YoloV3/tiny-YoloV3+RaspberryPi3/Ubuntu LaptopPC+NCS/NCS2+USB Camera+Python+OpenVINO

OpenVINO-YoloV3

YoloV3 / tiny-YoloV3 + RaspberryPi3 / Ubuntu LaptopPC + NCS/NCS2 + USB Camera + Python

Inspired from https://github.com/mystic123/tensorflow-yolo-v3.git

Performance comparison as a mobile application (Based on sensory comparison)
◯=HIGH, △=MEDIUM, ×=LOW

No.	Model	Speed	Accuracy	Adaptive distance
1	SSD	×	◯	ALL
2	MobileNet-SSD	△	△	Short distance
3	YoloV3	×	◯	ALL
4	tiny-YoloV3	◯	△	Long distance

My articles

[24 FPS] Boost RaspberryPi3 with four Neural Compute Stick 2 (NCS2) MobileNet-SSD / YoloV3 [48 FPS for Core i7]
[13 FPS] NCS2 x 4 + Full size YoloV3 performance has been tripled
Support for local training and OpenVINO of One Class tiny-YoloV3 with a proprietary data set

Change history

[Mar 01, 2019]　Improve accuracy. Fixed preprocessing and postprocessing bug.
[Mar 17, 2019]　Added a training procedure with your own data set.
[Apr 03, 2019]　Work on OpenVINO 2019 R1 started.
[Apr 14, 2019]　Compatible with 2019 R1.
[Apr 26, 2019]　Compatible with 2019 R1.0.1.

Operation sample

To raise the detection rate, lower the threshold by yourself.
The default threshold is 40%.

**
【Note】 Due to the performance difference of ARM <-> Core series, performance is degraded in RaspberryPi3.**

Python Version YoloV3 / tiny-YoloV3 (Dec 28, 2018 Operation confirmed)

YoloV3

$ python3 openvino_yolov3_test.py

tiny-YoloV3 + NCS2 MultiStick

$ python3 openvino_tiny-yolov3_MultiStick_test.py -numncs 1

YoloV3 + NCS2 MultiStick (Pretty slow)

$ python3 openvino_yolov3_MultiStick_test.py -numncs 4

CPP Version YoloV3 / tiny-YoloV3 (Dec 16, 2018 Operation confirmed)

cpp version is here "cpp/object_detection_demo_yolov3_async"

Environment

LattePanda Alpha (Intel 7th Core m3-7y30) or LaptopPC (Intel 8th Core i7-8750H)
Ubuntu 16.04 x86_64
RaspberryPi3
Raspbian Stretch armv7l
OpenVINO toolkit 2019 R1.0.1 (2019.1.133)
Python 3.5
OpenCV 4.1.0-openvino
Tensorflow v1.12.0 or Tensorflow-GPU v1.12.0 (pip install)
YoloV3 (MS-COCO)
tiny-YoloV3 (MS-COCO)
USB Camera (PlaystationEye) / Movie file (mp4)
Intel Neural Compute Stick v1 / v2

OpenVINO Supported Layers (As of Apr 14, 2019)

Supported Devices (https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_Supported_Devices.html#supported_layers)

Layers

GPU

CPU

MYRIAD(VPU)

GNA

FPGA

ShapeInfer

Activation-Clamp

Supported

Activation-ELU

Supported

Not Supported

Supported

Activation-Leaky ReLU

Supported

Activation-PReLU

Supported

Not Supported

Supported

Activation-ReLU

Supported

Activation-ReLU6

Supported

Not Supported

Supported

Activation-Sigmoid/Logistic

Supported

Not Supported

Supported

Activation-TanH

Supported

Not Supported

Supported

ArgMax

Supported

Not Supported

Supported

BatchNormalization

Supported

Not Supported

Supported

Concat

Supported

Const

Supported

Not Supported

Convolution-Dilated

Supported

Not Supported

Supported

Convolution-Dilated 3D

Not Supported

Supported

Not Supported

Convolution-Grouped

Supported

Not Supported

Supported

Convolution-Grouped 3D

Not Supported

Supported

Not Supported

Convolution-Ordinary

Supported

Convolution-Ordinary 3D

Not Supported

Supported

Not Supported

Crop

Supported

Not Supported

Supported

CTCGreedyDecoder

Supported

Not Supported

Supported

Deconvolution

Supported

Not Supported

Supported

Deconvolution 3D

Not Supported

Supported

Not Supported

DetectionOutput

Supported

Not Supported

Supported

Eltwise-Max

Supported

Not Supported

Supported

Eltwise-Mul

Supported

Not Supported

Supported

Eltwise-Sum

Supported

Flatten

Supported

Not Supported

Supported

FullyConnected (Inner Product)

