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

Related tags

Deep Learningpythonmodelsonnx
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

Downloads GitHub PyPI CodeQL

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

image

$ 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]

image

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

image

$ 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

image

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

image

$ 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]

image

7. Issues

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

You might also like...
Simple ONNX operation generator. Simple Operation Generator for ONNX.
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

Katsuya Hyodo 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

Sayed Nadim 10 Jan 1, 2023
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
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

NVIDIA Corporation 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

Zhao Hengrun 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.

Microsoft 58 Dec 18, 2022
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

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

Ibai Gorordo 18 Nov 6, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX
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

Ibai Gorordo 14 Dec 9, 2022
Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)
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

null 349 Dec 8, 2022
Ranger deep learning optimizer rewrite to use newest components
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

Less Wright 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)

  • 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] image

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

      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] image

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

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

  • 1.0.8(Jun 7, 2022)

  • 1.0.7(May 25, 2022)

  • 1.0.6(May 15, 2022)

  • 1.0.5(May 12, 2022)

  • 1.0.4(May 5, 2022)

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

  • 1.0.2(May 3, 2022)

  • 1.0.1(Apr 16, 2022)

  • 1.0.0(Apr 15, 2022)

Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
GitHub Repository
Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic Scenes", ICCV 2021.

Deep 3D Mask Volume for View Synthesis of Dynamic Scenes Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic S

Ken Lin 17 Oct 12, 2022
The official implementation of Equalization Loss for Long-Tailed Object Recognition (CVPR 2020) based on Detectron2

Equalization Loss for Long-Tailed Object Recognition Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan ⚠️ We re

Jingru Tan 197 Dec 25, 2022
Source code for paper: Knowledge Inheritance for Pre-trained Language Models

Knowledge-Inheritance Source code paper: Knowledge Inheritance for Pre-trained Language Models (preprint). The trained model parameters (in Fairseq fo

THUNLP 31 Nov 19, 2022
Learning from Synthetic Humans, CVPR 2017

Learning from Synthetic Humans (SURREAL) Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev and Cordelia Schmid,

Gul Varol 538 Dec 18, 2022
Volsdf - Volume Rendering of Neural Implicit Surfaces

Volume Rendering of Neural Implicit Surfaces Project Page | Paper | Data This re

Lior Yariv 221 Jan 07, 2023
Blender Add-on that sets a Material's Base Color to one of Pantone's Colors of the Year

Blender PCOY (Pantone Color of the Year) MCMC (Mid-Century Modern Colors) HG71 (House & Garden Colors 1971) Blender Add-ons That Assign a Custom Color

Don Schnitzius 15 Nov 20, 2022
The official MegEngine implementation of the ICCV 2021 paper: GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning

[ICCV 2021] GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning This is the official implementation of our ICCV2021 paper GyroFlow. Our pres

MEGVII Research 36 Sep 07, 2022
QuadTree Attention for Vision Transformers (ICLR2022)

This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and seman

tangshitao 222 Dec 28, 2022
It's A ML based Web Site build with python and Django to find the breed of the dog

ML-Based-Dog-Breed-Identifier This is a Django Based Web Site To Identify the Breed of which your DOG belogs All You Need To Do is to Follow These Ste

Sanskar Dwivedi 2 Oct 12, 2022
This repository contains all code and data for the Inside Out Visual Place Recognition task

Inside Out Visual Place Recognition This repository contains code and instructions to reproduce the results for the Inside Out Visual Place Recognitio

15 May 21, 2022
Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images.

IAug_CDNet Official Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images. Overview We propose a

53 Dec 02, 2022
Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Tom-R.T.Kvalvaag 2 Dec 17, 2021
(ICONIP 2020) MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image

MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image This repo contains the source code for MobileHand, real-time estimation of 3D

90 Dec 12, 2022
Parameterising Simulated Annealing for the Travelling Salesman Problem

Parameterising Simulated Annealing for the Travelling Salesman Problem

Gary Sun 55 Jun 15, 2022
Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

Almaz 2 Dec 08, 2022
Source code for From Stars to Subgraphs

GNNAsKernel Official code for From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Visualizations GNN-AK(+) GNN-AK(+) with Subgra

44 Dec 19, 2022
keyframes-CNN-RNN(action recognition)

keyframes-CNN-RNN(action recognition) Environment: python=3.7 pytorch=1.2 Datasets: Following the format of UCF101 action recognition. Run steps: Mo

4 Feb 09, 2022
DvD-TD3: Diversity via Determinants for TD3 version

DvD-TD3: Diversity via Determinants for TD3 version The implementation of paper Effective Diversity in Population Based Reinforcement Learning. Instal

3 Feb 11, 2022
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping.

P-Lambda 437 Dec 30, 2022