Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

Overview

scc4onnx

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

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

Downloads GitHub PyPI CodeQL

Key concept

  • Allow the user to specify the name of the input OP to change the input order.
  • All number of dimensions can be freely changed, not only 4 dimensions such as NCHW and NHWC.
  • Simply rewrite the input order of the input OP to the specified order and extrapolate Transpose after the input OP so that it does not affect the processing of subsequent OPs.
  • Allows the user to change the channel order of RGB and BGR by specifying options.

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 scc4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/scc4onnx:latest

### docker build
$ docker build -t pinto0309/scc4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/scc4onnx:latest
$ cd /workdir

2. CLI Usage

$ scc4onnx -h

usage:
  scc4onnx [-h]
  --input_onnx_file_path INPUT_ONNX_FILE_PATH
  --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
  [--input_op_names_and_order_dims INPUT_OP_NAME ORDER_DIM]
  [--channel_change_inputs INPUT_OP_NAME DIM]
  [--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.

  --input_op_names_and_order_dims INPUT_OP_NAME ORDER_DIM
      Specify the name of the input_op to be dimensionally changed and the order of the
      dimensions after the change.
      The name of the input_op to be dimensionally changed can be specified multiple times.

      e.g.
      --input_op_names_and_order_dims aaa [0,3,1,2] \
      --input_op_names_and_order_dims bbb [0,2,3,1] \
      --input_op_names_and_order_dims ccc [0,3,1,2,4,5]

  --channel_change_inputs INPUT_OP_NAME DIM
      Change the channel order of RGB and BGR.
      If the original model is RGB, it is transposed to BGR.
      If the original model is BGR, it is transposed to RGB.
      It can be selectively specified from among the OP names specified
      in --input_op_names_and_order_dims.
      OP names not specified in --input_op_names_and_order_dims are ignored.
      Multiple times can be specified as many times as the number of OP names specified
      in --input_op_names_and_order_dims.
      --channel_change_inputs op_name dimension_number_representing_the_channel
      dimension_number_representing_the_channel must specify the dimension position before
      the change in input_op_names_and_order_dims.
      For example, dimension_number_representing_the_channel is 1 for NCHW and 3 for NHWC.

      e.g.
      --channel_change_inputs aaa 3 \
      --channel_change_inputs bbb 1 \
      --channel_change_inputs ccc 5

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

3. In-script Usage

$ python
>>> from scc4onnx import order_conversion
>>> help(order_conversion)
Help on function order_conversion in module scc4onnx.onnx_input_order_converter:

order_conversion(
  input_op_names_and_order_dims: Union[dict, NoneType] = None,
  channel_change_inputs: Union[dict, NoneType] = None,
  input_onnx_file_path: Union[str, NoneType] = '',
  output_onnx_file_path: Union[str, NoneType] = '',
  onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, 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.
    
    input_op_names_and_order_dims: Optional[dict]
        Specify the name of the input_op to be dimensionally changed and
        the order of the dimensions after the change.
        The name of the input_op to be dimensionally changed
        can be specified multiple times.
    
        e.g.
        input_op_names_and_order_dims = {
            "input_op_name1": [0,3,1,2],
            "input_op_name2": [0,2,3,1],
            "input_op_name3": [0,3,1,2,4,5],
        }
    
    channel_change_inputs: Optional[dict]
        Change the channel order of RGB and BGR.
        If the original model is RGB, it is transposed to BGR.
        If the original model is BGR, it is transposed to RGB.
        It can be selectively specified from among the OP names
        specified in input_op_names_and_order_dims.
        OP names not specified in input_op_names_and_order_dims are ignored.
        Multiple times can be specified as many times as the number
        of OP names specified in input_op_names_and_order_dims.
        channel_change_inputs = {"op_name": dimension_number_representing_the_channel}
        dimension_number_representing_the_channel must specify
        the dimension position after the change in input_op_names_and_order_dims.
        For example, dimension_number_representing_the_channel is 1 for NCHW and 3 for NHWC.
    
        e.g.
        channel_change_inputs = {
            "aaa": 1,
            "bbb": 3,
            "ccc": 2,
        }
    
    non_verbose: Optional[bool]
        Do not show all information logs. Only error logs are displayed.
        Default: False
    
    Returns
    -------
    order_converted_graph: onnx.ModelProto
        Order converted onnx ModelProto

