Brutex
/
cavis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

DL4J CNN2D layers NHWC support (#376) 

* First steps for DL4J NHWC support

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

* Conv2d NHWC forward pass works

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

* Conv2d NHWC backprop

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

* Conv2d backprop + fixes; subsampling fwd/bwd; improve tests

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

* Zero padding layer NHWC support

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

* Cropping2D NHWC support

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

* Deconv2d NHWC + clean up NHWC test framework code duplication

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

* CnnLossLayer NHWC support

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

* Upsampling and batchnorm NHWC support

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

* Space to depth

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

* Depthwise pt1

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

* Depthwise pt2 and LRN

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

* SpaceToBatch

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

* LocallyConnected2D

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

* Fix depthwise nhwc support

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

* Upsampling NHWC - workaround for #8857

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

* Workaround for #8859 - SpaceToDepth

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

* Batch normalization workaround - #8860

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

* cuDNN fixes

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

* Switch cudnn conv2d to permute based impl due to 'true' NHWC not working

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

* cuDNN subsampling helper NHWC fix

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

* Upsampling/batchnorm fixes

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

* Small fixes

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

* CNN2D NHWC gradient checks (make CNNGradientCheckTest parameterized)

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

* Gradient checks, SConv2d, bunch of fixes

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

* Small fixes

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

* Global pooling NHWC support

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

* Also test both float and double for cuDNN NHWC tests

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

* Javadoc

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

* Ignore failing keras import test until next PR

Signed-off-by: Alex Black <blacka101@gmail.com>
master
Alex Black 2020-04-22 22:54:29 +10:00 committed by GitHub
parent 2c80b18f1d
commit 2a488efb1b
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
52 changed files with 3446 additions and 556 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

155
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
View File

@ -21,6 +21,7 @@ import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -34,6 +35,8 @@ import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.junit.Ignore;
import org.junit.Test;
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;
@ -51,6 +54,7 @@ import static org.junit.Assert.*;
/**
* Created by nyghtowl on 9/1/15.
*/
@RunWith(Parameterized.class)
public class CNNGradientCheckTest extends BaseDL4JTest {
private static final boolean PRINT_RESULTS = true;
private static final boolean RETURN_ON_FIRST_FAILURE = false;
@ -62,6 +66,17 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
private CNN2DFormat format;
public CNNGradientCheckTest(CNN2DFormat format){
this.format = format;
}
@Parameterized.Parameters(name = "{0}")
public static Object[] params(){
return CNN2DFormat.values();
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
@ -69,6 +84,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
@Test
public void testGradientCNNMLN() {
if(this.format != CNN2DFormat.NCHW) //Only test NCHW due to flat input format...
return;
//Parameterized test, testing combinations of:
// (a) activation function
// (b) Whether to test at random initialization, or after some learning (i.e., 'characteristic mode of operation')
@ -144,6 +162,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
@Test
public void testGradientCNNL1L2MLN() {
if(this.format != CNN2DFormat.NCHW) //Only test NCHW due to flat input format...
return;
//Parameterized test, testing combinations of:
// (a) activation function
// (b) Whether to test at random initialization, or after some learning (i.e., 'characteristic mode of operation')
@ -311,10 +332,12 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM};
boolean nchw = format == CNN2DFormat.NCHW;
for (String afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(4 * minibatchSize, nOut);
for (int i = 0; i < 4 * minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -330,13 +353,13 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(height, width, inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
String msg = format + " - poolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
+ afn;
if (PRINT_RESULTS) {
@ -377,8 +400,11 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int[] padding = {0, 0};
int size = 2;
boolean nchw = format == CNN2DFormat.NCHW;
for (int minibatchSize : minibatchSizes) {
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
MultiLayerConfiguration conf =
@ -393,8 +419,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(8 * 8 * 3)
.nOut(4).build())
.setInputType(InputType.convolutionalFlat(height, width,
inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -438,10 +463,13 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM};
boolean nchw = format == CNN2DFormat.NCHW;
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -461,14 +489,13 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3 * 3 * 3)
.nOut(4).build())
.setInputType(InputType.convolutionalFlat(height, width,
inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
String msg = format + " - poolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
+ afn;
if (PRINT_RESULTS) {
@ -508,10 +535,13 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM};
boolean nchw = format == CNN2DFormat.NCHW;
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -533,8 +563,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(2 * 2 * 2)
.nOut(4).build())
.setInputType(InputType.convolutionalFlat(height, width,
inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -558,8 +587,6 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
@Test
public void testCnnLocallyConnected2D() {
int nOut = 3;
int[] minibatchSizes = {2};
int width = 5;
int height = 5;
@ -569,11 +596,15 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Activation[] activations = {Activation.SIGMOID, Activation.TANH, Activation.SOFTPLUS};
int[] minibatch = {2, 1, 3};
boolean nchw = format == CNN2DFormat.NCHW;
for( int i=0; i<inputDepths.length; i++ ){
int inputDepth = inputDepths[i];
Activation afn = activations[i];
int minibatchSize = minibatch[i];
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).updater(new NoOp())
@ -590,7 +621,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(2 * 2 * 2).nOut(nOut)
.build())
.setInputType(InputType.convolutionalFlat(height, width, inputDepth)).build();
.setInputType(InputType.convolutional(height, width, inputDepth, format)).build();
assertEquals(ConvolutionMode.Truncate,
((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
@ -626,11 +657,15 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.getRandom().setSeed(12345);
boolean nchw = format == CNN2DFormat.NCHW;
for (int inputDepth : inputDepths) {
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -649,7 +684,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(2 * 2 * 2).nOut(nOut)
.build())
.setInputType(InputType.convolutionalFlat(height, width, inputDepth)).build();
.setInputType(InputType.convolutional(height, width, inputDepth, format)).build();
assertEquals(ConvolutionMode.Truncate,
((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
@ -691,13 +726,17 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.getRandom().setSeed(12345);
boolean nchw = format == CNN2DFormat.NCHW;
for( int i=0; i<minibatchSizes.length; i++ ){
int inputDepth = inputDepths[i];
int minibatchSize = minibatchSizes[i];
int height = heights[i];
int k = kernelSizes[i];
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
@ -713,7 +752,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.stride(1, 1).padding(0, 0).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(height, width, inputDepth)).build();
.setInputType(InputType.convolutional(height, width, inputDepth, format)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -748,13 +787,16 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.getRandom().setSeed(12345);
boolean nchw = format == CNN2DFormat.NCHW;
for (int inputDepth : inputDepths) {
for (int minibatchSize : minibatchSizes) {
for (int stride : strides) {
for (int k : kernelSizes) {
for (boolean convFirst : new boolean[]{true, false}) {
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -775,7 +817,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(1, convFirst ? poolLayer : convLayer)
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(height, width, inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -822,11 +864,15 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int[] inputDepths = {1, 3, 2};
int[][] zeroPadLayer = new int[][]{{0, 0, 0, 0}, {1, 1, 0, 0}, {2, 2, 2, 2}};
boolean nchw = format == CNN2DFormat.NCHW;
for( int i=0; i<minibatchSizes.length; i++ ){
int minibatchSize = minibatchSizes[i];
int inputDepth = inputDepths[i];
int[] zeroPad = zeroPadLayer[i];
INDArray input = Nd4j.rand(DataType.DOUBLE, new int[]{minibatchSize, inputDepth, height, width});
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
MultiLayerConfiguration conf =
@ -840,7 +886,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
padding).nIn(3).nOut(3).build())//output: (6-2+0)/1+1 = 5
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(4).build())
.setInputType(InputType.convolutional(height, width, inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -849,8 +895,14 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
//Check zero padding activation shape
org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer zpl =
(org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer) net.getLayer(1);
val expShape = new long[]{minibatchSize, inputDepth, height + zeroPad[0] + zeroPad[1],
long[] expShape;
if(nchw){
expShape = new long[]{minibatchSize, inputDepth, height + zeroPad[0] + zeroPad[1],
width + zeroPad[2] + zeroPad[3]};
} else {
expShape = new long[]{minibatchSize, height + zeroPad[0] + zeroPad[1],
width + zeroPad[2] + zeroPad[3], inputDepth};
}
INDArray out = zpl.activate(input, false, LayerWorkspaceMgr.noWorkspaces());
assertArrayEquals(expShape, out.shape());
@ -888,6 +940,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.getRandom().setSeed(12345);
boolean nchw = format == CNN2DFormat.NCHW;
for (int i = 0; i < minibatchSizes.length; i++) {
int minibatchSize = minibatchSizes[i];
int k = kernelSizes[i];
@ -900,7 +954,10 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int w = d * width;
int h = d * height;
INDArray input = Nd4j.rand(minibatchSize, w * h * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, h, w} : new long[]{minibatchSize, h, w, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int j = 0; j < minibatchSize; j++) {
labels.putScalar(new int[]{j, j % nOut}, 1.0);
@ -920,7 +977,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
MultiLayerConfiguration conf = b.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(h, w, inputDepth)).build();
.setInputType(InputType.convolutional(h, w, inputDepth, format)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -945,8 +1002,6 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
@Test
public void testSeparableConv2D() {
int nOut = 2;
int[] minibatchSizes = new int[]{1, 3};
int width = 6;
int height = 6;
int inputDepth = 3;
@ -959,6 +1014,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
ConvolutionMode[] cms = new ConvolutionMode[]{Truncate, Truncate, Truncate, Truncate, Truncate};
int[] mb = new int[]{1, 1, 1, 3, 3};
boolean nchw = format == CNN2DFormat.NCHW;
for (int t = 0; t < ks.length; t++) {
int k = ks[t];
@ -971,7 +1028,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int w = d * width;
int h = d * height;
INDArray input = Nd4j.rand(minibatchSize, w * h * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, h, w} : new long[]{minibatchSize, h, w, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -992,7 +1050,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
MultiLayerConfiguration conf = b.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(h, w, inputDepth)).build();
.setInputType(InputType.convolutional(h, w, inputDepth, format)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -1017,7 +1075,6 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
@Test
public void testCnnDilated() {
int nOut = 2;
int minibatchSize = 2;
int width = 8;
int height = 8;
@ -1031,9 +1088,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int[] ds = new int[]{2, 2, 3, 3, 2};
ConvolutionMode[] cms = new ConvolutionMode[]{Same, Truncate, Truncate, Same, Truncate};
boolean nchw = format == CNN2DFormat.NCHW;
for (int t = 0; t < sub.length; t++) {
boolean subsampling = sub[t];
int s = stride[t];
int k = kernel[t];
@ -1044,7 +1101,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int w = d * width;
int h = d * height;
INDArray input = Nd4j.rand(minibatchSize, w * h * inputDepth);
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, h, w} : new long[]{minibatchSize, h, w, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -1076,7 +1134,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
MultiLayerConfiguration conf = b.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(h, w, inputDepth)).build();
.setInputType(InputType.convolutional(h, w, inputDepth, format)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
@ -1114,11 +1172,14 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
int[] inputDepths = {1, 2, 3, 2};
int[] minibatchSizes = {2, 1, 3, 2};
boolean nchw = format == CNN2DFormat.NCHW;
for (int i = 0; i < cropTestCases.length; i++) {
int inputDepth = inputDepths[i];
int minibatchSize = minibatchSizes[i];
int[] crop = cropTestCases[i];
INDArray input = Nd4j.rand(new int[]{minibatchSize, inputDepth, height, width});
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
MultiLayerConfiguration conf =
@ -1134,7 +1195,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
.layer(new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG).kernelSize(3, 3).stride(3, 3).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutional(height, width, inputDepth))
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
@ -1143,12 +1204,18 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
//Check cropping activation shape
org.deeplearning4j.nn.layers.convolution.Cropping2DLayer cl =
(org.deeplearning4j.nn.layers.convolution.Cropping2DLayer) net.getLayer(1);
val expShape = new long[]{minibatchSize, inputDepth, height - crop[0] - crop[1],
long[] expShape;
if(nchw){
expShape = new long[]{minibatchSize, inputDepth, height - crop[0] - crop[1],
width - crop[2] - crop[3]};
} else {
expShape = new long[]{minibatchSize, height - crop[0] - crop[1],
width - crop[2] - crop[3], inputDepth};
}
INDArray out = cl.activate(input, false, LayerWorkspaceMgr.noWorkspaces());
assertArrayEquals(expShape, out.shape());
String msg = "minibatch=" + minibatchSize + ", channels=" + inputDepth + ", zeroPad = "
String msg = format + " - minibatch=" + minibatchSize + ", channels=" + inputDepth + ", zeroPad = "
+ Arrays.toString(crop);
if (PRINT_RESULTS) {
@ -1181,6 +1248,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Truncate, Truncate, Truncate, Truncate, Truncate};
int[] mb = new int[]{1,1,1,3,3};
boolean nchw = format == CNN2DFormat.NCHW;
for( int t=0; t<ks.length; t++ ){
int k = ks[t];
@ -1188,8 +1257,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
ConvolutionMode cm = cms[t];
int minibatchSize = mb[t];
INDArray input = Nd4j.rand(minibatchSize, width * height * nIn);
long[] inShape = nchw ? new long[]{minibatchSize, nIn, height, width} : new long[]{minibatchSize, height, width, nIn};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(minibatchSize, nOut);
for (int i = 0; i < minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
@ -1211,7 +1280,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
MultiLayerConfiguration conf = b.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutionalFlat(height, width, nIn)).build();
.setInputType(InputType.convolutional(height, width, nIn, format)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();

