[api] Refactor ImageFeatureExtractor

api/src/main/java/ai/djl/modality/cv/translator/ImageFeatureExtractor.java

@@ -13,14 +13,15 @@
 package ai.djl.modality.cv.translator;
 
 import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.types.DataType;
 import ai.djl.translate.TranslatorContext;
 
 import java.util.Map;
 
 /**
  * A generic {@link ai.djl.translate.Translator} for Image Classification feature extraction tasks.
  */
-public class ImageFeatureExtractor extends BaseImageTranslator<byte[]> {
+public class ImageFeatureExtractor extends BaseImageTranslator<float[]> {
 
     /**
      * Constructs an Image Classification using {@link Builder}.
@@ -33,8 +34,8 @@ public class ImageFeatureExtractor extends BaseImageTranslator<byte[]> {
 
     /** {@inheritDoc} */
     @Override
-    public byte[] processOutput(TranslatorContext ctx, NDList list) {
-        return list.get(0).toByteArray();
+    public float[] processOutput(TranslatorContext ctx, NDList list) {
+        return list.get(0).toType(DataType.FLOAT32, false).toFloatArray();
     }
 
     /**

api/src/main/java/ai/djl/modality/cv/translator/ImageFeatureExtractorFactory.java

@@ -21,20 +21,21 @@
 import java.util.Map;
 
 /** A {@link TranslatorFactory} that creates an {@link ImageClassificationTranslator}. */
-public class ImageFeatureExtractorFactory extends BaseImageTranslatorFactory<byte[]>
+public class ImageFeatureExtractorFactory extends BaseImageTranslatorFactory<float[]>
         implements Serializable {
 
     private static final long serialVersionUID = 1L;
 
     /** {@inheritDoc} */
     @Override
-    protected Translator<Image, byte[]> buildBaseTranslator(Model model, Map<String, ?> arguments) {
+    protected Translator<Image, float[]> buildBaseTranslator(
+            Model model, Map<String, ?> arguments) {
         return ImageFeatureExtractor.builder(arguments).build();
     }
 
     /** {@inheritDoc} */
     @Override
-    public Class<byte[]> getBaseOutputType() {
-        return byte[].class;
+    public Class<float[]> getBaseOutputType() {
+        return float[].class;
     }
 }

examples/src/main/java/ai/djl/examples/inference/face/FeatureExtraction.java

@@ -16,17 +16,11 @@
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
-import ai.djl.modality.cv.transform.Normalize;
-import ai.djl.modality.cv.transform.ToTensor;
-import ai.djl.ndarray.NDArray;
-import ai.djl.ndarray.NDList;
+import ai.djl.modality.cv.translator.ImageFeatureExtractorFactory;
 import ai.djl.repository.zoo.Criteria;
 import ai.djl.repository.zoo.ZooModel;
 import ai.djl.training.util.ProgressBar;
-import ai.djl.translate.Pipeline;
 import ai.djl.translate.TranslateException;
-import ai.djl.translate.Translator;
-import ai.djl.translate.TranslatorContext;
 
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -35,6 +29,8 @@
 import java.nio.file.Path;
 import java.nio.file.Paths;
 import java.util.Arrays;
+import java.util.List;
+import java.util.stream.Collectors;
 
 public final class FeatureExtraction {
 
@@ -55,13 +51,25 @@ public static void main(String[] args) throws IOException, ModelException, Trans
     public static float[] predict(Image img)
             throws IOException, ModelException, TranslateException {
         img.getWrappedImage();
+
+        List<Float> mean =
+                Arrays.asList(
+                        127.5f / 255.0f,
+                        127.5f / 255.0f,
+                        127.5f / 255.0f,
+                        128.0f / 255.0f,
+                        128.0f / 255.0f,
+                        128.0f / 255.0f);
+        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
+
         Criteria<Image, float[]> criteria =
                 Criteria.builder()
                         .setTypes(Image.class, float[].class)
                         .optModelUrls(
                                 "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
                         .optModelName("face_feature") // specify model file prefix
-                        .optTranslator(new FaceFeatureTranslator())
+                        .optArgument("normalize", normalize)
+                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
                         .optProgress(new ProgressBar())
                         .optEngine("PyTorch") // Use PyTorch engine
                         .build();
@@ -71,44 +79,4 @@ public static float[] predict(Image img)
             return predictor.predict(img);
         }
     }
-
-    private static final class FaceFeatureTranslator implements Translator<Image, float[]> {
-
-        FaceFeatureTranslator() {}
-
-        /** {@inheritDoc} */
-        @Override
-        public NDList processInput(TranslatorContext ctx, Image input) {
-            NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
-            Pipeline pipeline = new Pipeline();
-            pipeline
-                    // .add(new Resize(160))
-                    .add(new ToTensor())
-                    .add(
-                            new Normalize(
-                                    new float[] {127.5f / 255.0f, 127.5f / 255.0f, 127.5f / 255.0f},
-                                    new float[] {
-                                        128.0f / 255.0f, 128.0f / 255.0f, 128.0f / 255.0f
-                                    }));
-
-            return pipeline.transform(new NDList(array));
-        }
-
-        /** {@inheritDoc} */
-        @Override
-        public float[] processOutput(TranslatorContext ctx, NDList list) {
-            NDList result = new NDList();
-            long numOutputs = list.singletonOrThrow().getShape().get(0);
-            for (int i = 0; i < numOutputs; i++) {
-                result.add(list.singletonOrThrow().get(i));
-            }
-            float[][] embeddings =
-                    result.stream().map(NDArray::toFloatArray).toArray(float[][]::new);
-            float[] feature = new float[embeddings.length];
-            for (int i = 0; i < embeddings.length; i++) {
-                feature[i] = embeddings[i][0];
-            }
-            return feature;
-        }
-    }
 }

examples/src/test/java/ai/djl/examples/inference/face/FeatureExtractionTest.java

@@ -15,7 +15,6 @@
 import ai.djl.ModelException;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
-import ai.djl.testing.TestRequirements;
 import ai.djl.translate.TranslateException;
 
 import org.testng.Assert;
@@ -29,11 +28,24 @@ public class FeatureExtractionTest {
 
     @Test
     public void testFeatureComparison() throws ModelException, TranslateException, IOException {
-        TestRequirements.linux();
-
         Path imageFile = Paths.get("src/test/resources/kana1.jpg");
         Image img = ImageFactory.getInstance().fromFile(imageFile);
         float[] feature = FeatureExtraction.predict(img);
         Assert.assertEquals(feature.length, 512);
+        float[] expected = {
+            -0.040261813f,
+            -0.019486334f,
+            -0.09802657f,
+            0.017009983f,
+            0.037828982f,
+            0.030801114f,
+            -0.02714689f,
+            0.042024296f,
+            -0.009838469f,
+            -0.005961003f
+        };
+        float[] sub = new float[10];
+        System.arraycopy(feature, 0, sub, 0, 10);
+        Assert.assertEquals(sub, expected);
     }
 }