Arm NN

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Installation

Arm NN is packaged at science:machinelearning OBS project, and developed at https://github.com/ARM-software/armnn.

You can install ARM-NN from:

Current status

Current (2019-11-27) options enabled on aarch64 Arm NN:

Tumbleweed Upcoming Leap 15.2 Leap 15.1
NEON support Icon-checked.png Icon-checked.png Icon-checked.png
openCL support (GPU)* Icon-checked.png Icon-checked.png Icon-checked.png
Caffe support Icon-checked.png Icon-checked.png Icon-checked.png
ONNX support Icon-checked.png Icon-cross.png (no python-onnx package
wait for SR#751093)
Icon-cross.png (no python-onnx package)
TensorFlow support Icon-checked.png Icon-cross.png (TensorFlow fails to build, requires SR#751187) Icon-cross.png (TensorFlow fails to build)
TensorFlowLite support Icon-checked.png Icon-checked.png Icon-cross.png (No flatbuffers package)

* openCL support (GPU) has been tested on openSUSE Tumbleweed with HiKey960 board which includes a Mali Bifrost G71, with openCL 2.x support. According to [1] and [2], it requires a GPU with openCL 1.2 + cl_arm_non_uniform_work_group_size (for better performances openCL 2.0). Upstream tests are on Mali GPU.

Known issues

openCL backend

  • If you do not have /usr/lib64/libOpenCL.so file, you must link it to libOpenCL.so.1 file:
 sudo ln -s /usr/lib64/libOpenCL.so.1 /usr/lib64/libOpenCL.so
  • If you get the following error:
 An error occurred when preparing the network workloads:  in create_kernel src/core/CL/CLKernelLibrary.cpp:1087: Non uniform workgroup size is not supported!!

you have an openCL 1.x without cl_arm_non_uniform_work_group_size support, which is mandatory for ArmNN openCL.

Tests/Examples

On all tests, you may select the compute mode with -c CpuRef (standard C++, slow) or -c CpuAcc (NEON accelerated) or -c GpuAcc (GPU accelerated, requires openCL) option.

SimpleSample

Run SimpleSample and enter a number when prompted (here 458):

 Please enter a number: 
 458
 Your number was 458

Caffe backend

CaffeInception_BN-Armnn

CaffeInception_BN-Armnn example uses a Caffe model on top of ARM-NN for image classification. You need to get the data and the model, so please download:

Arm NN is not able to use this model as is and it should be converted:

  • batch size to be set to 1 (instead of 10)
  • Arm NN does not support all Caffe syntaxes, so some previous neural-network model files require updates to the latest Caffe syntax

So, you need to:

  • Copy deploy.prototxt to deploy_armnn.prototxt and update the file to set the batch size to 1:
 --- models/deploy.prototxt      2019-10-01 13:25:13.502886667 +0000
 +++ models/deploy_armnn.prototxt        2019-10-01 13:38:55.860972787 +0000
 @@ -3,7 +3,7 @@ layer {
 name: "data"
 type: "Input"
 top: "data"
 -  input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
 +  input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } }
 }
 layer { 
  • and run the following convert.py script from the 'models/' folder (requires python3-caffe):
 #!/usr/bin/python3
 import caffe
 net = caffe.Net('deploy.prototxt', 'Inception21k.caffemodel', caffe.TEST)
 new_net = caffe.Net('deploy_armnn.prototxt', 'Inception21k.caffemodel', caffe.TEST)
 new_net.save('Inception-BN-batchsize1.caffemodel')

Now, you can run CaffeInception_BN-Armnn --data-dir=data --model-dir=models :

 ArmNN v20190800
 = Prediction values for test #0
 Top(1) prediction is 3694 with value: 0.255735
 Top(2) prediction is 3197 with value: 0.0031263
 Top(3) prediction is 1081 with value: 0.000757725
 Top(4) prediction is 567 with value: 0.000526447
 Top(5) prediction is 559 with value: 9.72124e-05
 Total time for 1 test cases: 0.088 seconds
 Average time per test case: 88.260 ms
 Overall accuracy: 1.000
 Runtime::UnloadNetwork(): Unloaded network with ID: 0