17
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GlobalPoolingGradientCheckTests.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.gradientcheck;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -115,15 +116,17 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
//Basic test of global pooling w/ CNN
Nd4j.getRandom().setSeed(12345L);
for(boolean nchw : new boolean[]{true, false}) {
int inputDepth = 3;
int inputH = 5;
int inputW = 4;
int layerDepth = 4;
int nOut = 2;
int[] minibatchSizes = new int[] {1, 3};
int[] minibatchSizes = new int[]{1, 3};
PoolingType[] poolingTypes =
new PoolingType[] {PoolingType.AVG, PoolingType.SUM, PoolingType.MAX, PoolingType.PNORM};
new PoolingType[]{PoolingType.AVG, PoolingType.SUM, PoolingType.MAX, PoolingType.PNORM};
for (int miniBatchSize : minibatchSizes) {
for (PoolingType pt : poolingTypes) {
@ -137,14 +140,14 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
.layer(1, new GlobalPoolingLayer.Builder().poolingType(pt).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(nOut).build())
.setInputType(InputType.convolutional(inputH, inputW, inputDepth)).build();
.setInputType(InputType.convolutional(inputH, inputW, inputDepth, nchw ? CNN2DFormat.NCHW : CNN2DFormat.NHWC)).build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
mln.init();
Random r = new Random(12345L);
INDArray input = Nd4j.rand(new int[] {miniBatchSize, inputDepth, inputH, inputW}).subi(0.5);
long[] inShape = nchw ? new long[]{miniBatchSize, inputDepth, inputH, inputW} : new long[]{miniBatchSize, inputH, inputW, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape).subi(0.5);
INDArray labels = Nd4j.zeros(miniBatchSize, nOut);
for (int i = 0; i < miniBatchSize; i++) {
@ -153,8 +156,7 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
}
if (PRINT_RESULTS) {
System.out.println(
"testCnnGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = " + miniBatchSize);
System.out.println("testCnnGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = " + miniBatchSize + " - " + (nchw ? "NCHW" : "NHWC"));
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
@ -167,6 +169,7 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
}
}
}
}
@Test
public void testLSTMWithMasking() {

883
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvDataFormatTests.java Normal file
View File

@ -0,0 +1,883 @@
/* ******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License 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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.layers.convolution;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.CnnLossLayer;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test;
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;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;
@RunWith(Parameterized.class)
public class ConvDataFormatTests extends BaseDL4JTest {
private final DataType dataType;
public ConvDataFormatTests(DataType dataType){
this.dataType = dataType;
}
@Parameterized.Parameters(name = "{0}")
public static Object[] params(){
return new DataType[]{DataType.FLOAT, DataType.DOUBLE};
}
@Test
public void testConv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getConv2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getConv2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getConv2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getConv2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSubsampling2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSubsampling2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getSubsampling2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getSubsampling2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getSubsampling2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testDepthwiseConv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getDepthwiseConv2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getDepthwiseConv2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getDepthwiseConv2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getDepthwiseConv2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSeparableConv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSeparableConv2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getSeparableConv2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getSeparableConv2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getSeparableConv2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testDeconv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getDeconv2DNet2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getDeconv2DNet2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getDeconv2DNet2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getDeconv2DNet2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testLRN() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getLrnLayer(CNN2DFormat.NCHW, true, cm))
.net2(getLrnLayer(CNN2DFormat.NCHW, false, cm))
.net3(getLrnLayer(CNN2DFormat.NHWC, true, cm))
.net4(getLrnLayer(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testZeroPaddingLayer(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getZeroPaddingNet(CNN2DFormat.NCHW, true))
.net2(getZeroPaddingNet(CNN2DFormat.NCHW, false))
.net3(getZeroPaddingNet(CNN2DFormat.NHWC, true))
.net4(getZeroPaddingNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testCropping2DLayer(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getCropping2dNet(CNN2DFormat.NCHW, true))
.net2(getCropping2dNet(CNN2DFormat.NCHW, false))
.net3(getCropping2dNet(CNN2DFormat.NHWC, true))
.net4(getCropping2dNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testUpsampling2d(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getUpsamplingNet(CNN2DFormat.NCHW, true))
.net2(getUpsamplingNet(CNN2DFormat.NCHW, false))
.net3(getUpsamplingNet(CNN2DFormat.NHWC, true))
.net4(getUpsamplingNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testBatchNormNet(){
try {
for(boolean useLogStd : new boolean[]{true, false}) {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = (helpers ? "With helpers" : "No helpers") + " - " + (useLogStd ? "logstd" : "std");
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getBatchNormNet(useLogStd, CNN2DFormat.NCHW, true))
.net2(getBatchNormNet(useLogStd, CNN2DFormat.NCHW, false))
.net3(getBatchNormNet(useLogStd, CNN2DFormat.NHWC, true))
.net4(getBatchNormNet(useLogStd, CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testCnnLossLayer() {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labelsNHWC = TestUtils.randomOneHot(this.dataType,2*6*6, 3);
labelsNHWC = labelsNHWC.reshape(2,6,6,3);
INDArray labelsNCHW = labelsNHWC.permute(0,3,1,2).dup();
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getCnnLossNet(CNN2DFormat.NCHW, true, ConvolutionMode.Same))
.net2(getCnnLossNet(CNN2DFormat.NCHW, false, ConvolutionMode.Same))
.net3(getCnnLossNet(CNN2DFormat.NHWC, true, ConvolutionMode.Same))
.net4(getCnnLossNet(CNN2DFormat.NHWC, false, ConvolutionMode.Same))
.inNCHW(inNCHW)
.labelsNCHW(labelsNCHW)
.labelsNHWC(labelsNHWC)
.testLayerIdx(1)
.nhwcOutput(true)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSpaceToDepthNet(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSpaceToDepthNet(CNN2DFormat.NCHW, true))
.net2(getSpaceToDepthNet(CNN2DFormat.NCHW, false))
.net3(getSpaceToDepthNet(CNN2DFormat.NHWC, true))
.net4(getSpaceToDepthNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSpaceToBatchNet(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 16, 16);
INDArray labels = TestUtils.randomOneHot(8, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSpaceToBatchNet(CNN2DFormat.NCHW, true))
.net2(getSpaceToBatchNet(CNN2DFormat.NCHW, false))
.net3(getSpaceToBatchNet(CNN2DFormat.NHWC, true))
.net4(getSpaceToBatchNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testLocallyConnected() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getLocallyConnectedNet(CNN2DFormat.NCHW, true, cm))
.net2(getLocallyConnectedNet(CNN2DFormat.NCHW, false, cm))
.net3(getLocallyConnectedNet(CNN2DFormat.NHWC, true, cm))
.net4(getLocallyConnectedNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
private MultiLayerNetwork getConv2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getSubsampling2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new SubsamplingLayer.Builder()
.kernelSize(2, 2)
.stride(1, 1)
.dataFormat(format)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new SubsamplingLayer.Builder()
.kernelSize(2, 2)
.stride(1, 1)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getSeparableConv2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new SeparableConvolution2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new SeparableConvolution2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getDepthwiseConv2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new DepthwiseConvolution2D.Builder()
.depthMultiplier(2)
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new DepthwiseConvolution2D.Builder()
.depthMultiplier(2)
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getLrnLayer(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new LocalResponseNormalization.Builder()
.dataFormat(format)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new LocalResponseNormalization.Builder()
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getZeroPaddingNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new ZeroPaddingLayer.Builder(2,2)
.dataFormat(format).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new ZeroPaddingLayer.Builder(2,2).build(),
format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getCropping2dNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new Cropping2D.Builder(2,2)
.dataFormat(format).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new Cropping2D.Builder(2,2)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getUpsamplingNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new Upsampling2D.Builder(2)
.dataFormat(format).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new Upsampling2D.Builder(2)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getDeconv2DNet2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new Deconvolution2D.Builder().nOut(2)
.activation(Activation.TANH)
.kernelSize(2,2)
.stride(2,2)
.build(), format, cm, null);
} else {
return getNetWithLayer(new Deconvolution2D.Builder().nOut(2)
.activation(Activation.TANH)
.kernelSize(2,2)
.stride(2,2)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getBatchNormNet(boolean logStdev, CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new BatchNormalization.Builder()
.useLogStd(logStdev)
.dataFormat(format)
.helperAllowFallback(false)
.nOut(3).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new BatchNormalization.Builder()
.useLogStd(logStdev)
.helperAllowFallback(false)
.nOut(3).build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getSpaceToDepthNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new SpaceToDepthLayer.Builder()
.blocks(2)
.dataFormat(format)
.build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new SpaceToDepthLayer.Builder()
.blocks(2)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getSpaceToBatchNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new SpaceToBatchLayer.Builder()
.blocks(2, 2)
.dataFormat(format)
.build(), format, ConvolutionMode.Same, InputType.convolutional(16, 16, 3, format));
} else {
return getNetWithLayer(new SpaceToBatchLayer.Builder()
.blocks(2, 2)
.build(), format, ConvolutionMode.Same, InputType.convolutional(16, 16, 3, format));
}
}
private MultiLayerNetwork getLocallyConnectedNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new LocallyConnected2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.build(), format, cm, null);
} else {
return getNetWithLayer(new LocallyConnected2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getNetWithLayer(Layer layer, CNN2DFormat format, ConvolutionMode cm, InputType inputType) {
NeuralNetConfiguration.ListBuilder builder = new NeuralNetConfiguration.Builder()
.dataType(this.dataType)
.seed(12345)
.convolutionMode(cm)
.list()
.layer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build())
.layer(layer)
.layer(new OutputLayer.Builder().activation(Activation.SOFTMAX).nOut(10).build())
.setInputType(inputType != null ? inputType : InputType.convolutional(12, 12, 3, format));
MultiLayerNetwork net = new MultiLayerNetwork(builder.build());
net.init();
return net;
}
private MultiLayerNetwork getCnnLossNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm){
NeuralNetConfiguration.ListBuilder builder = new NeuralNetConfiguration.Builder()
.seed(12345)
.convolutionMode(cm)
.list()
.layer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build());
if(setOnLayerAlso){
builder.layer(new CnnLossLayer.Builder().format(format).activation(Activation.SOFTMAX).build());
} else {
builder.layer(new CnnLossLayer.Builder().activation(Activation.SOFTMAX).build());
}
builder.setInputType(InputType.convolutional(12, 12, 3, format));
MultiLayerNetwork net = new MultiLayerNetwork(builder.build());
net.init();
return net;
}
@AllArgsConstructor
@Data
@NoArgsConstructor
@Builder
private static class TestCase {
private String msg;
private MultiLayerNetwork net1;
private MultiLayerNetwork net2;
private MultiLayerNetwork net3;
private MultiLayerNetwork net4;
private INDArray inNCHW;
private INDArray labelsNCHW;
private INDArray labelsNHWC;
private int testLayerIdx;
private boolean nhwcOutput;
}
public static void testHelper(TestCase tc) {
tc.net2.params().assign(tc.net1.params());
tc.net3.params().assign(tc.net1.params());
tc.net4.params().assign(tc.net1.params());
//Test forward pass:
INDArray inNCHW = tc.inNCHW;
INDArray inNHWC = tc.inNCHW.permute(0, 2, 3, 1).dup();
INDArray l0_1 = tc.net1.feedForward(inNCHW).get(tc.testLayerIdx + 1);
INDArray l0_2 = tc.net2.feedForward(inNCHW).get(tc.testLayerIdx + 1);
INDArray l0_3 = tc.net3.feedForward(inNHWC).get(tc.testLayerIdx + 1);
INDArray l0_4 = tc.net4.feedForward(inNHWC).get(tc.testLayerIdx + 1);
assertEquals(tc.msg, l0_1, l0_2);
assertEquals(tc.msg, l0_1, l0_3.permute(0, 3, 1, 2));
assertEquals(tc.msg, l0_1, l0_4.permute(0, 3, 1, 2));
INDArray out1 = tc.net1.output(inNCHW);
INDArray out2 = tc.net2.output(inNCHW);
INDArray out3 = tc.net3.output(inNHWC);
INDArray out4 = tc.net4.output(inNHWC);
assertEquals(tc.msg, out1, out2);
if(!tc.nhwcOutput) {
assertEquals(tc.msg, out1, out3);
assertEquals(tc.msg, out1, out4);
} else {
assertEquals(tc.msg, out1, out3.permute(0,3,1,2)); //NHWC to NCHW
assertEquals(tc.msg, out1, out4.permute(0,3,1,2));
}
//Test backprop
Pair<Gradient, INDArray> p1 = tc.net1.calculateGradients(inNCHW, tc.labelsNCHW, null, null);
Pair<Gradient, INDArray> p2 = tc.net2.calculateGradients(inNCHW, tc.labelsNCHW, null, null);
Pair<Gradient, INDArray> p3 = tc.net3.calculateGradients(inNHWC, tc.labelsNHWC, null, null);
Pair<Gradient, INDArray> p4 = tc.net4.calculateGradients(inNHWC, tc.labelsNHWC, null, null);
//Inpput gradients
assertEquals(tc.msg, p1.getSecond(), p2.getSecond());
assertEquals(tc.msg, p1.getSecond(), p3.getSecond().permute(0,3,1,2)); //Input gradients for NHWC input are also in NHWC format
assertEquals(tc.msg, p1.getSecond(), p4.getSecond().permute(0,3,1,2));
List<String> diff12 = differentGrads(p1.getFirst(), p2.getFirst());
List<String> diff13 = differentGrads(p1.getFirst(), p3.getFirst());
List<String> diff14 = differentGrads(p1.getFirst(), p4.getFirst());
assertEquals(tc.msg + " " + diff12, 0, diff12.size());
assertEquals(tc.msg + " " + diff13, 0, diff13.size());
assertEquals(tc.msg + " " + diff14, 0, diff14.size());
assertEquals(tc.msg, p1.getFirst().gradientForVariable(), p2.getFirst().gradientForVariable());
assertEquals(tc.msg, p1.getFirst().gradientForVariable(), p3.getFirst().gradientForVariable());
assertEquals(tc.msg, p1.getFirst().gradientForVariable(), p4.getFirst().gradientForVariable());
tc.net1.fit(inNCHW, tc.labelsNCHW);
tc.net2.fit(inNCHW, tc.labelsNCHW);
tc.net3.fit(inNHWC, tc.labelsNHWC);
tc.net4.fit(inNHWC, tc.labelsNHWC);
assertEquals(tc.msg, tc.net1.params(), tc.net2.params());
assertEquals(tc.msg, tc.net1.params(), tc.net3.params());
assertEquals(tc.msg, tc.net1.params(), tc.net4.params());
//Test serialization
MultiLayerNetwork net1a = TestUtils.testModelSerialization(tc.net1);
MultiLayerNetwork net2a = TestUtils.testModelSerialization(tc.net2);
MultiLayerNetwork net3a = TestUtils.testModelSerialization(tc.net3);
MultiLayerNetwork net4a = TestUtils.testModelSerialization(tc.net4);
out1 = tc.net1.output(inNCHW);
assertEquals(tc.msg, out1, net1a.output(inNCHW));
assertEquals(tc.msg, out1, net2a.output(inNCHW));
if(!tc.nhwcOutput) {
assertEquals(tc.msg, out1, net3a.output(inNHWC));
assertEquals(tc.msg, out1, net4a.output(inNHWC));
} else {
assertEquals(tc.msg, out1, net3a.output(inNHWC).permute(0,3,1,2)); //NHWC to NCHW
assertEquals(tc.msg, out1, net4a.output(inNHWC).permute(0,3,1,2));
}
}
private static List<String> differentGrads(Gradient g1, Gradient g2){
List<String> differs = new ArrayList<>();
Map<String,INDArray> m1 = g1.gradientForVariable();
Map<String,INDArray> m2 = g2.gradientForVariable();
for(String s : m1.keySet()){
INDArray a1 = m1.get(s);
INDArray a2 = m2.get(s);
if(!a1.equals(a2)){
differs.add(s);
}
}
return differs;
}
}

