Cyrille Nofficial
456f327788
* add flip-image option * add command to list models * add option to override image size when training is launched * add option to disable aws spot instance
366 lines
14 KiB
Go
366 lines
14 KiB
Go
package main
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import (
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"context"
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"flag"
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"fmt"
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"github.com/cyrilix/robocar-base/cli"
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"github.com/cyrilix/robocar-tools/dkimpt"
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"github.com/cyrilix/robocar-tools/part"
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"github.com/cyrilix/robocar-tools/pkg/data"
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"github.com/cyrilix/robocar-tools/pkg/models"
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"github.com/cyrilix/robocar-tools/pkg/train"
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"github.com/cyrilix/robocar-tools/record"
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"github.com/cyrilix/robocar-tools/video"
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mqtt "github.com/eclipse/paho.mqtt.golang"
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"go.uber.org/zap"
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"log"
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"os"
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)
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const (
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DefaultClientId = "robocar-tools"
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DefaultTrainSliceSize = 0
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)
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func main() {
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var mqttBroker, username, password, clientId string
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var framePath string
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var fps int
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var frameTopic, objectsTopic, roadTopic, recordTopic string
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var withObjects, withRoad bool
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var recordsPath string
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var trainArchiveName string
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var trainSliceSize int
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var bucket, ociImage string
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var debug bool
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mqttQos := cli.InitIntFlag("MQTT_QOS", 0)
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_, mqttRetain := os.LookupEnv("MQTT_RETAIN")
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flag.Usage = func() {
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fmt.Printf("Usage of %s:\n", os.Args[0])
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fmt.Printf(" display\n \tDisplay events on live frames\n")
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fmt.Printf(" record \n \tRecord event for tensorflow training\n")
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fmt.Printf(" training \n \tManage training\n")
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fmt.Printf(" models \n \tManage models\n")
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fmt.Printf(" import-donkey-records \n \tCopy donkeycar records to new format\n")
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}
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err := cli.SetIntDefaultValueFromEnv(&trainSliceSize, "RC_TRAIN_SLICE_SIZE", DefaultTrainSliceSize)
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if err != nil {
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log.Printf("unable to init TRAIN_SLICE_SIZE: %v", err)
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}
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cli.SetDefaultValueFromEnv(&ociImage, "TRAIN_OCI_IMAGE", "")
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cli.SetDefaultValueFromEnv(&bucket, "TRAIN_BUCKET", "")
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flag.BoolVar(&debug, "debug", false, "Display debug logs")
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displayFlags := flag.NewFlagSet("display", flag.ExitOnError)
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cli.InitMqttFlagSet(displayFlags, DefaultClientId, &mqttBroker, &username, &password, &clientId, &mqttQos, &mqttRetain)
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displayFlags.StringVar(&frameTopic, "mqtt-topic-frame", os.Getenv("MQTT_TOPIC_FRAME"), "Mqtt topic that contains frame to display, use MQTT_TOPIC_FRAME if args not set")
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displayFlags.StringVar(&framePath, "frame-path", "", "Directory path where to read jpeg frame to inject in frame topic")
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displayFlags.IntVar(&fps, "frame-per-second", 25, "Video frame per second of frame to publish")
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displayFlags.StringVar(&objectsTopic, "mqtt-topic-objects", os.Getenv("MQTT_TOPIC_OBJECTS"), "Mqtt topic that contains detected objects, use MQTT_TOPIC_OBJECTS if args not set")
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displayFlags.BoolVar(&withObjects, "with-objects", false, "Display detected objects")
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displayFlags.StringVar(&roadTopic, "mqtt-topic-road", os.Getenv("MQTT_TOPIC_ROAD"), "Mqtt topic that contains road description, use MQTT_TOPIC_ROAD if args not set")
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displayFlags.BoolVar(&withRoad, "with-road", false, "Display detected road")
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recordFlags := flag.NewFlagSet("record", flag.ExitOnError)
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cli.InitMqttFlagSet(recordFlags, DefaultClientId, &mqttBroker, &username, &password, &clientId, &mqttQos, &mqttRetain)
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recordFlags.StringVar(&recordTopic, "mqtt-topic-records", os.Getenv("MQTT_TOPIC_RECORDS"), "Mqtt topic that contains record data for training, use MQTT_TOPIC_RECORDS if args not set")
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recordFlags.StringVar(&recordsPath, "record-path", os.Getenv("RECORD_PATH"), "Path where to write records files, use RECORD_PATH if args not set")
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var basedir, destdir string
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impdkFlags := flag.NewFlagSet("import-donkey-records", flag.ExitOnError)
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impdkFlags.StringVar(&basedir, "from", "", "source directory")
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impdkFlags.StringVar(&destdir, "to", "", "destination directory")
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trainingFlags := flag.NewFlagSet("training", flag.ExitOnError)
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trainingFlags.Usage = func(){
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fmt.Printf("Usage of %s %s:\n", os.Args[0], trainingFlags.Name())
