This document describes the process of setting up and running the Arm® Ethos™-U55 Image Classification example.
Use case solves classical computer vision problem: image classification. The ML sample was developed using MobileNet v2 model trained on ImageNet dataset.
Use case code could be found in source/use_case/img_class directory.
See Prerequisites
In addition to the already specified build option in the main documentation, Image Classification use case specifies:
img_class_MODEL_TFLITE_PATH - Path to the NN model file in TFLite format. Model will be processed and included into the application axf file. The default value points to one of the delivered set of models. Note that the parameters img_class_LABELS_TXT_FILE,TARGET_PLATFORM and ETHOS_U55_ENABLED should be aligned with the chosen model, i.e.:
ETHOS_U55_ENABLED is set to On or 1, the NN model is assumed to be optimized. The model will naturally fall back to the Arm® Cortex®-M CPU if an unoptimized model is supplied.ETHOS_U55_ENABLED is set to Off or 0, the NN model is assumed to be unoptimized. Supplying an optimized model in this case will result in a runtime error.img_class_FILE_PATH: Path to the directory containing images, or path to a single image file, to be used file(s) in the application. The default value points to the resources/img_class/samples folder containing the delivered set of images. See more in the Add custom input data section.
img_class_IMAGE_SIZE: The NN model requires input images to be of a specific size. This parameter defines the size of the image side in pixels. Images are considered squared. Default value is 224, which is what the supplied MobilenetV2-1.0 model expects.
img_class_LABELS_TXT_FILE: Path to the labels' text file to be baked into the application. The file is used to map classified classes index to the text label. Change this parameter to point to the custom labels file to map custom NN model output correctly.
The default value points to the delivered labels.txt file inside the delivery package.
img_class_ACTIVATION_BUF_SZ: The intermediate/activation buffer size reserved for the NN model. By default, it is set to 2MiB and should be enough for most models.
USE_CASE_BUILD: set to img_class to build only this example.
In order to build ONLY Image Classification example application add to the cmake command line specified in Building -DUSE_CASE_BUILD=img_class.
Note: This section describes the process for configuring the build for
MPS3: SSE-300for different target platform see Building.
Create a build directory folder and navigate inside:
mkdir build_img_class && cd build_img_class
On Linux, execute the following command to build only Image Classification application to run on the Ethos-U55 Fast Model when providing only the mandatory arguments for CMake configuration:
cmake ../ -DUSE_CASE_BUILD=img_class
To configure a build that can be debugged using Arm-DS, we can just specify the build type as Debug and use the Arm Compiler toolchain file:
cmake .. \ -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \ -DCMAKE_BUILD_TYPE=Debug \ -DUSE_CASE_BUILD=img_class
Also see:
Note: If re-building with changed parameters values, it is highly advised to clean the build directory and re-run the CMake command.
If the CMake command succeeds, build the application as follows:
make -j4
Add VERBOSE=1 to see compilation and link details.
Results of the build will be placed under build/bin folder:
bin ├── ethos-u-img_class.axf ├── ethos-u-img_class.htm ├── ethos-u-img_class.map ├── images-img_class.txt └── sectors └── img_class ├── dram.bin └── itcm.bin
Where:
ethos-u-img_class.axf: The built application binary for the Image Classification use case.
ethos-u-img_class.map: Information from building the application (e.g. libraries used, what was optimized, location of objects)
ethos-u-img_class.htm: Human readable file containing the call graph of application functions.
sectors/: Folder containing the built application, split into files for loading into different FPGA memory regions.
Images-img_class.txt: Tells the FPGA which memory regions to use for loading the binaries in sectors/** folder.
The application performs inference on input data found in the folder, or an individual file set by the CMake parameter img_class_FILE_PATH.
To run the application with your own images, first create a folder to hold them and then copy the custom images into this folder, for example:
mkdir /tmp/custom_images cp custom_image1.bmp /tmp/custom_images/
Note: Clean the build directory before re-running the CMake command.
