Experimental Python implementation of OpenVINO Inference Engine (very slow, limited functionality). All codes are written in Python. Easy to read and modify.

Related tags

Deep Learningpythonaideep-learningexperimentalinferencemnistmnist-classificationconvolutionconvolutional-neural-networkinference-engineinference-libraryopenvino
Overview

PyOpenVINO - An Experimental Python Implementation of OpenVINO Inference Engine (minimum-set)

Description

The PyOpenVINO is a spin-off product from my deep learning algorithm study work. This project is aiming at neither practical performance nor rich functionalities. PyOpenVINO can load an OpenVINO IR model (.xml/.bin) and run it. The implementation is quite straightforward and naive. No Optimization technique is used. Thus, the code is easy to read and modify. Supported API is quite limited, but it mimics OpenVINO IE Python API. So, you can easily read and modify the sample code too.

  • Developed as a spin-off from my deep learning study work.
  • Very slow and limited functionality. Not a general DL inference engine.
  • Naive and straightforward code: (I hope) This is a good reference for learning deep-learning technology.
  • Extensible ops: Ops are implemented as plugins. You can easily add your ops as needed.

How to run

Steps 1 and 2 are optional since the converted MNIST IR model is provided.

  1. (Optional) Train a model and generate a 'saved_model' with TensorFlow
python mnist-tf-training.py

The trained model data will be created under ./mnist-savedmodel directory.

  1. (Optional) Convert TF saved_model into OpenVINO IR model
    Prerequisite: You need to have OpenVINO installed (Model Optimizer is required).
convert-model.bat

Converted IR model (.xml/.bin) will be generated in ./models directory.

  1. Run pyOpenVINO sample program
python test_pyopenvino.py

You'll see the output like this.

pyopenvino>python test_pyopenvino.py
inputs: [{'name': 'conv2d_input', 'type': 'Parameter', 'version': 'opset1', 'data': {'element_type': 'f32', 'shape': (1, 1, 28, 28)}, 'output': {0: {'precision': 'FP32', 'dims': (1, 1, 28, 28)}}}]
outputs: [{'name': 'Func/StatefulPartitionedCall/output/_11:0', 'type': 'Result', 'version': 'opset1', 'input': {0: {'precision': 'FP32', 'dims': (1, 10)}}}]
# node_name, time (sec)
conv2d_input Parameter, 0.0
conv2d_input/scale_copy Const, 0.0
StatefulPartitionedCall/sequential/conv2d/Conv2D Convolution, 0.11315417289733887
StatefulPartitionedCall/sequential/conv2d/BiasAdd/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/conv2d/BiasAdd/Add Add, 0.0
StatefulPartitionedCall/sequential/conv2d/Relu ReLU, 0.0010142326354980469
StatefulPartitionedCall/sequential/max_pooling2d/MaxPool MaxPool, 0.020931482315063477
StatefulPartitionedCall/sequential/conv2d_1/Conv2D/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/conv2d_1/Conv2D Convolution, 0.04333162307739258
StatefulPartitionedCall/sequential/conv2d_1/BiasAdd/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/conv2d_1/BiasAdd/Add Add, 0.0
StatefulPartitionedCall/sequential/conv2d_1/Relu ReLU, 0.0
StatefulPartitionedCall/sequential/max_pooling2d_1/MaxPool MaxPool, 0.006029367446899414
StatefulPartitionedCall/sequential/target_conv_layer/Conv2D/ReadVariableOp Const, 0.0010688304901123047
StatefulPartitionedCall/sequential/target_conv_layer/Conv2D Convolution, 0.004073381423950195
StatefulPartitionedCall/sequential/target_conv_layer/BiasAdd/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/target_conv_layer/BiasAdd/Add Add, 0.0
StatefulPartitionedCall/sequential/target_conv_layer/Relu ReLU, 0.0
StatefulPartitionedCall/sequential/target_conv_layer/Relu/Transpose/value6071024 Const, 0.0
StatefulPartitionedCall/sequential/target_conv_layer/Relu/Transpose Transpose, 0.0
StatefulPartitionedCall/sequential/flatten/Const Const, 0.0
StatefulPartitionedCall/sequential/flatten/Reshape Reshape, 0.0
StatefulPartitionedCall/sequential/dense/MatMul/ReadVariableOp Const, 0.0010004043579101562
StatefulPartitionedCall/sequential/dense/MatMul MatMul, 0.0013704299926757812
StatefulPartitionedCall/sequential/dense/BiasAdd/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/dense/BiasAdd/Add Add, 0.0
StatefulPartitionedCall/sequential/dense/Relu ReLU, 0.0
StatefulPartitionedCall/sequential/dense_1/MatMul/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/dense_1/MatMul MatMul, 0.0
StatefulPartitionedCall/sequential/dense_1/BiasAdd/ReadVariableOp Const, 0.0
StatefulPartitionedCall/sequential/dense_1/BiasAdd/Add Add, 0.0
StatefulPartitionedCall/sequential/dense_1/Softmax SoftMax, 0.0009992122650146484
Func/StatefulPartitionedCall/output/_11:0 Result, 0.0
@TOTAL_TIME, 0.21120882034301758
0.21120882034301758 sec/inf
Raw result: {'Func/StatefulPartitionedCall/output/_11:0': array([[7.8985136e-07, 2.0382247e-08, 9.9999917e-01, 1.0367385e-10,
        1.0184062e-10, 1.6024957e-12, 2.0729640e-10, 1.6014919e-08,
        6.5354638e-10, 9.5946295e-14]], dtype=float32)}
Result: [2 0 1 7 8 6 3 4 5 9]
  1. Run Draw-and-Inter demo
python draw-and-infer.py