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fmt.Printf(" list\n \tList existing training jobs\n")
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fmt.Printf(" archive\n \tBuild tar.gz archive for training\n")
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fmt.Printf(" run\n \tRun training job\n")
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}
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var modelPath, roleArn, trainJobName string
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var withFlipImage bool
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var trainImageHeight, trainImageWidth int
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var enableSpotTraining bool
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trainingRunFlags := flag.NewFlagSet("run", flag.ExitOnError)
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trainingRunFlags.StringVar(&bucket, "bucket", os.Getenv("RC_TRAIN_BUCKET"), "AWS bucket where store data required, use RC_TRAIN_BUCKET if arg not set")
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trainingRunFlags.StringVar(&recordsPath, "record-path", os.Getenv("RECORD_PATH"), "Input data path where records and img files are stored, use RECORD_PATH if arg not set")
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trainingRunFlags.StringVar(&modelPath, "output-model-path", "", "Path where to write output model archive")
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trainingRunFlags.IntVar(&trainSliceSize, "slice-size", trainSliceSize, "Number of record to shift with image, use RC_TRAIN_SLICE_SIZE if args not set")
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trainingRunFlags.StringVar(&ociImage, "oci-image", os.Getenv("RC_TRAIN_OCI_IMAGE"), "OCI image to run (required), use RC_TRAIN_OCI_IMAGE if args not set")
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trainingRunFlags.StringVar(&roleArn, "role-arn", os.Getenv("RC_TRAIN_ROLE"), "AWS ARN role to use to run training (required), use RC_TRAIN_ROLE if arg not set")
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trainingRunFlags.StringVar(&trainJobName, "job-name", "", "Training job name (required)")
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trainingRunFlags.BoolVar(&withFlipImage, "with-flip-image", withFlipImage, "Flip horiontal image and reverse steering to increase data into training archive")
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trainingRunFlags.IntVar(&trainImageHeight, "image-height", 128, "Pixels image height")
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trainingRunFlags.IntVar(&trainImageWidth, "image-width", 160, "Pixels image width")
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trainingRunFlags.BoolVar(&enableSpotTraining, "enable-spot-training", true, "Train models using managed spot training")
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trainingListJobFlags := flag.NewFlagSet("list", flag.ExitOnError)
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trainArchiveFlags := flag.NewFlagSet("archive", flag.ExitOnError)
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trainArchiveFlags.StringVar(&recordsPath, "record-path", os.Getenv("RECORD_PATH"), "Path where records files are stored, use RECORD_PATH if args not set")
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trainArchiveFlags.StringVar(&trainArchiveName, "output", os.Getenv("TRAIN_ARCHIVE_NAME"), "Zip archive file name, use TRAIN_ARCHIVE_NAME if args not set")
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trainArchiveFlags.IntVar(&trainSliceSize, "slice-size", trainSliceSize, "Number of record to shift with image, use TRAIN_SLICE_SIZE if args not set")
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trainArchiveFlags.BoolVar(&withFlipImage, "with-flip-image", withFlipImage, "Flip horiontal image and reverse steering to increase data into training archive")
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modelsFlags := flag.NewFlagSet("models", flag.ExitOnError)
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modelsFlags.Usage = func() {
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fmt.Printf("Usage of %s %s:\n", os.Args[0], modelsFlags.Name())
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fmt.Printf(" list\n \tList existing models\n")
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fmt.Printf(" download\n \tDownload existing models\n")
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}
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modelsListFlags := flag.NewFlagSet("list", flag.ExitOnError)
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modelsListFlags.StringVar(&bucket, "bucket", os.Getenv("RC_TRAIN_BUCKET"), "AWS bucket where store data required, use RC_TRAIN_BUCKET if arg not set")
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var modelPathBucket string
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modelsDownloadFlags := flag.NewFlagSet("download", flag.ExitOnError)
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modelsDownloadFlags.StringVar(&bucket, "bucket", os.Getenv("RC_TRAIN_BUCKET"), "AWS bucket where store data required, use RC_TRAIN_BUCKET if arg not set")
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modelsDownloadFlags.StringVar(&modelPathBucket, "model", "", "S3 Model key into bucket (mandatory)")
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modelsDownloadFlags.StringVar(&trainArchiveName, "output", os.Getenv("TRAIN_ARCHIVE_NAME"), "Zip archive file name, use TRAIN_ARCHIVE_NAME if args not set")
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flag.Parse()
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config := zap.NewDevelopmentConfig()
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if debug {
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config.Level = zap.NewAtomicLevelAt(zap.DebugLevel)
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} else {
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config.Level = zap.NewAtomicLevelAt(zap.InfoLevel)
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}
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lgr, err := config.Build()
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if err != nil {
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log.Fatalf("unable to init logger: %v", err)
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}
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defer func() {
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if err := lgr.Sync(); err != nil {
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log.Printf("unable to Sync logger: %v\n", err)
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}
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}()
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zap.ReplaceGlobals(lgr)
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// Switch on the subcommand
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// Parse the flags for appropriate FlagSet
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// FlagSet.Parse() requires a set of arguments to parse as input
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// os.Args[2:] will be all arguments starting after the subcommand at os.Args[1]
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switch flag.Arg(0) {
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case displayFlags.Name():
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if err := displayFlags.Parse(os.Args[2:]); err == flag.ErrHelp {
