Merge pull request #2 from KonduitAI/asto_ops_wrapper

[WIP] New ops wrapper

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTests.java

@@ -50,6 +50,7 @@ import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import java.io.File;
+import java.io.IOException;
 import java.util.*;
 
 import static org.junit.Assert.*;
@@ -816,6 +817,37 @@ public class Word2VecTests extends BaseDL4JTest {
         assertEquals(vec1.getWordVectorMatrix("money"), vec2.getWordVectorMatrix("money"));
     }
 
+    @Test
+    public void testWordsNearestSum() throws IOException {
+        log.info("Load & Vectorize Sentences....");
+        SentenceIterator iter = new BasicLineIterator(inputFile);
+        TokenizerFactory t = new DefaultTokenizerFactory();
+        t.setTokenPreProcessor(new CommonPreprocessor());
+
+        log.info("Building model....");
+        Word2Vec vec = new Word2Vec.Builder()
+                .minWordFrequency(5)
+                .iterations(1)
+                .layerSize(100)
+                .seed(42)
+                .windowSize(5)
+                .iterate(iter)
+                .tokenizerFactory(t)
+                .build();
+
+        log.info("Fitting Word2Vec model....");
+        vec.fit();
+        log.info("Writing word vectors to text file....");
+        log.info("Closest Words:");
+        Collection<String> lst = vec.wordsNearestSum("day", 10);
+        log.info("10 Words closest to 'day': {}", lst);
+        assertTrue(lst.contains("week"));
+        assertTrue(lst.contains("night"));
+        assertTrue(lst.contains("year"));
+        assertTrue(lst.contains("years"));
+        assertTrue(lst.contains("time"));
+    }
+
     private static void printWords(String target, Collection<String> list, Word2Vec vec) {
         System.out.println("Words close to [" + target + "]:");
         for (String word : list) {

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/embeddings/reader/impl/BasicModelUtils.java

@@ -351,7 +351,8 @@ public class BasicModelUtils<T extends SequenceElement> implements ModelUtils<T>
         if (lookupTable instanceof InMemoryLookupTable) {
             InMemoryLookupTable l = (InMemoryLookupTable) lookupTable;
             INDArray syn0 = l.getSyn0();
-            INDArray weights = syn0.norm2(0).rdivi(1).muli(words);
+            INDArray temp = syn0.norm2(0).rdivi(1).reshape(words.shape());
+            INDArray weights = temp.muli(words);
             INDArray distances = syn0.mulRowVector(weights).sum(1);
             INDArray[] sorted = Nd4j.sortWithIndices(distances, 0, false);
             INDArray sort = sorted[0];

libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp

@@ -47,7 +47,7 @@ CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, 1, 0) {
     NDArray factorT(output->dataType(), block.launchContext()); // = NDArrayFactory::create(factor, block.launchContext());
     factorT.p(0, factor);
     // this is contrast calculation
-    *output = (*input - mean) * factorT + mean;
+    output->assign((*input - mean) * factorT + mean);
 
     return Status::OK();
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java

@@ -33,6 +33,7 @@ import org.nd4j.linalg.api.blas.params.MMulTranspose;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.NoOp;
+import org.nd4j.linalg.api.ops.custom.*;
 import org.nd4j.linalg.api.ops.impl.broadcast.BiasAdd;
 import org.nd4j.linalg.api.ops.impl.broadcast.BiasAddGrad;
 import org.nd4j.linalg.api.ops.impl.controlflow.compat.Enter;
@@ -2649,6 +2650,33 @@ public class DifferentialFunctionFactory {
         return new NextIteration(sameDiff, x).outputVariable();
     }
 
