cmd/rc-tools/rc-tools.go

@@ -102,7 +102,7 @@ func main() {
 		fmt.Printf("  run\n  \tRun training job\n")
 	}
 
-	var modelPath, roleArn, trainJobName, modelType string
+	var modelPath, roleArn, trainJobName string
 	var horizon int
 	var withFlipImage bool
 	var trainImageHeight, trainImageWidth int
@@ -120,7 +120,6 @@ func main() {
 	trainingRunFlags.IntVar(&trainImageHeight, "image-height", 128, "Pixels image height")
 	trainingRunFlags.IntVar(&trainImageWidth, "image-width", 160, "Pixels image width")
 	trainingRunFlags.IntVar(&horizon, "horizon", 0, "Upper zone image to crop (in pixels)")
-	trainingRunFlags.StringVar(&modelType, "model-type", train.ModelTypeCategorical.String(), "Type model to build")
 
 	trainingRunFlags.BoolVar(&enableSpotTraining, "enable-spot-training", true, "Train models using managed spot training")
 	trainingListJobFlags := flag.NewFlagSet("list", flag.ExitOnError)
@@ -238,7 +237,7 @@ func main() {
 				trainingRunFlags.PrintDefaults()
 				os.Exit(0)
 			}
-			runTraining(bucket, ociImage, roleArn, trainJobName, recordsPath, train.ParseModelType(modelType), trainSliceSize, trainImageWidth, trainImageHeight, horizon, withFlipImage, modelPath, enableSpotTraining)
+			runTraining(bucket, ociImage, roleArn, trainJobName, recordsPath, trainSliceSize, trainImageWidth, trainImageHeight, horizon, withFlipImage, modelPath, enableSpotTraining)
 		case trainArchiveFlags.Name():
 			if err := trainArchiveFlags.Parse(os.Args[3:]); err == flag.ErrHelp {
 				trainArchiveFlags.PrintDefaults()
@@ -356,7 +355,7 @@ func runDisplay(client mqtt.Client, framePath string, frameTopic string, fps int
 	}
 }
 
-func runTraining(bucketName, ociImage, roleArn, jobName, dataDir string, modelType train.ModelType, sliceSize, imgWidth, imgHeight int, horizon int, withFlipImage bool, outputModel string, enableSpotTraining bool) {
+func runTraining(bucketName, ociImage, roleArn, jobName, dataDir string, sliceSize, imgWidth, imgHeight int, horizon int, withFlipImage bool, outputModel string, enableSpotTraining bool) {
 
 	l := zap.S()
 	if bucketName == "" {
@@ -379,12 +378,8 @@ func runTraining(bucketName, ociImage, roleArn, jobName, dataDir string, modelTy
 		l.Fatalf("invalid value for sie-slice, only '0' or '2' are allowed")
 	}
 
-	if modelType == train.ModelTypeUnknown {
-		l.Fatalf("invalid model type: %v", modelType)
-	}
-
 	training := train.New(bucketName, ociImage, roleArn)
-	err := training.TrainDir(context.Background(), jobName, dataDir, modelType, imgWidth, imgHeight, sliceSize, horizon, withFlipImage, outputModel, enableSpotTraining)
+	err := training.TrainDir(context.Background(), jobName, dataDir, imgWidth, imgHeight, sliceSize, horizon, withFlipImage, outputModel, enableSpotTraining)
 
 	if err != nil {
 		l.Fatalf("unable to run training: %v", err)

pkg/train/train.go

@@ -12,40 +12,9 @@ import (
 	"github.com/cyrilix/robocar-tools/pkg/models"
 	"go.uber.org/zap"
 	"strconv"
-	"strings"
 	"time"
 )
 
-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
-)
-
 func New(bucketName string, ociImage, roleArn string) *Training {
 	return &Training{
 		config:       awsutils.MustLoadConfig(),
@@ -66,7 +35,7 @@ type Training struct {
 	outputBucket string
 }
 