4. CLI Execution

$ scc4onnx \
--input_onnx_file_path crestereo_next_iter2_240x320.onnx \
--output_onnx_file_path crestereo_next_iter2_240x320_ord.onnx \
--input_op_names_and_order_dims left [0,2,3,1] \
--input_op_names_and_order_dims right [0,2,3,1] \
--channel_change_inputs left 1 \
--channel_change_inputs right 1

5. In-script Execution

from scc4onnx import order_conversion

order_converted_graph = order_conversion(
    onnx_graph=graph,
    input_op_names_and_order_dims={"left": [0,2,3,1], "right": [0,2,3,1]},
    channel_change_inputs={"left": 1, "right": 1},
    non_verbose=True,
)

6. Sample

6-1. Transpose only

image

$ scc4onnx \
--input_onnx_file_path crestereo_next_iter2_240x320.onnx \
--output_onnx_file_path crestereo_next_iter2_240x320_ord.onnx \
--input_op_names_and_order_dims left [0,2,3,1] \
--input_op_names_and_order_dims right [0,2,3,1]

image image

6-2. Transpose + RGB<->BGR

image

$ scc4onnx \
--input_onnx_file_path crestereo_next_iter2_240x320.onnx \
--output_onnx_file_path crestereo_next_iter2_240x320_ord.onnx \
--input_op_names_and_order_dims left [0,2,3,1] \
--input_op_names_and_order_dims right [0,2,3,1] \
--channel_change_inputs left 1 \
--channel_change_inputs right 1

image

6-3. RGB<->BGR only

image

$ scc4onnx \
--input_onnx_file_path crestereo_next_iter2_240x320.onnx \
--output_onnx_file_path crestereo_next_iter2_240x320_ord.onnx \
--channel_change_inputs left 1 \
--channel_change_inputs right 1

image

7. Issues

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

You might also like...
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX

ONNX msg_chn_wacv20 depth completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20 model in

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for prediction.

Predicitng_viability Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for

Python project to take sound as input and output as RGB + Brightness values suitable for DMX

sound-to-light Python project to take sound as input and output as RGB + Brightness values suitable for DMX Current goals: Get one pixel working: Vary

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt. This is done by

An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

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.

A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .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-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

Releases(1.0.5)
  • 1.0.5(Sep 9, 2022)

    • Add short form parameter
      $ scc4onnx -h
      
      usage:
        scc4onnx [-h]
        -if INPUT_ONNX_FILE_PATH
        -of OUTPUT_ONNX_FILE_PATH
        [-ioo INPUT_OP_NAME ORDER_DIM]
        [-cci INPUT_OP_NAME DIM]
        [-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.
      
        -ioo INPUT_OP_NAMES_AND_ORDER_DIMS INPUT_OP_NAMES_AND_ORDER_DIMS, --input_op_names_and_order_dims INPUT_OP_NAMES_AND_ORDER_DIMS INPUT_OP_NAMES_AND_ORDER_DIMS
            Specify the name of the input_op to be dimensionally changed and the order of the
            dimensions after the change.
            The name of the input_op to be dimensionally changed can be specified multiple times.
      
            e.g.
            --input_op_names_and_order_dims aaa [0,3,1,2] \
            --input_op_names_and_order_dims bbb [0,2,3,1] \
            --input_op_names_and_order_dims ccc [0,3,1,2,4,5]
      
        -cci CHANNEL_CHANGE_INPUTS CHANNEL_CHANGE_INPUTS, --channel_change_inputs CHANNEL_CHANGE_INPUTS CHANNEL_CHANGE_INPUTS
            Change the channel order of RGB and BGR.
            If the original model is RGB, it is transposed to BGR.
            If the original model is BGR, it is transposed to RGB.
            It can be selectively specified from among the OP names specified
            in --input_op_names_and_order_dims.
            OP names not specified in --input_op_names_and_order_dims are ignored.
            Multiple times can be specified as many times as the number of OP names specified
            in --input_op_names_and_order_dims.
            --channel_change_inputs op_name dimension_number_representing_the_channel
            dimension_number_representing_the_channel must specify the dimension position before
            the change in input_op_names_and_order_dims.
            For example, dimension_number_representing_the_channel is 1 for NCHW and 3 for NHWC.
      
            e.g.
            --channel_change_inputs aaa 3 \
            --channel_change_inputs bbb 1 \
            --channel_change_inputs ccc 5
      
        -n, --non_verbose
            Do not show all information logs. Only error logs are displayed.
      