+    public SDVariable adjustContrast(SDVariable in, SDVariable factor) {
+        return new AdjustContrast(sameDiff, in, factor).outputVariable();
+    }
+
+    public SDVariable adjustContrastV2(SDVariable in, SDVariable factor) {
+        return new AdjustContrastV2(sameDiff, in, factor).outputVariable();
+    }
+
+    public SDVariable bitCast(SDVariable in, SDVariable dataType) {
+        return new BitCast(sameDiff, in, dataType).outputVariable();
+    }
+
+    public SDVariable compareAndBitpack(SDVariable threshold) {
+        return new CompareAndBitpack(sameDiff, threshold).outputVariable();
+    }
+
+    public SDVariable divideNoNan(SDVariable in1, SDVariable in2) {
+        return new DivideNoNan(sameDiff, in1, in2).outputVariable();
+    }
+
+    public SDVariable drawBoundingBoxes(SDVariable boxes, SDVariable colors) {
+        return new DrawBoundingBoxes(sameDiff, boxes, colors).outputVariable();
+    }
+
+    public SDVariable fakeQuantWithMinMaxVarsPerChannel(SDVariable x, SDVariable min, SDVariable max) {
+        return new FakeQuantWithMinMaxVarsPerChannel(sameDiff,x,min,max).outputVariable();
+    }
 
     public String toString() {
         return "DifferentialFunctionFactory{methodNames=" + methodNames + "}";

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java

@@ -581,8 +581,14 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.random.impl.ProbablisticMerge.class,
             org.nd4j.linalg.api.ops.random.impl.Range.class,
             org.nd4j.linalg.api.ops.random.impl.TruncatedNormalDistribution.class,
-            org.nd4j.linalg.api.ops.random.impl.UniformDistribution.class
+            org.nd4j.linalg.api.ops.random.impl.UniformDistribution.class,
-
+            org.nd4j.linalg.api.ops.custom.AdjustContrast.class,
+            org.nd4j.linalg.api.ops.custom.AdjustContrastV2.class,
+            org.nd4j.linalg.api.ops.custom.BitCast.class,
+            org.nd4j.linalg.api.ops.custom.CompareAndBitpack.class,
+            org.nd4j.linalg.api.ops.custom.DivideNoNan.class,
+            org.nd4j.linalg.api.ops.custom.DrawBoundingBoxes.class,
+            org.nd4j.linalg.api.ops.custom.FakeQuantWithMinMaxVarsPerChannel.class
     );
 
     static {

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java

@@ -0,0 +1,30 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.base.Preconditions;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+public class AdjustContrast extends BaseAdjustContrast {
+
+    public AdjustContrast() {super();}
+
+    public AdjustContrast(INDArray in, double factor, INDArray out) {
+        super(in, factor, out);
+    }
+
+    public AdjustContrast(SameDiff sameDiff, SDVariable in, SDVariable factor) {
+        super(sameDiff,new SDVariable[]{in,factor});
+    }
+
+    @Override
+    public String opName() {
+        return "adjust_contrast";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "AdjustContrast";
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrastV2.java

@@ -0,0 +1,30 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.base.Preconditions;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+public class AdjustContrastV2 extends BaseAdjustContrast {
+
+    public AdjustContrastV2() {super();}
+
+    public AdjustContrastV2(INDArray in, double factor, INDArray out) {
+        super(in, factor, out);
+    }
+
+    public AdjustContrastV2(SameDiff sameDiff, SDVariable in, SDVariable factor) {
+        super( sameDiff,new SDVariable[]{in,factor});
+    }
+
+    @Override
+    public String opName() {
+        return "adjust_contrast_v2";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "AdjustContrast";
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/BaseAdjustContrast.java

@@ -0,0 +1,25 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.base.Preconditions;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+public abstract class BaseAdjustContrast extends DynamicCustomOp {
+    public BaseAdjustContrast() {
+    }
+
+    public BaseAdjustContrast(INDArray in, double factor, INDArray out) {
+        Preconditions.checkArgument(in.rank() >= 3,
+                String.format("AdjustContrast: op expects rank of input array to be >= 3, but got %d instead", in.rank()));
+        inputArguments.add(in);
+        outputArguments.add(out);
+
+        addTArgument(factor);
+    }
+
+    public BaseAdjustContrast(SameDiff sameDiff, SDVariable[] vars) {
+        super("", sameDiff, vars);
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/BitCast.java

