Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

Overview

snc4onnx

Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

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 \
&& pip install -U onnx-simplifier \
&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \
&& pip install -U snc4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/snc4onnx:latest

### docker build
$ docker build -t pinto0309/snc4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/snc4onnx:latest
$ cd /workdir

2. CLI Usage

$ snc4onnx -h

usage:
  snc4onnx [-h]
    --input_onnx_file_paths INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...]
    --srcop_destop SRCOP_DESTOP [SRCOP_DESTOP ...]
    [--op_prefixes_after_merging OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...]]
    [--output_onnx_file_path OUTPUT_ONNX_FILE_PATH]
    [--output_of_onnx_file_in_the_process_of_fusion]
    [--non_verbose]

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

  --input_onnx_file_paths INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...]
    Input onnx file paths. At least two onnx files must be specified.

  --srcop_destop SRCOP_DESTOP [SRCOP_DESTOP ...]
    The names of the output OP to join from and the input OP to join to are
    out1 in1 out2 in2 out3 in3 .... format.
    In other words, to combine model1 and model2,
    --srcop_destop model1_out1 model2_in1 model1_out2 model2_in2
    Also, --srcop_destop can be specified multiple times.
    The first --srcop_destop specifies the correspondence between model1 and model2,
    and the second --srcop_destop specifies the correspondence
    between model1 and model2 combined and model3.
    It is necessary to take into account that the prefix specified
    in op_prefixes_after_merging is given at the beginning of each OP name.
    e.g. To combine model1 with model2 and model3.
    --srcop_destop model1_src_op1 model2_dest_op1 model1_src_op2 model2_dest_op2 ...
    --srcop_destop comb_model12_src_op1 model3_dest_op1 comb_model12_src_op2 model3_dest_op2 ...

  --op_prefixes_after_merging OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...]
    Since a single ONNX file cannot contain multiple OPs with the same name,
    a prefix is added to all OPs in each input ONNX model to avoid duplication.
    Specify the same number of paths as input_onnx_file_paths.
    e.g. --op_prefixes_after_merging model1_prefix model2_prefix model3_prefix ...

  --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
    Output onnx file path.

  --output_of_onnx_file_in_the_process_of_fusion
    Output of onnx files in the process of fusion.

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

3. In-script Usage

$ python
>>> from snc4onnx import combine
>>> help(combine)

Help on function combine in module snc4onnx.onnx_network_combine:

combine(
  srcop_destop: List[str],
  op_prefixes_after_merging: Union[List[str], NoneType] = [],
  input_onnx_file_paths: Union[List[str], NoneType] = [],
  onnx_graphs: Union[List[onnx.onnx_ml_pb2.ModelProto], NoneType] = [],
  output_onnx_file_path: Union[str, NoneType] = '',
  output_of_onnx_file_in_the_process_of_fusion: Union[bool, NoneType] = False,
  non_verbose: Union[bool, NoneType] = False
) -> onnx.onnx_ml_pb2.ModelProto

    Parameters
    ----------
    srcop_destop: List[str]
        The names of the output OP to join from and the input OP to join to are
        [["out1","in1"], ["out2","in2"], ["out3","in3"]] format.

        In other words, to combine model1 and model2,
        srcop_destop =
            [
                ["model1_out1_opname","model2_in1_opname"],
                ["model1_out2_opname","model2_in2_opname"]
            ]

        The first srcop_destop specifies the correspondence between model1 and model2,
        and the second srcop_destop specifies the correspondence
        between model1 and model2 combined and model3.
        It is necessary to take into account that the prefix specified
        in op_prefixes_after_merging is given at the beginning of each OP name.

