package classify import ( "io/ioutil" "sync" "testing" "github.com/photoprism/photoprism/pkg/fs" tensorflow "github.com/tensorflow/tensorflow/tensorflow/go" "github.com/stretchr/testify/assert" ) var assetsPath = fs.Abs("../../assets") var modelPath = assetsPath + "/nasnet" var examplesPath = assetsPath + "/examples" var once sync.Once var testInstance *TensorFlow // NewTest returns a new TensorFlow test instance. func NewTest(t *testing.T) *TensorFlow { once.Do(func() { testInstance = New(assetsPath, false) if err := testInstance.loadModel(); err != nil { t.Fatal(err) } }) return testInstance } func TestTensorFlow_LabelsFromFile(t *testing.T) { t.Run("chameleon_lime.jpg", func(t *testing.T) { tensorFlow := NewTest(t) result, err := tensorFlow.File(examplesPath + "/chameleon_lime.jpg") assert.Nil(t, err) if err != nil { t.Fatal(err) } assert.NotNil(t, result) assert.IsType(t, Labels{}, result) assert.Equal(t, 1, len(result)) t.Log(result) assert.Equal(t, "chameleon", result[0].Name) assert.Equal(t, 7, result[0].Uncertainty) }) t.Run("not existing file", func(t *testing.T) { tensorFlow := NewTest(t) result, err := tensorFlow.File(examplesPath + "/notexisting.jpg") assert.Contains(t, err.Error(), "no such file or directory") assert.Empty(t, result) }) t.Run("disabled true", func(t *testing.T) { tensorFlow := New(assetsPath, true) result, err := tensorFlow.File(examplesPath + "/chameleon_lime.jpg") assert.Nil(t, err) if err != nil { t.Fatal(err) } assert.Nil(t, result) assert.IsType(t, Labels{}, result) assert.Equal(t, 0, len(result)) t.Log(result) }) } func TestTensorFlow_Labels(t *testing.T) { if testing.Short() { t.Skip("skipping test in short mode.") } t.Run("chameleon_lime.jpg", func(t *testing.T) { tensorFlow := NewTest(t) if imageBuffer, err := ioutil.ReadFile(examplesPath + "/chameleon_lime.jpg"); err != nil { t.Error(err) } else { result, err := tensorFlow.Labels(imageBuffer) t.Log(result) assert.NotNil(t, result) if err != nil { t.Fatal(err) } assert.IsType(t, Labels{}, result) assert.Equal(t, 1, len(result)) assert.Equal(t, "chameleon", result[0].Name) assert.Equal(t, 100-93, result[0].Uncertainty) } }) t.Run("dog_orange.jpg", func(t *testing.T) { tensorFlow := NewTest(t) if imageBuffer, err := ioutil.ReadFile(examplesPath + "/dog_orange.jpg"); err != nil { t.Error(err) } else { result, err := tensorFlow.Labels(imageBuffer) t.Log(result) assert.NotNil(t, result) if err != nil { t.Fatal(err) } assert.IsType(t, Labels{}, result) assert.Equal(t, 1, len(result)) assert.Equal(t, "dog", result[0].Name) assert.Equal(t, 34, result[0].Uncertainty) } }) t.Run("Random.docx", func(t *testing.T) { tensorFlow := NewTest(t) if imageBuffer, err := ioutil.ReadFile(examplesPath + "/Random.docx"); err != nil { t.Error(err) } else { result, err := tensorFlow.Labels(imageBuffer) assert.Empty(t, result) assert.Error(t, err) } }) t.Run("6720px_white.jpg", func(t *testing.T) { tensorFlow := NewTest(t) if imageBuffer, err := ioutil.ReadFile(examplesPath + "/6720px_white.jpg"); err != nil { t.Error(err) } else { result, err := tensorFlow.Labels(imageBuffer) if err != nil { t.Fatal(err) } assert.Empty(t, result) } }) t.Run("disabled true", func(t *testing.T) { tensorFlow := New(assetsPath, true) if imageBuffer, err := ioutil.ReadFile(examplesPath + "/dog_orange.jpg"); err != nil { t.Error(err) } else { result, err := tensorFlow.Labels(imageBuffer) t.Log(result) assert.Nil(t, result) assert.Nil(t, err) assert.IsType(t, Labels{}, result) assert.Equal(t, 0, len(result)) } }) } func TestTensorFlow_LoadModel(t *testing.T) { t.Run("model loaded", func(t *testing.T) { tf := NewTest(t) assert.True(t, tf.ModelLoaded()) }) t.Run("model path does not exist", func(t *testing.T) { tensorFlow := New(assetsPath+"foo", false) if err := tensorFlow.loadModel(); err != nil { assert.Contains(t, err.Error(), "Could not find SavedModel") } else { t.Fatal("err should NOT be nil") } }) } func TestTensorFlow_BestLabels(t *testing.T) { t.Run("labels not loaded", func(t *testing.T) { tensorFlow := New(assetsPath, false) p := make([]float32, 1000) p[666] = 0.5 result := tensorFlow.bestLabels(p) assert.Empty(t, result) }) t.Run("labels loaded", func(t *testing.T) { tensorFlow := New(assetsPath, false) if err := tensorFlow.loadLabels(modelPath); err != nil { t.Fatal(err) } p := make([]float32, 1000) p[8] = 0.7 p[1] = 0.5 result := tensorFlow.bestLabels(p) assert.Equal(t, "chicken", result[0].Name) assert.Equal(t, "bird", result[0].Categories[0]) assert.Equal(t, "image", result[0].Source) t.Log(result) }) } func TestTensorFlow_MakeTensor(t *testing.T) { t.Run("cat_brown.jpg", func(t *testing.T) { tensorFlow := NewTest(t) imageBuffer, err := ioutil.ReadFile(examplesPath + "/cat_brown.jpg") if err != nil { t.Fatal(err) } result, err := tensorFlow.createTensor(imageBuffer, "jpeg") assert.Equal(t, tensorflow.DataType(0x1), result.DataType()) assert.Equal(t, int64(1), result.Shape()[0]) assert.Equal(t, int64(224), result.Shape()[2]) }) t.Run("Random.docx", func(t *testing.T) { tensorFlow := NewTest(t) imageBuffer, err := ioutil.ReadFile(examplesPath + "/Random.docx") assert.Nil(t, err) result, err := tensorFlow.createTensor(imageBuffer, "jpeg") assert.Empty(t, result) assert.EqualError(t, err, "image: unknown format") }) } func Test_ConvertTF(t *testing.T) { result := convertTF(uint32(98765432)) assert.Equal(t, float32(3024.898), result) }