[examples] include all engines in example project build.gradle

Change-Id: I06de6be02f20bc1f09b9a2a2d067f5c25aba9c8f

docs/load_model.md

@@ -185,7 +185,7 @@ You can use [ModelZoo.listModels()](https://javadoc.io/static/ai.djl/api/0.13.0/
 
 #### List available models using DJL command line
 
-Use the following command to list models in examples module for MXNet engine:
+Use the following command to list models in the DJL model zoo:
 
 ```shell
 ./gradlew :examples:listmodels
@@ -200,8 +200,8 @@ Use the following command to list models in examples module for MXNet engine:
 
 ```
 
-You can list models from your model folder and only list models for specific Engine with debug log:
+You can list models from your model folder with debug log:
 
 ```shell
-./gradlew :examples:listmodels -Dai.djl.default_engine=PyTorch -Dai.djl.logging.level=debug -Dai.djl.repository.zoo.location=file:///mymodels
+./gradlew :examples:listmodels -Dai.djl.logging.level=debug -Dai.djl.repository.zoo.location=file:///mymodels
 ```

engines/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/MxBertQATranslator.java

@@ -111,7 +111,7 @@ public String processOutput(TranslatorContext ctx, NDList list) {
         NDArray endLogits = output.get(1).reshape(new Shape(1, -1));
         int startIdx = (int) startLogits.argMax(1).getLong();
         int endIdx = (int) endLogits.argMax(1).getLong();
-        return tokens.subList(startIdx, endIdx + 1).toString();
+        return tokenizer.tokenToString(tokens.subList(startIdx, endIdx + 1));
     }
 
     /**

engines/mxnet/mxnet-model-zoo/src/test/java/ai/djl/mxnet/integration/MxSymbolBlockTest.java

@@ -96,7 +96,7 @@ public void testForward() throws IOException, ModelNotFoundException, MalformedM
     @Test
     public void trainWithNewParam()
             throws IOException, ModelNotFoundException, MalformedModelException {
-        if ("MXNet".equals(Engine.getInstance().getEngineName())) {
+        if ("MXNet".equals(Engine.getDefaultEngineName())) {
             // TODO: WARN The gradMeans (but not predictions or loss) changed during the upgrade
             // to MXNet 1.8. The issue affect only CPU, but GPU has not changed.
             TestRequirements.gpu();

examples/build.gradle

@@ -8,13 +8,9 @@ dependencies {
     implementation project(":basicdataset")
     implementation project(":model-zoo")
 
-    if (System.getProperty("ai.djl.default_engine") == "PyTorch") {
-        runtimeOnly project(":engines:pytorch:pytorch-model-zoo")
-    } else if (System.getProperty("ai.djl.default_engine") == "TensorFlow") {
-        runtimeOnly project(":engines:tensorflow:tensorflow-model-zoo")
-    } else {
-        runtimeOnly project(":engines:mxnet:mxnet-model-zoo")
-    }
+    runtimeOnly project(":engines:pytorch:pytorch-model-zoo")
+    runtimeOnly project(":engines:tensorflow:tensorflow-model-zoo")
+    runtimeOnly project(":engines:mxnet:mxnet-model-zoo")
 
     testImplementation("org.testng:testng:${testng_version}") {
         exclude group: "junit", module: "junit"

examples/docs/biggan.md

@@ -30,7 +30,7 @@ Use the following commands to run the project:
 
 ```
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.BigGAN -Dai.djl.default_engine=PyTorch
+./gradlew run -Dmain=ai.djl.examples.inference.BigGAN
 ```
 
 ### Output

examples/docs/face_detection.md

@@ -26,8 +26,8 @@ Use the following command to run the project:
 
 ```
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.face.RetinaFaceDetection -Dai.djl.default_engine=PyTorch
-./gradlew run -Dmain=ai.djl.examples.inference.face.LightFaceDetection -Dai.djl.default_engine=PyTorch
+./gradlew run -Dmain=ai.djl.examples.inference.face.RetinaFaceDetection
+./gradlew run -Dmain=ai.djl.examples.inference.face.LightFaceDetection
 ```
 