@@ -0,0 +1,32 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+import org.nd4j.linalg.factory.Nd4j;
+
+public class BitCast extends DynamicCustomOp {
+    public BitCast() {}
+
+    public BitCast(INDArray in, int dataType, INDArray out) {
+        inputArguments.add(in);
+        outputArguments.add(out);
+        iArguments.add(Long.valueOf(dataType));
+    }
+
+    public BitCast(SameDiff sameDiff, SDVariable in, SDVariable dataType) {
+        super("", sameDiff, new SDVariable[]{in, dataType});
+    }
+
+    @Override
+    public String opName() {
+        return "bitcast";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "Bitcast";
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/CompareAndBitpack.java

@@ -0,0 +1,31 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+import org.nd4j.linalg.factory.Nd4j;
+
+public class CompareAndBitpack extends DynamicCustomOp {
+    public CompareAndBitpack() {}
+
+    public CompareAndBitpack(INDArray in, double threshold, INDArray out) {
+        inputArguments.add(in);
+        inputArguments.add(Nd4j.scalar(threshold));
+        outputArguments.add(out);
+    }
+
+    public CompareAndBitpack(SameDiff sameDiff, SDVariable threshold) {
+        super("", sameDiff, new SDVariable[]{threshold});
+    }
+
+    @Override
+    public String opName() {
+        return "compare_and_bitpack";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "CompareAndBitpack";
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/DivideNoNan.java

@@ -0,0 +1,32 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.apache.commons.math3.analysis.function.Divide;
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+public class DivideNoNan extends DynamicCustomOp {
+    public DivideNoNan() {
+    }
+
+    public DivideNoNan(INDArray in1, INDArray in2, INDArray out) {
+        inputArguments.add(in1);
+        inputArguments.add(in2);
+        outputArguments.add(out);
+    }
+
+    public DivideNoNan(SameDiff sameDiff, SDVariable in1, SDVariable in2) {
+        super("", sameDiff, new SDVariable[]{in1, in2});
+    }
+
+    @Override
+    public String opName() {
+        return "divide_no_nan";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "DivNoNan";
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/DrawBoundingBoxes.java

@@ -0,0 +1,32 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+public class DrawBoundingBoxes extends DynamicCustomOp {
+    public DrawBoundingBoxes() {}
+
+    public DrawBoundingBoxes(INDArray images, INDArray boxes, INDArray colors,
+                             INDArray output) {
+        inputArguments.add(images);
+        inputArguments.add(boxes);
+        inputArguments.add(colors);
+        outputArguments.add(output);
+    }
+
+    public DrawBoundingBoxes(SameDiff sameDiff, SDVariable boxes, SDVariable colors) {
+        super("", sameDiff, new SDVariable[]{boxes, colors});
+    }
+
+    @Override
+    public String opName() {
+        return "draw_bounding_boxes";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "DrawBoundingBoxes";
+    }
+}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FakeQuantWithMinMaxVarsPerChannel.java

@@ -0,0 +1,36 @@
+package org.nd4j.linalg.api.ops.custom;
+
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.base.Preconditions;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+public class FakeQuantWithMinMaxVarsPerChannel extends DynamicCustomOp {
+    public FakeQuantWithMinMaxVarsPerChannel() {}
+
+    public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max,
+                                             INDArray output) {
+        Preconditions.checkArgument(min.isVector() && max.isVector() &&
+                        min.length() == max.length(),
+                "FakeQuantWithMinMaxVarsPerChannel: min and max should be 1D tensors with the same length");
+        inputArguments.add(x);
+        inputArguments.add(min);
+        inputArguments.add(max);
+        outputArguments.add(output);
+    }
+
+    public FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff, SDVariable x, SDVariable min, SDVariable max) {
+        super("", sameDiff, new SDVariable[]{x, min, max});
+    }
+
+    @Override
+    public String opName() {
+        return "fake_quant_with_min_max_vars_per_channel";
+    }
+
+    @Override
+    public String tensorflowName() {
+        return "FakeQuantWithMinMaxVarsPerChannel";
+    }
+}