CaffeResNet-Armnn

CaffeResNet-Armnn example uses a Caffe model on top of ARM-NN for image classification. You need to get the data and the model, so please download:

And run CaffeResNet-Armnn --data-dir=data --model-dir=models :

 ArmNN v20190800
 
 = Prediction values for test #0
 Top(1) prediction is 21 with value: 0.466987
 Top(2) prediction is 7 with value: 0.000633067
 Top(3) prediction is 1 with value: 2.17822e-06
 Top(4) prediction is 0 with value: 6.27832e-08
 = Prediction values for test #1
 Top(1) prediction is 2 with value: 0.511024
 Top(2) prediction is 0 with value: 2.7405e-07
 Total time for 2 test cases: 0.205 seconds
 Average time per test case: 102.741 ms
 Overall accuracy: 1.000
 Runtime::UnloadNetwork(): Unloaded network with ID: 0

CaffeMnist-Armnn

CaffeMnist-Armnn example uses a Caffe model on top of ARM-NN for handwritten digits recognition. In this example, this is number 7.

You need to get the data and the model, so please install arm-ml-example:

As CaffeMnist-Armnn requires is slightly different naming, you need to rename the files:

 cp -r /usr/share/armnn-mnist/* /tmp/
 mv /tmp/data/t10k-labels-idx1-ubyte /tmp/data/t10k-labels.idx1-ubyte
 mv /tmp/data/t10k-images-idx3-ubyte /tmp/data/t10k-images.idx3-ubyte

And run CaffeMnist-Armnn --data-dir=/tmp/data/ --model-dir=/tmp/model/:

 ArmNN v20190800
 
 = Prediction values for test #0
 Top(1) prediction is 7 with value: 1
 Top(2) prediction is 0 with value: 0
 = Prediction values for test #1
 Top(1) prediction is 2 with value: 1
 Top(2) prediction is 0 with value: 0
 = Prediction values for test #5
 Top(1) prediction is 1 with value: 1
 Top(2) prediction is 0 with value: 0
 = Prediction values for test #8
 Top(1) prediction is 5 with value: 1
 Top(2) prediction is 0 with value: 0
 = Prediction values for test #9
 Top(1) prediction is 9 with value: 1
 Top(2) prediction is 0 with value: 0
 Total time for 5 test cases: 0.008 seconds
 Average time per test case: 1.569 ms
 Overall accuracy: 1.000
 Runtime::UnloadNetwork(): Unloaded network with ID: 0


MNIST Caffe example

MNIST Caffe example uses a Caffe model on top of ARM-NN for handwritten digits recognition. In this example, this is number 7.

You must install ARM ML examples and associated data from:

Go to the data folder:

 cd /usr/share/armnn-mnist/

and run mnist_caffe:

 Predicted: 7
 Actual: 7

ONNX backend

OnnxMnist-Armnn

OnnxMnist-Armnn example uses an ONNX model on top of ARM-NN for handwritten digits recognition. In this example, this is number 7.

You need to get the data, so please install arm-ml-example:

And download the model from https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz

As OnnxMnist-Armnn requires is slightly different naming, you need to rename the files:

 cp -r /usr/share/armnn-mnist/* /tmp/
 mv /tmp/data/t10k-labels-idx1-ubyte /tmp/data/t10k-labels.idx1-ubyte
 mv /tmp/data/t10k-images-idx3-ubyte /tmp/data/t10k-images.idx3-ubyte

For the model:

 wget https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz
 tar xzf mnist.tar.gz
 cp mnist/model.onnx /tmp/model/mnist_onnx.onnx 

And run OnnxMnist-Armnn --data-dir=/tmp/data/ --model-dir=/tmp/model/ -i 1:

 ArmNN v20190800
 = Prediction values for test #0
 Top(1) prediction is 7 with value: 28.34
 Top(2) prediction is 3 with value: 9.42895
 Top(3) prediction is 2 with value: 8.64272
 Top(4) prediction is 1 with value: 0.627583
 Top(5) prediction is 0 with value: -1.25672
 Total time for 1 test cases: 0.002 seconds
 Average time per test case: 2.278 ms
 Overall accuracy: 1.000
 Runtime::UnloadNetwork(): Unloaded network with ID: 0


OnnxMobileNet-Armnn

OnnxMobileNet-Armnn example uses an ONNX model on top of ARM-NN for image classification. In this example, it will look for shark, dog and cat.