2
deeplearning4j/deeplearning4j-cuda/src/main/java/org/deeplearning4j/nn/layers/BaseCudnnHelper.java
View File

@ -178,8 +178,6 @@ public abstract class BaseCudnnHelper {
}
}
protected static final int TENSOR_FORMAT = CUDNN_TENSOR_NCHW;
protected final DataType nd4jDataType;
protected final int dataType;
protected final int dataTypeSize;

76
deeplearning4j/deeplearning4j-cuda/src/main/java/org/deeplearning4j/nn/layers/convolution/CudnnConvolutionHelper.java
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2020 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
@ -22,6 +23,7 @@ import lombok.extern.slf4j.Slf4j;
import lombok.val;
import com.jakewharton.byteunits.BinaryByteUnit;
import org.bytedeco.javacpp.Pointer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo;
@ -138,7 +140,21 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel,
int[] strides, int[] pad, INDArray biasGradView, INDArray weightGradView, IActivation afn,
AlgoMode mode, BwdFilterAlgo bwdFilterAlgo, BwdDataAlgo bwdDataAlgo,
ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
//AB 2020/04/21 - cuDNN does have NHWC support (with limitations) however I have been unable to get it working
// correctly on NHWC data, even after updating all descriptors, tensor format, etc.
//Therefore: all computation here is done in NCHW format only
//As of a future (next?) release we'll likely switch to C++ for cuDNN support
boolean origNHWC = false;
if(format == CNN2DFormat.NHWC){
input = input.permute(0,3,1,2); //NHWC to NCHW
delta = delta.permute(0,3,1,2);
origNHWC = true;
}
int TENSOR_FORMAT = CUDNN_TENSOR_NCHW;
int code;
val miniBatch = input.size(0);
@ -147,7 +163,7 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
val kH = weights.size(2);
val kW = weights.size(3);
CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, null);
CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, null, CNN2DFormat.NCHW); //Note hardcoded NCHW due to above
input = args.getInput();
val inH = input.size(2);
val inW = input.size(3);
@ -344,12 +360,30 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
interval(0, epsNext.size(3) - (args.isManualPadRight() ? 1 : 0)));
}
if(origNHWC){
epsNext = epsNext.permute(0,2,3,1); //NCHW to NHWC
}
return new Pair<>(retGradient, epsNext);
}
@Override
public INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad,
AlgoMode mode, FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
AlgoMode mode, FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) {
//AB 2020/04/21 - cuDNN does have NHWC support (with limitations) however I have been unable to get it working
// correctly on NHWC data, even after updating all descriptors, tensor format, etc.
//Therefore: all computation here is done in NCHW format only
//As of a future (next?) release we'll likely switch to C++ for cuDNN support
boolean origNHWC = false;
if(format == CNN2DFormat.NHWC){
input = input.permute(0,3,1,2); //NHWC to NCHW
origNHWC = true;
}
int TENSOR_FORMAT = CUDNN_TENSOR_NCHW;
int code;
val miniBatch = input.size(0);
@ -358,7 +392,7 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
val kH = weights.size(2);
val kW = weights.size(3);
CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, null);
CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, null, CNN2DFormat.NCHW); //Note hardcoded NCHW due to above
input = args.getInput();
val inH = input.size(2);
val inW = input.size(3);
@ -499,6 +533,10 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
if (CudaEnvironment.getInstance().getConfiguration().isDebug())
context.syncOldStream();
if(origNHWC){
z = z.permute(0,2,3,1); //NCHW to NHWC
}
return z;
}
@ -593,7 +631,7 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
* @return
*/
public static CudnnForwardArgs getCudnnForwardArgs(INDArray input, int[] kernel, int[] strides, int[] padding, int[] dilation,
ConvolutionMode convolutionMode, PoolingType poolingType){
ConvolutionMode convolutionMode, PoolingType poolingType, CNN2DFormat format){
INDArray origInput = input;
//Check if we need to dup the input: views, non-contiguous, etc. CuDNN also seems to have has issues if strides
@ -602,16 +640,19 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
input = input.dup('c');
}
boolean nchw = format == CNN2DFormat.NCHW;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
val inH = input.size(2);
val inW = input.size(3);
val inH = input.size(hIdx);
val inW = input.size(wIdx);
boolean manualPadBottom = false;
boolean manualPadRight = false;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
padding = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
int[] padBottomRight = ConvolutionUtils.getSameModeBottomRightPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
if(!Arrays.equals(padding, padBottomRight)){
@ -626,9 +667,17 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
manualPadRight = (padding[1] != padBottomRight[1]);
//NCHW format
val newShape = new long[]{input.size(0), input.size(1),
long[] newShape;
if(nchw){
newShape = new long[]{input.size(0), input.size(1),
input.size(2) + (manualPadBottom ? 1 : 0),
input.size(3) + (manualPadRight ? 1 : 0)};
} else {
newShape = new long[]{input.size(0),
input.size(1) + (manualPadBottom ? 1 : 0),
input.size(2) + (manualPadRight ? 1 : 0),
input.size(3)};
}
INDArray newInput;
if(poolingType == null || poolingType != PoolingType.MAX){
newInput = Nd4j.create(input.dataType(), newShape);
@ -638,15 +687,22 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
// if the 'real' (non-padding) values are all < 0, we take the real value, not the padding value
newInput = Nd4j.valueArrayOf(newShape, Double.NEGATIVE_INFINITY, input.dataType());
}
if(nchw){
newInput.put(new INDArrayIndex[]{all(), all(), interval(0,input.size(2)),
interval(0, input.size(3))}, input);
} else {
newInput.put(new INDArrayIndex[]{all(), interval(0,input.size(1)),
interval(0, input.size(2)), all()}, input);
}
input = newInput;
//Now: we've manually applied the "extra" bottom/right padding only - if required. Consequently, we
// now have the same amount of padding required for top/bottom, and left/right - which we'll let
// CuDNN handle
}
} else {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, padding, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, padding, convolutionMode, dilation, format); //Also performs validation
}
return new CudnnForwardArgs(manualPadBottom, manualPadRight, input, origInput, padding, outSize);

61
deeplearning4j/deeplearning4j-cuda/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/CudnnSubsamplingHelper.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.layers.convolution.subsampling;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.bytedeco.javacpp.Pointer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -114,23 +115,29 @@ public class CudnnSubsamplingHelper extends BaseCudnnHelper implements Subsampli
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides,
int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode,
int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(dilation[0] != 1 || dilation[1] != 1){
//CuDNN doesn't support dilated subsampling
return null;
}
boolean nchw = format == CNN2DFormat.NCHW;
int chIdx = nchw ? 1 : 3;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
//We require the output as one of the arguments for backprop here
//TODO we could add cache mode support here somehow...
INDArray reduced = activate(input, true, kernel, strides, pad, poolingType, convolutionMode, dilation, workspaceMgr);
INDArray reduced = activate(input, true, kernel, strides, pad, poolingType, convolutionMode, dilation, format, workspaceMgr);
val miniBatch = input.size(0);
val depth = input.size(1);
val depth = input.size(chIdx);
CudnnConvolutionHelper.CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, poolingType);
CudnnConvolutionHelper.CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, poolingType, format);
input = args.getInput();
val inH = input.size(2);
val inW = input.size(3);
val inH = input.size(hIdx);
val inW = input.size(wIdx);
val srcStride = input.stride();
int[] outSize = args.getOutSize();
int outH = outSize[0];
@ -160,23 +167,26 @@ public class CudnnSubsamplingHelper extends BaseCudnnHelper implements Subsampli
epsilon = epsilon.dup('c');
}
input = input.dup();
val deltaStride = epsilon.stride();
if (Nd4j.getExecutioner() instanceof GridExecutioner)
((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, (int) miniBatch, (int) depth, (int) inH, (int) inW,
(int) srcStride[0], (int) srcStride[1], (int) srcStride[2], (int) srcStride[3]));
(int) srcStride[0], (int) srcStride[chIdx], (int) srcStride[hIdx], (int) srcStride[wIdx]));
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.deltaTensorDesc, dataType, (int) miniBatch, (int) depth, (int) outH, (int) outW,
(int) deltaStride[0], (int) deltaStride[1], (int) deltaStride[2], (int) deltaStride[3]));
(int) deltaStride[0], (int) deltaStride[chIdx], (int) deltaStride[hIdx], (int) deltaStride[wIdx]));
checkCudnn(cudnnSetPooling2dDescriptor(cudnnContext.poolingDesc, poolingMode, CUDNN_PROPAGATE_NAN, kernel[0],
kernel[1], pad[0], pad[1], strides[0], strides[1]));
INDArray outEpsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), new long[] {(int) miniBatch, (int) depth, (int) inH, (int) inW}, 'c');
long[] outEpsShape = nchw ? new long[] {miniBatch, depth, inH, inW} : new long[] {miniBatch, inH, inW, depth};
INDArray outEpsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), outEpsShape, 'c');
val dstStride = outEpsilon.stride();
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, (int) miniBatch, (int) depth, (int) inH, (int) inW,
(int) dstStride[0], (int) dstStride[1], (int) dstStride[2], (int) dstStride[3]));
(int) dstStride[0], (int) dstStride[chIdx], (int) dstStride[hIdx], (int) dstStride[wIdx]));
Allocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(input, epsilon, reduced, outEpsilon);
@ -198,9 +208,16 @@ public class CudnnSubsamplingHelper extends BaseCudnnHelper implements Subsampli
//Note that: if we had to manually pad for SAME mode, we have to 'undo' this manual padding for the epsilon
// we return. The returned epsilon (i.e., dL/dIn array) has to be the same shape as the *original* input.
if(args.isManualPadBottom() || args.isManualPadRight()) {
if(nchw){
outEpsilon = outEpsilon.get(all(), all(),
interval(0, outEpsilon.size(2) - (args.isManualPadBottom() ? 1 : 0)),
interval(0, outEpsilon.size(3) - (args.isManualPadRight() ? 1 : 0)));
} else {
outEpsilon = outEpsilon.get(all(),
interval(0, outEpsilon.size(1) - (args.isManualPadBottom() ? 1 : 0)),
interval(0, outEpsilon.size(2) - (args.isManualPadRight() ? 1 : 0)),
all());
}
}
return new Pair<>(retGradient, outEpsilon);
@ -209,19 +226,24 @@ public class CudnnSubsamplingHelper extends BaseCudnnHelper implements Subsampli
@Override
public INDArray activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad,
PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(dilation[0] != 1 || dilation[1] != 1){
//CuDNN doesn't support dilated subsampling
return null;
}
val miniBatch = input.size(0);
val inDepth = input.size(1);
boolean nchw = format == CNN2DFormat.NCHW;
int chIdx = nchw ? 1 : 3;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
CudnnConvolutionHelper.CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, poolingType);
val miniBatch = input.size(0);
val inDepth = input.size(nchw ? 1 : 3);
CudnnConvolutionHelper.CudnnForwardArgs args = getCudnnForwardArgs(input, kernel, strides, pad, dilation, convolutionMode, poolingType, format);
input = args.getInput();
val inH = input.size(2);
val inW = input.size(3);
val inH = input.size(nchw ? 2 : 1);
val inW = input.size(nchw ? 3 : 2);
val srcStride = input.stride();
val outSize = args.getOutSize();
int outH = outSize[0];
@ -246,13 +268,14 @@ public class CudnnSubsamplingHelper extends BaseCudnnHelper implements Subsampli
checkCudnn(cudnnSetPooling2dDescriptor(cudnnContext.poolingDesc, poolingMode, CUDNN_PROPAGATE_NAN, kernel[0],
kernel[1], pad[0], pad[1], strides[0], strides[1]));
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, (int) miniBatch, (int) inDepth, (int) inH, (int) inW,
(int) srcStride[0], (int) srcStride[1], (int) srcStride[2], (int) srcStride[3]));
(int) srcStride[0], (int) srcStride[chIdx], (int) srcStride[hIdx], (int) srcStride[wIdx]));
INDArray reduced = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), new long[] {(int) miniBatch, (int) inDepth, outH, outW}, 'c');
long[] outShape = nchw ? new long[] {miniBatch, inDepth, outH, outW} : new long[] {miniBatch, outH, outW, inDepth};
INDArray reduced = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
val dstStride = reduced.stride();
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, (int) miniBatch, (int) inDepth, (int) outH, (int) outW,
(int) dstStride[0], (int) dstStride[1], (int) dstStride[2], (int) dstStride[3]));
(int) dstStride[0], (int) dstStride[chIdx], (int) dstStride[hIdx], (int) dstStride[wIdx]));
Allocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(input, reduced);