 Your output should look like the following:

examples/docs/face_recognition.md

@@ -27,7 +27,7 @@ Use the following command to run the project:
 
 ```
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.face.FeatureExtraction -Dai.djl.default_engine=PyTorch
+./gradlew run -Dmain=ai.djl.examples.inference.face.FeatureExtraction
 ```
 
 Your output should look like the following:
@@ -38,7 +38,7 @@ Your output should look like the following:
 
 ```
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.face.FeatureComparison -Dai.djl.default_engine=PyTorch
+./gradlew run -Dmain=ai.djl.examples.inference.face.FeatureComparison
 ```
 
 Your output should look like the following:

examples/docs/object_detection_with_tensorflow_saved_model.md

@@ -45,7 +45,7 @@ Use the following command to run the project:
 
 ```
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.ObjectDetectionWithTensorflowSavedModel -Dai.djl.default_engine=TensorFlow
+./gradlew run -Dmain=ai.djl.examples.inference.ObjectDetectionWithTensorflowSavedModel
 ```
 
 Your output should look like the following:

examples/docs/sentiment_analysis.md

@@ -29,5 +29,5 @@ Follow [setup](../../docs/development/setup.md) to configure your development en
 
 ```
 cd examples
-./gradlew run -Dai.djl.default_engine=PyTorch -Dmain=ai.djl.examples.inference.SentimentAnalysis
+./gradlew run -Dmain=ai.djl.examples.inference.SentimentAnalysis
 ```

examples/docs/super_resolution.md

@@ -30,7 +30,7 @@ Use the following commands to run the project:
 
 ```
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.sr.SuperResolution -Dai.djl.default_engine=TensorFlow
+./gradlew run -Dmain=ai.djl.examples.inference.sr.SuperResolution
 ```
 
 ### Output

examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java

@@ -56,6 +56,7 @@ public static Classifications predict() throws IOException, ModelException, Tran
                         .setTypes(Image.class, Classifications.class)
                         .optFilter("backbone", "inceptionv3")
                         .optFilter("dataset", "ucf101")
+                        .optEngine("MXNet")
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/BertClassification.java

@@ -14,7 +14,6 @@
 
 import ai.djl.MalformedModelException;
 import ai.djl.ModelException;
-import ai.djl.engine.Engine;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.Classifications;
 import ai.djl.modality.nlp.DefaultVocabulary;
@@ -57,21 +56,14 @@ public static void main(String[] args) throws IOException, ModelException, Trans
         inputs.add("class3\tDJL is good");
 
         Classifications[] results = predict(inputs);
-        if (results == null) {
-            logger.info("This example only works for TensorFlow Engine");
-        } else {
-            for (int i = 0; i < inputs.size(); i++) {
-                logger.info("Prediction for: " + inputs.get(i) + "\n" + results[i].toString());
-            }
+        for (int i = 0; i < inputs.size(); i++) {
+            logger.info("Prediction for: " + inputs.get(i) + "\n" + results[i].toString());
         }
     }
 
     public static Classifications[] predict(List<String> inputs)
             throws MalformedModelException, ModelNotFoundException, IOException,
                     TranslateException {
-        if (!"TensorFlow".equals(Engine.getInstance().getEngineName())) {
-            return null;
-        }
         // refer to
         // https://medium.com/delvify/bert-rest-inference-from-the-fine-tuned-model-499997b32851 and
         // https://github.com/google-research/bert
@@ -84,6 +76,7 @@ public static Classifications[] predict(List<String> inputs)
                         .setTypes(String[].class, Classifications[].class)
                         .optModelUrls(modelUrl)
                         .optTranslator(new MyTranslator(vocabularyPath, 128))
+                        .optEngine("TensorFlow")
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/BertQaInference.java

@@ -15,6 +15,7 @@
 
 import ai.djl.Application;
 import ai.djl.ModelException;
+import ai.djl.engine.Engine;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.nlp.qa.QAInput;
 import ai.djl.repository.zoo.Criteria;
@@ -68,6 +69,7 @@ public static String predict() throws IOException, TranslateException, ModelExce
                         .optApplication(Application.NLP.QUESTION_ANSWER)
                         .setTypes(QAInput.class, String.class)
                         .optFilter("backbone", "bert")
+                        .optEngine(Engine.getDefaultEngineName())
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/BigGAN.java

