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
Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation

OoD_Gen-Chest_Xray Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation Requirements (Installations) Install the following libra

Enoch Tetteh 2 Oct 01, 2022
[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition (CVPR 2021) arXiv Prerequisite PyTorch = 1.2.0 Python3 torchvision PIL argpar

51 Nov 11, 2022
Generative code template for PixelBeasts 10k NFT project.

generator-template Generative code template for combining transparent png attributes into 10,000 unique images. Used for the PixelBeasts 10k NFT proje

Yohei Nakajima 9 Aug 24, 2022
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Seulki Park 70 Jan 03, 2023
[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links

LinkBERT: A Knowledgeable Language Model Pretrained with Document Links This repo provides the model, code & data of our paper: LinkBERT: Pretraining

Michihiro Yasunaga 264 Jan 01, 2023
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

This repository holds the implementation for paper Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Download our preproc

Qitian Wu 42 Dec 27, 2022
The official implementation of the Hybrid Self-Attention NEAT algorithm

PUREPLES - Pure Python Library for ES-HyperNEAT About This is a library of evolutionary algorithms with a focus on neuroevolution, implemented in pure

Adrian Westh 91 Dec 12, 2022
BABEL: Bodies, Action and Behavior with English Labels [CVPR 2021]

BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL labels about 43 hours of mocap sequences from AMASS [1] with action labels.

113 Dec 28, 2022
A python interface for training Reinforcement Learning bots to battle on pokemon showdown

The pokemon showdown Python environment A Python interface to create battling pokemon agents. poke-env offers an easy-to-use interface for creating ru

Haris Sahovic 184 Dec 30, 2022
Barlow Twins and HSIC

Barlow Twins and HSIC Unofficial Pytorch implementation for Barlow Twins and HSIC_SSL on small datasets (CIFAR10, STL10, and Tiny ImageNet). Correspon

Yao-Hung Hubert Tsai 49 Nov 24, 2022
Human4D Dataset tools for processing and visualization

HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media HUMAN4D constitutes a large and multimodal 4D dataset that contains a variet

tofis 15 Nov 09, 2022
Half Instance Normalization Network for Image Restoration

HINet Half Instance Normalization Network for Image Restoration, based on https://github.com/megvii-model/HINet. Dependencies NumPy PyTorch, preferabl

Holy Wu 4 Jun 06, 2022
Migration of Edge-based Distributed Federated Learning

FedFly: Towards Migration in Edge-based Distributed Federated Learning About the research Due to mobility, a device participating in Federated Learnin

qub-blesson 11 Nov 13, 2022
Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

EfficientZero (NeurIPS 2021) Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021. Environments Effi

Weirui Ye 671 Jan 03, 2023
[ WSDM '22 ] On Sampling Collaborative Filtering Datasets

On Sampling Collaborative Filtering Datasets This repository contains the implementation of many popular sampling strategies, along with various expli

Noveen Sachdeva 17 Dec 08, 2022
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation(mCOLT/mRASP2), ACL2021

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation(mCOLT/mRASP2), ACL2021 The code for training mCOLT/mRASP2, a multilingua

104 Jan 01, 2023
Command-line tool for downloading and extending the RedCaps dataset.

RedCaps Downloader This repository provides the official command-line tool for downloading and extending the RedCaps dataset. Users can seamlessly dow

RedCaps dataset 33 Dec 14, 2022
A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)

Library | Paper | Slack We released two versions of OAG-BERT in CogDL package. OAG-BERT is a heterogeneous entity-augmented academic language model wh

THUDM 58 Dec 17, 2022
Code & Data for Enhancing Photorealism Enhancement

Enhancing Photorealism Enhancement Stephan R. Richter, Hassan Abu AlHaija, Vladlen Koltun Paper | Website (with side-by-side comparisons) | Video (Pap

Intelligent Systems Lab Org 1.1k Dec 31, 2022
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

Deep learning based state estimation: incorporating Transformer and LSTM to Kalman Filter with EM algorithm Overview Kalman Filter requires the true p

zshicode 57 Dec 27, 2022