Responsibility of each layer

[tucan9389]

There are three layers for using tflite pose estimation model:

• TFLiteImageInterpretor: the wrapper class for image preprocessing before inference
• PoseEstimator: a simple protocol for abstraction
• PoseNetPoseEstimator: concrete implement for PoseNet model

TFLiteImageInterpretor

TFLiteImageInterpretor is similar to VNCoreMLRequest. It has some pre-process logic for image and a tflite interpretor. Only TFLiteImageInterpretor class has a dependency with TensorFlowLiteSwift.

PoseEstimator

A simple protocol for abstraction. Users can create the concrete type conforming to PoseEstimator protocol and then use the estimator instance with the protocol, not concrete type. So except for the creation point, there is no use of the concrete type.

PoseNetPoseEstimator

PoseNetPoseEstimator is concrete type of PoseEstimator. It has PoseNet model specific information like input/output shape and normalization type.
For example, tensorflow/examples's PoseNet model needs following information:

• input: [1, 257, 257, 3]
• output: [1, 9, 9, 17]
• input range: 0.0~1.0
• input type: float32
• quantized: false

TODO

• preprocess
• inference
• postprocess