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java

@@ -26,8 +26,7 @@ import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.CustomOp;
 import org.nd4j.linalg.api.ops.DynamicCustomOp;
-import org.nd4j.linalg.api.ops.custom.Flatten;
+import org.nd4j.linalg.api.ops.custom.*;
-import org.nd4j.linalg.api.ops.custom.ScatterUpdate;
 import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
 import org.nd4j.linalg.api.ops.executioner.OpStatus;
 import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
@@ -807,4 +806,118 @@ public class CustomOpsTests extends BaseNd4jTest {
 
         Nd4j.getExecutioner().commit();
     }
+
+    @Test
+    public void testAdjustContrast() {
+        INDArray in = Nd4j.linspace(DataType.DOUBLE, 1.0, 1.0, 4*4*3).reshape(4,4,3);
+        INDArray out = Nd4j.zeros(4,4,3);
+
+        INDArray expected = Nd4j.createFromArray(new double[]{-21.5, -20.5, -19.5,  -15.5, -14.5, -13.5,  -9.5,  -8.5,  -7.5,  -3.5,  -2.5,  -1.5,
+                2.5,   3.5,   4.5,    8.5,   9.5,  10.5,  14.5,  15.5,  16.5,  20.5,  21.5,  22.5,
+                26.5,  27.5,  28.5,   32.5,  33.5,  34.5,  38.5,  39.5,  40.5,  44.5,  45.5,  46.5,
+                50.5,  51.5,  52.5,   56.5,  57.5,  58.5,  62.5,  63.5,  64.5,  68.5,  69.5,  70.5
+        }).reshape(4,4,3);
+        Nd4j.exec(new AdjustContrast(in, 2.0, out));
+
+        assertArrayEquals(out.shape(), in.shape());
+        assertEquals(expected, out);
+    }
+
+    @Test
+    public void testAdjustContrastV2() {
+        INDArray in = Nd4j.linspace(DataType.DOUBLE,1.0,1.0, 4*4*3).reshape(4,4,3);
+        INDArray out = Nd4j.createUninitialized(4,4,3);
+
+        INDArray expected = Nd4j.createFromArray(new double[]{-21.5, -20.5, -19.5,  -15.5, -14.5, -13.5,  -9.5,  -8.5,  -7.5,  -3.5,  -2.5,  -1.5,
+                2.5,   3.5,   4.5,    8.5,   9.5,  10.5,  14.5,  15.5,  16.5,  20.5,  21.5,  22.5,
+                26.5,  27.5,  28.5,   32.5,  33.5,  34.5,  38.5,  39.5,  40.5,  44.5,  45.5,  46.5,
+                50.5,  51.5,  52.5,   56.5,  57.5,  58.5,  62.5,  63.5,  64.5,  68.5,  69.5,  70.5
+        }).reshape(4,4,3);
+
+        Nd4j.exec(new AdjustContrastV2(in, 2.0, out));
+
+        assertArrayEquals(out.shape(), in.shape());
+        assertEquals(expected, out);
+    }
+
+    @Test
+    public void testBitCast() {
+        INDArray in = Nd4j.linspace(DataType.FLOAT, 1.0f, 1.0f, 8).reshape(2,2,2);
+        INDArray out = Nd4j.createUninitialized(2,2);
+
+        Nd4j.exec(new BitCast(in, DataType.DOUBLE.toInt(), out));
+
+        INDArray expected = Nd4j.createFromArray(new double[]{2., 512., 8192., 131072.032 }).reshape(2,2);
+        assertArrayEquals(new long[]{2,2}, out.shape());
+        assertEquals(expected, out);
+    }
+
+    @Test
+    public void testCompareAndBitpack() {
+        INDArray in = Nd4j.createFromArray(new double[]{-12.f, -11.f, -10.f, -9.f, -8.f, -7.f, -6.f, -5.f, -4.f, -3.f,
+                -2.f, -1.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}).reshape( 2,3,4);
+        INDArray out = Nd4j.createUninitialized(DataType.UBYTE, 2,3,4);
+        INDArray expected = Nd4j.createFromArray(new byte[]{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}).
