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

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

Issues
Owner
Yasunori Shimura
Yasunori Shimura
This is a repository for a No-Code object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems.

OpenVINO Inference API This is a repository for an object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operati

BMW TechOffice MUNICH 70 Aug 1, 2022
Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code

Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code.

Yasunori Shimura 7 Jul 27, 2022
This is a repository for a semantic segmentation inference API using the OpenVINO toolkit

BMW-IntelOpenVINO-Segmentation-Inference-API This is a repository for a semantic segmentation inference API using the OpenVINO toolkit. It's supported

BMW TechOffice MUNICH 34 Aug 1, 2022
A very tiny, very simple, and very secure file encryption tool.

Picocrypt is a very tiny (hence "Pico"), very simple, yet very secure file encryption tool. It uses the modern ChaCha20-Poly1305 cipher suite as well

Evan Su 704 Aug 11, 2022
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python

dev 23 Jul 29, 2022
The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data This repository provides the implementation details for

null 118 Aug 7, 2022
PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

Amin Rezaei 152 Aug 12, 2022
📚 A collection of Jupyter notebooks for learning and experimenting with OpenVINO 👓

A collection of ready-to-run Python* notebooks for learning and experimenting with OpenVINO developer tools. The notebooks are meant to provide an introduction to OpenVINO basics and teach developers how to leverage our APIs for optimized deep learning inference in their applications.

OpenVINO Toolkit 704 Aug 6, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.

null 7.1k Aug 8, 2022
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

null 7.1k Aug 7, 2022
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Demonstration of OpenVINO techniques - Model-division and a simplest-way to support custom layers Description: Model Optimizer in Intel(r) OpenVINO(tm

Yasunori Shimura 11 Nov 28, 2021
MoveNet Single Pose on OpenVINO

MoveNet Single Pose tracking on OpenVINO Running Google MoveNet Single Pose models on OpenVINO. A convolutional neural network model that runs on RGB

null 30 Jul 20, 2022
Running Google MoveNet Multipose Tracking models on OpenVINO.

MoveNet MultiPose Tracking on OpenVINO

null 53 Aug 12, 2022
WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

HeadPoseEstimation-WHENet-yolov4-onnx-openvino ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L 1. Usage $ git clone htt

Katsuya Hyodo 47 Aug 9, 2022
Semi-automated OpenVINO benchmark_app with variable parameters

Semi-automated OpenVINO benchmark_app with variable parameters. User can specify multiple options for any parameters in the benchmark_app and the progam runs the benchmark with all combinations of given options.

Yasunori Shimura 8 Apr 11, 2022
BasicRL: easy and fundamental codes for deep reinforcement learning。It is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up.

BasicRL: easy and fundamental codes for deep reinforcement learning BasicRL is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up. It is

RayYoh 12 Apr 28, 2022
FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data, a relatively complete set of integrated multi-source data download terminal software fast is developed. The software contains most of the data sources required in the process of GNSS scientific research and learning. The way of parallel download greatly improves the efficiency of download.

ChangChuntao 21 Jul 12, 2022
ScriptProfilerPy - Module to visualize where your python script is slow

ScriptProfiler helps you track where your code is slow It provides: Code lines t

Lucas BLP 3 Jun 2, 2022