You need to get the mobilenetv2 model for ONNX, so:

For the data, you need to download:

  • an image of a shark, rename it shark.jpg and place it to data/ folder.
  • an image of a dog, rename it Dog.jpg and place it to data/ folder.
  • an image of a cat, rename it Cat.jpg and place it to data/ folder.


And run OnnxMobileNet-Armnn --data-dir=data --model-dir=models -i 3:

 ArmNN v20190800
 Performance test running in DEBUG build - results may be inaccurate.
 = Prediction values for test #0
 Top(1) prediction is 273 with value: 16.4625
 Top(2) prediction is 227 with value: 13.9884
 Top(3) prediction is 225 with value: 11.6609
 Top(4) prediction is 168 with value: 11.3706
 Top(5) prediction is 159 with value: 9.35255
 = Prediction values for test #1
 Top(1) prediction is 281 with value: 16.7145
 Top(2) prediction is 272 with value: 5.43621
 Top(3) prediction is 271 with value: 5.3766
 Top(4) prediction is 51 with value: 5.24998
 Top(5) prediction is 24 with value: 2.50436
 = Prediction values for test #2
 Top(1) prediction is 2 with value: 21.4471
 Top(2) prediction is 0 with value: 4.55977
 Total time for 3 test cases: 0.164 seconds
 Average time per test case: 54.651 ms
 Overall accuracy: 0.667
 Runtime::UnloadNetwork(): Unloaded network with ID: 0


TensorFlow backend

MNIST TensorFlow example

MNIST TensorFlow example uses a TensorFlow model on top of ARM-NN for handwritten digits recognition. In this example, this is number 7.

You must install ARM ML examples (and associated data) from:

Go to the data folder:

 cd /usr/share/armnn-mnist/

and run mnist_tf:

 Predicted: 7
 Actual: 7


mnist-draw - Web app

MNIST Draw is single page website that enables users to hand-draw and classify digits between 0 and 9 using machine learning. A machine learning model trained against the MNIST dataset is used for classification.

The project is a modified version of mnist-draw, which uses the Arm NN SDK to perform inferences on an Arm Cortex-A CPU. The application runs on any ARM system and can be accessed over a network using a browser.

There is no RPM package yet, so you need to build it and run it manually.

Install dependencies:

zypper in armnn-devel python3-pillow gcc gcc-c++ make tensorflow-devel

Compile from sources:

cd /tmp
git clone https://github.com/ARM-software/Tool-Solutions/
cd Tool-Solutions/ml-tool-examples/mnist-draw
make -C armnn-draw
chmod a+w . -R # Fix permissions

If you want to use GpuAcc instead of CpuAcc, you can update cgi-bin/mnist.py by replacing mnist_tf_convol argument from:

 completed = subprocess.run(['./armnn-draw/mnist_tf_convol', '1', '1', 'image.txt'], stderr=subprocess.PIPE, check=True)

to:

 completed = subprocess.run(['./armnn-draw/mnist_tf_convol', '2', '1', 'image.txt'], stderr=subprocess.PIPE, check=True)

Run it:

python3 -m http.server --cgi 8080

And access it from your web browser, e.g: http://192.168.0.4:8080 if your board has IP 192.168.0.4.