@@ -14,7 +14,6 @@
 
 import ai.djl.Application;
 import ai.djl.ModelException;
-import ai.djl.engine.Engine;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
 import ai.djl.repository.zoo.Criteria;
@@ -36,11 +35,6 @@ public final class BigGAN {
     private BigGAN() {}
 
     public static void main(String[] args) throws ModelException, TranslateException, IOException {
-        if (!"PyTorch".equals(Engine.getInstance().getEngineName())) {
-            logger.info("This example only works for PyTorch Engine");
-            return;
-        }
-
         Image[] generatedImages = BigGAN.generate();
         logger.info("Using PyTorch Engine. {} images generated.", generatedImages.length);
         saveImages(generatedImages);
@@ -58,13 +52,13 @@ private static void saveImages(Image[] generatedImages) throws IOException {
     }
 
     public static Image[] generate() throws IOException, ModelException, TranslateException {
-
         Criteria<int[], Image[]> criteria =
                 Criteria.builder()
                         .optApplication(Application.CV.IMAGE_GENERATION)
                         .setTypes(int[].class, Image[].class)
                         .optFilter("size", "256")
                         .optArgument("truncation", 0.4f)
+                        .optEngine("PyTorch")
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java

@@ -53,7 +53,7 @@ public static DetectedObjects predict() throws IOException, ModelException, Tran
         Image img = ImageFactory.getInstance().fromFile(imageFile);
 
         String backbone;
-        if ("TensorFlow".equals(Engine.getInstance().getEngineName())) {
+        if ("TensorFlow".equals(Engine.getDefaultEngineName())) {
             backbone = "mobilenet_v2";
         } else {
             backbone = "resnet50";
@@ -64,6 +64,7 @@ public static DetectedObjects predict() throws IOException, ModelException, Tran
                         .optApplication(Application.CV.OBJECT_DETECTION)
                         .setTypes(Image.class, DetectedObjects.class)
                         .optFilter("backbone", backbone)
+                        .optEngine(Engine.getDefaultEngineName())
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java

@@ -14,7 +14,6 @@
 
 import ai.djl.Application;
 import ai.djl.ModelException;
-import ai.djl.engine.Engine;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
@@ -69,17 +68,11 @@ public final class ObjectDetectionWithTensorflowSavedModel {
     private ObjectDetectionWithTensorflowSavedModel() {}
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        if (!"TensorFlow".equals(Engine.getInstance().getEngineName())) {
-            logger.info("This example only works for TensorFlow Engine");
-            return;
-        }
-
         DetectedObjects detection = ObjectDetectionWithTensorflowSavedModel.predict();
         logger.info("{}", detection);
     }
 
     public static DetectedObjects predict() throws IOException, ModelException, TranslateException {
-
         Path imageFile = Paths.get("src/test/resources/dog_bike_car.jpg");
         Image img = ImageFactory.getInstance().fromFile(imageFile);
 
@@ -94,6 +87,7 @@ public static DetectedObjects predict() throws IOException, ModelException, Tran
                         // saved_model.pb file is in the subfolder of the model archive file
                         .optModelName("ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model")
                         .optTranslator(new MyTranslator())
+                        .optEngine("TensorFlow")
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java

@@ -30,6 +30,7 @@
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.nio.file.Paths;
+import java.util.Collections;
 import java.util.List;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -60,7 +61,7 @@ public static Joints predict() throws IOException, ModelException, TranslateExce
 
         if (person == null) {
             logger.warn("No person found in image.");
-            return null;
+            return new Joints(Collections.emptyList());
         }
 
         return predictJointsInPerson(person);
@@ -78,6 +79,7 @@ private static Image predictPersonInImage(Image img)
                         .optFilter("backbone", "resnet50")
                         .optFilter("flavor", "v1")
                         .optFilter("dataset", "voc")
+                        .optEngine("MXNet")
                         .optProgress(new ProgressBar())
                         .build();
 