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displayFlags.PrintDefaults()
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os.Exit(0)
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}
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client, err := cli.Connect(mqttBroker, username, password, clientId)
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if err != nil {
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zap.S().Fatalf("unable to connect to mqtt bus: %v", err)
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}
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defer client.Disconnect(50)
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runDisplay(client, framePath, frameTopic, fps, objectsTopic, roadTopic, withObjects, withRoad)
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case recordFlags.Name():
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if err := recordFlags.Parse(os.Args[2:]); err == flag.ErrHelp {
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recordFlags.PrintDefaults()
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os.Exit(0)
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}
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client, err := cli.Connect(mqttBroker, username, password, clientId)
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if err != nil {
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log.Fatalf("unable to connect to mqtt bus: %v", err)
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}
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defer client.Disconnect(50)
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runRecord(client, recordsPath, recordTopic)
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case impdkFlags.Name():
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if err := impdkFlags.Parse(os.Args[2:]); err == flag.ErrHelp {
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impdkFlags.PrintDefaults()
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os.Exit(0)
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}
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runImportDonkeyRecords(basedir, destdir)
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case trainingFlags.Name():
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if err := trainingFlags.Parse(os.Args[2:]); err == flag.ErrHelp {
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trainingFlags.PrintDefaults()
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os.Exit(0)
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}
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switch trainingFlags.Arg(0) {
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case trainingListJobFlags.Name():
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if err:= trainingListJobFlags.Parse(os.Args[3:]); err == flag.ErrHelp {
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trainingListJobFlags.PrintDefaults()
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os.Exit(0)
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}
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runTrainList()
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case trainingRunFlags.Name():
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if err := trainingRunFlags.Parse(os.Args[3:]); err == flag.ErrHelp {
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trainingRunFlags.PrintDefaults()
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os.Exit(0)
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}
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runTraining(bucket, ociImage, roleArn, trainJobName, recordsPath, trainSliceSize, withFlipImage, modelPath,
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trainImageHeight, trainImageWidth, enableSpotTraining)
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case trainArchiveFlags.Name():
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if err := trainArchiveFlags.Parse(os.Args[3:]); err == flag.ErrHelp {
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trainArchiveFlags.PrintDefaults()
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os.Exit(0)
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}
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runTrainArchive(recordsPath, trainArchiveName, trainSliceSize, withFlipImage)
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default:
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trainingFlags.PrintDefaults()
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os.Exit(0)
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}
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case modelsFlags.Name():
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if err := modelsFlags.Parse(os.Args[2:]); err == flag.ErrHelp {
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modelsFlags.PrintDefaults()
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os.Exit(0)
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}
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switch modelsFlags.Arg(0) {
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case modelsListFlags.Name():
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if err:= modelsListFlags.Parse(os.Args[3:]); err == flag.ErrHelp {
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modelsListFlags.PrintDefaults()
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os.Exit(0)
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}
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runModelsList(bucket)
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case modelsDownloadFlags.Name():
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if err:= modelsDownloadFlags.Parse(os.Args[3:]); err == flag.ErrHelp {
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modelsDownloadFlags.PrintDefaults()
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os.Exit(0)
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}
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if trainArchiveName == "" {
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zap.S().Error("output model file is mandatory")
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modelsDownloadFlags.PrintDefaults()
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os.Exit(1)
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}
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runModelsDownload(bucket, modelPathBucket, trainArchiveName)
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default:
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modelsFlags.PrintDefaults()
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os.Exit(0)
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}
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default:
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flag.PrintDefaults()
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os.Exit(1)
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}
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}
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func runRecord(client mqtt.Client, recordsDir, recordTopic string) {
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r, err := record.New(client, recordsDir, recordTopic)
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if err != nil {
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zap.S().Fatalf("unable to init record part: %v", err)
