Inception V3 on Qualcomm Robotics RB3 DSP

• Qualcomm Robotics RB3 Development Kit
• Camera

• Qualcomm Robotics RB3 kit
• Hexagon SDK Download
• ImageNet
• Cross compiler

Build Hexagon NN DSP library for the Qualcomm Robotics RB3 CDSP

• Download the Hexagon SDK version 3.4.2 for Linux from here.

• Unzip the file and install the SDK using the following commands

  $ cd qualcomm_hexagon_sdk_3_4_2_linux/
  $ chmod +x qualcomm_hexagon_sdk_3_4_2_eval.bin
  $ ./qualcomm_hexagon_sdk_3_4_2_eval.bin

This will pop up a window for the installer, follow the instructions accordingly and complete the installation.

Note: If you encounter an error “Malformed \uxxxx encoding” like here, change the PS1 prompt and then execute the bin file

• Set the environment variable $HEXAGON_SDK_ROOT by the command

  $ source /work/Qualcomm/Hexagon_SDK/3.4.2/setup_sdk_env.source

• Compile Hexagon NN using the following commands and generate a hexagon library

  $ cd $HEXAGON_SDK_ROOT/libs/hexagon_nn/2.5/
  $ make tree VERBOSE=1 CDSP_FLAG=1 V=hexagon_Release_dynamic_toolv82_v65 V65=1
  

• The libhexagon_nn_skel.so generated here is the shared library for the CDSP

  

Note: If you get a Hexagon clang error, it is because the Hexagon SDK 3.4.2 has 8.3.02 version of Hexagon Clang instead of 8.2.07. To continue with the build process, Download the Hexagon SDK 3.4.1 for Linux from here and build using all the steps listed till now.

Create a soft link between the 3.4.1 SDK and 3.4.2 SDK , so that Hexagon clang 8.2.07 can be used to build SDK 3.4.2.

In this project, you will use an Inception v3 model’s frozen graph for the image classification. In the following steps, download TensorFlow, Bazel and generate a Hexagon DSP compatible version of the Inception V3 frozen graph.

• Download TensorFlow Inception V3 model Frozen graph using

  $ curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -o /tmp/inceptionv3.tgz and unzip it to /tmp directory.

  $ tar -xzf /tmp/inceptionv3.tgz -C /tmp/

• Download and install Bazel (version 0.20.0) using the following commands:

  $ wget https://github.com/bazelbuild/bazel/releases/download/0.20.0/bazel-0.20.0-installer-linux-x86_64.sh
  chmod +x bazel-0.21.0-installer-linux-x86_64.sh
  $ ./bazel-0.21.0-installer-linux-x86_64.sh –-user

  Make sure to add /home/bin to $PATH to avoid bazel not found error.

  

• Download and build TensorFlow(version 1.13.2) using the following commands

  $ wget https://github.com/tensorflow/tensorflow/archive/v1.13.2.tar.gz
  $ tar -zxvf v1.13.2.tar.gz
  $ cd tensorflow-1.13.2
  $ ./configure
  

  To complete the configuration, specify the location of Python and select the appropriate options to build TensorFlow.

  

• Build transform_graph tool in TensorFlow using the following commands

  $ touch WORKSPACE
  $ bazel build tensorflow/tools/graph_transforms:transform_graph

  The build would take a bit longer.

  

  (More information about tranform_graph can be found here)

• Quantize protobuf using Graph Transform Tool

  $ bazel-bin/tensorflow/tools/graph_transforms/transform_graph --in_graph=/tmp/classify_image_graph_def.pb --out_graph=/tmp/inception_v3_quantized.pb --inputs="Mul" --outputs='softmax' --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3") remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes fold_constants strip_unused_nodes sort_by_execution_order'

• Convert the quantized protobuf into a C file (iv3.c) using the following commands

  $ cd $HEXAGON_SDK_ROOT/examples/hexagon_nn/
  $ virtualenv -p python2 env2

  If this gives an error “virtualenv not found”/ “pip missing”, install pip using

  $ sudo apt install python-pip

  and install virtualenv using

  $ sudo apt-get install python-virtualenv)
  $ source env2/bin/activate
  $ pip install -r environments/req2.txt
  

