Issue #2693 Implement PtNDArrayEx.multiBoxPrior with validation

engines/mxnet/mxnet-engine/src/test/java/ai/djl/mxnet/engine/MxNDArrayExTest.java

@@ -0,0 +1,55 @@
+package ai.djl.mxnet.engine;
+
+import ai.djl.engine.Engine;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.ndarray.internal.NDArrayEx;
+import ai.djl.ndarray.types.DataType;
+import ai.djl.ndarray.types.Shape;
+import org.testng.annotations.Test;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+public class MxNDArrayExTest {
+ public static void main(String[] args) {
+ MxNDArrayExTest mxNDArrayExTest = new MxNDArrayExTest();
+ mxNDArrayExTest.testMultiBoxPrior();
+ }
+
+ @Test
+ public void testMultiBoxPrior() {
+ Engine engine = Engine.getInstance();
+
+ try (NDManager manager = NDManager.newBaseManager()) {
+
+ Shape shape = new Shape(1, 64, 32, 32);
+ DataType dataType = DataType.FLOAT32;
+ NDArray mxNDArray = manager.create(shape, dataType);
+ NDArrayEx mxNDArrayEx = mxNDArray.getNDArrayInternal();
+
+ List<Float> sizes = new ArrayList<Float>(Arrays.asList(0.2f, 0.272f));
+ List<Float> ratios = new ArrayList<Float>(Arrays.asList(1f, 2f, 0.5f));
+ List<Float> steps = new ArrayList<Float>(Arrays.asList(0.2f, 0.272f));
+ List<Float> offsets = new ArrayList<Float>(Arrays.asList(0.2f, 0.272f));
+ boolean clip = false;
+ try (NDList result = mxNDArrayEx.multiBoxPrior(sizes, ratios, steps, offsets, clip)) {
+ NDArray firstitem = result.get(0);
+ Shape shapeResult = firstitem.getShape();
+
+ assert(shapeResult.getLayout().length==2);
+ assert(shapeResult.get(0)==6534);
+ assert(shapeResult.get(1)==4);
+ for (NDArray item: result) {
+ System.out.println(item);
+ //ND: (1, 4096, 4) cpu() float32
+ }
+
+ }
+ System.out.println("Done!");
+ }
+ }
+}
+

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

@@ -26,6 +26,8 @@
 
 import java.util.List;
 
+import static ai.djl.pytorch.jni.JniUtils.sqrt;
+
 /** {@code PtNDArrayEx} is the PyTorch implementation of the {@link NDArrayEx}. */
 public class PtNDArrayEx implements NDArrayEx {
 
@@ -694,7 +696,59 @@ public NDList multiBoxPrior(
  List<Float> steps,
  List<Float> offsets,
  boolean clip) {
- throw new UnsupportedOperationException("Not implemented");
+ //Based on https:/apache/mxnet/blob/a720b15b5fa011a9610dfaeabf0792443c6abec5/src/operator/contrib/multibox_prior.cc
+
+ NDManager ndManager = array.getManager().getParentManager();
+
+ Float step_x = steps.get(1);
+ Float step_y = steps.get(0);
+ int num_sizes = sizes.size();
+ int num_ratios = ratios.size();
+ int count = 0;
+ float[][] out = new float[num_sizes][num_ratios];
+
+ //Based on https:/apache/mxnet/blob/a720b15b5fa011a9610dfaeabf0792443c6abec5/src/operator/contrib/multibox_prior-inl.h#L116
+
+ PtNDArray input = array;
+ //int in_height = in_data[mboxprior_enum::kData].size(2);
+ //int in_width = in_data[mboxprior_enum::kData].size(3);
+ int in_height = input.getInt(2);
+ int in_width = input.getInt(3);
+
+ for (int r = 0; r < in_height; ++r) {
+ float center_y = (r + offsets.get(0)) * step_y;
+ for (int c = 0; c < in_width; ++c) {
+ float center_x = (c + offsets.get(1)) * step_x;
+ // ratio = first ratio, various sizes
+ float ratio = num_ratios > 0 ? (float)Math.sqrt(ratios.get(0)) : 1.f;
+ for (int i = 0; i < num_sizes; ++i) {
+ float size = sizes.get(i);
+ float w = size * in_height / in_width * ratio / 2;
+ float h = size / ratio / 2;
+
+ out[count][0] = center_x - w; // xmin
+ out[count][1] = center_y - h; // ymin
+ out[count][2] = center_x + w; // xmax
+ out[count][3] = center_y + h; // ymax
+ ++count;
+ }
+ // various ratios, size = min_size = size[0]
+ float size = sizes.get(0);
+ for (int j = 1; j < num_ratios; ++j) {
+ float ratioLocal = (float)Math.sqrt(ratios.get(j));
+ float w = size * in_height / in_width * ratioLocal / 2;
+ float h = size / ratioLocal / 2;
+ out[count][0] = center_x - w; // xmin
+ out[count][1] = center_y - h; // ymin
+ out[count][2] = center_x + w; // xmax
+ out[count][3] = center_y + h; // ymax
+ ++count;
+ }
+ }
+ }
+ NDArray ndArray = ndManager.create(out);
+ NDList outResult = new NDList(ndArray);
+ return outResult;
  }
 
  /** {@inheritDoc} */

engines/pytorch/pytorch-engine/src/test/java/ai/djl/pytorch/engine/PtNDArrayExTest.java

@@ -0,0 +1,49 @@
+package ai.djl.pytorch.engine;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.ndarray.types.DataType;
+import ai.djl.ndarray.types.Shape;
+import org.testng.annotations.Test;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+
+public class PtNDArrayExTest {
+ public static void main(String[] args) {
+ PtNDArrayExTest ptNDArrayExTest = new PtNDArrayExTest();
+ ptNDArrayExTest.testMultiBoxPrior();
+ }
+
+ @Test
+ public void testMultiBoxPrior() {
+ try (PtNDManager manager = (PtNDManager) NDManager.newBaseManager()) {
+ Shape shape = new Shape(1, 64, 32, 32);
+ DataType dataType = DataType.FLOAT32;
+ PtNDArray ptNDArray = manager.create(shape, dataType);
+ PtNDArrayEx ptNDArrayEx = new PtNDArrayEx(ptNDArray);
+
+ List<Float> sizes = new ArrayList<Float>(Arrays.asList(0.2f, 0.272f));
+ List<Float> ratios = new ArrayList<Float>(Arrays.asList(1f, 2f, 0.5f));
+ List<Float> steps = new ArrayList<Float>(Arrays.asList(0.2f, 0.272f));
+ List<Float> offsets = new ArrayList<Float>(Arrays.asList(0.2f, 0.272f));
+ boolean clip = false;
+ try (NDList result = ptNDArrayEx.multiBoxPrior(sizes, ratios, steps, offsets, clip)) {
+ NDArray firstitem = result.get(0);
+ Shape shapeResult = firstitem.getShape();
+ assert(shapeResult.getLayout().length==2);
+ assert(shapeResult.get(0)==6534);
+ assert(shapeResult.get(1)==4);
+ for (NDArray item: result) {
+ System.out.println(item);
+ //ND: (6534, 4) cpu() float32
+ }
+ }
+
+ System.out.println("Done!");
+ }
+ }
+}