How to Operate

  • Left click to draw points.
  • Right click to clear the canvas.
    This demo program is using 'numpy' kernels for performance.
    draw-and-infer

A Littile Description of the Implementation

IR model internal representation

This inference engine uses networkx.DiGraph as the internal representation of the IR model. IR model will be translated into nodes and edges.
The nodes represent the ops, and it holds the attributes of the ops (e.g., strides, dilations, etc.).
The edges represent the connection between the nodes. The edges hold the port number for both ends.
The intermediate output from the nodes (feature maps) will be stored in the data attributes in the output port of the node (G.nodes[node_id_num]['output'][port_num]['data'] = feat_map)

An example of the contents (attributes) of a node

node id= 14
 name : StatefulPartitionedCall/sequential/target_conv_layer/Conv2D
 type : Convolution
 version : opset1
 data :
     auto_pad : valid
     dilations : 1, 1
     pads_begin : 0, 0
     pads_end : 0, 0
     strides : 1, 1
 input :
     0 :
         precision : FP32
         dims : (1, 64, 5, 5)
     1 :
         precision : FP32
         dims : (64, 64, 3, 3)
 output :
     2 :
         precision : FP32
         dims : (1, 64, 3, 3)

An example of the contents of an edge

format = (from-layer, from-port, to-layer, to-port)

edge_id= (0, 2)
   {'connection': (0, 0, 2, 0)}

Ops plugins

Operators are implemented as plugins. You can develop an Op in Python and place the file in the op_plugins directory. The inference_engine of pyOpenVINO will search the Python source files in the op_plugins directory at the start time and register them as the Ops plugin.
The file name of the Ops plugin will be treated as the Op name, so it must match the layer type attribute field in the IR XML file.
The inference engine will call the compute() function of the plugin to perform the calculation. The compute() function is the only API between the inference engine and the plugin. The inference engine will collect the required input data and pass it to the compute() function. The input data is in the form of Python dict. ({port_num:data[, port_num:data[, ...]]})
The op needs to calculate the result from the input data and return it as a Python dict. ({port_num:result[, port_num:result[, ...]]})

Kernel implementation: NumPy version and Naive version

Not all, but some Ops have dual kernel implementation, a naive implementation (easy to read), and a NumPy version implementation (a bit faster).
The NumPy version might be x10+ faster than the naive version.
The kernel type can be specified with Executable_Network.kernel_type attribute. You can specify eitgher one of 'naive' (default) or 'numpy'. Please refer to the sample program test_pyopenvino.py for the details.

END

Owner
Yasunori Shimura
Yasunori Shimura
GitHub Repository
implicit displacement field

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

Yifan Wang 100 Dec 19, 2022
PyTorch implementation of the paper Ultra Fast Structure-aware Deep Lane Detection

PyTorch implementation of the paper Ultra Fast Structure-aware Deep Lane Detection

1.4k Jan 06, 2023
PyTorch implementation of ENet

PyTorch-ENet PyTorch (v1.1.0) implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from the lua-torc

David Silva 333 Dec 29, 2022
An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicity.

Fast Face Classification (F²C) This is the code of our paper An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicit

33 Jun 27, 2021
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

PointCNN: Convolution On X-Transformed Points Created by Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Introduction PointCNN

Yangyan Li 1.3k Dec 21, 2022
This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

Tianhong Dai 150 Dec 23, 2022
PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

FullSubNet This Git repository for the official PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech E

郝翔 357 Jan 04, 2023
LiDAR R-CNN: An Efficient and Universal 3D Object Detector

LiDAR R-CNN: An Efficient and Universal 3D Object Detector Introduction This is the official code of LiDAR R-CNN: An Efficient and Universal 3D Object

TuSimple 295 Jan 05, 2023
Control-Robot-Arm-using-PS4-Controller - A Robotic Arm based on Raspberry Pi and Arduino that controlled by PS4 Controller

Control-Robot-Arm-using-PS4-Controller You can see all details about this Robot

MohammadReza Sharifi 5 Jan 01, 2022
A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

Neuraxle Pipelines Code Machine Learning Pipelines - The Right Way. Neuraxle is a Machine Learning (ML) library for building machine learning pipeline

Neuraxio 555 Dec 24, 2022
PConv-Keras - Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions". Try at: www.fixmyphoto.ai

Partial Convolutions for Image Inpainting using Keras Keras implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions", https

Mathias Gruber 871 Jan 05, 2023
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
PyTorch implementation of Densely Connected Time Delay Neural Network

Densely Connected Time Delay Neural Network PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Conne

Ya-Qi Yu 64 Oct 11, 2022
This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). More details can be found at the project page. Framework Grounding Physical

Zhenfang Chen 31 Jan 06, 2023
codes for IKM (arXiv2021, Submitted to IEEE Trans)

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution This repository is for IKM introduced in the following paper Yuanfei

Yuanfei Huang 9 Dec 29, 2022
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt

Youssef Chafiqui 2 Jan 07, 2022
PlaidML is a framework for making deep learning work everywhere.

A platform for making deep learning work everywhere. Documentation | Installation Instructions | Building PlaidML | Contributing | Troubleshooting | R

PlaidML 4.5k Jan 02, 2023
Measures input lag without dedicated hardware, performing motion detection on recorded or live video

What is InputLagTimer? This tool can measure input lag by analyzing a video where both the game controller and the game screen can be seen on a webcam

Bruno Gonzalez 4 Aug 18, 2022
StackNet is a computational, scalable and analytical Meta modelling framework

StackNet This repository contains StackNet Meta modelling methodology (and software) which is part of my work as a PhD Student in the computer science

Marios Michailidis 1.3k Dec 15, 2022
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

mani 1.2k Jan 07, 2023