54
deeplearning4j/deeplearning4j-cuda/src/main/java/org/deeplearning4j/nn/layers/normalization/CudnnBatchNormalizationHelper.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.layers.normalization;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.bytedeco.javacpp.Pointer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseCudnnHelper;
@ -124,12 +125,21 @@ public class CudnnBatchNormalizationHelper extends BaseCudnnHelper implements Ba
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, long[] shape, INDArray gamma, INDArray beta,
INDArray dGammaView, INDArray dBetaView, double eps, LayerWorkspaceMgr layerWorkspaceMgr) {
INDArray dGammaView, INDArray dBetaView, double eps, CNN2DFormat format, LayerWorkspaceMgr layerWorkspaceMgr) {
boolean nchw = format == CNN2DFormat.NCHW;
this.eps = eps;
int cudnnTensorFormat = nchw ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
int chIdx = nchw ? 1 : 3;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
val miniBatch = (int) input.size(0);
val depth = (int) input.size(1);
val inH = (int) input.size(2);
val inW = (int) input.size(3);
val depth = (int) input.size(chIdx);
val inH = (int) input.size(hIdx);
val inW = (int) input.size(wIdx);
final boolean isHalf = (input.dataType() == DataType.HALF);
INDArray gammaOrig = null;
@ -164,16 +174,17 @@ public class CudnnBatchNormalizationHelper extends BaseCudnnHelper implements Ba
((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, (int) miniBatch, (int) depth, (int) inH, (int) inW,
(int) srcStride[0], (int) srcStride[1], (int) srcStride[2], (int) srcStride[3]));
(int) srcStride[0], (int) srcStride[chIdx], (int) srcStride[hIdx], (int) srcStride[wIdx]));
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.deltaTensorDesc, dataType, (int) miniBatch, (int) depth, (int) inH, (int) inW,
(int) deltaStride[0], (int) deltaStride[1], (int) deltaStride[2], (int) deltaStride[3]));
(int) deltaStride[0], (int) deltaStride[chIdx], (int) deltaStride[hIdx], (int) deltaStride[wIdx]));
INDArray nextEpsilon = layerWorkspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), new long[] {miniBatch, depth, inH, inW}, 'c');
long[] nextEpsShape = nchw ? new long[] {miniBatch, depth, inH, inW} : new long[] {miniBatch, inH, inW, depth};
INDArray nextEpsilon = layerWorkspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), nextEpsShape, 'c');
val dstStride = ArrayUtil.toInts(nextEpsilon.stride());
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, depth, inH, inW,
dstStride[0], dstStride[1], dstStride[2], dstStride[3]));
checkCudnn(cudnnSetTensor4dDescriptor(cudnnContext.gammaBetaTensorDesc, TENSOR_FORMAT, toCudnnDataType(gamma.data().dataType()), (int)shape[0],
dstStride[0], dstStride[chIdx], dstStride[hIdx], dstStride[wIdx]));
checkCudnn(cudnnSetTensor4dDescriptor(cudnnContext.gammaBetaTensorDesc, cudnnTensorFormat, toCudnnDataType(gamma.data().dataType()), (int)shape[0],
(int)shape[1], shape.length > 2 ? (int)shape[2] : 1, shape.length > 3 ? (int)shape[3] : 1));
Allocator allocator = AtomicAllocator.getInstance();
@ -215,9 +226,15 @@ public class CudnnBatchNormalizationHelper extends BaseCudnnHelper implements Ba
@Override
public INDArray preOutput(INDArray x, boolean training, long[] shape, INDArray gamma, INDArray beta, INDArray mean,
INDArray var, double decay, double eps, LayerWorkspaceMgr workspaceMgr) {
INDArray var, double decay, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
boolean nchw = format == CNN2DFormat.NCHW;
int cudnnTensorFormat = nchw ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
int chIdx = nchw ? 1 : 3;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
this.eps = eps;
final boolean isHalf = (x.dataType() == DataType.HALF);
final boolean isHalf = (x.dataType() == DataType.FLOAT16);
INDArray origGamma = gamma;
INDArray origBeta = beta;
INDArray origMean = mean;
@ -238,21 +255,22 @@ public class CudnnBatchNormalizationHelper extends BaseCudnnHelper implements Ba
decay = 0.0; //From cudnn docs: runningMean = newMean*factor + runningMean*(1-factor). -> 0 = "in-place modification of running mean disabled"
val miniBatch = (int) x.size(0);
val inDepth = (int) x.size(1);
val inH = (int) x.size(2);
val inW = (int) x.size(3);
val inDepth = (int) x.size(chIdx);
val inH = (int) x.size(hIdx);
val inW = (int) x.size(wIdx);
val srcStride = ArrayUtil.toInts(x.stride());
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, miniBatch, inDepth, inH, inW,
srcStride[0], srcStride[1], srcStride[2], srcStride[3]));
srcStride[0], srcStride[chIdx], srcStride[hIdx], srcStride[wIdx]));
INDArray activations = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, x.dataType(), new long[] {miniBatch, inDepth, inH, inW}, 'c');
long[] actShape = nchw ? new long[] {miniBatch, inDepth, inH, inW} : new long[] {miniBatch, inH, inW, inDepth};
INDArray activations = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, x.dataType(), actShape, 'c');
val dstStride = ArrayUtil.toInts(activations.stride());
checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, inDepth, inH, inW,
dstStride[0], dstStride[1], dstStride[2], dstStride[3]));
dstStride[0], dstStride[chIdx], dstStride[hIdx], dstStride[wIdx]));
checkCudnn(cudnnSetTensor4dDescriptor(cudnnContext.gammaBetaTensorDesc, TENSOR_FORMAT, toCudnnDataType(mean.data().dataType()), (int)shape[0],
checkCudnn(cudnnSetTensor4dDescriptor(cudnnContext.gammaBetaTensorDesc, cudnnTensorFormat, toCudnnDataType(mean.data().dataType()), (int)shape[0],
(int)shape[1], shape.length > 2 ? (int)shape[2] : 1, shape.length > 3 ? (int)shape[3] : 1));
Allocator allocator = AtomicAllocator.getInstance();

226
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/TestUtils.java
View File