MNIST-Draw-Web-Interface-Image.gif

TensorFlow Lite backend

TensorFlow Lite examples may print some errors (depending on your images), since your Cat image maybe recognized as a 'Tabby Cat' (label 282) and not the expected 'Tiger Cat' (label 283), see https://github.com/ARM-software/armnn/issues/165#issuecomment-538299546

To run TfLite*-Armnn examples, you need to download the models and extract them to models/ folder:

 # Only the *.tflite files are needed, but more files are in the archives
 wget http://download.tensorflow.org/models/tflite/mnasnet_1.3_224_09_07_2018.tgz
 tar xzf mnasnet_*.tgz
 mv mnasnet_*/ models
 pushd models
 wget http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz
 tar xzf inception_v3_quant.tgz
 wget http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz
 tar xzf mobilenet_v1_1.0_224_quant.tgz
 wget http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz
 tar xzf mobilenet_v2_1.0_224_quant.tgz
 popd

You may also get the labels from the MobileNet V1 archive: https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip

For the data, you need to download:

  • an image of a shark, rename it shark.jpg and place it to data/ folder.
  • an image of a dog, rename it Dog.jpg and place it to data/ folder.
  • an image of a cat, rename it Cat.jpg and place it to data/ folder.

TfLiteInceptionV3Quantized-Armnn example

Once you have the models/ and data/ folders ready, you can run TfLiteInceptionV3Quantized-Armnn --data-dir=data --model-dir=models

TfLiteMnasNet-Armnn example

Once you have the models/ and data/ folders ready, you can run TfLiteMnasNet-Armnn --data-dir=data --model-dir=models

TfLiteMobilenetQuantized-Armnn example

Once you have the models/ and data/ folders ready, you can run TfLiteMobilenetQuantized-Armnn --data-dir=data --model-dir=models

TfLiteMobilenetV2Quantized-Armnn example

Once you have the models/ and data/ folders ready, you can run TfLiteMobilenetV2Quantized-Armnn --data-dir=data --model-dir=models

Additionnal (downstream) tests

Additional downstream tests are packaged separately, in armnn-extratests package.

TI ArmnnExamples

TI provides an additional test ArmnnExamples which allow to run models from all supported backends (Caffe, TensorFlow, TensorFlowLite, ONNX) on images, video (filter on .mp4, .mov and .avi, but you can change extension to workaround the filter and use .ogv, and others), and live stream from a webcam. More information on http://software-dl.ti.com/processor-sdk-linux/esd/docs/latest/linux/Foundational_Components_ArmNN.html#arm-nn-mobilenet-demo

You need to download a number of files (tests files from tidl-api git repo, mobilenet model, and mobilenet labels):

git clone git://git.ti.com/tidl/tidl-api.git # Can be skipped if you want to use your own test files
wget http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz
tar xzf *.tgz
wget https://raw.githubusercontent.com/leferrad/tensorflow-mobilenet/master/imagenet/labels.txt 
sudo mkdir -p /usr/share/arm/armnn/models/
sudo cp labels.txt /usr/share/arm/armnn/models/
sudo chmod 666 /usr/share/arm/armnn/models/labels.txt # TO BE FIXED


Test with the baseball.jpg image from tidl-api/ :

ArmnnExamples -f tensorflow-binary -i input -s '1 224 224 3' -o MobilenetV1/Predictions/Reshape_1 -d ./tidl-api/examples/classification/images/baseball.jpg -m ./mobilenet_v1_1.0_224_frozen.pb -c CpuAcc --number_frame 10


Test with the test2.mp4 video clip from tidl-api/, it displays the video, with top match and FPS (requires h.264 decoder to be installed):

 ArmnnExamples -f tensorflow-binary -i input -s '1 224 224 3' -o MobilenetV1/Predictions/Reshape_1 -d ./tidl-api/examples/classification/clips/test2.mp4 -m ./mobilenet_v1_1.0_224_frozen.pb -c CpuAcc --number_frame 100

You may also use warplane ogv file (just change the extension to .mp4)


Test with a live stream from a camera, it displays the video, with top match and FPS (camera_live_input0 is for /dev/video0, camera_live_input1 is for /dev/video1, etc.):

 ArmnnExamples -f tensorflow-binary -i input -s '1 224 224 3' -o MobilenetV1/Predictions/Reshape_1 -d camera_live_input0 -m ./mobilenet_v1_1.0_224_frozen.pb -c CpuAcc --number_frame 100