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DL4J/DataVec: Fix Yolo2OutputLayer and ObjectDetectionRecordReader support for NHWC data format (#483)

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* Fix Yolo2OutputLayer for NHWC data format

Signed-off-by: Alex Black <blacka101@gmail.com>

* ObjectDetectionRecordReader NHWC support

Signed-off-by: Alex Black <blacka101@gmail.com>
This commit is contained in:
Alex Black 2020-06-05 11:49:02 +10:00 committed by GitHub
parent 45ebd4899c
commit ee3e059b12
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6 changed files with 260 additions and 158 deletions
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39
datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/recordreader/objdetect/ObjectDetectionRecordReader.java
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@ -49,7 +49,7 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.point;
/**
* An image record reader for object detection.
* <p>
* Format of returned values: 4d array, with dimensions [minibatch, 4+C, h, w]
* Format of returned values: 4d array, with dimensions [minibatch, 4+C, h, w] (nchw) or [minibatch, h, w, 4+C] (nhwc)
* Where the image is quantized into h x w grid locations.
* <p>
* Note that this matches the format required for Deeplearning4j's Yolo2OutputLayer
@ -61,26 +61,48 @@ public class ObjectDetectionRecordReader extends BaseImageRecordReader {
private final int gridW;
private final int gridH;
private final ImageObjectLabelProvider labelProvider;
private final boolean nchw;
protected Image currentImage;
/**
* As per {@link #ObjectDetectionRecordReader(int, int, int, int, int, boolean, ImageObjectLabelProvider)} but hardcoded
* to NCHW format
*/
public ObjectDetectionRecordReader(int height, int width, int channels, int gridH, int gridW, ImageObjectLabelProvider labelProvider) {
this(height, width, channels, gridH, gridW, true, labelProvider);
}
/**
* Create ObjectDetectionRecordReader with
*
* @param height Height of the output images
* @param width Width of the output images
* @param channels Number of channels for the output images
* @param gridH Grid/quantization size (along height dimension) - Y axis
* @param gridW Grid/quantization size (along height dimension) - X axis
* @param nchw If true: return NCHW format labels with array shape [minibatch, 4+C, h, w]; if false, return
* NHWC format labels with array shape [minibatch, h, w, 4+C]
* @param labelProvider ImageObjectLabelProvider - used to look up which objects are in each image
*/
public ObjectDetectionRecordReader(int height, int width, int channels, int gridH, int gridW, ImageObjectLabelProvider labelProvider) {
public ObjectDetectionRecordReader(int height, int width, int channels, int gridH, int gridW, boolean nchw, ImageObjectLabelProvider labelProvider) {
super(height, width, channels, null, null);
this.gridW = gridW;
this.gridH = gridH;
this.nchw = nchw;
this.labelProvider = labelProvider;
this.appendLabel = labelProvider != null;
}
/**
* As per {@link #ObjectDetectionRecordReader(int, int, int, int, int, boolean, ImageObjectLabelProvider, ImageTransform)}
* but hardcoded to NCHW format
*/
public ObjectDetectionRecordReader(int height, int width, int channels, int gridH, int gridW,
ImageObjectLabelProvider labelProvider, ImageTransform imageTransform) {
this(height, width, channels, gridH, gridW, true, labelProvider, imageTransform);
}
/**
* When imageTransform != null, object is removed if new center is outside of transformed image bounds.
*
@ -90,13 +112,16 @@ public class ObjectDetectionRecordReader extends BaseImageRecordReader {
* @param gridH Grid/quantization size (along height dimension) - Y axis
* @param gridW Grid/quantization size (along height dimension) - X axis
* @param labelProvider ImageObjectLabelProvider - used to look up which objects are in each image
* @param nchw If true: return NCHW format labels with array shape [minibatch, 4+C, h, w]; if false, return
* NHWC format labels with array shape [minibatch, h, w, 4+C]
* @param imageTransform ImageTransform - used to transform image and coordinates
*/
public ObjectDetectionRecordReader(int height, int width, int channels, int gridH, int gridW,
public ObjectDetectionRecordReader(int height, int width, int channels, int gridH, int gridW, boolean nchw,
ImageObjectLabelProvider labelProvider, ImageTransform imageTransform) {
super(height, width, channels, null, null);
this.gridW = gridW;
this.gridH = gridH;
this.nchw = nchw;
this.labelProvider = labelProvider;
this.appendLabel = labelProvider != null;
this.imageTransform = imageTransform;
@ -182,6 +207,10 @@ public class ObjectDetectionRecordReader extends BaseImageRecordReader {
exampleNum++;
}
if(!nchw) {
outImg = outImg.permute(0, 2, 3, 1); //NCHW to NHWC
outLabel = outLabel.permute(0, 2, 3, 1);
}
return new NDArrayRecordBatch(Arrays.asList(outImg, outLabel));
}
@ -256,6 +285,8 @@ public class ObjectDetectionRecordReader extends BaseImageRecordReader {
imageLoader = new NativeImageLoader(height, width, channels, imageTransform);
}
Image image = this.imageLoader.asImageMatrix(dataInputStream);
if(!nchw)
image.setImage(image.getImage().permute(0,2,3,1));
Nd4j.getAffinityManager().ensureLocation(image.getImage(), AffinityManager.Location.DEVICE);
List<Writable> ret = RecordConverter.toRecord(image.getImage());
@ -264,6 +295,8 @@ public class ObjectDetectionRecordReader extends BaseImageRecordReader {
int nClasses = labels.size();
INDArray outLabel = Nd4j.create(1, 4 + nClasses, gridH, gridW);
label(image, imageObjectsForPath, outLabel, 0);
if(!nchw)
outLabel = outLabel.permute(0,2,3,1); //NCHW to NHWC
ret.add(new NDArrayWritable(outLabel));
}
return ret;