@ -16,74 +16,131 @@
package org.deeplearning4j;
import org.apache.commons.compress.utils.IOUtils;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.layers.BaseLayer;
import org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.convolution.ConvolutionLayer;
import org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer;
import org.deeplearning4j.nn.layers.normalization.BatchNormalization;
import org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization;
import org.deeplearning4j.nn.layers.recurrent.LSTM;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import org.nd4j.linalg.learning.regularization.WeightDecay;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.*;
import java.lang.reflect.Field;
import java.util.List;
import java.util.Random;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
public class TestUtils {
public static MultiLayerNetwork testModelSerialization(MultiLayerNetwork net){
MultiLayerNetwork restored;
try {
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ModelSerializer.writeModel(net, baos, true);
byte[] bytes = baos.toByteArray();
ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
MultiLayerNetwork restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
assertEquals(net.getLayerWiseConfigurations(), restored.getLayerWiseConfigurations());
assertEquals(net.params(), restored.params());
return restored;
} catch (IOException e){
//Should never happen
throw new RuntimeException(e);
}
//Also check the MultiLayerConfiguration is serializable (required by Spark etc)
MultiLayerConfiguration conf = net.getLayerWiseConfigurations();
serializeDeserializeJava(conf);
return restored;
}
public static ComputationGraph testModelSerialization(ComputationGraph net){
ComputationGraph restored;
try {
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ModelSerializer.writeModel(net, baos, true);
byte[] bytes = baos.toByteArray();
ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
ComputationGraph restored = ModelSerializer.restoreComputationGraph(bais, true);
restored = ModelSerializer.restoreComputationGraph(bais, true);
assertEquals(net.getConfiguration(), restored.getConfiguration());
assertEquals(net.params(), restored.params());
return restored;
} catch (IOException e){
//Should never happen
throw new RuntimeException(e);
}
//Also check the ComputationGraphConfiguration is serializable (required by Spark etc)
ComputationGraphConfiguration conf = net.getConfiguration();
serializeDeserializeJava(conf);
return restored;
}
public static INDArray randomOneHot(int examples, int nOut){
private static <T> T serializeDeserializeJava(T object){
byte[] bytes;
try(ByteArrayOutputStream baos = new ByteArrayOutputStream(); ObjectOutputStream oos = new ObjectOutputStream(baos)){
oos.writeObject(object);
oos.close();
bytes = baos.toByteArray();
} catch (IOException e){
//Should never happen
throw new RuntimeException(e);
}
T out;
try(ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(bytes))){
out = (T)ois.readObject();
} catch (IOException | ClassNotFoundException e){
throw new RuntimeException(e);
}
assertEquals(object, out);
return out;
}
public static INDArray randomOneHot(long examples, long nOut){
return randomOneHot(examples, nOut, new Random(12345));
}
public static INDArray randomOneHot(int examples, int nOut, long rngSeed){
public static INDArray randomOneHot(DataType dataType, long examples, long nOut){
return randomOneHot(dataType, examples, nOut, new Random(12345));
}
public static INDArray randomOneHot(long examples, long nOut, long rngSeed){
return randomOneHot(examples, nOut, new Random(rngSeed));
}
public static INDArray randomOneHot(int examples, int nOut, Random rng){
INDArray arr = Nd4j.create(examples, nOut);
public static INDArray randomOneHot(long examples, long nOut, Random rng) {
return randomOneHot(Nd4j.defaultFloatingPointType(), examples,nOut, rng);
}
public static INDArray randomOneHot(DataType dataType, long examples, long nOut, Random rng){
INDArray arr = Nd4j.create(dataType, examples, nOut);
for( int i=0; i<examples; i++ ){
arr.putScalar(i, rng.nextInt(nOut), 1.0);
arr.putScalar(i, rng.nextInt((int) nOut), 1.0);
}
return arr;
}
@ -115,4 +172,143 @@ public class TestUtils {
Nd4j.getExecutioner().exec(new BernoulliDistribution(ret, p));
return ret;
}
public static void writeStreamToFile(File out, InputStream is) throws IOException {
byte[] b = IOUtils.toByteArray(is);
try (OutputStream os = new BufferedOutputStream(new FileOutputStream(out))) {
os.write(b);
}
}
public static L1Regularization getL1Reg(List<Regularization> l){
for(Regularization r : l){
if(r instanceof L1Regularization){
return (L1Regularization) r;
}
}
return null;
}
public static L2Regularization getL2Reg(BaseLayer baseLayer){
return getL2Reg(baseLayer.getRegularization());
}
public static L2Regularization getL2Reg(List<Regularization> l){
for(Regularization r : l){
if(r instanceof L2Regularization){
return (L2Regularization) r;
}
}
return null;
}
public static WeightDecay getWeightDecayReg(BaseLayer bl){
return getWeightDecayReg(bl.getRegularization());
}
public static WeightDecay getWeightDecayReg(List<Regularization> l){
for(Regularization r : l){
if(r instanceof WeightDecay){
return (WeightDecay) r;
}
}
return null;
}
public static double getL1(BaseLayer layer) {
List<Regularization> l = layer.getRegularization();
return getL1(l);
}
public static double getL1(List<Regularization> l){
L1Regularization l1Reg = null;
for(Regularization reg : l){
if(reg instanceof L1Regularization)
l1Reg = (L1Regularization) reg;
}
assertNotNull(l1Reg);
return l1Reg.getL1().valueAt(0,0);
}
public static double getL2(BaseLayer layer) {
List<Regularization> l = layer.getRegularization();
return getL2(l);
}
public static double getL2(List<Regularization> l){
L2Regularization l2Reg = null;
for(Regularization reg : l){
if(reg instanceof L2Regularization)
l2Reg = (L2Regularization) reg;
}
assertNotNull(l2Reg);
return l2Reg.getL2().valueAt(0,0);
}
public static double getL1(AbstractSameDiffLayer layer){
return getL1(layer.getRegularization());
}
public static double getL2(AbstractSameDiffLayer layer){
return getL2(layer.getRegularization());
}
public static double getWeightDecay(BaseLayer layer) {
return getWeightDecayReg(layer.getRegularization()).getCoeff().valueAt(0,0);
}
public static void removeHelper(Layer layer) throws Exception {
removeHelpers(new Layer[]{layer});
}
public static void removeHelpers(Layer[] layers) throws Exception {
for(Layer l : layers){
if(l instanceof ConvolutionLayer){
Field f1 = ConvolutionLayer.class.getDeclaredField("helper");
f1.setAccessible(true);
f1.set(l, null);
} else if(l instanceof SubsamplingLayer){
Field f2 = SubsamplingLayer.class.getDeclaredField("helper");
f2.setAccessible(true);
f2.set(l, null);
} else if(l instanceof BatchNormalization) {
Field f3 = BatchNormalization.class.getDeclaredField("helper");
f3.setAccessible(true);
f3.set(l, null);
} else if(l instanceof LSTM){
Field f4 = LSTM.class.getDeclaredField("helper");
f4.setAccessible(true);
f4.set(l, null);
} else if(l instanceof LocalResponseNormalization){
Field f5 = LocalResponseNormalization.class.getDeclaredField("helper");
f5.setAccessible(true);
f5.set(l, null);
}
if(l.getHelper() != null){
throw new IllegalStateException("Did not remove helper for layer: " + l.getClass().getSimpleName());
}
}
}
public static void assertHelperPresent(Layer layer){
}
public static void assertHelpersPresent(Layer[] layers) throws Exception {
for(Layer l : layers){
//Don't use instanceof here - there are sub conv subclasses
if(l.getClass() == ConvolutionLayer.class || l instanceof SubsamplingLayer || l instanceof BatchNormalization || l instanceof LSTM){
Preconditions.checkNotNull(l.getHelper(), l.conf().getLayer().getLayerName());
}
}
}
public static void assertHelpersAbsent(Layer[] layers) throws Exception {
for(Layer l : layers){
Preconditions.checkState(l.getHelper() == null, l.conf().getLayer().getLayerName());
}
}
}

967
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/convolution/ConvDataFormatTests.java Normal file
View File