        e.g. To combine model1 with model2 and model3.
        srcop_destop =
            [
                [
                    ["model1_src_op1","model2_dest_op1"],
                    ["model1_src_op2","model2_dest_op2"]
                ],
                [
                    ["combined_model1.2_src_op1","model3_dest_op1"],
                    ["combined_model1.2_src_op2","model3_dest_op2"]
                ],
                ...
            ]

    op_prefixes_after_merging: List[str]
        Since a single ONNX file cannot contain multiple OPs with the same name,
        a prefix is added to all OPs in each input ONNX model to avoid duplication.
        Specify the same number of paths as input_onnx_file_paths.
        e.g. op_prefixes_after_merging = ["model1_prefix","model2_prefix","model3_prefix", ...]

    input_onnx_file_paths: Optional[List[str]]
        Input onnx file paths. At least two onnx files must be specified.
        Either input_onnx_file_paths or onnx_graphs must be specified.
        onnx_graphs If specified, ignore input_onnx_file_paths and process onnx_graphs.
        e.g. input_onnx_file_paths = ["model1.onnx", "model2.onnx", "model3.onnx", ...]

    onnx_graphs: Optional[List[onnx.ModelProto]]
        List of onnx.ModelProto. At least two onnx graphs must be specified.
        Either input_onnx_file_paths or onnx_graphs must be specified.
        onnx_graphs If specified, ignore input_onnx_file_paths and process onnx_graphs.
        e.g. onnx_graphs = [graph1, graph2, graph3, ...]

    output_onnx_file_path: Optional[str]
        Output onnx file path.
        If not specified, .onnx is not output.
        Default: ''

    output_of_onnx_file_in_the_process_of_fusion: Optional[bool]
        Output of onnx files in the process of fusion.
        Default: False

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

    Returns
    -------
    combined_graph: onnx.ModelProto
        Combined onnx ModelProto

4. CLI Execution

$ snc4onnx \
--input_onnx_file_paths crestereo_init_iter2_120x160.onnx crestereo_next_iter2_240x320.onnx \
--srcop_destop output flow_init \
--op_prefixes_after_merging init next

5. In-script Execution

5-1. ONNX files

from snc4onnx import combine

combined_graph = combine(
    srcop_destop = [
        ['output', 'flow_init']
    ],
    op_prefixes_after_merging = [
        'init',
        'next',
    ],
    input_onnx_file_paths = [
        'crestereo_init_iter2_120x160.onnx',
        'crestereo_next_iter2_240x320.onnx',
    ],
    non_verbose = True,
)

5-2. onnx.ModelProtos

from snc4onnx import combine

combined_graph = combine(
    srcop_destop = [
        ['output', 'flow_init']
    ],
    op_prefixes_after_merging = [
        'init',
        'next',
    ],
    onnx_graphs = [
        graph1,
        graph2,
        graph3,
    ],
    non_verbose = True,
)

6. Sample

6-1 INPUT <-> OUTPUT

  • Summary

    image

  • Model.1

    image

  • Model.2

    image

  • Merge

    $ snc4onnx \
    --input_onnx_file_paths crestereo_init_iter2_120x160.onnx crestereo_next_iter2_240x320.onnx \
    --op_prefixes_after_merging init next \
    --srcop_destop output flow_init
  • Result

    image image

6-2 INPUT + INPUT

  • Summary

    image

  • Model.1

    image

  • Model.2

    image

  • Merge

    $ snc4onnx \
    --input_onnx_file_paths objectron_camera_224x224.onnx objectron_chair_224x224.onnx \
    --srcop_destop input_1 input_1 \
    --op_prefixes_after_merging camera chair \
    --output_onnx_file_path objectron_camera_chair_224x224.onnx
  • Result

    image image

7. Reference

  1. https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md
  2. https://github.com/PINTO0309/sne4onnx
  3. https://github.com/PINTO0309/snd4onnx
  4. https://github.com/PINTO0309/scs4onnx
  5. https://github.com/PINTO0309/sog4onnx
  6. https://github.com/PINTO0309/PINTO_model_zoo

8. Issues

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

You might also like...
Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.
Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.

ONNX Object Localization Network Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX. Ori

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

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.