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}
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defer r.Stop()
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cli.HandleExit(r)
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err = r.Start()
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if err != nil {
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zap.S().Fatalf("unable to start service: %v", err)
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}
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}
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func runTrainArchive(basedir, archiveName string, sliceSize int, withFlipImage bool) {
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err := data.WriteArchive(basedir, archiveName, sliceSize, withFlipImage)
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if err != nil {
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zap.S().Fatalf("unable to build archive file %v: %v", archiveName, err)
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}
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}
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func runImportDonkeyRecords(basedir, destdir string) {
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if destdir == "" || basedir == "" {
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zap.S().Fatal("invalid arg")
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}
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err := dkimpt.ImportDonkeyRecords(basedir, destdir)
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if err != nil {
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zap.S().Fatalf("unable to import files from %v to %v: %v", basedir, destdir, err)
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}
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}
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func runDisplay(client mqtt.Client, framePath string, frameTopic string, fps int, objectsTopic string, roadTopic string, withObjects bool, withRoad bool) {
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if framePath != "" {
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camera, err := video.NewCameraFake(client, frameTopic, framePath, fps)
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if err != nil {
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log.Fatalf("unable to load fake camera: %v", err)
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}
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if err = camera.Start(); err != nil {
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log.Fatalf("unable to start fake camera: %v", err)
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}
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defer camera.Stop()
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}
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p := part.NewPart(client, frameTopic,
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objectsTopic, roadTopic,
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withObjects, withRoad)
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defer p.Stop()
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cli.HandleExit(p)
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err := p.Start()
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if err != nil {
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zap.S().Fatalf("unable to start service: %v", err)
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}
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}
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func runTraining(bucketName string, ociImage string, roleArn string, jobName, dataDir string, sliceSize int, withFlipImage bool,
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outputModel string, imgHeight int, imgWidth int, enableSpotTraining bool) {
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l := zap.S()
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if bucketName == "" {
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l.Fatalf("no bucket define, see help")
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}
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if ociImage == "" {
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l.Fatalf("no oci image define, see help")
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}
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if jobName == "" {
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l.Fatalf("no job name define, see help")
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}
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if dataDir == "" {
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l.Fatalf("no training data define, see help")
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}
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if outputModel == "" {
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l.Fatalf("no output model path define, see help")
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}
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if sliceSize != 0 && sliceSize != 2 {
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l.Fatalf("invalid value for sie-slice, only '0' or '2' are allowed")
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}
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training := train.New(bucketName, ociImage, roleArn)
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err := training.TrainDir(context.Background(), jobName, dataDir, imgHeight, imgWidth, sliceSize, withFlipImage,
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outputModel, enableSpotTraining)
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if err != nil {
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l.Fatalf("unable to run training: %v", err)
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}
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}
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func runTrainList() {
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err := train.ListJob(context.Background())
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if err != nil {
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zap.S().Fatalf("unable to list training jobs: %w", err)
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}
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}
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func runModelsList(bucketName string) {
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err := models.ListModels(context.Background(), bucketName)
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if err != nil {
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zap.S().Fatalf("unable to list models: %s", err)
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}
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}
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func runModelsDownload(bucketName, modelPath, output string) {
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err := models.DownloadArchiveToFile(context.Background(), bucketName, modelPath, output)
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if err != nil {
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zap.S().Fatalf("unable to download model: %s", err)
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}
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} |