robocars/robocar-steering-tflite-edgetpu
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feat: implement linear model and autodetect parameters

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This commit is contained in:
Cyrille Nofficial
2022-06-10 13:50:22 +02:00
parent 1e6966495c
commit acf6066fea
5 changed files with 223 additions and 5 deletions
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13
pkg/steering/steering.go
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@ -19,9 +19,10 @@ import (
"time"
)
func NewPart(client mqtt.Client, modelPath, steeringTopic, cameraTopic string, edgeVerbosity int, imgWidth, imgHeight, horizon int) *Part {
func NewPart(client mqtt.Client, modelType tools.ModelType, modelPath, steeringTopic, cameraTopic string, edgeVerbosity int, imgWidth, imgHeight, horizon int) *Part {
return &Part{
client: client,
modelType: modelType,
modelPath: modelPath,
steeringTopic: steeringTopic,
cameraTopic: cameraTopic,
@ -42,6 +43,7 @@ type Part struct {
options *tflite.InterpreterOptions
interpreter *tflite.Interpreter
modelType tools.ModelType
modelPath string
model *tflite.Model
edgeVebosity int
@ -214,7 +216,14 @@ func (p *Part) Value(img image.Image) (float32, float32, error) {
output := p.interpreter.GetOutputTensor(0).UInt8s()
zap.L().Debug("raw steering", zap.Uint8s("result", output))
steering, score := tools.LinearBin(output, 15, -1, 2.0)
var steering, score float64
switch p.modelType {
case tools.ModelTypeCategorical:
steering, score = tools.LinearBin(output, 15, -1, 2.0)
case tools.ModelTypeLinear:
steering = 2*(float64(output[0])/255.) - 1.
score = 0.6
}
zap.L().Debug("found steering",
zap.Float64("steering", steering),
zap.Float64("score", score),