Supported

Gather

Not Supported

Supported

Not Supported

Supported

Gemm

Not Supported

Supported

Not Supported

Supported

GRN

Supported

Not Supported

Supported

Interp

Supported

Not Supported

Supported

LRN (Norm)

Supported

Not Supported

Supported

LSTMCell

Supported

Not Supported

GRUCell

Supported

Not Supported

RNNCell

Supported

Not Supported

LSTMSequence

Supported

Not Supported

GRUSequence

Supported

Not Supported

RNNSequence

Supported

Not Supported

Memory

Not Supported

Supported

Not Supported

Supported

Not Supported

Supported

MVN

Supported

Not Supported

Supported

Normalize

Supported

Not Supported

Supported

Pad

Supported

Not Supported

Supported

Permute

Supported

Not Supported

Supported

Pooling(AVG,MAX)

Supported

Pooling(AVG,MAX) 3D

Not Supported

Supported

Not Supported

Power

Supported

PriorBox

Supported

Not Supported

Supported

PriorBoxClustered

Supported

Not Supported

Supported

Proposal

Supported

Not Supported

Supported

PSROIPooling

Supported

Not Supported

Supported

RegionYolo

Supported

Not Supported

Supported

ReorgYolo

Supported

Not Supported

Supported

Resample

Supported

Not Supported

Supported

Reshape

Supported

Not Supported

Supported

RNN

Not Supported

Supported

Not Supported

ROIPooling

Supported

Not Supported

Supported

ScaleShift

Supported

SimplerNMS

Supported

Not Supported

Supported

Slice

Supported

SoftMax

Supported

Not Supported

Supported

SpatialTransformer

Not Supported

Supported

Not Supported

Supported

Split

Supported

TensorIterator

Not Supported

Supported

Not Supported

Tile

Supported

Not Supported

Supported

Unpooling

Supported

Not Supported

Upsampling

Supported

Not Supported

OpenVINO - Python API

https://docs.openvinotoolkit.org/latest/_inference_engine_ie_bridges_python_docs_api_overview.html

Environment construction procedure

1. Work with LaptopPC (Ubuntu 16.04)

1.OpenVINO 2019R1.0.1 Full-Install. Execute the following command.

$ cd ~
$ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1ciX7cHqCh8lLFYI0HKkhC3r_fMirrlKk" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1ciX7cHqCh8lLFYI0HKkhC3r_fMirrlKk" -o l_openvino_toolkit_p_2019.1.133.tgz
$ tar -zxf l_openvino_toolkit_p_2019.1.133.tgz
$ rm l_openvino_toolkit_p_2019.1.133.tgz
$ cd l_openvino_toolkit_p_2019.1.133
$ sudo -E ./install_openvino_dependencies.sh

## GUI version installer
$ sudo ./install_GUI.sh
 or
## CUI version installer
$ sudo ./install.sh

2.Configure the Model Optimizer. Execute the following command.

$ cd /opt/intel/openvino/install_dependencies/
$ sudo -E ./install_openvino_dependencies.sh
$ nano ~/.bashrc
source /opt/intel/openvino/bin/setupvars.sh

$ source ~/.bashrc
$ cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites/
$ sudo ./install_prerequisites.sh

3.【Optional execution】 Additional installation steps for the Intel® Movidius™ Neural Compute Stick v1 and Intel® Neural Compute Stick v2

$ sudo usermod -a -G users "$(whoami)"
$ cat < 97-usbboot.rules
SUBSYSTEM=="usb", ATTRS{idProduct}=="2150", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
EOF

$ sudo cp 97-usbboot.rules /etc/udev/rules.d/
$ sudo udevadm control --reload-rules
$ sudo udevadm trigger
$ sudo ldconfig
$ rm 97-usbboot.rules

4.【Optional execution】 Additional installation steps for processor graphics (GPU, Intel HD Graphics series only)

$ cd /opt/intel/openvino/install_dependencies/
$ sudo -E su
$ uname -r
4.15.0-42-generic #<--- display kernel version sample

### Execute only when the kernel version is older than 4.14
$ ./install_4_14_kernel.sh

$ ./install_NEO_OCL_driver.sh
$ sudo reboot

2. Work with RaspberryPi (Raspbian Stretch)

[Note] Only the execution environment is introduced.

1.Execute the following command.

$ sudo apt update
$ sudo apt upgrade
$ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1NFt6g6ZkneHioU2P7rUJ8BFpQhIazbym" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1NFt6g6ZkneHioU2P7rUJ8BFpQhIazbym" -o l_openvino_toolkit_raspbi_p_2019.1.133.tgz
$ tar -zxvf l_openvino_toolkit_raspbi_p_2019.1.133.tgz
$ rm l_openvino_toolkit_raspbi_p_2019.1.133.tgz
$ sed -i "s||$(pwd)/inference_engine_vpu_arm|" inference_engine_vpu_arm/bin/setupvars.sh

2.Execute the following command.

