AI: Update log messages and tests in internal/ai/classify #127 #5011

Signed-off-by: Michael Mayer <michael@photoprism.app>

internal/ai/classify/model.go

@@ -149,7 +149,7 @@ func (m *Model) Run(img []byte, confidenceThreshold int) (result Labels, err err
 		nil)
 
 	if err != nil {
-		return result, fmt.Errorf("classify: %s (run inference)", err.Error())
+		return result, fmt.Errorf("classify: %s (run inference)", clean.Error(err))
 	}
 
 	if len(output) < 1 {
@@ -173,7 +173,7 @@ func (m *Model) loadLabels(modelPath string) (err error) {
 
 	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)
+		log.Infof("vision: model does not seem to have tags at %s, trying %s", clean.Log(modelPath), clean.Log(m.defaultLabelsPath))
 		m.labels, err = tensorflow.LoadLabels(m.defaultLabelsPath, numLabels)
 	}
 	return err
@@ -197,29 +197,30 @@ func (m *Model) loadModel() (err error) {
 	modelPath := path.Join(m.assetsPath, m.modelPath)
 
 	if len(m.meta.Tags) == 0 {
-		infos, err := tensorflow.GetModelInfo(modelPath)
+		infos, modelErr := tensorflow.GetModelInfo(modelPath)
-		if err != nil {
+		if modelErr != nil {
-			log.Errorf("classify: could not get the model info at %s: %v", clean.Log(modelPath), err)
+			log.Errorf("classify: could not get info from model in %s (%s)", clean.Log(modelPath), clean.Error(modelErr))
 		} else if len(infos) == 1 {
 			log.Debugf("classify: model info: %+v", infos[0])
 			m.meta.Merge(&infos[0])
 		} else {
-			log.Warnf("classify: found %d metagraphs... that's too many", len(infos))
+			log.Warnf("classify: found %d metagraphs, which is too many", len(infos))
 		}
 	}
 
 	m.model, err = tensorflow.SavedModel(modelPath, m.meta.Tags)
+
 	if err != nil {
 		return err
 	}
 
 	if !m.meta.IsComplete() {
-		input, output, err := tensorflow.GetInputAndOutputFromSavedModel(m.model)
+		input, output, modelErr := tensorflow.GetInputAndOutputFromSavedModel(m.model)
-		if err != nil {
+		if modelErr != nil {
-			log.Errorf("classify: could not get info from signatures: %v", err)
+			log.Errorf("classify: could not get info from signatures (%s)", clean.Error(modelErr))
-			input, output, err = tensorflow.GuessInputAndOutput(m.model)
+			input, output, modelErr = tensorflow.GuessInputAndOutput(m.model)
-			if err != nil {
+			if modelErr != nil {
-				return fmt.Errorf("classify: %w", err)
+				return fmt.Errorf("classify: %w", modelErr)
 			}
 		}
 
@@ -232,7 +233,7 @@ 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", nil)
+			return fmt.Errorf("classify: could not add softmax (%s)", clean.Error(err))
 		}
 	}
 

internal/ai/classify/model_external_test.go

@@ -129,15 +129,15 @@ func TestExternalModel_AllModels(t *testing.T) {
 
 	for k, v := range modelsInfo {
 		t.Run(k, func(*testing.T) {
-			log.Infof("Testing model %s", k)
+			log.Infof("vision: testing model %s", k)
 
 			downloadedModel := downloadRemoteModel(t, fmt.Sprintf("%s/%s", baseUrl, k), tmpPath)
-			log.Infof("Model downloaded to %s", downloadedModel)
+			log.Infof("vision: model downloaded to %s", downloadedModel)
 
 			if v.Labels != "" {
 				modelPath := filepath.Join(tmpPath, downloadedModel)
 
-				t.Logf("Model path: %s", modelPath)
+				t.Logf("vision: model path is %s", modelPath)
 				downloadLabels(t, fmt.Sprintf("%s/%s", baseUrl, v.Labels), modelPath)
 			}
 

internal/ai/classify/model_test.go

@@ -37,7 +37,7 @@ func TestModel_CenterCrop(t *testing.T) {
 	model.meta.Input.ResizeOperation = tensorflow.CenterCrop
 
 	t.Run("nasnet padding", func(t *testing.T) {
-		testModel_BasicLabels(t, model, 6)
+		runBasicLabelsTest(t, model, 6)
 	})
 }
 