+                reshape(2,3,4);
+
+        Nd4j.exec(new CompareAndBitpack(in ,2.0, out));
+        assertArrayEquals(new long[]{2,3,4}, out.shape());
+    }
+
+    @Test
+    public void testDivideNoNan() {
+        INDArray in1 = Nd4j.rand(DataType.DOUBLE, 2,3,4);
+        INDArray in2 = Nd4j.rand(DataType.DOUBLE, 2,3,4);
+        INDArray out = Nd4j.createUninitialized(DataType.DOUBLE, 2,3,4);
+
+        Nd4j.exec(new DivideNoNan(in1, in2, out));
+        assertArrayEquals(new long[]{2,3,4}, out.shape());
+    }
+
+    @Test
+    public void testDrawBoundingBoxes() {
+        INDArray images = Nd4j.linspace(DataType.FLOAT, 1.0f, 1.0f, 2*4*5*3).reshape(2,4,5,3);
+        INDArray boxes = Nd4j.createFromArray(new float[]{ 0.0f , 0.0f , 1.0f , 1.0f,
+                0.1f, 0.2f, 0.9f, 0.8f,
+                0.3f, 0.3f, 0.7f, 0.7f,
+                0.4f, 0.4f, 0.6f, 0.6f}).reshape(2,2,4);
+        INDArray colors = Nd4j.createFromArray(new float[]{
+                201.0f, 202.0f, 203.0f, 127.0f, 128.0f, 129.0f}).
+                reshape(2,3);
+        INDArray output = Nd4j.create(DataType.FLOAT, images.shape());
+        INDArray expected = Nd4j.createFromArray(new float[]{127.f, 128.f, 129.f,    127.f, 128.f, 129.f,    127.f, 128.f, 129.f,
+                127.f, 128.f, 129.f,    201.f, 202.f, 203.f,
+                127.f, 128.f,  129.f,    19.f,  20.f,  21.f,     22.f,  23.f,  24.f,    127.f, 128.f, 129.f,    201.f, 202.f, 203.f,
+                127.f, 128.f,  129.f,   127.f, 128.f, 129.f,    127.f, 128.f, 129.f,    127.f, 128.f, 129.f,    201.f, 202.f, 203.f,
+                201.f, 202.f,  203.f,    201.f ,202.f ,203.f,   201.f, 202.f, 203.f,    201.f, 202.f, 203.f,    201.f, 202.f, 203.f,
+
+                61.f,  62.f,   63.f,    201.f, 202.f, 203.f,    201.f, 202.f, 203.f,     70.f,  71.f,  72.f,     73.f,  74.f,  75.f,
+                76.f,  77.f,   78.f,    127.f, 128.f, 129.f,    127.f, 128.f, 129.f,     85.f,  86.f,  87.f,     88.f,  89.f,  90.f,
+                91.f,  92.f,   93.f,    201.f, 202.f, 203.f,    201.f, 202.f, 203.f,    100.f, 101.f, 102.f,    103.f, 104.f, 105.f,
+                106.f, 107.f,  108.f,    109.f, 110.f, 111.f,    112.f, 113.f, 114.f,    115.f, 116.f, 117.f,    118.f, 119.f, 120.f}).
+                reshape(2,4,5,3);
+
+        Nd4j.exec(new DrawBoundingBoxes(images, boxes, colors, output));
+
+        assertArrayEquals(images.shape(), output.shape());
+        assertEquals(expected, output);
+    }
+
+    @Test
+    public void FakeQuantWithMinMaxVarsPerChannel() {
+
+        INDArray x = Nd4j.createFromArray(new float[]{-63.80f, -63.75f, -63.4f, -63.5f, 0.0f, 0.1f}).
+                reshape(1,2,3,1);
+
+        INDArray min = Nd4j.createFromArray(new float[]{-63.65f});
+        INDArray max = Nd4j.createFromArray(new float[]{0.1f});
+
+        INDArray output = Nd4j.createUninitialized(DataType.FLOAT, 1,2,3,1);
+        INDArray expected = Nd4j.createFromArray(new float[]{-63.75f, -63.75f, -63.5f, -63.5f, 0.f, 0.f}).
+                reshape(1,2,3,1);
+
+        Nd4j.exec(new FakeQuantWithMinMaxVarsPerChannel(x,min,max,output));
+
+        assertEquals(expected, output);
+    }
 }