@ -0,0 +1,967 @@
/* ******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License 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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.convolution;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.CuDNNTestUtils;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test;
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;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;
@RunWith(Parameterized.class)
public class ConvDataFormatTests extends BaseDL4JTest {
private final DataType dataType;
public ConvDataFormatTests(DataType dataType){
this.dataType = dataType;
}
@Parameterized.Parameters(name = "{0}")
public static Object[] params(){
return new DataType[]{DataType.FLOAT, DataType.DOUBLE};
}
@Test
public void testConv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getConv2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getConv2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getConv2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getConv2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSubsampling2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSubsampling2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getSubsampling2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getSubsampling2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getSubsampling2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testDepthwiseConv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getDepthwiseConv2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getDepthwiseConv2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getDepthwiseConv2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getDepthwiseConv2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSeparableConv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSeparableConv2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getSeparableConv2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getSeparableConv2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getSeparableConv2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testDeconv2d() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getDeconv2DNet2dNet(CNN2DFormat.NCHW, true, cm))
.net2(getDeconv2DNet2dNet(CNN2DFormat.NCHW, false, cm))
.net3(getDeconv2DNet2dNet(CNN2DFormat.NHWC, true, cm))
.net4(getDeconv2DNet2dNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testLRN() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getLrnLayer(CNN2DFormat.NCHW, true, cm))
.net2(getLrnLayer(CNN2DFormat.NCHW, false, cm))
.net3(getLrnLayer(CNN2DFormat.NHWC, true, cm))
.net4(getLrnLayer(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testZeroPaddingLayer(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getZeroPaddingNet(CNN2DFormat.NCHW, true))
.net2(getZeroPaddingNet(CNN2DFormat.NCHW, false))
.net3(getZeroPaddingNet(CNN2DFormat.NHWC, true))
.net4(getZeroPaddingNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testCropping2DLayer(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getCropping2dNet(CNN2DFormat.NCHW, true))
.net2(getCropping2dNet(CNN2DFormat.NCHW, false))
.net3(getCropping2dNet(CNN2DFormat.NHWC, true))
.net4(getCropping2dNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testUpsampling2d(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getUpsamplingNet(CNN2DFormat.NCHW, true))
.net2(getUpsamplingNet(CNN2DFormat.NCHW, false))
.net3(getUpsamplingNet(CNN2DFormat.NHWC, true))
.net4(getUpsamplingNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testBatchNormNet(){
try {
for(boolean useLogStd : new boolean[]{true, false}) {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = (helpers ? "With helpers" : "No helpers") + " - " + (useLogStd ? "logstd" : "std");
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getBatchNormNet(useLogStd, CNN2DFormat.NCHW, true))
.net2(getBatchNormNet(useLogStd, CNN2DFormat.NCHW, false))
.net3(getBatchNormNet(useLogStd, CNN2DFormat.NHWC, true))
.net4(getBatchNormNet(useLogStd, CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testCnnLossLayer() {
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labelsNHWC = TestUtils.randomOneHot(this.dataType,2*6*6, 3);
labelsNHWC = labelsNHWC.reshape(2,6,6,3);
INDArray labelsNCHW = labelsNHWC.permute(0,3,1,2).dup();
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getCnnLossNet(CNN2DFormat.NCHW, true, ConvolutionMode.Same))
.net2(getCnnLossNet(CNN2DFormat.NCHW, false, ConvolutionMode.Same))
.net3(getCnnLossNet(CNN2DFormat.NHWC, true, ConvolutionMode.Same))
.net4(getCnnLossNet(CNN2DFormat.NHWC, false, ConvolutionMode.Same))
.inNCHW(inNCHW)
.labelsNCHW(labelsNCHW)
.labelsNHWC(labelsNHWC)
.testLayerIdx(1)
.nhwcOutput(true)
.helpers(helpers)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSpaceToDepthNet(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSpaceToDepthNet(CNN2DFormat.NCHW, true))
.net2(getSpaceToDepthNet(CNN2DFormat.NCHW, false))
.net3(getSpaceToDepthNet(CNN2DFormat.NHWC, true))
.net4(getSpaceToDepthNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testSpaceToBatchNet(){
try {
for (boolean helpers : new boolean[]{false, true}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers" : "No helpers";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 16, 16);
INDArray labels = TestUtils.randomOneHot(8, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getSpaceToBatchNet(CNN2DFormat.NCHW, true))
.net2(getSpaceToBatchNet(CNN2DFormat.NCHW, false))
.net3(getSpaceToBatchNet(CNN2DFormat.NHWC, true))
.net4(getSpaceToBatchNet(CNN2DFormat.NHWC, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testLocallyConnected() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (ConvolutionMode cm : new ConvolutionMode[]{ConvolutionMode.Truncate, ConvolutionMode.Same}) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + cm + ")" : "No helpers (" + cm + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getLocallyConnectedNet(CNN2DFormat.NCHW, true, cm))
.net2(getLocallyConnectedNet(CNN2DFormat.NCHW, false, cm))
.net3(getLocallyConnectedNet(CNN2DFormat.NHWC, true, cm))
.net4(getLocallyConnectedNet(CNN2DFormat.NHWC, false, cm))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.helpers(helpers)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
@Test
public void testGlobalPooling() {
try {
for (boolean helpers : new boolean[]{false, true}) {
for (PoolingType pt : PoolingType.values()) {
Nd4j.getRandom().setSeed(12345);
Nd4j.getEnvironment().allowHelpers(helpers);
String msg = helpers ? "With helpers (" + pt + ")" : "No helpers (" + pt + ")";
System.out.println(" --- " + msg + " ---");
INDArray inNCHW = Nd4j.rand(this.dataType, 2, 3, 12, 12);
INDArray labels = TestUtils.randomOneHot(2, 10);
TestCase tc = TestCase.builder()
.msg(msg)
.net1(getGlobalPoolingNet(CNN2DFormat.NCHW, pt, true))
.net2(getGlobalPoolingNet(CNN2DFormat.NCHW, pt, false))
.net3(getGlobalPoolingNet(CNN2DFormat.NHWC, pt, true))
.net4(getGlobalPoolingNet(CNN2DFormat.NHWC, pt, false))
.inNCHW(inNCHW)
.labelsNCHW(labels)
.labelsNHWC(labels)
.testLayerIdx(1)
.build();
testHelper(tc);
}
}
} finally {
Nd4j.getEnvironment().allowHelpers(true);
}
}
private MultiLayerNetwork getConv2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getSubsampling2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new SubsamplingLayer.Builder()
.kernelSize(2, 2)
.stride(1, 1)
.dataFormat(format)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new SubsamplingLayer.Builder()
.kernelSize(2, 2)
.stride(1, 1)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getSeparableConv2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new SeparableConvolution2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new SeparableConvolution2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getDepthwiseConv2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new DepthwiseConvolution2D.Builder()
.depthMultiplier(2)
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new DepthwiseConvolution2D.Builder()
.depthMultiplier(2)
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getLrnLayer(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new LocalResponseNormalization.Builder()
.dataFormat(format)
.helperAllowFallback(false)
.build(), format, cm, null);
} else {
return getNetWithLayer(new LocalResponseNormalization.Builder()
.helperAllowFallback(false)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getZeroPaddingNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new ZeroPaddingLayer.Builder(2,2)
.dataFormat(format).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new ZeroPaddingLayer.Builder(2,2).build(),
format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getCropping2dNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new Cropping2D.Builder(2,2)
.dataFormat(format).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new Cropping2D.Builder(2,2)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getUpsamplingNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new Upsampling2D.Builder(2)
.dataFormat(format).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new Upsampling2D.Builder(2)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getDeconv2DNet2dNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new Deconvolution2D.Builder().nOut(2)
.activation(Activation.TANH)
.kernelSize(2,2)
.stride(2,2)
.build(), format, cm, null);
} else {
return getNetWithLayer(new Deconvolution2D.Builder().nOut(2)
.activation(Activation.TANH)
.kernelSize(2,2)
.stride(2,2)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getBatchNormNet(boolean logStdev, CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new BatchNormalization.Builder()
.useLogStd(logStdev)
.dataFormat(format)
.helperAllowFallback(false)
.nOut(3).build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new BatchNormalization.Builder()
.useLogStd(logStdev)
.helperAllowFallback(false)
.nOut(3).build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getSpaceToDepthNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new SpaceToDepthLayer.Builder()
.blocks(2)
.dataFormat(format)
.build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new SpaceToDepthLayer.Builder()
.blocks(2)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getSpaceToBatchNet(CNN2DFormat format, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new SpaceToBatchLayer.Builder()
.blocks(2, 2)
.dataFormat(format)
.build(), format, ConvolutionMode.Same, InputType.convolutional(16, 16, 3, format));
} else {
return getNetWithLayer(new SpaceToBatchLayer.Builder()
.blocks(2, 2)
.build(), format, ConvolutionMode.Same, InputType.convolutional(16, 16, 3, format));
}
}
private MultiLayerNetwork getLocallyConnectedNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm) {
if (setOnLayerAlso) {
return getNetWithLayer(new LocallyConnected2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.build(), format, cm, null);
} else {
return getNetWithLayer(new LocallyConnected2D.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.nOut(3)
.build(), format, cm, null);
}
}
private MultiLayerNetwork getGlobalPoolingNet(CNN2DFormat format, PoolingType pt, boolean setOnLayerAlso) {
if (setOnLayerAlso) {
return getNetWithLayer(new GlobalPoolingLayer.Builder(pt)
.poolingDimensions(format == CNN2DFormat.NCHW ? new int[]{2,3} : new int[]{1,2})
.build(), format, ConvolutionMode.Same, null);
} else {
return getNetWithLayer(new GlobalPoolingLayer.Builder(pt)
.build(), format, ConvolutionMode.Same, null);
}
}
private MultiLayerNetwork getCnnLossNet(CNN2DFormat format, boolean setOnLayerAlso, ConvolutionMode cm){
NeuralNetConfiguration.ListBuilder builder = new NeuralNetConfiguration.Builder()
.seed(12345)
.convolutionMode(cm)
.list()
.layer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build());
if(setOnLayerAlso){
builder.layer(new CnnLossLayer.Builder().format(format).activation(Activation.SOFTMAX).build());
} else {
builder.layer(new CnnLossLayer.Builder().activation(Activation.SOFTMAX).build());
}
builder.setInputType(InputType.convolutional(12, 12, 3, format));
MultiLayerNetwork net = new MultiLayerNetwork(builder.build());
net.init();
return net;
}
private MultiLayerNetwork getNetWithLayer(Layer layer, CNN2DFormat format, ConvolutionMode cm, InputType inputType) {
NeuralNetConfiguration.ListBuilder builder = new NeuralNetConfiguration.Builder()
.dataType(this.dataType)
.seed(12345)
.convolutionMode(cm)
.list()
.layer(new ConvolutionLayer.Builder()
.kernelSize(3, 3)
.stride(2, 2)
.activation(Activation.TANH)
.dataFormat(format)
.nOut(3)
.helperAllowFallback(false)
.build())
.layer(layer)
.layer(new OutputLayer.Builder().activation(Activation.SOFTMAX).nOut(10).build())
.setInputType(inputType != null ? inputType : InputType.convolutional(12, 12, 3, format));
MultiLayerNetwork net = new MultiLayerNetwork(builder.build());
net.init();
return net;
}
@AllArgsConstructor
@Data
@NoArgsConstructor
@Builder
private static class TestCase {
private String msg;
private MultiLayerNetwork net1;
private MultiLayerNetwork net2;
private MultiLayerNetwork net3;
private MultiLayerNetwork net4;
private INDArray inNCHW;
private INDArray labelsNCHW;
private INDArray labelsNHWC;
private int testLayerIdx;
private boolean nhwcOutput;
private boolean helpers;
}
public static void testHelper(TestCase tc) {
if(!tc.helpers){
try {
CuDNNTestUtils.removeHelpers(tc.net1.getLayers());
CuDNNTestUtils.removeHelpers(tc.net2.getLayers());
CuDNNTestUtils.removeHelpers(tc.net3.getLayers());
CuDNNTestUtils.removeHelpers(tc.net4.getLayers());
} catch (Throwable t){
throw new RuntimeException(t);
}
}
tc.net2.params().assign(tc.net1.params());
tc.net3.params().assign(tc.net1.params());
tc.net4.params().assign(tc.net1.params());
//Test forward pass:
INDArray inNCHW = tc.inNCHW;
INDArray inNHWC = tc.inNCHW.permute(0, 2, 3, 1).dup();
INDArray l0_1 = tc.net1.feedForward(inNCHW).get(tc.testLayerIdx + 1);
INDArray l0_2 = tc.net2.feedForward(inNCHW).get(tc.testLayerIdx + 1);
INDArray l0_3 = tc.net3.feedForward(inNHWC).get(tc.testLayerIdx + 1);
INDArray l0_4 = tc.net4.feedForward(inNHWC).get(tc.testLayerIdx + 1);
assertEquals(tc.msg, l0_1, l0_2);
if(l0_1.rank() == 4) {
assertEquals(tc.msg, l0_1, l0_3.permute(0, 3, 1, 2));
assertEquals(tc.msg, l0_1, l0_4.permute(0, 3, 1, 2));
} else {
assertEquals(tc.msg, l0_1, l0_3);
assertEquals(tc.msg, l0_1, l0_4);
}
INDArray out1 = tc.net1.output(inNCHW);
INDArray out2 = tc.net2.output(inNCHW);
INDArray out3 = tc.net3.output(inNHWC);
INDArray out4 = tc.net4.output(inNHWC);
assertEquals(tc.msg, out1, out2);
if(!tc.nhwcOutput) {
assertEquals(tc.msg, out1, out3);
assertEquals(tc.msg, out1, out4);
} else {
assertEquals(tc.msg, out1, out3.permute(0,3,1,2)); //NHWC to NCHW
assertEquals(tc.msg, out1, out4.permute(0,3,1,2));
}
//Test backprop
Pair<Gradient, INDArray> p1 = tc.net1.calculateGradients(inNCHW, tc.labelsNCHW, null, null);
Pair<Gradient, INDArray> p2 = tc.net2.calculateGradients(inNCHW, tc.labelsNCHW, null, null);
Pair<Gradient, INDArray> p3 = tc.net3.calculateGradients(inNHWC, tc.labelsNHWC, null, null);
Pair<Gradient, INDArray> p4 = tc.net4.calculateGradients(inNHWC, tc.labelsNHWC, null, null);
//Inpput gradients
assertEquals(tc.msg, p1.getSecond(), p2.getSecond());
assertEquals(tc.msg, p1.getSecond(), p3.getSecond().permute(0,3,1,2)); //Input gradients for NHWC input are also in NHWC format
assertEquals(tc.msg, p1.getSecond(), p4.getSecond().permute(0,3,1,2));
List<String> diff12 = differentGrads(p1.getFirst(), p2.getFirst());
List<String> diff13 = differentGrads(p1.getFirst(), p3.getFirst());
List<String> diff14 = differentGrads(p1.getFirst(), p4.getFirst());
assertEquals(tc.msg + " " + diff12, 0, diff12.size());
assertEquals(tc.msg + " " + diff13, 0, diff13.size());
assertEquals(tc.msg + " " + diff14, 0, diff14.size());
assertEquals(tc.msg, p1.getFirst().gradientForVariable(), p2.getFirst().gradientForVariable());
assertEquals(tc.msg, p1.getFirst().gradientForVariable(), p3.getFirst().gradientForVariable());
assertEquals(tc.msg, p1.getFirst().gradientForVariable(), p4.getFirst().gradientForVariable());
tc.net1.fit(inNCHW, tc.labelsNCHW);
tc.net2.fit(inNCHW, tc.labelsNCHW);
tc.net3.fit(inNHWC, tc.labelsNHWC);
tc.net4.fit(inNHWC, tc.labelsNHWC);
assertEquals(tc.msg, tc.net1.params(), tc.net2.params());
assertEquals(tc.msg, tc.net1.params(), tc.net3.params());
assertEquals(tc.msg, tc.net1.params(), tc.net4.params());
//Test serialization
MultiLayerNetwork net1a = TestUtils.testModelSerialization(tc.net1);
MultiLayerNetwork net2a = TestUtils.testModelSerialization(tc.net2);
MultiLayerNetwork net3a = TestUtils.testModelSerialization(tc.net3);
MultiLayerNetwork net4a = TestUtils.testModelSerialization(tc.net4);
if(!tc.helpers){
try {
CuDNNTestUtils.removeHelpers(net1a.getLayers());
CuDNNTestUtils.removeHelpers(net2a.getLayers());
CuDNNTestUtils.removeHelpers(net3a.getLayers());
CuDNNTestUtils.removeHelpers(net4a.getLayers());
} catch (Throwable t){
throw new RuntimeException(t);
}
}
out1 = tc.net1.output(inNCHW);
assertEquals(tc.msg, out1, net1a.output(inNCHW));
assertEquals(tc.msg, out1, net2a.output(inNCHW));
if(!tc.nhwcOutput) {
assertEquals(tc.msg, out1, net3a.output(inNHWC));
assertEquals(tc.msg, out1, net4a.output(inNHWC));
} else {
assertEquals(tc.msg, out1, net3a.output(inNHWC).permute(0,3,1,2)); //NHWC to NCHW
assertEquals(tc.msg, out1, net4a.output(inNHWC).permute(0,3,1,2));
}
}
private static List<String> differentGrads(Gradient g1, Gradient g2){
List<String> differs = new ArrayList<>();
Map<String,INDArray> m1 = g1.gradientForVariable();
Map<String,INDArray> m2 = g2.gradientForVariable();
for(String s : m1.keySet()){
INDArray a1 = m1.get(s);
INDArray a2 = m2.get(s);
if(!a1.equals(a2)){
differs.add(s);
}
}
return differs;
}
}

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java
View File

@ -320,7 +320,7 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
INDArray[] output = model.output(input);
}
@Test
@Test @Ignore //AB 2020/04/22 Ignored until Keras model import updated to use NHWC support
public void importAcganGenerator() throws Exception {
ComputationGraph model = importFunctionalModelH5Test("modelimport/keras/examples/acgan/acgan_generator_1_epochs.h5");
//System.out.println(model.summary()) ;

31
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/CNN2DFormat.java Normal file
View File

@ -0,0 +1,31 @@
package org.deeplearning4j.nn.conf;
/**
* CNN2DFormat defines the format of the activations (including input images) in to and out of all 2D convolution layers in
* Deeplearning4j. Default value is NCHW.<br>
* <br>
* NCHW = "channels first" - arrays of shape [minibatch, channels, height, width]<br>
* NHWC = "channels last" - arrays of shape [minibatch, height, width, channels]<br>
*
* @author Alex Black
*/
public enum CNN2DFormat {
NCHW,
NHWC;
/**
* Returns a string that explains the dimensions:<br>
* NCHW -> returns "[minibatch, channels, height, width]"<br>
* NHWC -> returns "[minibatch, height, width, channels]"
*/
public String dimensionNames(){
switch (this){
case NCHW:
return "[minibatch, channels, height, width]";
case NHWC:
return "[minibatch, height, width, channels]";
default:
throw new IllegalStateException("Unknown enum: " + this); //Should never happen
}
}
}

24
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/inputs/InputType.java
View File

@ -20,6 +20,7 @@ import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import lombok.NoArgsConstructor;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.layers.Convolution3D;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonIgnore;
@ -123,7 +124,12 @@ public abstract class InputType implements Serializable {
* @return InputTypeConvolutional
*/
public static InputType convolutional(long height, long width, long depth) {
return new InputTypeConvolutional(height, width, depth);
// return new InputTypeConvolutional(height, width, depth);
return convolutional(height, width, depth, CNN2DFormat.NCHW);
}
public static InputType convolutional(long height, long width, long depth, CNN2DFormat format){
return new InputTypeConvolutional(height, width, depth, format);
}
/**
@ -257,11 +263,18 @@ public abstract class InputType implements Serializable {
private long height;
private long width;
private long channels;
private CNN2DFormat format = CNN2DFormat.NCHW; //Default for JSON deserialization of older configurations
public InputTypeConvolutional(@JsonProperty("height") long height, @JsonProperty("width") long width, @JsonProperty("channels") long channels) {
public InputTypeConvolutional(@JsonProperty("height") long height, @JsonProperty("width") long width,
@JsonProperty("channels") long channels, @JsonProperty("format") CNN2DFormat format) {
this.height = height;
this.width = width;
this.channels = channels;
this.format = format;
}
public InputTypeConvolutional(long height, long width, long channels) {
this(height, width, channels, CNN2DFormat.NCHW);
}
/**
@ -292,7 +305,7 @@ public abstract class InputType implements Serializable {
@Override
public String toString() {
return "InputTypeConvolutional(h=" + height + ",w=" + width + ",c=" + channels + ")";
return "InputTypeConvolutional(h=" + height + ",w=" + width + ",c=" + channels + "," + format + ")";
}
@Override
@ -302,8 +315,13 @@ public abstract class InputType implements Serializable {
@Override
public long[] getShape(boolean includeBatchDim) {
if(format == CNN2DFormat.NCHW){
if(includeBatchDim) return new long[]{-1, channels, height, width};
else return new long[]{channels, height, width};
} else {
if(includeBatchDim) return new long[]{-1, height, width, channels};
else return new long[]{height, width, channels};
}
}
}

16
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/BatchNormalization.java
View File

