx:latest

### docker build
$ docker build -t pinto0309/sam4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/sam4onnx:latest
$ cd /workdir

2. CLI Usage

$ sam4onnx -h

usage:
    sam4onnx [-h]
    --input_onnx_file_path INPUT_ONNX_FILE_PATH
    --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
    [--op_name OP_NAME]
    [--attributes NAME DTYPE VALUE]
    [--input_constants NAME DTYPE VALUE]
    [--non_verbose]

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

  --input_onnx_file_path INPUT_ONNX_FILE_PATH
        Input onnx file path.

  --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
        Output onnx file path.

  --op_name OP_NAME
        OP name of the attributes to be changed.
        When --attributes is specified, --op_name must always be specified.
        e.g. --op_name aaa

  --attributes NAME DTYPE VALUE
        Parameter to change the attribute of the OP specified in --op_name.
        If the OP specified in --op_name has no attributes,
        it is ignored. attributes can be specified multiple times.
        --attributes name dtype value dtype is one of
        "float32" or "float64" or "int32" or "int64" or "str".
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

        e.g.
        --attributes alpha float32 [[1.0]]
        --attributes beta float32 [1.0]
        --attributes transA int64 0
        --attributes transB int64 0

  --input_constants NAME DTYPE VALUE
        Specifies the name of the constant to be changed.
        If you want to change only the constant,
        you do not need to specify --op_name and --attributes.
        input_constants can be specified multiple times.
        --input_constants constant_name numpy.dtype value

        e.g.
        --input_constants constant_name1 int64 0
        --input_constants constant_name2 float32 [[1.0,2.0,3.0],[4.0,5.0,6.0]]

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

3. In-script Usage

$ python
>>> from sam4onnx import modify
>>> help(modify)
Help on function modify in module sam4onnx.onnx_attr_const_modify:

modify(
    input_onnx_file_path: Union[str, NoneType] = '',
    output_onnx_file_path: Union[str, NoneType] = '',
    onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
    op_name: Union[str, NoneType] = '',
    attributes: Union[dict, NoneType] = None,
    input_constants: Union[dict, NoneType] = None,
    non_verbose: Union[bool, NoneType] = False
) -> onnx.onnx_ml_pb2.ModelProto

    Parameters
    ----------
    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.

    op_name: Optional[str]
        OP name of the attributes to be changed.
        When --attributes is specified, --op_name must always be specified.
        Default: ''
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

    attributes: Optional[dict]
        Specify output attributes for the OP to be generated.
        See below for the attributes that can be specified.

        {"attr_name1": numpy.ndarray, "attr_name2": numpy.ndarray, ...}

        e.g. attributes =
            {
                "alpha": np.asarray(1.0, dtype=np.float32),
                "beta": np.asarray(1.0, dtype=np.float32),
                "transA": np.asarray(0, dtype=np.int64),
                "transB": np.asarray(0, dtype=np.int64)
            }
        Default: None
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

    input_constants: Optional[dict]
        Specifies the name of the constant to be changed.
        If you want to change only the constant,
        you do not need to specify --op_name and --attributes.
        {"constant_name1": numpy.ndarray, "constant_name2": numpy.ndarray, ...}

        e.g.
        input_constants =
            {
                "constant_name1": np.asarray(0, dtype=np.int64),
                "constant_name2": np.asarray([[1.0,2.0,3.0],[4.0,5.0,6.0]], dtype=np.float32)
            }
        Default: None
        https://github.com/onnx/onnx/blob/main/docs/Operators.md

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

    Returns
    -------
    modified_graph: onnx.ModelProto
        Mddified onnx ModelProto

4. CLI Execution

$ sam4onnx \
--op_name Transpose_17 \
--input_onnx_file_path input.onnx \
--output_onnx_file_path output.onnx \
--attributes perm int64 [0,1]

5. In-script Execution

from sam4onnx import modify

modified_graph = modify(
    onnx_graph=graph,
    input_constants={"241": np.asarray([1], dtype=np.int64)},
    non_verbose=True,
)

6. Sample

6-1. Transpose - update `perm`

$ sam4onnx \
--op_name Transpose_17 \
--input_onnx_file_path hitnet_sf_finalpass_720x1280_nonopt.onnx \
--output_onnx_file_path hitnet_sf_finalpass_720x1280_nonopt_mod.onnx \
--attributes perm int64 [0,1]

6-2. Mul - update `Constant (170)` - From: `2`, To: `1`

$ sam4onnx \
--input_onnx_file_path hitnet_sf_finalpass_720x1280_nonopt.onnx \
--output_onnx_file_path hitnet_sf_finalpass_720x1280_nonopt_mod.onnx \
--input_constants 170 float32 1

6-3. Reshape - update `Constant (241)` - From: `[-1]`, To: `[1]`

$ sam4onnx \
--input_onnx_file_path hitnet_sf_finalpass_720x1280_nonopt.onnx \
--output_onnx_file_path hitnet_sf_finalpass_720x1280_nonopt_mod.onnx \
--input_constants 241 int64 [1]

7. Issues

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

You might also like...

