feat: implement linear model and autodetect parameters

pkg/steering/steering.go

@@ -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),

pkg/tools/tools.go

@@ -4,6 +4,37 @@ import (
 	"fmt"
 	"go.uber.org/zap"
 	"sort"
+	"strings"
+)
+
+type ModelType int
+
+func ParseModelType(s string) ModelType {
+	switch strings.ToLower(s) {
+	case "categorical":
+		return ModelTypeCategorical
+	case "linear":
+		return ModelTypeLinear
+	default:
+		return ModelTypeUnknown
+	}
+}
+
+func (m ModelType) String() string {
+	switch m {
+	case ModelTypeCategorical:
+		return "categorical"
+	case ModelTypeLinear:
+		return "linear"
+	default:
+		return "unknown"
+	}
+}
+
+const (
+	ModelTypeUnknown ModelType = iota
+	ModelTypeCategorical
+	ModelTypeLinear
 )
 
 // LinearBin  perform inverse linear_bin, taking

pkg/tools/tools_test.go

@@ -75,3 +75,28 @@ func Test_LinearBin(t *testing.T) {
 		})
 	}
 }
+
+func TestParseModelType(t *testing.T) {
+	type args struct {
+		s string
+	}
+	tests := []struct {
+		name string
+		args args
+		want ModelType
+	}{
+		{name: "categorical", args: args{s: "categorical"}, want: ModelTypeCategorical},
+		{name: "categorical-upper", args: args{s: "caTeGorical"}, want: ModelTypeCategorical},
+		{name: "linear", args: args{s: "linear"}, want: ModelTypeLinear},
+		{name: "linear-upper", args: args{s: "LineAr"}, want: ModelTypeLinear},
+		{name: "unknown", args: args{s: "1234"}, want: ModelTypeUnknown},
+		{name: "empty", args: args{s: ""}, want: ModelTypeUnknown},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if got := ParseModelType(tt.args.s); got != tt.want {
+				t.Errorf("ParseModelType() = %v, want %v", got, tt.want)
+			}
+		})
+	}
+}