This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

PyTorch Infer Utils This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model infer

Convert onnx models to pytorch.

onnx2torch onnx2torch is an ONNX to PyTorch converter. Our converter: Is easy to use – Convert the ONNX model with the function call convert; Is easy

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

XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward? Models Playground is here to help you do that. Models playground allows you to train your models right from the browser. PyTorch ,ONNX and TensorRT implementation of YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

Releases(1.0.11)
  • 1.0.11(Jan 2, 2023)

  • 1.0.10(Jan 2, 2023)

  • 1.0.9(Sep 7, 2022)

    • Add short form parameter

      $ snc4onnx -h
      
      usage:
        snc4onnx [-h]
          -if INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...]
          -sd SRCOP_DESTOP [SRCOP_DESTOP ...]
          [-opam OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...]]
          [-of OUTPUT_ONNX_FILE_PATH]
          [-f]
          [-n]
      
      optional arguments:
        -h, --help
          show this help message and exit.
      
        -if INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...], --input_onnx_file_paths INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...]
            Input onnx file paths. At least two onnx files must be specified.
      
        -sd SRCOP_DESTOP [SRCOP_DESTOP ...], --srcop_destop SRCOP_DESTOP [SRCOP_DESTOP ...]
            The names of the output OP to join from and the input OP to join to are
            out1 in1 out2 in2 out3 in3 ....
            format.
            In other words, to combine model1 and model2,
            --srcop_destop model1_out1 model2_in1 model1_out2 model2_in2
            Also, --srcop_destop can be specified multiple times.
            The first --srcop_destop specifies the correspondence between model1 and model2,
            and the second --srcop_destop specifies the correspondence between
            model1 and model2 combined and model3.
            It is necessary to take into account that the prefix specified
            in op_prefixes_after_merging is
            given at the beginning of each OP name.
            e.g. To combine model1 with model2 and model3.
            --srcop_destop model1_src_op1 model2_dest_op1 model1_src_op2 model2_dest_op2 ...
            --srcop_destop combined_model1.2_src_op1 model3_dest_op1 combined_model1.2_src_op2 model3_dest_op2 ...
      
        -opam OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...], --op_prefixes_after_merging OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...]
            Since a single ONNX file cannot contain multiple OPs with the same name,
            a prefix is added to all OPs in each input ONNX model to avoid duplication.
            Specify the same number of paths as input_onnx_file_paths.
            e.g. --op_prefixes_after_merging model1_prefix model2_prefix model3_prefix ...
      
        -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
            Output onnx file path.
      
        -f, --output_of_onnx_file_in_the_process_of_fusion
            Output of onnx files in the process of fusion.
      
        -n, --non_verbose
            Do not show all information logs. Only error logs are displayed.
      
    Source code(tar.gz)
    Source code(zip)
  • 1.0.8(Sep 6, 2022)

    1. Fixed a bug that caused INPUT names to be corrupted. There was a problem with the removal of prefixes added during the model merging process.
      • before: main_input -> put (bug)
      • after: main_input -> input
      • Stop using lstrip and change to forward matching logic with re.sub
    2. Added process to clean up OUTPUT prefixes as much as possible image
    Source code(tar.gz)
    Source code(zip)
  • 1.0.7(May 25, 2022)

  • 1.0.6(May 7, 2022)

  • 1.0.5(May 1, 2022)

  • 1.0.4(Apr 27, 2022)

    • Change op_prefixes_after_merging to optional
    • Added duplicate OP name check
      • If there is a duplicate OP name, the model cannot be combined and the process is aborted with the following error message.
        ERROR: 
        There is a duplicate OP name after merging models.
        op_name:input count:2, op_name:output count:2
        Avoid duplicate OP names by specifying a prefix in op_prefixes_after_merging.
        
    Source code(tar.gz)
    Source code(zip)
  • 1.0.3(Apr 24, 2022)

  • 1.0.2(Apr 11, 2022)

  • 1.0.1(Apr 10, 2022)

  • 1.0.0(Apr 10, 2022)

Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

IterMVS official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo' Introduction IterMVS is a novel lear

Fangjinhua Wang 127 Jan 04, 2023
A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squares.