Simple ONNX operation generator. Simple Operation Generator for ONNX.

sog4onnx Simple ONNX operation generator. Simple Operation Generator for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools Key concept V

6 May 15, 2022

Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.

Image Quality Evaluation Metrics Implementation of some common full reference image quality metrics. The repo is built based on full reference image q

10 Jan 1, 2023

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Milano (This is a research project, not an official NVIDIA product.) Documentation https://nvidia.github.io/Milano Milano (Machine learning autotuner

147 Dec 17, 2022

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

CBREN This is the Pytorch implementation for our IEEE TCSVT paper : CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhanceme

3 Nov 4, 2022

ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS.

ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS. It currently supports four examples for you to quickly experience the power of ONNX Runtime Web.

58 Dec 18, 2022

ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

18 Nov 6, 2022

ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

14 Dec 9, 2022

Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)

Image Classification Project Killer in PyTorch This repo is designed for those who want to start their experiments two days before the deadline and ki

349 Dec 8, 2022

Ranger deep learning optimizer rewrite to use newest components

Ranger21 - integrating the latest deep learning components into a single optimizer Ranger deep learning optimizer rewrite to use newest components Ran

266 Dec 28, 2022

Releases(1.0.12)

1.0.12(Jan 2, 2023)
What's Changed

Support for models with custom domains by @PINTO0309 in https://github.com/PINTO0309/sam4onnx/pull/2

New Contributors

@PINTO0309 made their first contribution in https://github.com/PINTO0309/sam4onnx/pull/2

Full Changelog: https://github.com/PINTO0309/sam4onnx/compare/1.0.11...1.0.12
Source code(tar.gz)
Source code(zip)

1.0.11(Sep 8, 2022)

Add short form parameter

$ sam4onnx -h

usage:
    sam4onnx [-h]
    -if INPUT_ONNX_FILE_PATH
    -of OUTPUT_ONNX_FILE_PATH
    [-on OP_NAME]
    [-a NAME DTYPE VALUE]
    [-da DELETE_ATTRIBUTES [DELETE_ATTRIBUTES ...]]
    [-ic NAME DTYPE VALUE]
    [-n]

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

  -if INPUT_ONNX_FILE_PATH, --input_onnx_file_path INPUT_ONNX_FILE_PATH
    Input onnx file path.

  -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
    Output onnx file path.

  -on OP_NAME, --op_name OP_NAME
    OP name of the attributes to be changed.
    When --attributes is specified, --op_name must always be specified.
    e.g. --op_name aaa

  -a ATTRIBUTES ATTRIBUTES ATTRIBUTES, --attributes ATTRIBUTES ATTRIBUTES ATTRIBUTES
    Parameter to change the attribute of the OP specified in --op_name.
    If the OP specified in --op_name has no attributes,
    it is ignored. attributes can be specified multiple times.
    --attributes name dtype value dtype is one of
    "float32" or "float64" or "int32" or "int64" or "str".
    https://github.com/onnx/onnx/blob/main/docs/Operators.md

    e.g.
    --attributes alpha float32 [[1.0]]
    --attributes beta float32 [1.0]
    --attributes transA int64 0
    --attributes transB int64 0

  -da DELETE_ATTRIBUTES [DELETE_ATTRIBUTES ...], --delete_attributes DELETE_ATTRIBUTES [DELETE_ATTRIBUTES ...]
    Parameter to delete the attribute of the OP specified in --op_name.
    If the OP specified in --op_name has no attributes,
    it is ignored. delete_attributes can be specified multiple times.
    --delete_attributes name1 name2 name3
    https://github.com/onnx/onnx/blob/main/docs/Operators.md

    e.g. --delete_attributes alpha beta

  -ic INPUT_CONSTANTS INPUT_CONSTANTS INPUT_CONSTANTS, --input_constants INPUT_CONSTANTS INPUT_CONSTANTS INPUT_CONSTANTS
    Specifies the name of the constant to be changed.
    If you want to change only the constant,
    you do not need to specify --op_name and --attributes.
    input_constants can be specified multiple times.
    --input_constants constant_name numpy.dtype value

    e.g.
    --input_constants constant_name1 int64 0
    --input_constants constant_name2 float32 [[1.0,2.0,3.0],[4.0,5.0,6.0]]
    --input_constants constant_name3 float32 [\'-Infinity\']

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

Source code(tar.gz)
Source code(zip)

1.0.10(Aug 7, 2022)
Supports the specification of -np.inf, -np.inf, "Infinity", and "-Infinity"

Source code(tar.gz)
Source code(zip)
1.0.9(Jul 17, 2022)
Support for constant rewriting when the same constant is shared. Valid only when op_name is specified. Generates a new constant that is different from the shared constant.