$ nano ~/.bashrc
### Add 1 row below
source /home/pi/inference_engine_vpu_arm/bin/setupvars.sh

$ source ~/.bashrc
### Successful if displayed as below
[setupvars.sh] OpenVINO environment initialized

$ sudo usermod -a -G users "$(whoami)"
$ sudo reboot

3.Update USB rule.

$ sh inference_engine_vpu_arm/install_dependencies/install_NCS_udev_rules.sh
### It is displayed as follows
Update udev rules so that the toolkit can communicate with your neural compute stick
[install_NCS_udev_rules.sh] udev rules installed

[Note] OpenCV 4.1.0 will be installed without permission when the work is finished. If you do not want to affect other environments, please edit environment variables after installation is completed.

Training with your own data set

See the article below.
A sample of one-class training with Darknet and tiny-YoloV3.
https://qiita.com/PINTO/items/7dd7135085a7249bf17a#support-for-local-training-and-openvino-of-one-class-tiny-yolov3-with-a-proprietary-data-set

How to install Bazel (version 0.17.2, x86_64 only)

1. Bazel introduction command

$ cd ~
$ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1dvR3pdM6vtkTWqeR-DpgVUoDV0EYWil5" > /dev/null
$ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
$ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1dvR3pdM6vtkTWqeR-DpgVUoDV0EYWil5" -o bazel
$ sudo cp ./bazel /usr/local/bin
$ rm ./bazel

2. Supplementary information

https://github.com/PINTO0309/Bazel_bin.git

How to check the graph structure of a ".pb" file [Part.1]

Simple structure analysis.

1. Build and run graph structure analysis program

$ cd ~
$ git clone -b v1.11.0 https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ git checkout -b v1.11.0
$ bazel build tensorflow/tools/graph_transforms:summarize_graph
$ bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=xxxx.pb

2. Sample of display result

YoloV3

Found 1 possible inputs: (name=inputs, type=float(1), shape=[?,416,416,3]) 
No variables spotted.
Found 1 possible outputs: (name=output_boxes, op=ConcatV2) 
Found 62002034 (62.00M) const parameters, 0 (0) variable parameters, and 0 control_edges
Op types used: 536 Const, 372 Identity, 87 Mul, 75 Conv2D, 72 FusedBatchNorm, 72 Maximum, 28 Add, \
24 Reshape, 14 ConcatV2, 9 Sigmoid, 6 Tile, 6 Range, 5 Pad, 4 SplitV, 3 Pack, 3 RealDiv, 3 Fill, \
3 Exp, 3 BiasAdd, 2 ResizeNearestNeighbor, 2 Sub, 1 Placeholder
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- \
--graph=/home/b920405/git/OpenVINO-YoloV3/pbmodels/frozen_yolo_v3.pb \
--show_flops \
--input_layer=inputs \
--input_layer_type=float \
--input_layer_shape=-1,416,416,3 \
--output_layer=output_boxes

tiny-YoloV3

Found 1 possible inputs: (name=inputs, type=float(1), shape=[?,416,416,3]) 
No variables spotted.
Found 1 possible outputs: (name=output_boxes, op=ConcatV2) 
Found 8858858 (8.86M) const parameters, 0 (0) variable parameters, and 0 control_edges
Op types used: 134 Const, 63 Identity, 21 Mul, 16 Reshape, 13 Conv2D, 11 FusedBatchNorm, 11 Maximum, \
10 ConcatV2, 6 Sigmoid, 6 MaxPool, 4 Tile, 4 Add, 4 Range, 3 RealDiv, 3 SplitV, 2 Pack, 2 Fill, \
2 Exp, 2 Sub, 2 BiasAdd, 1 Placeholder, 1 ResizeNearestNeighbor
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- \
--graph=/home/b920405/git/OpenVINO-YoloV3/pbmodels/frozen_tiny_yolo_v3.pb \
--show_flops \
--input_layer=inputs \
--input_layer_type=float \
--input_layer_shape=-1,416,416,3 \
--output_layer=output_boxes

How to check the graph structure of a ".pb" file [Part.2]

Convert to text format.

1. Run graph structure analysis program

$ python3 tfconverter.py
### ".pbtxt" in ProtocolBuffer format is output.
### The size of the generated text file is huge.

How to check the graph structure of a ".pb" file [Part.3]

Use Tensorboard.

1. Run log output program for Tensorboard

import tensorflow as tf
from tensorflow.python.platform import gfile

with tf.Session() as sess:
    model_filename ="xxxx.pb"
    with gfile.FastGFile(model_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        g_in = tf.import_graph_def(graph_def)

    LOGDIR="path/to/logs"
    train_writer = tf.summary.FileWriter(LOGDIR)
    train_writer.add_graph(sess.graph)