added uncertain training
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Linrador
parent
2d5e522475
commit
cb7f32d24d
@ -443,6 +443,9 @@ func trainingNextHandler(w http.ResponseWriter, r *http.Request) {
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forceNew := r.URL.Query().Get("force") == "1" ||
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strings.EqualFold(r.URL.Query().Get("force"), "true")
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preferUncertain := strings.EqualFold(r.URL.Query().Get("mode"), "uncertain") ||
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strings.EqualFold(r.URL.Query().Get("sampleMode"), "uncertain")
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root, err := trainingRootDir()
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if err != nil {
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trainingWriteError(w, http.StatusInternalServerError, err.Error())
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@ -452,7 +455,7 @@ func trainingNextHandler(w http.ResponseWriter, r *http.Request) {
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refreshPrediction := r.URL.Query().Get("refresh") == "1" ||
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strings.EqualFold(r.URL.Query().Get("refresh"), "true")
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if !forceNew {
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if !forceNew && !preferUncertain {
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var startedAtMs int64
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if refreshPrediction {
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@ -476,9 +479,22 @@ func trainingNextHandler(w http.ResponseWriter, r *http.Request) {
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}
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}
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startedAtMs := publishAnalysisStarted("", 4, "Neues Trainingsbild wird vorbereitet…")
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startedAtMs := publishAnalysisStarted("", 4, func() string {
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if preferUncertain {
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return "Unsichere Prediction wird gesucht…"
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}
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return "Neues Trainingsbild wird vorbereitet…"
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}())
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var sample *TrainingSample
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if preferUncertain {
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sample, err = trainingCreateUncertainNextSampleWithProgress(startedAtMs)
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} else {
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sample, err = trainingCreateNextSampleWithProgress(startedAtMs)
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}
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sample, err := trainingCreateNextSampleWithProgress(startedAtMs)
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if err != nil {
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publishAnalysisError(startedAtMs, "", "Trainingsbild konnte nicht erstellt werden.", err)
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trainingWriteError(w, http.StatusInternalServerError, err.Error())
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@ -1824,6 +1840,153 @@ func trainingCreateNextSample() (*TrainingSample, error) {
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return sample, nil
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}
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func trainingCreateUncertainNextSampleWithProgress(startedAtMs int64) (*TrainingSample, error) {
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const attempts = 8
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root, err := trainingRootDir()
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if err != nil {
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return nil, err
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}
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var best *TrainingSample
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bestScore := -1.0
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errs := []string{}
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for i := 0; i < attempts; i++ {
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publishAnalysisStep(
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startedAtMs,
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i+1,
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attempts+1,
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"",
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fmt.Sprintf("Unsicherer Kandidat %d/%d wird analysiert…", i+1, attempts),
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)
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sample, err := trainingCreateNextSample()
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if err != nil {
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errs = append(errs, err.Error())
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continue
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}
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score := trainingPredictionUncertaintyScore(sample.Prediction)
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if score > bestScore {
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if best != nil {
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trainingDeleteSampleFiles(root, best.SampleID)
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}
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best = sample
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bestScore = score
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} else {
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trainingDeleteSampleFiles(root, sample.SampleID)
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}
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}
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if best == nil {
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if len(errs) > 0 {
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return nil, errors.New(strings.Join(errs, "; "))
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}
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return nil, errors.New("keine unsicheren Trainingskandidaten gefunden")
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}
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publishAnalysisStep(
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startedAtMs,
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attempts+1,
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attempts+1,
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best.SourceFile,
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fmt.Sprintf("Unsicherer Kandidat gewählt · Score %.0f%%", bestScore*100),
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)
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return best, nil
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}
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func trainingDeleteSampleFiles(root string, sampleID string) {
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sampleID = strings.TrimSpace(sampleID)
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if sampleID == "" || strings.Contains(sampleID, "/") || strings.Contains(sampleID, "\\") {
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return
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}
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_ = os.Remove(filepath.Join(root, "samples", sampleID+".json"))
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_ = os.Remove(filepath.Join(root, "frames", sampleID+".jpg"))
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}
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func trainingPredictionUncertaintyScore(pred TrainingPrediction) float64 {
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if !pred.ModelAvailable {
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return 0.10
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}
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scores := []float64{}
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addScore := func(score float64) {
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if math.IsNaN(score) || math.IsInf(score, 0) {
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return
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}
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if score <= 0 {
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return
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}
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scores = append(scores, clamp01(score))
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}
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if strings.TrimSpace(pred.SexPosition) != "" &&
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strings.TrimSpace(pred.SexPosition) != "unknown" {
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addScore(pred.SexPositionScore)
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}
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for _, box := range pred.Boxes {
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addScore(box.Score)
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}
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if len(pred.Boxes) == 0 {
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for _, item := range pred.BodyPartsPresent {
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addScore(item.Score)
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}
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for _, item := range pred.ObjectsPresent {
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addScore(item.Score)
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}
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for _, item := range pred.ClothingPresent {
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addScore(item.Score)
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}
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}
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if len(scores) == 0 {
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// Modell ist verfügbar, erkennt aber nichts.
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// Das kann ein nützliches Hard-Negative oder ein False-Negative sein.
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return 0.35
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}
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sum := 0.0
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for _, score := range scores {
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// Höchste Unsicherheit ungefähr im mittleren Bereich.
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// 0.55 ist absichtlich etwas niedriger als 0.75,
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// damit Low/Mid-Confidence-Fälle bevorzugt werden.
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distance := math.Abs(score - 0.55)
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uncertainty := 1.0 - distance/0.55
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sum += clamp01(uncertainty)
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}
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avg := sum / float64(len(scores))
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// Viele Boxen mit mittlerer Confidence sind besonders wertvoll.
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boxBonus := math.Min(0.12, float64(len(pred.Boxes))*0.025)
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// Sehr niedrige Confidence soll ebenfalls auftauchen,
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// aber nicht alle Ergebnisse dominieren.
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lowConfidenceBonus := 0.0
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for _, score := range scores {
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if score >= 0.25 && score <= 0.75 {
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lowConfidenceBonus = 0.08
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break
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}
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}
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return clamp01(avg + boxBonus + lowConfidenceBonus)
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}
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func trainingCreateNextSampleWithProgress(startedAtMs int64) (*TrainingSample, error) {
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publishAnalysisStep(startedAtMs, 1, 4, "", "Video wird ausgewählt…")
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@ -184,6 +184,8 @@ type TrainingNotice = {
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progress?: number
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}
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type TrainingSampleMode = 'random' | 'uncertain'
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function trainingNoticeClass(kind: TrainingNoticeKind) {
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switch (kind) {
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case 'success':
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@ -2042,6 +2044,7 @@ export default function TrainingTab(props: {
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const [boxLabel, setBoxLabel] = useState('')
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const [activeBoxIndex, setActiveBoxIndex] = useState<number | null>(null)
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const [imageReloadKey, setImageReloadKey] = useState(0)
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const [trainingSampleMode, setTrainingSampleMode] = useState<TrainingSampleMode>('random')
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const [expandedCorrectionSections, setExpandedCorrectionSections] = useState({
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sexPosition: false,
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people: false,
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@ -2293,15 +2296,20 @@ export default function TrainingTab(props: {
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forceNew?: boolean
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refreshPrediction?: boolean
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preserveNotice?: boolean
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mode?: TrainingSampleMode
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}) => {
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const mode = opts?.mode ?? trainingSampleMode
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const uncertainMode = mode === 'uncertain' && !opts?.refreshPrediction
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setLoading(true)
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setAnalysisProgress(8)
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setAnalysisStep(
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opts?.refreshPrediction
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? 'Aktuelles Bild wird neu analysiert…'
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: opts?.forceNew
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? 'Neues Trainingsbild wird gesucht…'
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: 'Trainingsbild wird geladen…'
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: uncertainMode
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? 'Unsichere Prediction wird gesucht…'
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: opts?.forceNew
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? 'Neues Trainingsbild wird gesucht…'
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: 'Trainingsbild wird geladen…'
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)
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if (!opts?.preserveNotice) {
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@ -2314,11 +2322,16 @@ export default function TrainingTab(props: {
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if (opts?.forceNew) params.set('force', '1')
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if (opts?.refreshPrediction) params.set('refresh', '1')
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if (uncertainMode) params.set('mode', 'uncertain')
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const url = `/api/training/next${params.toString() ? `?${params.toString()}` : ''}`
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setAnalysisProgress(25)
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setAnalysisStep('Bild wird vorbereitet…')
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setAnalysisStep(
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uncertainMode
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? 'Mehrere Kandidaten werden bewertet…'
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: 'Bild wird vorbereitet…'
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)
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const res = await fetch(url, { cache: 'no-store' })
