AI: Added some intelligence to label loading.

Now when loading labels internal/ai/tensorflow package will try to look
for all the files that match the glob label*.txt and will return the
labels that match the expected number. Some models add a first label
called background, which is a bias.
Also, a new parameter has been added to models to allow a second path to
look for the label files. This path is set to nasnet asset on
internal/ai/vision.

internal/ai/classify/model.go

@@ -25,6 +25,7 @@ type Model struct {
 	model             *tf.SavedModel
 	modelPath         string
 	assetsPath        string
+	defaultLabelsPath string
 	labels            []string
 	disabled          bool
 	meta              *tensorflow.ModelInfo
@@ -32,7 +33,7 @@ type Model struct {
 }
 
 // NewModel returns new TensorFlow classification model instance.
-func NewModel(assetsPath, modelPath string, meta *tensorflow.ModelInfo, disabled bool) *Model {
+func NewModel(assetsPath, modelPath, defaultLabelsPath string, meta *tensorflow.ModelInfo, disabled bool) *Model {
 	if meta == nil {
 		meta = new(tensorflow.ModelInfo)
 	}
@@ -40,6 +41,7 @@ func NewModel(assetsPath, modelPath string, meta *tensorflow.ModelInfo, disabled
 	return &Model{
 		modelPath:         modelPath,
 		assetsPath:        assetsPath,
+		defaultLabelsPath: defaultLabelsPath,
 		meta:              meta,
 		disabled:          disabled,
 	}
@@ -47,7 +49,7 @@ func NewModel(assetsPath, modelPath string, meta *tensorflow.ModelInfo, disabled
 
 // NewNasnet returns new Nasnet TensorFlow classification model instance.
 func NewNasnet(assetsPath string, disabled bool) *Model {
-	return NewModel(assetsPath, "nasnet", &tensorflow.ModelInfo{
+	return NewModel(assetsPath, "nasnet", "", &tensorflow.ModelInfo{
 		TFVersion: "1.12.0",
 		Tags:      []string{"photoprism"},
 		Input: &tensorflow.PhotoInput{
@@ -159,7 +161,13 @@ func (m *Model) Run(img []byte, confidenceThreshold int) (result Labels, err err
 }
 
 func (m *Model) loadLabels(modelPath string) (err error) {
-	m.labels, err = tensorflow.LoadLabels(modelPath)
+	numLabels := int(m.meta.Output.NumOutputs)
+
+	m.labels, err = tensorflow.LoadLabels(modelPath, numLabels)
+	if os.IsNotExist(err) {
+		log.Infof("Model does not seem to have tags at %s, trying %s", modelPath, m.defaultLabelsPath)
+		m.labels, err = tensorflow.LoadLabels(m.defaultLabelsPath, numLabels)
+	}
 	return err
 }
 
@@ -216,7 +224,7 @@ func (m *Model) loadModel() (err error) {
 	if m.meta.Output.OutputsLogits {
 		_, err = tensorflow.AddSoftmax(m.model.Graph, m.meta)
 		if err != nil {
-			return fmt.Errorf("classify: could not add softmax: %w")
+			return fmt.Errorf("classify: could not add softmax: %w", nil)
 		}
 	}
 

internal/ai/nsfw/model.go

@@ -170,7 +170,7 @@ func (m *Model) loadModel() error {
 	if m.meta.Output.OutputsLogits {
 		_, err = tensorflow.AddSoftmax(m.model.Graph, m.meta)
 		if err != nil {
-			return fmt.Errorf("nsfw: could not add softmax: %w")
+			return fmt.Errorf("nsfw: could not add softmax: %w", nil)
 		}
 	}
 
@@ -178,7 +178,7 @@ func (m *Model) loadModel() error {
 }
 
 func (m *Model) loadLabels(modelPath string) (err error) {
-	m.labels, err = tensorflow.LoadLabels(modelPath)
+	m.labels, err = tensorflow.LoadLabels(modelPath, int(m.meta.Output.NumOutputs))
 	return nil
 }
 

internal/ai/tensorflow/labels.go

@@ -2,19 +2,17 @@ package tensorflow
 
 import (
 	"bufio"
+	"io/fs"
 	"os"
+	"path/filepath"
 )
 
-// LoadLabels loads the labels of classification models from the specified path and returns them.
+func loadLabelsFromPath(path string) (labels []string, err error) {
-func LoadLabels(modelPath string) (labels []string, err error) {
+	log.Infof("tensorflow: loading model labels from %s", path)
-	modelLabels := modelPath + "/labels.txt"
-
-	log.Infof("tensorflow: loading model labels from labels.txt")
-
-	f, err := os.Open(modelLabels)
 
+	f, err := os.Open(path)
 	if err != nil {
-		return labels, err
+		return nil, err
 	}
 
 	defer f.Close()
@@ -30,3 +28,34 @@ func LoadLabels(modelPath string) (labels []string, err error) {
 
 	return labels, err
 }
+
+// LoadLabels loads the labels of classification models from the specified path and returns them.
+func LoadLabels(modelPath string, expectedLabels int) (labels []string, err error) {
+
+	dir := os.DirFS(modelPath)
+	matches, err := fs.Glob(dir, "labels*.txt")
+	if err != nil {
+		return nil, err
+	}
+
+	for i := range matches {
+		labels, err := loadLabelsFromPath(filepath.Join(modelPath, matches[i]))
+		if err != nil {
+			return nil, err
+		}
+
+		switch expectedLabels - len(labels) {
+		case 0:
+			log.Infof("Found a valid labels file: %s", matches[i])
+			return labels, nil
+		case 1:
+			log.Infof("Found a valid labels file %s but we have to add bias", matches[i])
+
+			return append([]string{"background"}, labels...), nil
+		default:
+			log.Infof("File not valid. Expected %d labels and have %d",
+				expectedLabels, len(labels))
+		}
+	}
+	return nil, os.ErrNotExist
+}

internal/ai/vision/model.go

@@ -104,17 +104,18 @@ func (m *Model) ClassifyModel() *classify.Model {
 		// Set default thumbnail resolution if no tags are configured.
 		if m.Resolution <= 0 {
 			m.Resolution = DefaultResolution
-		} else {
+		}
+
 		if m.Meta.Input == nil {
 			m.Meta.Input = new(tensorflow.PhotoInput)
 		}
 
 		m.Meta.Input.SetResolution(m.Resolution)
 		m.Meta.Input.Channels = 3
-		}
 
 		// Try to load custom model based on the configuration values.
-		if model := classify.NewModel(AssetsPath, m.Path, m.Meta, m.Disabled); model == nil {
+		defaultPath := filepath.Join(AssetsPath, "nasnet")
+		if model := classify.NewModel(AssetsPath, m.Path, defaultPath, m.Meta, m.Disabled); model == nil {
 			return nil
 		} else if err := model.Init(); err != nil {
 			log.Errorf("vision: %s (init %s)", err, m.Path)
@@ -222,14 +223,14 @@ func (m *Model) NsfwModel() *nsfw.Model {
 		// Set default thumbnail resolution if no tags are configured.
 		if m.Resolution <= 0 {
 			m.Resolution = DefaultResolution
-		} else {
+		}
+
 		if m.Meta.Input == nil {
 			m.Meta.Input = new(tensorflow.PhotoInput)
 		}
 
 		m.Meta.Input.SetResolution(m.Resolution)
 		m.Meta.Input.Channels = 3
-		}
 
 		if m.Meta == nil {
 			m.Meta = &tensorflow.ModelInfo{}