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export BASEDIR=~/armnn-pi
export PATH=$BASEDIR/boost.build/bin:$PATH
export PATH=$BASEDIR/protobuf-host/bin:$PATH
export LD_LIBRARY_PATH=$BASEDIR/protobuf-host/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$BASEDIR/armnn/build:$LD_LIBRARY_PATH
export ARMNN_INCLUDE=$BASEDIR/armnn/include
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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.
ArmNN SDK can be built on SP7021 with RPIOS.
Please see the attach file to see the detail process.
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1. Arm Compute Library
It is in $BASEDIR/ComputeLibrary
It's library built is in $BASEDIR/ComputeLibrary/build
1.1 Running a DNN with random weights and inputs
Arm compute library comes with examples for most common DNN architectures like: AlexNet, MobileNet, ResNet, Inception v3, Inception v4, Squeezenet, etc.
All available examples source code can be found in this example location: $BASEDIR/ComputeLibrary/example .example
All available examples can be found in this example build location: $BASEDIR/ComputeLibrary/build/example
Each model architecture can be tested with graph_[dnn_model] application. For example, to run the MobileNet v2 DNN model with random weights, run the example application without any argument:
cd $BASEDIR/ComputeLibrary/build/examples export LD_LIBRARY_PATH=$BASEDIR/ComputeLibrary/build:$LD_LIBRARY_PATH ./graph_mobilenet_v2
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grep "Caff"
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Download the model files:
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
change the batch size to 1
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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 1Original 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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3. Python interface to Arm NN (PyArmNN)
cd ~/armnn-pi/armnn/python/pyarmnn/examples/
python3 tflite_mobilenetv1_quantized.py
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python3 onnx_mobilenetv2.py
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