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Arm NN SDK provides a set of tests which can also be considered as demos showing what Arm NN does and how to use it. They load neural network models of various formats (Caffe, TensorFlow, TensorFlow Lite, ONNX), run the inference on a specified input data and output the inference result.

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cd $BASEDIR/ComputeLibrary/build/examples

export LD_LIBRARY_PATH=$BASEDIR/ComputeLibrary/build:$LD_LIBRARY_PATH

./graph_mobilenet_v2

The application will create the DNN with random weights and run it with random inputs. If inference finishes, the "Test passed" message should be printed.

1.2 Running AlexNet using graph API
Download the archive file to the example location folder from this link:
cd $BASEDIR/ComputeLibrary/build/examples
curl -L -o computer_library_alexnet.zip

https://developer.arm.com/-/media/Arm%20Developer%20Community/Images/Tutorial%20Guide%20Diagrams%20and%20Screenshots/Machine%20Learning/Running%20AlexNet%20on%20Pi%20with%20Compute%20Library/compute_library_alexnet.zip?revision=c1a232fa-f328-451f-9bd6-250b83511e01_

Create a new sub-folder and unzip the file.
mkdir assets_alexnet
unzip compute_library_alexnet.zip -d assets_alexnet

Set environment variables for execution:
export LD_LIBRARY_PATH=$BASEDIR/ComputeLibrary/build/examples:$LD_LIBRARY_PATH
export PATH_ASSETS=$BASEDIR/ComputeLibrary/build/examples/assets_alexnet

Run the program.
./graph_alexnet --data=$PATH_ASSETS --image=$PATH_ASSETS/go_kart.ppm --labels=$PATH_ASSETS/labels.txt

grep "Caff"
-rwxr-xr-x 1 pi pi 915248 Sep 2 05:51 CaffeAlexNet-Armnn
-rwxr-xr-x 1 pi pi 725688 Sep 2 05:23 CaffeCifar10AcrossChannels-Armnn
-rwxr-xr-x 1 pi pi 915252 Sep 2 05:41 CaffeInception_BN-Armnn
-rwxr-xr-x 1 pi pi 729920 Sep 2 05:49 CaffeMnist-Armnn
-rwxr-xr-x 1 pi pi 915236 Sep 2 05:15 CaffeResNet-Armnn
-rwxr-xr-x 1 pi pi 915248 Sep 2 05:33 CaffeVGG-Armnn
-rwxr-xr-x 1 pi pi 920148 Sep 2 05:43 CaffeYolo-Armnn
Two important limitations might require preprocessing of the Caffe model file prior to running an Arm NN Caffe test.
First, Arm NN tests require batch size to be set to 1.
Second, Arm NN does not support all Caffe syntaxes, therefore some older neural network model files will require updates to the latest Caffe syntax.
For example, if a Caffe model has a batch size different from one or uses an older Caffe version defined by files model_name.prototxt and model_name.caffemodel, create a copy of the .prototxt file (new_model_name.prototxt), modify this file to use the new Caffe syntax and change the batch size to 1 and finally run the following python script:
import caffe
net = caffe.Net('model_name.prototxt', 'model_name.caffemodel', caffe.TEST)
new_net = caffe.Net('new_model_name.prototxt', 'model_name.caffemodel', caffe.TEST)
new_net.save('new_model_name.caffemodel')

1. 1. CaffeAlexNet-Armnn
Use A linux host with py-caffe installed

Download the model files:
cd ~/ArmnnTests
curl -L -o deploy.prototxt https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt
curl -L -o bvlc_alexnet.caffemodel http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
cp deploy.prototxt bvlc_alexnet_1.prototxt
nano bvlc_alexnet_1.prototxt

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2.2.2 CaffeInception_BN-Armnn

Use A linux host with py-caffe installed
Download the model files:
cd ~/ArmnnTests
curl -L -o deploy.prototxt https://raw.githubusercontent.com/pertusa/InceptionBN-21K-for-Caffe/master/deploy.prototxt
curl -L -o Inception21k.caffemodel http://www.dlsi.ua.es/~pertusa/deep/Inception21k.caffemodel
cp deploy.prototxt Inception-BN-batchsize1.prototxt
nano Inception-BN-batchsize1.prototxt

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2.1.3 CaffeMnist-Armnn

Use A linux host with py-caffe installed
Download the model files:
cd ~/ArmnnTests
curl -L -o lenet.prototxt https://raw.githubusercontent.com/BVLC/caffe/master/examples/mnist/lenet.prototxt
curl -L -o lenet_iter_9000_ori.caffemodel https://github.com/ARM-software/ML-examples/blob/master/armnn-mnist/model/lenet_iter_9000.caffemodel
cp lenet.prototxt lenet_iter_9000.prototxt
nano lenet_iter_9000.prototxt
change the batch size to 1
Original content:
name: "LeNet"

layer {

name: "data"

type: "Input"

top: "data"

input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
Modified content:
name: "LeNet"

layer {

name: "data"

type: "Input"

top: "data"

input_param { shape: { dim: 1 dim: 1 dim: 28 dim: 28 } }

Run the following python script to transform the network
python3
import caffe
net = caffe.Net(lenet.prototxt', lenet_iter_9000_ori.caffemodel', caffe.TEST)
new_net = caffe.Net(' lenet_iter_9000.prototxt', lenet_iter_9000_ori.caffemodel', caffe.TEST)
new_net.save(' lenet_iter_9000.caffemodel')

Copy lenet_iter_9000.caffemodel to ~/ArmnnTests/models in SP7021
Find a .jpg file containing a shark (great white shark). Rename it to shark.jpg and copy it to the data folder on SP7021.
Download the two archives below and unpack them:

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TfInceptionV3-Armnn --data-dir=data --model-dir=models

This is not an execution error. This occurs because the TfInceptionV3-Armnn test expects a specific type of dog, cat and shark to be found so if a different type/breed of these animals is passed to the test, it returns a case failed.
The expected inputs for this test are:

ID	Label	File name
208	Golden Retriever	Dog.jpg
283	Tiger Cat	Cat.jpg
3	White Shark	shark.jpg

The complete list of supported objects can be found in https://github.com/ARM-software/armnn/blob/branches/armnn_18_11/tests/TfLiteMobilenetQuantized-Armnn/labels.txt

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For a more complete Arm NN experience, there are examples located in /armnn-pi/armnn/python/pyarmnn/examples/, which require requests, PIL and maybe some other Python3 modules. You may install the missing modules using pip3.

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Version	Old Version 10	New Version 11
Changes made by	akira.yu	akira.yu
Saved on	Oct 12, 2020	Oct 12, 2020

Versions Compared

Key

name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }