unit test

api/src/main/java/ai/djl/modality/cv/transform/ToOneHot.java

@@ -20,6 +20,11 @@
 public class ToOneHot implements Transform {
     private final int numClass;
 
+    /**
+     * Creates a {@code toOneHot} {@link Transform} that converts the sparse label to one-hot label.
+     *
+     * @param numClass number of classes
+     */
     public ToOneHot(int numClass) {
         this.numClass = numClass;
     }

api/src/main/java/ai/djl/modality/cv/transform/Transpose.java

@@ -0,0 +1,37 @@
+/*
+ * Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
+ * with the License. A copy of the License is located at
+ *
+ * http://aws.amazon.com/apache2.0/
+ *
+ * or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
+ * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
+ * and limitations under the License.
+ */
+package ai.djl.modality.cv.transform;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.translate.Transform;
+
+/** A {@link Transform} that transposes the image. */
+public class Transpose implements Transform {
+
+    private int[] axis;
+
+    /**
+     * Creates a {@code Resize} {@link Transform} that transposes to the given size.
+     *
+     * @param axis the new order of axis
+     */
+    public Transpose(int... axis) {
+        this.axis = axis;
+    }
+
+    /** {@inheritDoc} */
+    @Override
+    public NDArray transform(NDArray array) {
+        return array.transpose(axis);
+    }
+}

api/src/main/java/ai/djl/modality/cv/util/NDImageUtils.java

@@ -30,7 +30,7 @@ private NDImageUtils() {}
      *
      * @param image the image to resize
      * @param size the desired size
-     * @return the resized NDList
+     * @return the resized NDArray
      */
     public static NDArray resize(NDArray image, int size) {
         return resize(image, size, size, Image.Interpolation.BILINEAR);
@@ -42,7 +42,7 @@ public static NDArray resize(NDArray image, int size) {
      * @param image the image to resize
      * @param width the desired width
      * @param height the desired height
-     * @return the resized NDList
+     * @return the resized NDArray
      */
     public static NDArray resize(NDArray image, int width, int height) {
         return resize(image, width, height, Image.Interpolation.BILINEAR);
@@ -55,7 +55,7 @@ public static NDArray resize(NDArray image, int width, int height) {
      * @param width the desired width
      * @param height the desired height
      * @param interpolation the desired interpolation
-     * @return the resized NDList
+     * @return the resized NDArray
      */
     public static NDArray resize(
             NDArray image, int width, int height, Image.Interpolation interpolation) {
@@ -136,6 +136,7 @@ public static NDArray toTensor(NDArray image) {
      * <p>Converts the labels {@link NDArray} to one-hot labels.
      *
      * @param label the label to convert
+     * @param numClass the number of classes
      * @return the converted label
      */
     public static NDArray toOneHot(NDArray label, int numClass) {
@@ -346,6 +347,6 @@ public static boolean isCHW(Shape shape) {
         } else if (shape.get(2) == 1 || shape.get(2) == 3) {
             return false;
         }
-        throw new IllegalArgumentException("Image is not CHW or HWC");
+        throw new IllegalArgumentException("Image is neither CHW nor HWC");
     }
 }

api/src/main/java/ai/djl/ndarray/internal/NDFormat.java

@@ -61,6 +61,10 @@ public static String format(
             sb.append(" hasGradient");
         }
 
+        if (DEBUG) {
+            sb.append(" ->");
+        }
+
         NDFormat format;
         DataType dataType = array.getDataType();
 

api/src/main/java/ai/djl/nn/Parameter.java

@@ -113,6 +113,11 @@ public void setShape(Shape shape) {
         this.shape = shape;
     }
 
+    /**
+     * Gets the shape of this {@code Parameter}.
+     *
+     * @return the shape of this {@code Parameter}
+     */
     public Shape getShape() {
         return this.shape;
     }
@@ -173,6 +178,12 @@ public void setInitializer(Initializer initializer) {
         this.initializer = initializer;
     }
 
+    /**
+     * Gets the {@link Initializer} for this {@code Parameter}, if not already set. If overwrite
+     * flag is true, sets the initializer regardless.
+     *
+     * @return the initializer of this {@code Parameter}
+     */
     public Initializer getInitializer() {
         return initializer;
     }

api/src/main/java/ai/djl/training/loss/SoftmaxCrossEntropyLoss.java

@@ -55,8 +55,8 @@ public SoftmaxCrossEntropyLoss(String name) {
      * @param name the name of the loss
      * @param weight the weight to apply on the loss value, default 1
      * @param classAxis the axis that represents the class probabilities, default -1
-     * @param sparseLabel whether labels are 1-D integer array of [batch_size] (false) or 2-D
-     *     probabilities of [batch_size, n-class] (true), default true
+     * @param sparseLabel whether labels are rank-1 integer array of [batch_size] (false) or rank-2
+     *     one-hot of [batch_size, n-class] (true), default true
      * @param fromLogit if true, the inputs are assumed to be the numbers before being applied with
      *     softmax. Then logSoftmax will be applied to input, default true
      */
@@ -73,6 +73,7 @@ public SoftmaxCrossEntropyLoss(
     /**
      * Creates a new instance of {@code SoftmaxCrossEntropyLoss} from the output of softmax.
      *
+     * @param name the name of the loss function
      * @param fromSoftmax the input prediction is from the output of softmax, default false
      */
     public SoftmaxCrossEntropyLoss(String name, boolean fromSoftmax) {
@@ -97,8 +98,6 @@ public NDArray evaluate(NDList label, NDList prediction) {
         NDArray loss;
         NDArray lab = label.singletonOrThrow();
         if (sparseLabel) {
-            // TODO: should support only one label and the transformation of label, if needed, is
-            // done outside. Keep this function unique-purposed.
             NDIndex pickIndex =
                     new NDIndex()
                             .addAllDim(Math.floorMod(classAxis, pred.getShape().dimension()))

api/src/main/java/ai/djl/training/tracker/FixedPerVarTracker.java

@@ -42,6 +42,11 @@ public float getNewValue(int numUpdate) {
         return valueMap.getOrDefault(this.parameterId, this.value);
     }
 
+    /**
+     * Set parameterId for this Tracker.
+     *
+     * @param parameterId the parameter Id
+     */
     public void setParameterId(String parameterId) {
         this.parameterId = parameterId;
     }
@@ -59,20 +64,40 @@ public static Builder builder() {
     public static final class Builder {
 
         private float value;
-        private Map<String, Float> valueMap = new ConcurrentHashMap<String, Float>();
+        private Map<String, Float> valueMap = new ConcurrentHashMap<>();
 
+        /** Create a builder for {@link FixedPerVarTracker}. */
         private Builder() {}
 
+        /**
+         * Set the default learning rate.
+         *
+         * @param value the default learning rate
+         * @return builder
+         */
         public Builder setDefaultValue(float value) {
             this.value = value;
             return this;
         }
 
+        /**
+         * Add a kv pair of parameter and its learning rate.
+         *
+         * @param parameterId the parameter id
+         * @param value the default learning rate
+         * @return builder
+         */
         public Builder put(String parameterId, float value) {
             this.valueMap.put(parameterId, value);
             return this;
         }
 
+        /**
+         * Add kv pairs of parameter and its learning rate.
+         *
+         * @param valueMap stores parameterId and learning rate
+         * @return builder
+         */
         public Builder putAll(Map<String, Float> valueMap) {
             this.valueMap.putAll(valueMap);
             return this;

engines/paddlepaddle/paddlepaddle-native/src/main/native/ai_djl_paddlepaddle_jni_PaddleLibrary_inference.cc

@@ -45,7 +45,7 @@ JNIEXPORT void JNICALL Java_ai_djl_paddlepaddle_jni_PaddleLibrary_loadExtraDir(
   int size = vec_arg.size();
   argv.reserve(vec_arg.size());
   for (auto& arg : vec_arg) {
-    argv.push_back(const_cast<char*>(arg.data()));
+    argv.emplace_back(const_cast<char*>(arg.data()));
   }
   char** array = argv.data();
   std::cout << "Pending Paddle fix to proceed with the option" << std::endl;

engines/pytorch/pytorch-engine/src/main/java/ai/djl/pytorch/engine/PtNDArrayEx.java

@@ -571,30 +571,22 @@ public PtNDArray resize(int width, int height, int interpolation) {
             if (result.getDataType() != DataType.FLOAT32) {
                 result = result.toType(DataType.FLOAT32, true);
             }
-
             int dim = result.getShape().dimension();
             if (dim == 3) {
                 result = result.expandDims(0);
             }
-            boolean orderHWC = false;
-            if (result.getShape().get(1) != 3) {
-                // HWC order -> CHW order
-                orderHWC = true;
-                result = result.transpose(0, 3, 1, 2);
-            }
+            // change from HWC to CHW order
+            result = result.transpose(0, 3, 1, 2);
             result =
                     JniUtils.interpolate(
-                            array.getManager().from(result),
-                            new long[] {height, width},
-                            getInterpolationMode(interpolation),
-                            false);
-            if (orderHWC) {
-                result = result.transpose(0, 2, 3, 1);
-            }
+                                    array.getManager().from(result),
+                                    new long[] {height, width},
+                                    getInterpolationMode(interpolation),
+                                    false)
+                            .transpose(0, 2, 3, 1);
             if (dim == 3) {
                 result = result.squeeze(0);
             }
-
             array.attach(subManager.getParentManager());
             result.attach(subManager.getParentManager());
             return (PtNDArray) result;

engines/pytorch/pytorch-native/src/main/native/ai_djl_pytorch_jni_PyTorchLibrary_inference.cc

@@ -262,7 +262,7 @@ JNIEXPORT jlongArray JNICALL Java_ai_djl_pytorch_jni_PyTorchLibrary_moduleGetPar
   auto* module_ptr = reinterpret_cast<torch::jit::script::Module*>(jhandle);
   std::vector<jlong> jptrs;
   for (const auto& tensor : module_ptr->parameters()) {
-    jptrs.push_back(reinterpret_cast<uintptr_t>(new torch::Tensor(tensor)));
+    jptrs.emplace_back(reinterpret_cast<uintptr_t>(new torch::Tensor(tensor)));
   }
   size_t len = jptrs.size();
   jlongArray jarray = env->NewLongArray(len);
@@ -277,7 +277,7 @@ JNIEXPORT jobjectArray JNICALL Java_ai_djl_pytorch_jni_PyTorchLibrary_moduleGetP
   auto* module_ptr = reinterpret_cast<torch::jit::script::Module*>(jhandle);
   std::vector<std::string> jptrs;
   for (const auto& named_tensor : module_ptr->named_parameters()) {
-    jptrs.push_back(named_tensor.name);
+    jptrs.emplace_back(named_tensor.name);
   }
   return djl::utils::jni::GetStringArrayFromVec(env, jptrs);
   API_END_RETURN()

examples/build.gradle

@@ -8,7 +8,6 @@ dependencies {
     implementation project(":basicdataset")
     implementation project(":model-zoo")
     implementation project(":extensions:timeseries")
-    implementation 'org.testng:testng:7.1.0'
 
     runtimeOnly project(":engines:pytorch:pytorch-model-zoo")
     runtimeOnly project(":engines:tensorflow:tensorflow-model-zoo")