39
datavec/datavec-data/datavec-data-image/src/test/java/org/datavec/image/recordreader/TestObjectDetectionRecordReader.java
View File

@ -56,6 +56,7 @@ public class TestObjectDetectionRecordReader {
@Test
public void test() throws Exception {
for(boolean nchw : new boolean[]{true, false}) {
ImageObjectLabelProvider lp = new TestImageObjectDetectionLabelProvider();
File f = testDir.newFolder();
@ -73,10 +74,10 @@ public class TestObjectDetectionRecordReader {
URI[] u = new FileSplit(new File(path)).locations();
Arrays.sort(u);
RecordReader rr = new ObjectDetectionRecordReader(h, w, c, gH, gW, lp);
RecordReader rr = new ObjectDetectionRecordReader(h, w, c, gH, gW, nchw, lp);
rr.initialize(new CollectionInputSplit(u));
RecordReader imgRR = new ImageRecordReader(h, w, c);
RecordReader imgRR = new ImageRecordReader(h, w, c, nchw);
imgRR.initialize(new CollectionInputSplit(u));
List<String> labels = rr.getLabels();
@ -135,24 +136,32 @@ public class TestObjectDetectionRecordReader {
}
INDArray lArr = ((NDArrayWritable) next.get(1)).get();
if(nchw) {
assertArrayEquals(new long[]{1, 4 + 2, gH, gW}, lArr.shape());
} else {
assertArrayEquals(new long[]{1, gH, gW, 4 + 2}, lArr.shape());
}
if(!nchw)
expLabels = expLabels.permute(0,2,3,1); //NCHW to NHWC
assertEquals(expLabels, lArr);
}
rr.reset();
Record record = rr.nextRecord();
RecordMetaDataImageURI metadata = (RecordMetaDataImageURI)record.getMetaData();
RecordMetaDataImageURI metadata = (RecordMetaDataImageURI) record.getMetaData();
assertEquals(new File(path, "000012.jpg"), new File(metadata.getURI()));
assertEquals(3, metadata.getOrigC());
assertEquals((int)origH[0], metadata.getOrigH());
assertEquals((int)origW[0], metadata.getOrigW());
assertEquals((int) origH[0], metadata.getOrigH());
assertEquals((int) origW[0], metadata.getOrigW());
List<Record> out = new ArrayList<>();
List<RecordMetaData> meta = new ArrayList<>();
out.add(record);
meta.add(metadata);
record = rr.nextRecord();
metadata = (RecordMetaDataImageURI)record.getMetaData();
metadata = (RecordMetaDataImageURI) record.getMetaData();
out.add(record);
meta.add(metadata);
@ -164,27 +173,27 @@ public class TestObjectDetectionRecordReader {
int[] nonzeroCount = {5, 10};
ImageTransform transform = new ResizeImageTransform(37, 42);
RecordReader rrTransform = new ObjectDetectionRecordReader(42, 37, c, gH, gW, lp, transform);
RecordReader rrTransform = new ObjectDetectionRecordReader(42, 37, c, gH, gW, nchw, lp, transform);
rrTransform.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform.hasNext()) {
List<Writable> next = rrTransform.next();
assertEquals(37, transform.getCurrentImage().getWidth());
assertEquals(42, transform.getCurrentImage().getHeight());
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
INDArray labelArray = ((NDArrayWritable) next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
ImageTransform transform2 = new ResizeImageTransform(1024, 2048);
RecordReader rrTransform2 = new ObjectDetectionRecordReader(2048, 1024, c, gH, gW, lp, transform2);
RecordReader rrTransform2 = new ObjectDetectionRecordReader(2048, 1024, c, gH, gW, nchw, lp, transform2);
rrTransform2.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform2.hasNext()) {
List<Writable> next = rrTransform2.next();
assertEquals(1024, transform2.getCurrentImage().getWidth());
assertEquals(2048, transform2.getCurrentImage().getHeight());
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
INDArray labelArray = ((NDArrayWritable) next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
@ -194,19 +203,19 @@ public class TestObjectDetectionRecordReader {
new ResizeImageTransform(2048, 4096),
new FlipImageTransform(-1)
);
RecordReader rrTransform3 = new ObjectDetectionRecordReader(2048, 1024, c, gH, gW, lp, transform3);
RecordReader rrTransform3 = new ObjectDetectionRecordReader(2048, 1024, c, gH, gW, nchw, lp, transform3);
rrTransform3.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform3.hasNext()) {
List<Writable> next = rrTransform3.next();
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
INDArray labelArray = ((NDArrayWritable) next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
//Test that doing a downscale with the native image loader directly instead of a transform does not cause an exception:
ImageTransform transform4 = new FlipImageTransform(-1);
RecordReader rrTransform4 = new ObjectDetectionRecordReader(128, 128, c, gH, gW, lp, transform4);
RecordReader rrTransform4 = new ObjectDetectionRecordReader(128, 128, c, gH, gW, nchw, lp, transform4);
rrTransform4.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform4.hasNext()) {
@ -215,10 +224,12 @@ public class TestObjectDetectionRecordReader {
assertEquals((int) origW[i], transform4.getCurrentImage().getWidth());
assertEquals((int) origH[i], transform4.getCurrentImage().getHeight());
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
INDArray labelArray = ((NDArrayWritable) next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
}
}
//2 images: 000012.jpg and 000019.jpg