Next set img_class_FILE_PATH to the location of this folder when building:
cmake .. \ -Dimg_class_FILE_PATH=/tmp/custom_images/ \ -DUSE_CASE_BUILD=img_class
The images found in the img_class_FILE_PATH folder will be picked up and automatically converted to C++ files during the CMake configuration stage and then compiled into the application during the build phase for performing inference with.
The log from the configuration stage should tell you what image directory path has been used:
-- User option img_class_FILE_PATH is set to /tmp/custom_images -- User option img_class_IMAGE_SIZE is set to 224 ... -- Generating image files from /tmp/custom_images ++ Converting custom_image1.bmp to custom_image1.cc ... -- Defined build user options: ... -- img_class_FILE_PATH=/tmp/custom_images -- img_class_IMAGE_SIZE=224
After compiling, your custom images will have now replaced the default ones in the application.
Note: The CMake parameter IMAGE_SIZE should match the model input size. When building the application, if the size of any image does not match IMAGE_SIZE then it will be rescaled and padded so that it does.
The application performs inference using the model pointed to by the CMake parameter MODEL_TFLITE_PATH.
Note: If you want to run the model using Ethos-U55, ensure your custom model has been run through the Vela compiler successfully before continuing. See Optimize model with Vela compiler.
To run the application with a custom model you will need to provide a labels_<model_name>.txt file of labels associated with the model. Each line of the file should correspond to one of the outputs in your model. See the provided labels_mobilenet_v2_1.0_224.txt file for an example.
Then, you must set img_class_MODEL_TFLITE_PATH to the location of the Vela processed model file and img_class_LABELS_TXT_FILE to the location of the associated labels file.
An example:
cmake .. \ -Dimg_class_MODEL_TFLITE_PATH=<path/to/custom_model_after_vela.tflite> \ -Dimg_class_LABELS_TXT_FILE=<path/to/labels_custom_model.txt> \ -DUSE_CASE_BUILD=img_class
Note: Clean the build directory before re-running the CMake command.
The .tflite model file pointed to by img_class_MODEL_TFLITE_PATH and labels text file pointed to by img_class_LABELS_TXT_FILE will be converted to C++ files during the CMake configuration stage and then compiled into the application for performing inference with.
The log from the configuration stage should tell you what model path and labels file have been used:
-- User option img_class_MODEL_TFLITE_PATH is set to <path/to/custom_model_after_vela.tflite> ... -- User option img_class_LABELS_TXT_FILE is set to <path/to/labels_custom_model.txt> ... -- Using <path/to/custom_model_after_vela.tflite> ++ Converting custom_model_after_vela.tflite to\ custom_model_after_vela.tflite.cc -- Generating labels file from <path/to/labels_custom_model.txt> -- writing to <path/to/build/generated/src/Labels.cc> ...
After compiling, your custom model will have now replaced the default one in the application.
The FVP is available publicly from Arm Ecosystem FVP downloads.
For Ethos-U55 evaluation, please download the MPS3 version of the Arm® Corstone™-300 model that contains Ethos-U55 and Cortex-M55. The model is currently only supported on Linux based machines. To install the FVP:
Unpack the archive
Run the install script in the extracted package
$./FVP_Corstone_SSE-300_Ethos-U55.sh
Pre-built application binary ethos-u-img_class.axf can be found in the bin/mps3-sse-300 folder of the delivery package. Assuming the install location of the FVP was set to ~/FVP_install_location, the simulation can be started by:
~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-img_class.axf
A log output should appear on the terminal:
telnetterminal0: Listening for serial connection on port 5000 telnetterminal1: Listening for serial connection on port 5001 telnetterminal2: Listening for serial connection on port 5002 telnetterminal5: Listening for serial connection on port 5003
This will also launch a telnet window with the sample application's standard output and error log entries containing information about the pre-built application version, TensorFlow Lite Micro library version used, data type as well as the input and output tensor sizes of the model compiled into the executable binary.