    Full Changelog: https://github.com/PINTO0309/scc4onnx/compare/1.0.4...1.0.5

    Source code(tar.gz)
    Source code(zip)
  • 1.0.4(May 25, 2022)

  • 1.0.3(May 15, 2022)

  • 1.0.2(May 10, 2022)

  • 1.0.1(Apr 19, 2022)

  • 1.0.0(Apr 18, 2022)

Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
Code for intrusion detection system (IDS) development using CNN models and transfer learning

Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus

Western OC2 Lab 38 Dec 12, 2022
This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

README This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et a

Raghav 42 Dec 15, 2022
This is the implementation of GGHL (A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection)

GGHL: A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection This is the implementation of GGHL πŸ‘‹ πŸ‘‹ πŸ‘‹ [Arxiv] [Google Drive][B

551 Dec 31, 2022
Text-Based Ideal Points

Text-Based Ideal Points Source code for the paper: Text-Based Ideal Points by Keyon Vafa, Suresh Naidu, and David Blei (ACL 2020). Update (June 29, 20

Keyon Vafa 37 Oct 09, 2022
A simple, high level, easy-to-use open source Computer Vision library for Python.

ZoomVision : Slicing Aid Detection A simple, high level, easy-to-use open source Computer Vision library for Python. Installation Installing dependenc

Nurettin Sinanoğlu 2 Mar 04, 2022
Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

Point-Unet This is an unofficial implementation of the MICCAI 2021 paper Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segment

Namt0d 9 Dec 07, 2022
Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Diffusion Probabilistic Models This repository provides a reference implementation of the method described in the paper: Deep Unsupervised Learning us

Jascha Sohl-Dickstein 238 Jan 02, 2023
Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Deep Causal Reasoning for Recommender Systems The codes are associated with the following paper: Deep Causal Reasoning for Recommendations, Yaochen Zh

Yaochen Zhu 22 Oct 15, 2022
Randstad Artificial Intelligence Challenge (powered by VGEN). Soluzione proposta da Stefano Fiorucci (anakin87) - primo classificato

Randstad Artificial Intelligence Challenge (powered by VGEN) Soluzione proposta da Stefano Fiorucci (anakin87) - primo classificato Struttura director

Stefano Fiorucci 1 Nov 13, 2021
An official source code for "Augmentation-Free Self-Supervised Learning on Graphs"

Augmentation-Free Self-Supervised Learning on Graphs An official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted

Namkyeong Lee 59 Dec 01, 2022
Official Pytorch Code for the paper TransWeather

TransWeather Official Code for the paper TransWeather, Arxiv Tech Report 2021 Paper | Website About this repo: This repo hosts the implentation code,

Jeya Maria Jose 81 Dec 30, 2022
Towards Fine-Grained Reasoning for Fake News Detection

FinerFact This is the PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection (Ar

Ahren_Jin 15 Dec 15, 2022
Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

AMRBART An implementation for ACL2022 paper "Graph Pre-training for AMR Parsing and Generation". You may find our paper here (Arxiv). Requirements pyt

xfbai 60 Jan 03, 2023
Main repository for the HackBio'2021 Virtual Internship Experience for #Team-Greider ❀️

Hello 🀟 #Team-Greider The team of 20 people for HackBio'2021 Virtual Bioinformatics Internship πŸ’ πŸ–¨οΈ πŸ‘¨β€πŸ’» HackBio: https://thehackbio.com πŸ’¬ Ask us

Siddhant Sharma 7 Oct 20, 2022
Implementation of Wasserstein adversarial attacks.

Stronger and Faster Wasserstein Adversarial Attacks Code for Stronger and Faster Wasserstein Adversarial Attacks, appeared in ICML 2020. This reposito

21 Oct 06, 2022
Efficient training of deep recommenders on cloud.

HybridBackend Introduction HybridBackend is a training framework for deep recommenders which bridges the gap between evolving cloud infrastructure and

Alibaba 111 Dec 23, 2022
Count the MACs / FLOPs of your PyTorch model.

THOP: PyTorch-OpCounter How to install pip install thop (now continously intergrated on Github actions) OR pip install --upgrade git+https://github.co

Ligeng Zhu 3.9k Dec 29, 2022
LabelImg is a graphical image annotation tool.

LabelImgPlus LabelImg is a graphical image annotation tool. This project is not updated with new functions now. More functions are supported with Labe

lzx1413 200 Dec 20, 2022
Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Wav2CLIP 🚧 WIP 🚧 Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP πŸ“„ πŸ”— Ho-Hsiang Wu, Prem Seetharaman

Descript 240 Dec 13, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022