39
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/YoloGradientCheckTests.java
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@ -24,9 +24,7 @@ import org.datavec.image.recordreader.objdetect.impl.VocLabelProvider;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.distribution.GaussianDistribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
@ -36,6 +34,8 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -50,17 +50,28 @@ import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertTrue;
/**
* @author Alex Black
*/
@RunWith(Parameterized.class)
public class YoloGradientCheckTests extends BaseDL4JTest {
static {
Nd4j.setDataType(DataType.DOUBLE);
}
private CNN2DFormat format;
public YoloGradientCheckTests(CNN2DFormat format){
this.format = format;
}
@Parameterized.Parameters(name = "{0}")
public static Object[] params(){
return CNN2DFormat.values();
}
@Rule
public TemporaryFolder testDir = new TemporaryFolder();
@ -97,8 +108,14 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
Nd4j.getRandom().setSeed(12345);
INDArray input = Nd4j.rand(new int[]{mb, depthIn, h, w});
INDArray labels = yoloLabels(mb, c, h, w);
INDArray input, labels;
if(format == CNN2DFormat.NCHW){
input = Nd4j.rand(DataType.DOUBLE, mb, depthIn, h, w);
labels = yoloLabels(mb, c, h, w);
} else {
input = Nd4j.rand(DataType.DOUBLE, mb, h, w, depthIn);
labels = yoloLabels(mb, c, h, w).permute(0,2,3,1);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
.dataType(DataType.DOUBLE)
@ -112,6 +129,7 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
.layer(new Yolo2OutputLayer.Builder()
.boundingBoxPriors(bbPrior)
.build())
.setInputType(InputType.convolutional(h, w, depthIn, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -120,7 +138,18 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
String msg = "testYoloOutputLayer() - minibatch = " + mb + ", w=" + w + ", h=" + h + ", l1=" + l1[i] + ", l2=" + l2[i];
System.out.println(msg);
INDArray out = net.output(input);
if(format == CNN2DFormat.NCHW){
assertArrayEquals(new long[]{mb, yoloDepth, h, w}, out.shape());
} else {
assertArrayEquals(new long[]{mb, h, w, yoloDepth}, out.shape());
}
net.fit(input, labels);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.minAbsoluteError(1e-6)
.labels(labels).subset(true).maxPerParam(100));
assertTrue(msg, gradOK);