  Enter the command below to generate Hexagon compatible iv3.c file

  $python $HEXAGON_SDK_ROOT/examples/hexagon_nn/scripts/tensorflow_to_hexagon_nn.py /tmp/inception_v3_quantized.pb $HEXAGON_SDK_ROOT/examples/hexagon_nn/tutorials/007-tensorflow-to-hexagon-nn/inceptionv3_v1.yaml > iv3.c

  And copy iv3.c to the folder as shown in the command

  $ cp iv3.c $HEXAGON_SDK_ROOT/libs/hexagon_nn/2.5/

• Download the patch using

  $ wget http://people.linaro.org/~srinivas.kandagatla/hexagon_nn_3.4.2_shared_lib.patch

• Patch Hexagon NN with the downloaded patch to compile as shared library for user space

  $ patch -p1

  $ make tree V=UbuntuARM_Debug_aarch64 CDSP_FLAG=1 V65=1 GRAPHINIT="iv3.c"

Step 1: Make hardware connections and power up Robotics Development Kit, set the Screen to avoid blanking

• Connect Robotics Development Kit with USB Camera on the right most port, and wireless USB for Keyboard and mouse on the other port.
• Connect Robotics Development Kit with HDMI cable and the other end of the cable to the HDMI Screen/monitor that is powered on.

• Connect the power cable to Robotics Development Kit and wait for the desktop to pop up.

  

• Open a terminal by navigating to System Tools > QTerminal as shown below

  

• Change the screen properties using the commands below, to avoid blanking of screen after a short period of inactivity on the board.

  $ xset s off
  $ xset dpms 0 0 0
  $ xset dpms s off

Step 2: Install fastrpc lib on Target Qualcomm Robotics Development Kit

• In the terminal, login as sudo and check if there are any old adsprpc libraries and remove them

  $ sudo su
  $ rm /usr/local/libadsp* /usr/local/libcdsp* /usr/local/bin/cdsprpcd /usr/local/bin/adsprpcd

• Copy the fastRPC libs to /usr/local/lib and /usr/local/bin

  $ cp /home/linaro/apps/libcdsp* /usr/local/lib/
  $ cp /home/linaro/apps/libadsp* /usr/local/lib/
  $ cp /home/linaro/apps/*rpcd /usr/local/bin

Step 3: Mount the dsp and start cdsprpcd daemon

• $ mkdir /dsp
  $ mount /dev/disk/by-partlabel/dsp_a /dsp
  $ cp /home/linaro/dsp/* /dsp/cdsp/
  $ export ADSP_LIBRARY_PATH=/dsp/cdsp
  $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
  $ cdsprpcd&
  

Step 4: Install OpenCV on the Target

• In another terminal, type in the following commands to download and install OpenCV

  $ git clone https://github.com/opencv/opencv.git
  $ cd ~/opencv
  $ mkdir build
  $ cd build
  $ cmake -D CMAKE_BUILD_TYPE=Release -D
  CMAKE_INSTALL_PREFIX=/usr/local

  (If this gives an error “cmake not installed”, install cmake with

  apt-get -y install cmake)
  $ make -j8
  $ sudo make install

Step 5: Run OpenCV ImageNet application for camera

• Download the application from the camera branch and build using the following commands

  $ git clone https://git.linaro.org/people/srinivas.kandagatla/ImagenetCv.git
  $ cd ImagenetCv
  

  Build application using

  $ cmake .
  $ make

  Run the application using the command $ ./ImagenetCv

  If this shows an error like “install libgtk2.0-dev”

  Install libgtk2.0-dev using the commands given below and run the application

  $ apt get update
  $ apt get upgrade
  $ apt get install libgtk2.0-dev

  Running the application pops up a window that shows video captured by the camera and the image classification being done

  

Step 6: Run OpenCV ImageNet application for local Images

• Download the application from the images branch and build using the following commands
• $ git clone https://git.linaro.org/people/srinivas.kandagatla/ImagenetCv.git
  $ cd ImagenetCv
  $ cmake .
  $ make
  $ ./ImagenetCv

  4 windows pop up, that show image classification being done on local images.

  

Qualcomm Robotics and Qualcomm Hexagon are products of Qualcomm Technologies, Inc. and/or its subsidiaries.