@ -20,6 +20,7 @@ import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -60,6 +61,7 @@ public class BatchNormalization extends FeedForwardLayer {
protected boolean lockGammaBeta = false;
protected boolean cudnnAllowFallback = true;
protected boolean useLogStd = false; //Default for deserialized models (1.0.0-beta3) and earlier: store variance as variance. Post 1.0.0-beta3: use log stdev instead
protected CNN2DFormat cnn2DFormat = CNN2DFormat.NCHW; //Default for deserialized models, 1.0.0-beta6 and earlier
private BatchNormalization(Builder builder) {
super(builder);
@ -71,6 +73,7 @@ public class BatchNormalization extends FeedForwardLayer {
this.lockGammaBeta = builder.lockGammaBeta;
this.cudnnAllowFallback = builder.cudnnAllowFallback;
this.useLogStd = builder.useLogStd;
this.cnn2DFormat = builder.cnn2DFormat;
initializeConstraints(builder);
}
@ -138,6 +141,7 @@ public class BatchNormalization extends FeedForwardLayer {
break;
case CNN:
nIn = ((InputType.InputTypeConvolutional) inputType).getChannels();
cnn2DFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
break;
case CNN3D:
nIn = ((InputType.InputTypeConvolutional3D) inputType).getChannels();
@ -307,6 +311,8 @@ public class BatchNormalization extends FeedForwardLayer {
*/
protected boolean useLogStd = true;
protected CNN2DFormat cnn2DFormat = CNN2DFormat.NCHW; //Default for deserialized models, 1.0.0-beta6 and earlier
public Builder(double decay, boolean isMinibatch) {
this.setDecay(decay);
this.setMinibatch(isMinibatch);
@ -329,6 +335,16 @@ public class BatchNormalization extends FeedForwardLayer {
public Builder() {}
/**
* Set the input and output array data format. Defaults to NCHW format - i.e., channels first.
* See {@link CNN2DFormat} for more details
* @param format Format to use
*/
public Builder dataFormat(CNN2DFormat format){
this.cnn2DFormat = format;
return this;
}
/**
* If doing minibatch training or not. Default: true. Under most circumstances, this should be set to true. If
* doing full batch training (i.e., all examples in a single DataSet object - very small data sets) then this

14
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CnnLossLayer.java
View File

@ -22,6 +22,7 @@ import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -62,10 +63,12 @@ import java.util.Map;
public class CnnLossLayer extends FeedForwardLayer {
protected ILossFunction lossFn;
protected CNN2DFormat format = CNN2DFormat.NCHW;
private CnnLossLayer(Builder builder) {
super(builder);
this.lossFn = builder.lossFn;
this.format = builder.format;
}
@Override
@ -114,12 +117,16 @@ public class CnnLossLayer extends FeedForwardLayer {
@Override
public void setNIn(InputType inputType, boolean override) {
//No op
if(inputType instanceof InputType.InputTypeConvolutional){
this.format = ((InputType.InputTypeConvolutional) inputType).getFormat();
}
}
public static class Builder extends BaseOutputLayer.Builder<Builder> {
protected CNN2DFormat format = CNN2DFormat.NCHW;
public Builder() {
this.activationFn = Activation.IDENTITY.getActivationFunction();
}
@ -132,6 +139,11 @@ public class CnnLossLayer extends FeedForwardLayer {
this.lossFn = lossFunction;
}
public Builder format(CNN2DFormat format){
this.format = format;
return this;
}
@Override
@SuppressWarnings("unchecked")
public Builder nIn(int nIn) {

29
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ConvolutionLayer.java
View File

@ -19,10 +19,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
@ -58,6 +55,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
protected int[] stride; // Default is 2. Down-sample by a factor of 2
protected int[] padding;
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat cnn2dDataFormat = CNN2DFormat.NCHW;
/**
* The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the {@link FwdAlgo},
@ -139,6 +137,9 @@ public class ConvolutionLayer extends FeedForwardLayer {
this.cudnnBwdFilterAlgo = builder.cudnnBwdFilterAlgo;
this.cudnnBwdDataAlgo = builder.cudnnBwdDataAlgo;
this.cudnnAllowFallback = builder.cudnnAllowFallback;
if(builder instanceof Builder) {
this.cnn2dDataFormat = ((Builder)builder).dataFormat;
}
initializeConstraints(builder);
}
@ -191,7 +192,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
}
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), ConvolutionLayer.class);
nOut, layerIndex, getLayerName(), cnn2dDataFormat, ConvolutionLayer.class);
}
@Override
@ -205,6 +206,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
this.nIn = c.getChannels();
}
this.cnn2dDataFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
}
@Override
@ -285,6 +287,8 @@ public class ConvolutionLayer extends FeedForwardLayer {
super();
}
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
@Override
protected boolean allowCausal() {
//Causal convolution - allowed for 1D only
@ -311,6 +315,17 @@ public class ConvolutionLayer extends FeedForwardLayer {
return this;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.dataFormat = format;
return this;
}
@Override
@SuppressWarnings("unchecked")
public ConvolutionLayer build() {
@ -359,6 +374,10 @@ public class ConvolutionLayer extends FeedForwardLayer {
public void setDilation(int... dilation) {
this.dilation = ValidationUtils.validate2NonNegative(dilation, false, "dilation");
}
public void setDataFormat(CNN2DFormat dataFormat){
this.dataFormat = dataFormat;
}
}
@Getter

8
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Deconvolution2D.java
View File

@ -22,6 +22,7 @@ import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -133,6 +134,13 @@ public class Deconvolution2D extends ConvolutionLayer {
super();
}
private CNN2DFormat format = CNN2DFormat.NCHW;
public Builder format(CNN2DFormat format){
this.format = format;
return this;
}
@Override
protected boolean allowCausal() {
//Causal convolution - allowed for 1D only

22
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/DepthwiseConvolution2D.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer;
@ -47,13 +48,14 @@ import java.util.*;
@EqualsAndHashCode(callSuper = true)
public class DepthwiseConvolution2D extends ConvolutionLayer {
int depthMultiplier;
protected int depthMultiplier;
protected DepthwiseConvolution2D(Builder builder) {
super(builder);
Preconditions.checkState(builder.depthMultiplier > 0, "Depth multiplier must be > 0, got %s", builder.depthMultiplier);
this.depthMultiplier = builder.depthMultiplier;
this.nOut = this.nIn * this.depthMultiplier;
this.cnn2dDataFormat = builder.cnn2DFormat;
initializeConstraints(builder);
}
@ -95,7 +97,7 @@ public class DepthwiseConvolution2D extends ConvolutionLayer {
}
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), DepthwiseConvolution2DLayer.class);
nOut, layerIndex, getLayerName(), cnn2dDataFormat, DepthwiseConvolution2DLayer.class);
}
@Override
@ -105,6 +107,7 @@ public class DepthwiseConvolution2D extends ConvolutionLayer {
if(nOut == 0 || override){
nOut = this.nIn * this.depthMultiplier;
}
this.cnn2dDataFormat = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Getter
@ -115,7 +118,9 @@ public class DepthwiseConvolution2D extends ConvolutionLayer {
* Set channels multiplier for depth-wise convolution
*
*/
public int depthMultiplier = 1;
protected int depthMultiplier = 1;
protected CNN2DFormat cnn2DFormat = CNN2DFormat.NCHW;
public Builder(int[] kernelSize, int[] stride, int[] padding) {
super(kernelSize, stride, padding);
@ -139,6 +144,17 @@ public class DepthwiseConvolution2D extends ConvolutionLayer {
return false;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.cnn2DFormat = format;
return this;
}
/**
* Set channels multiplier for depth-wise convolution
*

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/FeedForwardLayer.java
View File

@ -91,7 +91,7 @@ public abstract class FeedForwardLayer extends BaseLayer {
case CNN:
//CNN -> FF
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
return new CnnToFeedForwardPreProcessor(c.getHeight(), c.getWidth(), c.getChannels());
return new CnnToFeedForwardPreProcessor(c.getHeight(), c.getWidth(), c.getChannels(), c.getFormat());
case CNN3D:
//CNN3D -> FF
InputType.InputTypeConvolutional3D c3d = (InputType.InputTypeConvolutional3D) inputType;

12
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GlobalPoolingLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -127,7 +128,7 @@ public class GlobalPoolingLayer extends NoParamLayer {
if (collapseDimensions) {
return InputType.feedForward(conv.getChannels());
} else {
return InputType.convolutional(1, 1, conv.getChannels());
return InputType.convolutional(1, 1, conv.getChannels(), conv.getFormat());
}
case CNN3D:
InputType.InputTypeConvolutional3D conv3d = (InputType.InputTypeConvolutional3D) inputType;
@ -150,7 +151,14 @@ public class GlobalPoolingLayer extends NoParamLayer {
@Override
public void setNIn(InputType inputType, boolean override) {
//Not applicable
if(inputType.getType() == InputType.Type.CNN){
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
if(c.getFormat() == CNN2DFormat.NCHW){
poolingDimensions = new int[]{2,3};
} else {
poolingDimensions = new int[]{1,2};
}
}
}
@Override

21
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/InputTypeUtil.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.exception.DL4JInvalidConfigException;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -70,13 +71,13 @@ public class InputTypeUtil {
if (convolutionMode == ConvolutionMode.Same) {
long hOut = stride[0] * hIn;
long wOut = stride[1] * wIn;
return InputType.convolutional(hOut, wOut, outputDepth);
return InputType.convolutional(hOut, wOut, outputDepth, i.getFormat());
}
long hOut = sH * (hIn - 1) + kH - 2 * padH;
long wOut = sW * (wIn - 1) + kW - 2 * padW;
return InputType.convolutional(hOut, wOut, outputDepth);
return InputType.convolutional(hOut, wOut, outputDepth, i.getFormat());
}
public static InputType getOutputTypeDeconv3dLayer(InputType inputType, int[] kernelSize, int[] stride, int[] padding,
@ -332,10 +333,20 @@ public class InputTypeUtil {
return InputType.recurrent(outputDepth, outH);
}
/**
* @deprecated Use {@link #getOutputTypeCnnLayers(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, CNN2DFormat, Class)}
*/
@Deprecated
public static InputType getOutputTypeCnnLayers(InputType inputType, int[] kernelSize, int[] stride, int[] padding,
int[] dilation, ConvolutionMode convolutionMode, long outputDepth, long layerIdx, String layerName,
Class<?> layerClass) {
return getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode, outputDepth,
layerIdx, layerName, CNN2DFormat.NCHW, layerClass);
}
public static InputType getOutputTypeCnnLayers(InputType inputType, int[] kernelSize, int[] stride, int[] padding,
int[] dilation, ConvolutionMode convolutionMode, long outputDepth, long layerIdx, String layerName,
CNN2DFormat format, Class<?> layerClass) {
if (convolutionMode == null) {
String name = layerName == null ? "(not named)" : layerName;
@ -424,12 +435,12 @@ public class InputTypeUtil {
int outH = (int) Math.ceil(inHeight / ((double) stride[0]));
int outW = (int) Math.ceil(inWidth / ((double) stride[1]));
return InputType.convolutional(outH, outW, outputDepth);
return InputType.convolutional(outH, outW, outputDepth, format);
}
long hOut = (inHeight - kH + 2 * padH) / sH + 1;
long wOut = (inWidth - kW + 2 * padW) / sW + 1;
return InputType.convolutional(hOut, wOut, outputDepth);
return InputType.convolutional(hOut, wOut, outputDepth, format);
}
private static String getConfigErrorCommonLine(long layerIdx, String layerName, Class<?> layerClass,

22
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocalResponseNormalization.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
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;
@ -26,6 +27,7 @@ import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.EmptyParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.learning.regularization.Regularization;
@ -50,6 +52,7 @@ public class LocalResponseNormalization extends Layer {
protected double beta = 0.75; // decay rate
protected double alpha = 1e-4; // decay rate
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
private LocalResponseNormalization(Builder builder) {
super(builder);
@ -58,6 +61,7 @@ public class LocalResponseNormalization extends Layer {
this.alpha = builder.alpha;
this.beta = builder.beta;
this.cudnnAllowFallback = builder.cudnnAllowFallback;
this.dataFormat = builder.dataFormat;
}
@Override
@ -99,7 +103,8 @@ public class LocalResponseNormalization extends Layer {
@Override
public void setNIn(InputType inputType, boolean override) {
//No op
Preconditions.checkState(inputType.getType() == InputType.Type.CNN, "Only CNN input types can be used with LocalResponseNormalisation, got %s", inputType);
this.dataFormat = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
@ -184,8 +189,10 @@ public class LocalResponseNormalization extends Layer {
*/
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
public Builder(double k, double n, double alpha, double beta) {
this(k, n, alpha, beta, true);
this(k, n, alpha, beta, true, CNN2DFormat.NCHW);
}
public Builder(double k, double alpha, double beta) {
@ -263,6 +270,17 @@ public class LocalResponseNormalization extends Layer {
return this;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat dataFormat){
this.dataFormat = dataFormat;
return this;
}
@Override
public LocalResponseNormalization build() {
return new LocalResponseNormalization(this);