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const data = await res.json().catch(() => null)
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@ -2371,7 +2384,7 @@ export default function TrainingTab(props: {
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setAnalysisStep('')
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}, 500)
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}
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}, [])
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}, [trainingSampleMode])
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const reloadCurrentImage = useCallback(async () => {
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setDrawingBox(null)
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@ -3431,6 +3444,64 @@ export default function TrainingTab(props: {
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</button>
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</div>
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<div className="mt-3 rounded-xl bg-gray-50 p-2 ring-1 ring-black/5 dark:bg-white/[0.04] dark:ring-white/10">
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<button
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type="button"
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disabled={uiLocked}
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onClick={() => {
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const nextMode: TrainingSampleMode =
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trainingSampleMode === 'uncertain' ? 'random' : 'uncertain'
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setTrainingSampleMode(nextMode)
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if (!uiLocked) {
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void loadNext({
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forceNew: true,
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mode: nextMode,
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})
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}
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}}
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className={[
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'flex w-full items-center justify-between gap-3 rounded-lg px-2 py-1.5 text-left transition',
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'focus:outline-none focus:ring-2 focus:ring-indigo-500/40',
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uiLocked
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? 'cursor-not-allowed opacity-50'
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: 'hover:bg-white dark:hover:bg-white/10',
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].join(' ')}
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title="Wenn aktiv, werden bevorzugt Frames mit niedriger oder mittlerer Modell-Confidence geladen."
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aria-pressed={trainingSampleMode === 'uncertain'}
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>
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<span className="min-w-0">
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<span className="block text-[11px] font-bold text-gray-900 dark:text-white">
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Unsichere zuerst
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</span>
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<span className="mt-0.5 block text-[10px] leading-snug text-gray-500 dark:text-gray-400">
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Lädt bevorzugt Grenzfälle, die dem Modell am meisten helfen.
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</span>
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</span>
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<span
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className={[
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'relative inline-flex h-5 w-9 shrink-0 items-center rounded-full transition',
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trainingSampleMode === 'uncertain'
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? 'bg-indigo-600'
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: 'bg-gray-300 dark:bg-white/20',
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].join(' ')}
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aria-hidden="true"
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>
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<span
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className={[
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'inline-block h-4 w-4 rounded-full bg-white shadow transition',
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trainingSampleMode === 'uncertain'
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? 'translate-x-4'
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: 'translate-x-0.5',
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].join(' ')}
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/>
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</span>
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</button>
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</div>
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{trainingRunning || feedbackCount < requiredCount ? (
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<div className="mt-3">
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<div className="mb-1 flex items-center justify-between text-[10px] font-medium text-gray-500 dark:text-gray-400">
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@ -4394,7 +4465,11 @@ export default function TrainingTab(props: {
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disabled={uiLocked || frameBusy || !sample}
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onClick={() => void loadNext({ forceNew: true })}
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className="w-full justify-center px-2 text-xs sm:text-sm"
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title="Dieses Bild nicht bewerten und ein anderes laden."
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title={
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trainingSampleMode === 'uncertain'
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? 'Dieses Bild nicht bewerten und eine andere unsichere Prediction laden.'
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: 'Dieses Bild nicht bewerten und ein anderes laden.'
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}
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>
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<span className="sm:hidden">Skip</span>
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<span className="hidden sm:inline">Überspringen</span>
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