81d33f1b6d
This will allow the unit tests to be run on additional targets that need some addiitonal initialization (for example cornstone_300 from #46830). This particular change is broken out from the Cornstone PR #46830 to be able to have smaller more reviewable PRs. In the past, we have added state to the DebugLog() and GetCurrentTimeTicks() functions as a way to avoid having an InitializeTarget function. With this change, we are deciding to go with an explicit intitialization step instead. This change has added calls to tflite::InitializeTarget to the tests, benchmarks, and examples and converted the Arduino and SparkfunEdge to make use of this explicit initialization. The changes for the Arduino and SparkfunEdge have not been tested on actual hardware. Progress towards #46829
Image Recognition Example
Table of Contents
Introduction
This example shows how you can use Tensorflow Lite Micro to perform image recognition on a STM32F746 discovery kit with a STM32F4DIS-CAM camera module attached. It classifies the captured image into 1 of 10 different classes, and those classes are "Plane", "Car", "Bird", "Cat", "Deer", "Dog", "Frog", "Horse", "Ship", "Truck".
Hardware
STM32F746G-DISCO board (Cortex-M7)
STM32F4DIS-CAM Camera module
Building
These instructions have been tested on Ubuntu 16.04.
Building the test case
$ make -f tensorflow/lite/micro/tools/make/Makefile image_recognition_test
This will build and run the test case. As input, the test case uses the first 10 images of the test batch included in the CIFAR10 dataset. Details surrounding the dataset can be found in this paper.
Building the image recognition application
Prerequisites
Install mbed-cli: $ pip install mbed-cli
Install the arm-none-eabi-toolchain.
For Ubuntu, this can be done by installing the package gcc-arm-none-eabi. In
Ubuntu 16.04, the version included in the repository is 4.9.3 while the
recommended version is 6 and up. Later versions can be downloaded from
here
for Windows, Mac OS X and Linux.
Compiling and flashing
In order to generate the mbed project, run the following command: $ make -f tensorflow/lite/micro/tools/make/Makefile TAGS=disco_f746ng generate_image_recognition_mbed_project This will copy all of the necessary
files needed to build and flash the application.
Navigate to the output folder: $ cd tensorflow/lite/micro/tools/make/gen/linux_x86_64/prj/image_recognition/mbed/
The following instructions for compiling and flashing can also be found in the file README_MBED.md in the output folder.
To load the dependencies required, run: $ mbed config root . $ mbed deploy
In order to compile, run: mbed compile -m auto -t GCC_ARM --profile release
-m auto: Automatically detects the correct target if the Discovery board is
connected to the computer. If the board is not connected, replace auto with
DISCO_F746NG.
-t GCC_ARM: Specifies the toolchain used to compile. GCC_ARM indicates that
the arm-none-eabi-toolchain will be used.
--profile release: Build the release profile. The different profiles can be
found under mbed-os/tools/profiles/.
This will produce a file named mbed.bin in
BUILD/DISCO_F746NG/GCC_ARM-RELEASE/. To flash it to the board, simply copy the
file to the volume mounted as a USB drive. Alternatively, the -f option can be
appended to flash automatically after compilation.
On Ubuntu 16.04 (and possibly other Linux distributions) there may be an error
message when running mbed compile saying that the Python module pywin32
failed to install. This message can be ignored.