6
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/objdetect/Yolo2OutputLayer.java
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@ -21,6 +21,7 @@ import lombok.Getter;
import lombok.Setter;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -80,6 +81,8 @@ public class Yolo2OutputLayer extends org.deeplearning4j.nn.conf.layers.Layer {
@JsonDeserialize(using = BoundingBoxesDeserializer.class)
private INDArray boundingBoxes;
private CNN2DFormat format = CNN2DFormat.NCHW; //Default for serialization of old formats
private Yolo2OutputLayer() {
//No-arg constructor for Jackson JSON
}
@ -119,7 +122,8 @@ public class Yolo2OutputLayer extends org.deeplearning4j.nn.conf.layers.Layer {
@Override
public void setNIn(InputType inputType, boolean override) {
//No op
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
this.format = c.getFormat();
}
@Override

15
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/objdetect/Yolo2OutputLayer.java
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@ -19,6 +19,7 @@ package org.deeplearning4j.nn.layers.objdetect;
import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
@ -110,6 +111,12 @@ public class Yolo2OutputLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
Preconditions.checkState(labels.rank() == 4, "Expected rank 4 labels array with shape [minibatch, 4+numClasses, h, w]" +
" but got rank %s labels array with shape %s", labels.rank(), labels.shape());
boolean nchw = layerConf().getFormat() == CNN2DFormat.NCHW;
INDArray input = nchw ? this.input : this.input.permute(0,3,1,2); //NHWC to NCHW
INDArray labels = this.labels.castTo(input.dataType()); //Ensure correct dtype (same as params); no-op if already correct dtype
if(!nchw)
labels = labels.permute(0,3,1,2); //NHWC to NCHW
double lambdaCoord = layerConf().getLambdaCoord();
double lambdaNoObj = layerConf().getLambdaNoObj();
@ -119,7 +126,7 @@ public class Yolo2OutputLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
int b = (int) layerConf().getBoundingBoxes().size(0);
int c = (int) labels.size(1)-4;
INDArray labels = this.labels.castTo(input.dataType()); //Ensure correct dtype (same as params); no-op if already correct dtype
//Various shape arrays, to reuse
long[] nhw = new long[]{mb, h, w};
@ -380,13 +387,17 @@ public class Yolo2OutputLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
epsWH.addi(dLc_din_wh);
epsXY.addi(dLc_din_xy);
if(!nchw)
epsOut = epsOut.permute(0,2,3,1); //NCHW to NHWC
return epsOut;
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
return YoloUtils.activate(layerConf().getBoundingBoxes(), input, workspaceMgr);
boolean nchw = layerConf().getFormat() == CNN2DFormat.NCHW;
return YoloUtils.activate(layerConf().getBoundingBoxes(), input, nchw, workspaceMgr);
}
@Override

20
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/objdetect/YoloUtils.java
View File

@ -39,12 +39,23 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.*;
*/
public class YoloUtils {
/** Essentially: just apply activation functions... */
/** Essentially: just apply activation functions... For NCHW format. For NCHW format, use one of the other activate methods */
public static INDArray activate(INDArray boundingBoxPriors, INDArray input) {
return activate(boundingBoxPriors, input, LayerWorkspaceMgr.noWorkspaces());
return activate(boundingBoxPriors, input, true);
}
public static INDArray activate(@NonNull INDArray boundingBoxPriors, @NonNull INDArray input, LayerWorkspaceMgr layerWorkspaceMgr){
public static INDArray activate(INDArray boundingBoxPriors, INDArray input, boolean nchw) {
return activate(boundingBoxPriors, input, nchw, LayerWorkspaceMgr.noWorkspaces());
}
public static INDArray activate(@NonNull INDArray boundingBoxPriors, @NonNull INDArray input, LayerWorkspaceMgr layerWorkspaceMgr) {
return activate(boundingBoxPriors, input, true, layerWorkspaceMgr);
}
public static INDArray activate(@NonNull INDArray boundingBoxPriors, @NonNull INDArray input, boolean nchw, LayerWorkspaceMgr layerWorkspaceMgr){
if(!nchw)
input = input.permute(0,3,1,2); //NHWC to NCHW
long mb = input.size(0);
long h = input.size(2);
long w = input.size(3);
@ -83,6 +94,9 @@ public class YoloUtils {
INDArray outputClasses = output5.get(all(), all(), interval(5, 5+c), all(), all()); //Shape: [minibatch, C, H, W]
outputClasses.assign(postSoftmax5d);
if(!nchw)
output = output.permute(0,2,3,1); //NCHW to NHWC
return output;
}