3.15.3. TensorFlow Lite Introduction

Processor SDK Linux has integrated open source TensorFlow Lite for deep learning inference at the edge. Curently, TensorFlow Lite runs on Arm for Sitara devices (AM3/AM4/AM5/AM6). Supported version

  • TensorFlow Lite 1.12 TensorFlow Lite example applications

TensoreFlow Lite example applications are installed on filesystem at /usr/share/tensorflow-lite-<ver>/examples. One TensorFlow Lite model (mobilenet_v1_1.0_224_quant.tflite) is also installed at the same place for demonstration. To use other TensorFlwo Lite models, such as TensorFlow Lite Hosted Models, please download those models and then copy them to the target.

root@am57xx-evm:/usr/share/tensorflow-lite-1.12/examples# ls -l
total 8356
-rwxr-xr-x 1 root root 1158612 Sep 21  2019 benchmark_model
-rw-r--r-- 1 root root  940650 Sep 21  2019 grace_hopper.bmp
-rwxr-xr-x 1 root root 1076716 Sep 21  2019 label_image
-rw-r--r-- 1 root root   10484 Sep 21  2019 labels.txt
-rwxr-xr-x 1 root root 1055900 Sep 21  2019 minimal
-rw-r--r-- 1 root root 4276352 Sep 21  2019 mobilenet_v1_1.0_224_quant.tflite Running benchmark_model

The benchmark_model binary performs computation benchmarking for Tensorflow Lite models. Usage of benchmark_model:

usage: ./benchmark_model
        --num_runs=50   int32   number of runs
        --run_delay=-1  float   delay between runs in seconds
        --num_threads=1 int32   number of threads
        --benchmark_name=       string  benchmark name
        --output_prefix=        string  benchmark output prefix
        --warmup_runs=1 int32   how many runs to initialize model
        --graph=        string  graph file name
        --input_layer=  string  input layer names
        --input_layer_shape=    string  input layer shape
        --use_nnapi=false       bool    use nnapi api

Example of running benchmark_model on target using the pre-installed mobilenet_v1_1.0_224_quant.tflite model:

# cd /usr/share/tensorflow-lite-1.12/examples
# ./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite
Num runs: [50]
Inter-run delay (seconds): [-1]
Num threads: [1]
Benchmark name: []
Output prefix: []
Warmup runs: [1]
Graph: [mobilenet_v1_1.0_224_quant.tflite]
Input layers: []
Input shapes: []
Use nnapi : [0]
Loaded model mobilenet_v1_1.0_224_quant.tflite
resolved reporter
Initialized session in 4.452ms
Running benchmark for 1 iterations
count=1 curr=146161

Running benchmark for 50 iterations
count=50 first=137789 curr=137254 min=137079 max=143416 avg=137722 std=896

Average inference timings in us: Warmup: 146161, Init: 4452, no stats: 137722 Running label_image example

The label_image provides an image classification example using TensorFlow Lite. Options for label_image:

--accelerated, -a: [0|1], use Android NNAPI or not
--count, -c: loop interpreter->Invoke() for certain times
--input_mean, -b: input mean
--input_std, -s: input standard deviation
--image, -i: image_name.bmp
--labels, -l: labels for the model
--tflite_model, -m: model_name.tflite
--profiling, -p: [0|1], profiling or not
--num_results, -r: number of results to show
--threads, -t: number of threads
--verbose, -v: [0|1] print more information

Example of running label_image on target, using the pre-installed mobilenet_v1_1.0_224_quant.tflite model, grace_hopper.bmp, and labels.txt.

# cd /usr/share/tensorflow-lite-1.12/examples
# ./label_image -i grace_hopper.bmp -l labels.txt -m mobilenet_v1_1.0_224_quant.tflite
Loaded model mobilenet_v1_1.0_224_quant.tflite
resolved reporter
average time: 345.13 ms
0.780392: 653 military uniform
0.105882: 907 Windsor tie
0.0156863: 458 bow tie
0.0117647: 466 bulletproof vest
0.00784314: 835 suit