Reshape_156 onnx::Reshape_391 int64 [1, -1, 85]

Reshape_174 onnx::Reshape_391 int64 [1, -1, 85]

sam4onnx \ --input_onnx_file_path yolov7-tiny_test_sim.onnx \ --output_onnx_file_path yolov7-tiny_test_sim_mod.onnx \ --op_name Reshape_156 \ --input_constants onnx::Reshape_391 int64 [1,14400,85]

Reshape_156 onnx::Reshape_391 int64 [1, -1, 85] -> Reshape_156 onnx::Reshape_391_mod_3 int64 [1, 14400, 85]

Reshape_174 onnx::Reshape_391 int64 [1, -1, 85]

Source code(tar.gz)
Source code(zip)
1.0.8(Jun 7, 2022)
string attributes bug fixes

Source code(tar.gz)
Source code(zip)
1.0.7(May 25, 2022)
Security update

README update

Source code(tar.gz)
Source code(zip)
1.0.6(May 15, 2022)
eval() -> ast.literal_eval()

Source code(tar.gz)
Source code(zip)
1.0.5(May 12, 2022)
Fixed a bug that caused rewriting of direct constants to fail

Source code(tar.gz)
Source code(zip)
1.0.4(May 5, 2022)
Support for deletion of attributes

Source code(tar.gz)
Source code(zip)
1.0.3(May 5, 2022)
Support for additional attributes

Note that the correct attribute set according to the OP's opset is not checked, so any attribute can be added.

The figure below shows the addition of the attribute perm to Reshape, which does not originally exist.

Source code(tar.gz)
Source code(zip)
1.0.2(May 3, 2022)
Performance Improvement

Source code(tar.gz)
Source code(zip)
1.0.1(Apr 16, 2022)
Small fixes

Source code(tar.gz)
Source code(zip)
1.0.0(Apr 15, 2022)
Initial release

Source code(tar.gz)
Source code(zip)

Owner

Katsuya Hyodo

Hobby programmer. Intel Software Innovator Program member.

GitHub Repository

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

30 Sep 23, 2022

Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

EDM-subgenre-classifier This repository contains the code for "Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Fea

11 Dec 20, 2022

Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

5 Nov 21, 2022

This repo. is an implementation of ACFFNet, which is accepted for in Image and Vision Computing.

Attention-Guided-Contextual-Feature-Fusion-Network-for-Salient-Object-Detection This repo. is an implementation of ACFFNet, which is accepted for in I

5 Nov 21, 2022

A distributed deep learning framework that supports flexible parallelization strategies.

FlexFlow FlexFlow is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization stra

528 Dec 25, 2022

Attentive Implicit Representation Networks (AIR-Nets)

Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

29 Dec 07, 2022

📚 A collection of Jupyter notebooks for learning and experimenting with OpenVINO 👓

A collection of ready-to-run Python* notebooks for learning and experimenting with OpenVINO developer tools. The notebooks are meant to provide an introduction to OpenVINO basics and teach developers

840 Jan 03, 2023

PyTorch-Geometric Implementation of MarkovGNN: Graph Neural Networks on Markov Diffusion

MarkovGNN This is the official PyTorch-Geometric implementation of MarkovGNN paper under the title "MarkovGNN: Graph Neural Networks on Markov Diffusi

6 Sep 23, 2022

Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.

Computational Design and Dynamics of Soft Systems · This is a repository that contains the source code for generating the lecture notes, handouts, exe

4 Jul 21, 2022

The code from the paper Character Transformations for Non-Autoregressive GEC Tagging

Character Transformations for Non-Autoregressive GEC Tagging Milan Straka, Jakub Náplava, Jana Straková Charles University Faculty of Mathematics and

5 Dec 10, 2022

Predict multi paths to a moving person depending on his trajectory history.

Multi-future Trajectory Prediction The project is about using the Multiverse model to make possible multible-future trajectory prediction for a seen p

1 Jan 18, 2022

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-t

5.1k Jan 04, 2023

Código de um painel de auto atendimento feito em Python.

Painel de Auto-Atendimento O intuito desse projeto era fazer em Python um programa que simulasse um painel de auto atendimento, no maior estilo Mac Do

2 Nov 09, 2022

Astrostatistics class for the MSc degree in Astrophysics at the University of Milan-Bicocca (Italy)

Astrostatistics Davide Gerosa - [email protected] University of Mil

25 Jan 02, 2023

OpenCV, MediaPipe Pose Estimation, Affine Transform for Icon Overlay

Yoga Pose Identification and Icon Matching Project Goal Detect yoga poses performed by a user and overlay a corresponding icon image. Running the main

1 Dec 03, 2021

Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions"

Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions" Environment requirement This code is based on Python

1 Dec 19, 2021

Image processing in Python

scikit-image: Image processing in Python Website (including documentation): https://scikit-image.org/ Mailing list: https://mail.python.org/mailman3/l

5.2k Dec 31, 2022

Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Attention-based Transformation from Latent Features to Point Clouds This repository contains a PyTorch implementation of the paper: Attention-based Tr

12 Nov 11, 2022

Prediction of MBA refinance Index (Mortgage prepayment)

Prediction of MBA refinance Index (Mortgage prepayment) Deep Neural Network based Model The ability to predict mortgage prepayment is of critical use

1 Jan 16, 2022

PyTorch DepthNet Training on Still Box dataset

DepthNet training on Still Box Project page This code can replicate the results of our paper that was published in UAVg-17. If you use this repo in yo

115 Nov 21, 2022