examples/src/main/java/ai/djl/examples/inference/StyleTransfer.java

@@ -15,7 +15,6 @@
 import ai.djl.Application;
 import ai.djl.MalformedModelException;
 import ai.djl.ModelException;
-import ai.djl.engine.Engine;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
@@ -46,11 +45,6 @@ public enum Artist {
     }
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        if (!"PyTorch".equals(Engine.getInstance().getEngineName())) {
-            logger.info("This example only works for PyTorch Engine");
-            return;
-        }
-
         Artist artist = Artist.MONET;
         String imagePath = "src/test/resources/mountains.png";
         Image input = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
@@ -63,11 +57,6 @@ public static void main(String[] args) throws IOException, ModelException, Trans
     public static Image transfer(Image image, Artist artist)
             throws IOException, ModelNotFoundException, MalformedModelException,
                     TranslateException {
-
-        if (!"PyTorch".equals(Engine.getInstance().getEngineName())) {
-            return null;
-        }
-
         String modelName = "style_" + artist.toString().toLowerCase() + ".zip";
         String modelUrl =
                 "https://mlrepo.djl.ai/model/cv/image_generation/ai/djl/pytorch/cyclegan/0.0.1/"
@@ -80,6 +69,7 @@ public static Image transfer(Image image, Artist artist)
                         .optModelUrls(modelUrl)
                         .optProgress(new ProgressBar())
                         .optTranslatorFactory(new StyleTransferTranslatorFactory())
+                        .optEngine("PyTorch")
                         .build();
 
         try (ZooModel<Image, Image> model = criteria.loadModel();

examples/src/main/java/ai/djl/examples/inference/UniversalSentenceEncoder.java

@@ -15,7 +15,6 @@
 import ai.djl.Application;
 import ai.djl.MalformedModelException;
 import ai.djl.ModelException;
-import ai.djl.engine.Engine;
 import ai.djl.inference.Predictor;
 import ai.djl.ndarray.NDArray;
 import ai.djl.ndarray.NDArrays;
@@ -53,23 +52,14 @@ public static void main(String[] args) throws IOException, ModelException, Trans
         inputs.add("I am a sentence for which I would like to get its embedding");
 
         float[][] embeddings = UniversalSentenceEncoder.predict(inputs);
-        if (embeddings == null) {
-            logger.info("This example only works for TensorFlow Engine");
-        } else {
-            for (int i = 0; i < inputs.size(); i++) {
-                logger.info(
-                        "Embedding for: " + inputs.get(i) + "\n" + Arrays.toString(embeddings[i]));
-            }
+        for (int i = 0; i < inputs.size(); i++) {
+            logger.info("Embedding for: " + inputs.get(i) + "\n" + Arrays.toString(embeddings[i]));
         }
     }
 
     public static float[][] predict(List<String> inputs)
             throws MalformedModelException, ModelNotFoundException, IOException,
                     TranslateException {
-        if (!"TensorFlow".equals(Engine.getInstance().getEngineName())) {
-            return null;
-        }
-
         String modelUrl =
                 "https://storage.googleapis.com/tfhub-modules/google/universal-sentence-encoder/4.tar.gz";
 
@@ -79,6 +69,7 @@ public static float[][] predict(List<String> inputs)
                         .setTypes(String[].class, float[][].class)
                         .optModelUrls(modelUrl)
                         .optTranslator(new MyTranslator())
+                        .optEngine("TensorFlow")
                         .optProgress(new ProgressBar())
                         .build();
         try (ZooModel<String[], float[][]> model = criteria.loadModel();

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

@@ -13,7 +13,6 @@
 package ai.djl.examples.inference.face;
 
 import ai.djl.ModelException;
-import ai.djl.engine.Engine;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
 import ai.djl.translate.TranslateException;
@@ -30,11 +29,6 @@ public final class FeatureComparison {
     private FeatureComparison() {}
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        if (!"PyTorch".equals(Engine.getInstance().getEngineName())) {
-            logger.info("This example only works for PyTorch.");
-            return;
-        }
-
         Path imageFile1 = Paths.get("src/test/resources/kana1.jpg");
         Image img1 = ImageFactory.getInstance().fromFile(imageFile1);
         Path imageFile2 = Paths.get("src/test/resources/kana2.jpg");