After the application has started if img_class_FILE_PATH pointed to a single file (or a folder containing a single image) the inference starts immediately. In case of multiple inputs choice, it outputs a menu and waits for the user input from telnet terminal:
User input required Enter option number from: 1. Classify next image 2. Classify image at chosen index 3. Run classification on all images 4. Show NN model info 5. List images Choice:
“Classify next image” menu option will run single inference on the next in line image from the collection of the compiled images.
“Classify image at chosen index” menu option will run single inference on the chosen image.
Note: Please make sure to select image index in the range of supplied images during application build. By default, pre-built application has 4 images, indexes from 0 to 3.
“Run classification on all images” menu option triggers sequential inference executions on all built-in images.
“Show NN model info” menu option prints information about model data type, input and output tensor sizes:
INFO - uTFL version: 2.5.0 INFO - Model info: INFO - Model INPUT tensors: INFO - tensor type is UINT8 INFO - tensor occupies 150528 bytes with dimensions INFO - 0: 1 INFO - 1: 224 INFO - 2: 224 INFO - 3: 3 INFO - Quant dimension: 0 INFO - Scale[0] = 0.007812 INFO - ZeroPoint[0] = 128 INFO - Model OUTPUT tensors: INFO - tensor type is UINT8 INFO - tensor occupies 1001 bytes with dimensions INFO - 0: 1 INFO - 1: 1001 INFO - Quant dimension: 0 INFO - Scale[0] = 0.098893 INFO - ZeroPoint[0] = 58 INFO - Activation buffer (a.k.a tensor arena) size used: 521760 INFO - Number of operators: 1 INFO - Operator 0: ethos-u
“List Images” menu option prints a list of pair image indexes - the original filenames embedded in the application:
INFO - List of Files: INFO - 0 => cat.bmp INFO - 1 => dog.bmp INFO - 2 => kimono.bmp INFO - 3 => tiger.bmp
Please select the first menu option to execute Image Classification.
The following example illustrates application output for classification:
INFO - Running inference on image 0 => cat.bmp INFO - Final results: INFO - Total number of inferences: 1 INFO - 0) 282 (14.636096) -> tabby, tabby cat INFO - 1) 286 (14.537203) -> Egyptian cat INFO - 2) 283 (12.757138) -> tiger cat INFO - 3) 458 (7.021370) -> bow tie, bow-tie, bowtie INFO - 4) 288 (7.021370) -> lynx, catamount INFO - Profile for Inference: INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 2489726 INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 1098726 INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 471129 INFO - NPU ACTIVE cycles: 7489258 INFO - NPU IDLE cycles: 914 INFO - NPU total cycles: 7490172
It could take several minutes to complete one inference run (average time is 2-3 minutes).
The log shows the inference results for “image 0” (0 - index) that corresponds to “cat.bmp” in the sample image resource folder.
The profiling section of the log shows that for this inference:
Ethos-U55's PMU report:
7,490,172 total cycle: The number of NPU cycles
7,489,258 active cycles: number of NPU cycles that were used for computation
914 idle cycles: number of cycles for which the NPU was idle
2,489,726 AXI0 read beats: The number of AXI beats with read transactions from AXI0 bus. AXI0 is the bus where Ethos-U55 NPU reads and writes to the computation buffers (activation buf/tensor arenas).
1,098,726 AXI0 write beats: The number of AXI beats with write transactions to AXI0 bus.
471,129 AXI1 read beats: The number of AXI beats with read transactions from AXI1 bus. AXI1 is the bus where Ethos-U55 NPU reads the model (read only)
For FPGA platforms, CPU cycle count can also be enabled. For FVP, however, CPU cycle counters should not be used as the CPU model is not cycle-approximate or cycle-accurate.
The application prints the top 5 classes with indexes, confidence score and labels from associated labels_mobilenet_v2_1.0_224.txt file. The FVP window also shows the output on its LCD section.