GstInference/Supported backends/Tensorflow-Lite

GstInference
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InceptionV1 InceptionV2 InceptionV3 InceptionV4 TinyYoloV2 TinyYoloV3 YoloV2 ResNetV1 MobileNetV2 MobileNetV2 + SSD AlexNet
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TensorFlow Lite is an open-source software library that is part of TensorFlow™. This provides a deep learning framework for on-device inference. Tensorflow lite models can be used on Android and IOS, and also can be used on systems like Raspberry Pi and Arm64-based boards.

To use the Tensorflow-Lite backend on Gst-Inference be sure to run the R2Inference configure with the flag -Denable-tflite=true and use the property backend=tflite on the Gst-Inference plugins. GstInference depends on the C++ API of Tensorflow-Lite.

Installation

GstInference depends on the C++ API of Tensorflow-Lite. For installation steps, follow the steps in [R2Inference/Getting_started/Building_the_library| R2Inference/Building the library] section.

TensorFlow Python API and utilities can be installed with python pip, but it is not needed by GstInference.

Enabling the backend

To enable Tensorflow-Lite as a backend for GstInference you need to install R2Inference with TensorFlow-Lite support. To do this, use the option -Denable-tflite=true while following this wiki

Generating a Graph

GstInference uses Tensorflow-lite models for inference. You can generate a tflite model from a checkpoint file, from a saved session, or converting a Tensorflow frozen graph model. For examples on how to generate a graph please check the section R2Inference/Supported_backends/TensorFlow-Lite to create or convert models on the R2Inference wiki guide.

Properties

TensorFlow Lite API Reference has full documentation of the Tensorflow-Lite C++ API. Gst-Inference uses only the C++ API of Tensorflow-Lite and R2Inference takes care of devices and loading the models.

The following syntax is used to change backend options on Gst-Inference plugins:

backend::<property>

For example to change the backend to use Tensorflow-Lite with the inceptionv4 plugin you need to run the pipeline like this:

gst-launch-1.0 \
inceptionv4 name=net model-location=graph_inceptionv4.tflite backend=tflite backend::allow-fp16=0 backend::number-of-threads=4 \
videotestsrc ! tee name=t \
t. ! queue ! videoconvert ! videoscale ! net.sink_model \
t. ! queue ! net.sink_bypass \
net.src_bypass ! fakesink

To learn more about the Tensorflow-Lite C++ API, please check the Tensorflow-Lite API section on the R2Inference sub wiki.

Tools

The TensorFlow Python API installation includes a tool named Tensorboard, that can be used to visualize a model. If you want some examples and a more complete description please check the Tools section on the R2Inference wiki.