@@ -50,7 +50,7 @@ func TestModel_Padding(t *testing.T) {
 	model.meta.Input.ResizeOperation = tensorflow.Padding
 
 	t.Run("nasnet padding", func(t *testing.T) {
-		testModel_BasicLabels(t, model, 6)
+		runBasicLabelsTest(t, model, 6)
 	})
 }
 
@@ -63,11 +63,11 @@ func TestModel_ResizeBreakAspectRatio(t *testing.T) {
 	model.meta.Input.ResizeOperation = tensorflow.ResizeBreakAspectRatio
 
 	t.Run("nasnet break aspect ratio", func(t *testing.T) {
-		testModel_BasicLabels(t, model, 4)
+		runBasicLabelsTest(t, model, 4)
 	})
 }
 
-func testModel_BasicLabels(t *testing.T, model *Model, expectedUncertainty int) {
+func runBasicLabelsTest(t *testing.T, model *Model, expectedUncertainty int) {
 	result, err := model.File(examplesPath+"/zebra_green_brown.jpg", 10)
 
 	assert.NoError(t, err)
@@ -146,14 +146,14 @@ func TestModel_LabelsFromFile(t *testing.T) {
 			assert.Equal(t, 70, result[0].Uncertainty)
 		}
 	})
-	t.Run("not existing file", func(t *testing.T) {
+	t.Run("NotExistingFile", func(t *testing.T) {
 		tensorFlow := NewModelTest(t)
 
 		result, err := tensorFlow.File(examplesPath+"/notexisting.jpg", 10)
 		assert.Contains(t, err.Error(), "no such file or directory")
 		assert.Empty(t, result)
 	})
-	t.Run("disabled true", func(t *testing.T) {
+	t.Run("Disabled", func(t *testing.T) {
 		tensorFlow := NewNasnet(assetsPath, true)
 
 		result, err := tensorFlow.File(examplesPath+"/chameleon_lime.jpg", 10)
@@ -252,7 +252,7 @@ func TestModel_Run(t *testing.T) {
 			assert.Empty(t, result)
 		}
 	})
-	t.Run("disabled true", func(t *testing.T) {
+	t.Run("Disabled", func(t *testing.T) {
 		tensorFlow := NewNasnet(assetsPath, true)
 
 		if imageBuffer, err := os.ReadFile(examplesPath + "/dog_orange.jpg"); err != nil {
@@ -272,16 +272,16 @@ func TestModel_Run(t *testing.T) {
 }
 
 func TestModel_LoadModel(t *testing.T) {
-	t.Run("model loaded", func(t *testing.T) {
+	t.Run("Success", func(t *testing.T) {
 		tf := NewModelTest(t)
 		assert.True(t, tf.ModelLoaded())
 	})
-	t.Run("model path does not exist", func(t *testing.T) {
+	t.Run("NotFound", func(t *testing.T) {
 		tensorFlow := NewNasnet(assetsPath+"foo", false)
 		err := tensorFlow.loadModel()
 
 		if err != nil {
-			assert.Contains(t, err.Error(), "could not find SavedModel")
+			assert.Contains(t, err.Error(), "not find SavedModel")
 		}
 
 		assert.Error(t, err)
@@ -289,17 +289,7 @@ func TestModel_LoadModel(t *testing.T) {
 }
 
 func TestModel_BestLabels(t *testing.T) {
-	t.Run("labels not loaded", func(t *testing.T) {
+	t.Run("Success", func(t *testing.T) {
-		tensorFlow := NewNasnet(assetsPath, false)
-
-		p := make([]float32, 1000)
-
-		p[666] = 0.5
-
-		result := tensorFlow.bestLabels(p, 10)
-		assert.Empty(t, result)
-	})
-	t.Run("labels loaded", func(t *testing.T) {
 		tensorFlow := NewNasnet(assetsPath, false)
 
 		if err := tensorFlow.loadLabels(modelPath); err != nil {
@@ -317,4 +307,14 @@ func TestModel_BestLabels(t *testing.T) {
 		assert.Equal(t, "image", result[0].Source)
 		t.Log(result)
 	})
+	t.Run("NotLoaded", func(t *testing.T) {
+		tensorFlow := NewNasnet(assetsPath, false)
+
+		p := make([]float32, 1000)
+
+		p[666] = 0.5
+
+		result := tensorFlow.bestLabels(p, 10)
+		assert.Empty(t, result)
+	})
 }