-func (t *Training) TrainDir(ctx context.Context, jobName, basedir string, modelType ModelType, imgWidth, imgHeight, sliceSize int, horizon int, withFlipImage bool, outputModelFile string, enableSpotTraining bool) error {
+func (t *Training) TrainDir(ctx context.Context, jobName, basedir string, imgWidth, imgHeight, sliceSize int, horizon int, withFlipImage bool, outputModelFile string, enableSpotTraining bool) error {
 	l := zap.S()
 	l.Infof("run training with data from %s", basedir)
 	archive, err := data.BuildArchive(basedir, sliceSize, imgWidth, imgHeight, horizon, withFlipImage)
@@ -81,7 +50,14 @@ func (t *Training) TrainDir(ctx context.Context, jobName, basedir string, modelT
 	}
 	l.Info("")
 
-	err = t.runTraining(ctx, jobName, sliceSize, imgHeight, imgWidth, horizon, enableSpotTraining, modelType)
+	err = t.runTraining(
+		ctx,
+		jobName,
+		sliceSize,
+		imgHeight,
+		imgWidth,
+		enableSpotTraining,
+	)
 	if err != nil {
 		return fmt.Errorf("unable to run training: %w", err)
 	}
@@ -119,7 +95,7 @@ func List(bucketName string) error {
 	return nil
 }
 
-func (t *Training) runTraining(ctx context.Context, jobName string, slideSize, imgHeight, imgWidth, horizon int, enableSpotTraining bool, modelType ModelType) error {
+func (t *Training) runTraining(ctx context.Context, jobName string, slideSize int, imgHeight, imgWidth int, enableSpotTraining bool) error {
 	l := zap.S()
 	client := sagemaker.NewFromConfig(awsutils.MustLoadConfig())
 	l.Infof("Start training job '%s'", jobName)
@@ -149,9 +125,6 @@ func (t *Training) runTraining(ctx context.Context, jobName string, slideSize, i
 			"slide_size":       strconv.Itoa(slideSize),
 			"img_height":       strconv.Itoa(imgHeight),
 			"img_width":        strconv.Itoa(imgWidth),
-			"batch_size":       strconv.Itoa(32),
-			"model_type":       modelType.String(),
-			"horizon":          strconv.Itoa(horizon),
 		},
 		InputDataConfig: []types.Channel{
 			{

run_training.sh

@@ -3,8 +3,8 @@
 set +e
 set +x
 
-RECORDS_PATH=~/src/robocars/data/viva20/viva12/
+RECORDS_PATH=~/robocar/record-sim4-2
-#TRAINING_OPTS="--horizon=50"
+#TRAINING_OPTS="--horizon=20"
 TRAINING_OPTS=""
 MODEL_TYPE="categorical"
 #MODEL_TYPE="linear"
@@ -12,9 +12,8 @@ IMG_WIDTH=160
 IMG_HEIGHT=120
 HORIZON=20
 
-TRAINING_DIR=~/src/robocars/trainings
+TRAINING_DATA_DIR=/tmp/data
-TRAINING_DATA_DIR=${TRAINING_DIR}/data
+TRAINING_OUTPUT_DIR=/tmp/output
-TRAINING_OUTPUT_DIR=${TRAINING_DIR}/output
 TRAIN_ARCHIVE=${TRAINING_DATA_DIR}/train.zip
 
 #######################
@@ -32,9 +31,9 @@ go run ./cmd/rc-tools training archive \
 
 printf "\n\nRun training\n\n"
 podman run --rm -it \
-            -v /trainings/data:/opt/ml/input/data/train \
+            -v /tmp/data:/opt/ml/input/data/train \
-            -v /trainings/output:/opt/ml/model/ \
+            -v /tmp/output:/opt/ml/model/ \
-            localhost/tensorflow_without_gpu:old \
+            localhost/tensorflow_without_gpu \
                 python /opt/ml/code/train.py \
                       --img_height=${IMG_HEIGHT} \
                       --img_width=${IMG_WIDTH} \