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

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

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

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

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.

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

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

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

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

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
Backend code to use MCPI's python API to make infinite worlds with custom generation

inf-mcpi Backend code to use MCPI's python API to make infinite worlds with custom generation Does not save player-placed blocks! Generation is still

5 Oct 04, 2022
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time.

BBB Face Recognizer Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time. Instalati

Rafael Azevedo 232 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
Keras-1D-ACGAN-Data-Augmentation

Keras-1D-ACGAN-Data-Augmentation What is the ACGAN(Auxiliary Classifier GANs) ? Related Paper : [Abstract : Synthesizing high resolution photorealisti

Jae-Hoon Shim 7 Dec 23, 2022
Contextual Attention Network: Transformer Meets U-Net

Contextual Attention Network: Transformer Meets U-Net Contexual attention network for medical image segmentation with state of the art results on skin

Reza Azad 67 Nov 28, 2022
This implements one of result networks from Large-scale evolution of image classifiers

Exotic structured image classifier This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. Req

54 Nov 25, 2022
Multi-Agent Reinforcement Learning (MARL) method to learn scalable control polices for multi-agent target tracking.

scalableMARL Scalable Reinforcement Learning Policies for Multi-Agent Control CD. Hsu, H. Jeong, GJ. Pappas, P. Chaudhari. "Scalable Reinforcement Lea

Christopher Hsu 17 Nov 17, 2022
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
Keras Model Implementation Walkthrough

Keras Model Implementation Walkthrough

Luke Wood 17 Sep 27, 2022
MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images

MAg Paper Abstract File structure Dataset prepare Data description How to use MAg? Why not try the MAg_lib! Trained models Experiment and results Some

Calvin Pang 3 Apr 08, 2022
The Pytorch code of "Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification", CVPR 2022 (Oral).

DeepBDC for few-shot learning        Introduction In this repo, we provide the implementation of the following paper: "Joint Distribution Matters: Dee

FeiLong 116 Dec 19, 2022
This code provides various models combining dilated convolutions with residual networks

Overview This code provides various models combining dilated convolutions with residual networks. Our models can achieve better performance with less

Fisher Yu 1.1k Dec 30, 2022
Efficient neural networks for analog audio effect modeling

micro-TCN Efficient neural networks for audio effect modeling

Christian Steinmetz 94 Dec 29, 2022
QICK: Quantum Instrumentation Control Kit

QICK: Quantum Instrumentation Control Kit The QICK is a kit of firmware and software to use the Xilinx RFSoC to control quantum systems. It consists o

81 Dec 15, 2022
Official code of ICCV2021 paper "Residual Attention: A Simple but Effective Method for Multi-Label Recognition"

CSRA This is the official code of ICCV 2021 paper: Residual Attention: A Simple But Effective Method for Multi-Label Recoginition Demo, Train and Vali

163 Dec 22, 2022
A simple program for training and testing vit

Vit This is a simple program for training and testing vit. Key requirements: torch, torchvision and timm. Dataset I put 5 categories of the cub classi

xiezhenyu 2 Oct 11, 2022
Full-featured Decision Trees and Random Forests learner.

CID3 This is a full-featured Decision Trees and Random Forests learner. It can save trees or forests to disk for later use. It is possible to query tr

Alejandro Penate-Diaz 3 Aug 15, 2022
On Nonlinear Latent Transformations for GAN-based Image Editing - PyTorch implementation

On Nonlinear Latent Transformations for GAN-based Image Editing - PyTorch implementation On Nonlinear Latent Transformations for GAN-based Image Editi

Valentin Khrulkov 22 Oct 24, 2022
Stream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite

mqtt-camera-streamer Summary: Publish frames from a connected camera or MJPEG/RTSP stream to an MQTT topic, and view the feed in a browser on another

Robin Cole 183 Dec 16, 2022