W.I.P-Aim-Memory-Game A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squar

dE_soot 1 Dec 08, 2021
The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

SF-Net for fullband SE This is the repo of the manuscript "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Ban

Guochen Yu 36 Dec 02, 2022
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs

Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs ArXiv Abstract Convolutional Neural Networks (CNNs) have become the de f

Philipp Benz 12 Oct 24, 2022
Efficient Training of Visual Transformers with Small Datasets

Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.

Yahui Liu 112 Dec 25, 2022
Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

beringresearch 285 Jan 04, 2023
KoRean based ELECTRA pre-trained models (KR-ELECTRA) for Tensorflow and PyTorch

KoRean based ELECTRA (KR-ELECTRA) This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computa

12 Jun 03, 2022
Large-scale language modeling tutorials with PyTorch

Large-scale language modeling tutorials with PyTorch μ•ˆλ…•ν•˜μ„Έμš”. μ €λŠ” TUNiBμ—μ„œ λ¨Έμ‹ λŸ¬λ‹ μ—”μ§€λ‹ˆμ–΄λ‘œ 근무 쀑인 κ³ ν˜„μ›…μž…λ‹ˆλ‹€. 이 μžλ£ŒλŠ” λŒ€κ·œλͺ¨ μ–Έμ–΄λͺ¨λΈ κ°œλ°œμ— ν•„μš”ν•œ μ—¬λŸ¬κ°€μ§€ κΈ°μˆ λ“€μ„ μ†Œκ°œλ“œλ¦¬κΈ° μœ„ν•΄ λ§ˆλ ¨ν•˜μ˜€μœΌλ©° 기본적으둜

TUNiB 172 Dec 29, 2022
[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

CC 4.4k Dec 27, 2022
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited b

Facebook Research 61 Nov 28, 2022
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)

Official code of Retinal Vessel Segmentation with Pixel-wise Adaptive Filters and Consistency Training (ISBI 2022)

anonymous 14 Oct 27, 2022
Learning Compatible Embeddings, ICCV 2021

LCE Learning Compatible Embeddings, ICCV 2021 by Qiang Meng, Chixiang Zhang, Xiaoqiang Xu and Feng Zhou Paper: Arxiv We cannot release source codes pu

Qiang Meng 25 Dec 17, 2022
Image-retrieval-baseline - MUGE Multimodal Retrieval Baseline

MUGE Multimodal Retrieval Baseline This repo is implemented based on the open_cl

47 Dec 16, 2022
TeST: Temporal-Stable Thresholding for Semi-supervised Learning

TeST: Temporal-Stable Thresholding for Semi-supervised Learning TeST Illustration Semi-supervised learning (SSL) offers an effective method for large-

Xiong Weiyu 1 Jul 14, 2022
Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020).

SentiBERT Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020). https://arxiv.org/abs/20

Da Yin 66 Aug 13, 2022
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN

Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN Which Style Makes Me Attractive? Interpretable Control Discovery an

Bo Li 11 Dec 01, 2022
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

GCL: Graph Contrastive Learning Library for PyTorch 594 Jan 08, 2023
πŸƒβ€β™€οΈ A curated list about human motion capture, analysis and synthesis.

Awesome Human Motion πŸƒβ€β™€οΈ A curated list about human motion capture, analysis and synthesis. Contents Introduction Human Models Datasets Data Process

Dennis Wittchen 274 Dec 14, 2022
Prometheus exporter for Cisco Unified Computing System (UCS) Manager

prometheus-ucs-exporter Overview Use metrics from the UCS API to export relevant metrics to Prometheus This repository is a fork of Drew Stinnett's or

Marshall Wace 6 Nov 07, 2022
Implementation of light baking system for ray tracing based on Activision's UberBake

Vulkan Light Bakary MSU Graphics Group Student's Diploma Project Treefonov Andrey [GitHub] [LinkedIn] Project Goal The goal of the project is to imple

Andrey Treefonov 7 Dec 27, 2022