35
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected2D.java
View File

@ -17,6 +17,7 @@
package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -70,6 +71,7 @@ public class LocallyConnected2D extends SameDiffLayer {
private int[] inputSize;
private int[] outputSize;
private int featureDim;
protected CNN2DFormat format = CNN2DFormat.NCHW;
protected LocallyConnected2D(Builder builder) {
super(builder);
@ -84,6 +86,7 @@ public class LocallyConnected2D extends SameDiffLayer {
this.hasBias = builder.hasBias;
this.inputSize = builder.inputSize;
this.featureDim = kernel[0] * kernel[1] * (int) nIn;
this.format = builder.format;
}
private LocallyConnected2D() {
@ -97,17 +100,19 @@ public class LocallyConnected2D extends SameDiffLayer {
throw new IllegalArgumentException("Input size has to be specified for locally connected layers.");
}
int[] inputShape = new int[] {1, nIn, inputSize[0], inputSize[1]};
boolean nchw = format == CNN2DFormat.NCHW;
int[] inputShape = nchw ? new int[] {1, nIn, inputSize[0], inputSize[1]} : new int[] {1, inputSize[0], inputSize[1], nIn};
INDArray dummyInputForShapeInference = Nd4j.ones(inputShape);
if (cm == ConvolutionMode.Same) {
this.outputSize = ConvolutionUtils.getOutputSize(dummyInputForShapeInference, kernel, stride, null, cm,
dilation);
dilation, format);
this.padding = ConvolutionUtils.getSameModeTopLeftPadding(outputSize, inputSize, kernel, stride, dilation);
this.paddingBr = ConvolutionUtils.getSameModeBottomRightPadding(outputSize, inputSize, kernel, stride, dilation);
} else {
this.outputSize = ConvolutionUtils.getOutputSize(dummyInputForShapeInference, kernel, stride, padding, cm,
dilation);
dilation, format);
}
}
@ -123,7 +128,7 @@ public class LocallyConnected2D extends SameDiffLayer {
computeOutputSize();
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernel, stride, padding, new int[] {1, 1}, cm, nOut,
layerIndex, getLayerName(), LocallyConnected2D.class);
layerIndex, getLayerName(), format, LocallyConnected2D.class);
}
@Override
@ -133,6 +138,7 @@ public class LocallyConnected2D extends SameDiffLayer {
this.nIn = c.getChannels();
this.featureDim = kernel[0] * kernel[1] * (int) nIn;
}
this.format = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
@ -181,6 +187,10 @@ public class LocallyConnected2D extends SameDiffLayer {
int kH = kernel[0];
int kW = kernel[1];
boolean nchw = format == CNN2DFormat.NCHW;
if(!nchw)
layerInput = layerInput.permute(0,3,1,2); //NHWC to NCHW
if(padding[0] > 0 || padding[1] > 0 || (cm == ConvolutionMode.Same && (paddingBr[0] > 0 || paddingBr[1] > 0))){
//Note: for same mode, bottom/right padding can be 1 more than top/left padding
//NCHW format
@ -210,16 +220,15 @@ public class LocallyConnected2D extends SameDiffLayer {
SDVariable reshapeResult = sameDiff.reshape(mmulResult, outH, outW, miniBatch, nOut);
SDVariable permutedResult = sameDiff.permute(reshapeResult, 2, 3, 0, 1); // (mb, nOut, outH, outW)
SDVariable permutedResult = nchw ? reshapeResult.permute(2, 3, 0, 1) : reshapeResult.permute(2, 0, 1, 3); // (mb, nOut, outH, outW) or (mb, outH, outW, nOut)
if (hasBias) {
SDVariable b = paramTable.get(ConvolutionParamInitializer.BIAS_KEY);
SDVariable biasAddedResult = sameDiff.nn().biasAdd(permutedResult, b, true);
SDVariable biasAddedResult = sameDiff.nn().biasAdd(permutedResult, b, nchw);
return activation.asSameDiff("out", sameDiff, biasAddedResult);
} else {
return activation.asSameDiff("out", sameDiff, permutedResult);
}
}
@Override
@ -292,6 +301,7 @@ public class LocallyConnected2D extends SameDiffLayer {
*/
private boolean hasBias = true;
protected CNN2DFormat format = CNN2DFormat.NCHW;
/**
@ -386,6 +396,17 @@ public class LocallyConnected2D extends SameDiffLayer {
return this;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.format = format;
return this;
}
/**
* @param hasBias If true (default is false) the layer will have a bias
*/

21
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SeparableConvolution2D.java
View File

@ -20,6 +20,7 @@ import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer;
@ -85,6 +86,8 @@ public class SeparableConvolution2D extends ConvolutionLayer {
this.cudnnFwdAlgo = builder.cudnnFwdAlgo;
this.cudnnBwdFilterAlgo = builder.cudnnBwdFilterAlgo;
this.cudnnBwdDataAlgo = builder.cudnnBwdDataAlgo;
this.cnn2dDataFormat = builder.dataFormat;
initializeConstraints(builder);
}
@ -153,8 +156,10 @@ public class SeparableConvolution2D extends ConvolutionLayer {
+ "\"): Expected CNN input, got " + inputType);
}
CNN2DFormat format = ((InputType.InputTypeConvolutional)inputType).getFormat();
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), SeparableConvolution2DLayer.class);
nOut, layerIndex, getLayerName(), format, SeparableConvolution2DLayer.class);
}
@ -166,7 +171,8 @@ public class SeparableConvolution2D extends ConvolutionLayer {
* Set channels multiplier of channels-wise step in separable convolution
*
*/
public int depthMultiplier = 1;
protected int depthMultiplier = 1;
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
public Builder(int[] kernelSize, int[] stride, int[] padding) {
super(kernelSize, stride, padding);
@ -190,6 +196,17 @@ public class SeparableConvolution2D extends ConvolutionLayer {
return false;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.dataFormat = format;
return this;
}
/**
* Set channels multiplier of channels-wise step in separable convolution
*

22
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToBatchLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -26,6 +27,7 @@ import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.EmptyParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -65,12 +67,14 @@ public class SpaceToBatchLayer extends NoParamLayer {
protected int[] blocks;
protected int[][] padding;
protected CNN2DFormat format = CNN2DFormat.NCHW;
protected SpaceToBatchLayer(Builder builder) {
super(builder);
this.blocks = builder.blocks;
this.padding = builder.padding;
this.format = builder.format;
}
@Override
@ -112,7 +116,7 @@ public class SpaceToBatchLayer extends NoParamLayer {
}
InputType.InputTypeConvolutional i = (InputType.InputTypeConvolutional) inputType;
return InputType.convolutional((i.getHeight() + padding[0][0] + padding[0][1]) / blocks[0],
(i.getWidth() + padding[1][0] + padding[1][1]) / blocks[1], i.getChannels());
(i.getWidth() + padding[1][0] + padding[1][1]) / blocks[1], i.getChannels(), i.getFormat());
}
@Override
@ -123,7 +127,8 @@ public class SpaceToBatchLayer extends NoParamLayer {
@Override
public void setNIn(InputType inputType, boolean override) {
//No op: space to batch layer doesn't have nIn value
Preconditions.checkState(inputType.getType() == InputType.Type.CNN, "Only CNN input types can be used with SpaceToBatchLayer, got %s", inputType);
this.format = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
@ -158,6 +163,8 @@ public class SpaceToBatchLayer extends NoParamLayer {
*/
protected int[][] padding;
protected CNN2DFormat format = CNN2DFormat.NCHW;
/**
* @param blocks Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions
@ -193,6 +200,17 @@ public class SpaceToBatchLayer extends NoParamLayer {
this.setPadding(padding);
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public T dataFormat(CNN2DFormat format){
this.format = format;
return (T)this;
}
/**
* @param blocks Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions

36
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToDepthLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -56,12 +57,20 @@ import java.util.Map;
@EqualsAndHashCode(callSuper = true)
public class SpaceToDepthLayer extends NoParamLayer {
/**
* @deprecated Use {@link CNN2DFormat} instead
*/
@Deprecated
public enum DataFormat {
NCHW, NHWC
NCHW, NHWC;
public CNN2DFormat toFormat(){
return this == NCHW ? CNN2DFormat.NCHW : CNN2DFormat.NHWC;
}
}
protected int blockSize;
protected DataFormat dataFormat;
protected CNN2DFormat dataFormat;
protected SpaceToDepthLayer(Builder builder) {
@ -108,7 +117,7 @@ public class SpaceToDepthLayer extends NoParamLayer {
}
InputType.InputTypeConvolutional i = (InputType.InputTypeConvolutional) inputType;
return InputType.convolutional(i.getHeight() / blockSize, i.getWidth() / blockSize,
i.getChannels() * blockSize * blockSize);
i.getChannels() * blockSize * blockSize, i.getFormat());
}
@Override
@ -119,7 +128,7 @@ public class SpaceToDepthLayer extends NoParamLayer {
@Override
public void setNIn(InputType inputType, boolean override) {
//No op: space to batch layer doesn't have nIn value
this.dataFormat = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
@ -147,7 +156,7 @@ public class SpaceToDepthLayer extends NoParamLayer {
/**
* Data format for input activations. Note DL4J uses NCHW in most cases
*/
protected DataFormat dataFormat = DataFormat.NCHW;
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
/**
* @param blockSize Block size
@ -160,7 +169,12 @@ public class SpaceToDepthLayer extends NoParamLayer {
* @param blockSize Block size
* @param dataFormat Data format for input activations. Note DL4J uses NCHW in most cases
*/
@Deprecated
public Builder(int blockSize, DataFormat dataFormat) {
this(blockSize, dataFormat.toFormat());
}
public Builder(int blockSize, CNN2DFormat dataFormat) {
this.setBlockSize(blockSize);
this.setDataFormat(dataFormat);
}
@ -175,8 +189,20 @@ public class SpaceToDepthLayer extends NoParamLayer {
/**
* @param dataFormat Data format for input activations. Note DL4J uses NCHW in most cases
* @deprecated Use {@link #dataFormat(CNN2DFormat)}
*/
@Deprecated
public T dataFormat(DataFormat dataFormat) {
return dataFormat(dataFormat.toFormat());
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param dataFormat Format for activations (in and out)
*/
public T dataFormat(CNN2DFormat dataFormat) {
this.setDataFormat(dataFormat);
return (T) this;
}

22
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SubsamplingLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -58,6 +59,7 @@ public class SubsamplingLayer extends NoParamLayer {
protected int pnorm;
protected double eps;
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat cnn2dDataFormat = CNN2DFormat.NCHW;
/*
Default here for JSON deserialization of 1.0.0-beta4 and earlier models. New models default to false via builder.
This impacts average pooling only - whether the divisor should include or exclude padding along image edges.
@ -121,6 +123,7 @@ public class SubsamplingLayer extends NoParamLayer {
if (clone.dilation != null) {
clone.dilation = clone.dilation.clone();
}
return clone;
}
@ -153,12 +156,13 @@ public class SubsamplingLayer extends NoParamLayer {
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
((InputType.InputTypeConvolutional) inputType).getChannels(), layerIndex, getLayerName(),
SubsamplingLayer.class);
cnn2dDataFormat, SubsamplingLayer.class);
}
@Override
public void setNIn(InputType inputType, boolean override) {
//No op: subsampling layer doesn't have nIn value
this.cnn2dDataFormat = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
@ -229,6 +233,7 @@ public class SubsamplingLayer extends NoParamLayer {
* Dilation for kernel
*/
private int[] dilation = new int[] {1, 1};
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
public Builder(PoolingType poolingType, int[] kernelSize, int[] stride) {
super(poolingType, kernelSize, stride);
@ -307,6 +312,17 @@ public class SubsamplingLayer extends NoParamLayer {
return this;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.dataFormat = format;
return this;
}
/**
* Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions,
* which are also known as atrous convolutions.<br> NOTE: Kernel dilation is less common in practice for
@ -358,6 +374,10 @@ public class SubsamplingLayer extends NoParamLayer {
public void setDilation(int[] dilation) {
this.dilation = ValidationUtils.validate2NonNegative(dilation, false, "dilation");
}
public void setDataFormat(CNN2DFormat format){
this.dataFormat = format;
}
}
@NoArgsConstructor

28
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Upsampling2D.java
View File

@ -17,6 +17,7 @@
package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -59,10 +60,12 @@ public class Upsampling2D extends BaseUpsamplingLayer {
@JsonDeserialize(using = LegacyIntArrayDeserializer.class)
protected int[] size;
protected CNN2DFormat format = CNN2DFormat.NCHW;
protected Upsampling2D(UpsamplingBuilder builder) {
super(builder);
this.size = builder.size;
this.format = ((Builder)builder).format;
}
@Override
@ -97,7 +100,7 @@ public class Upsampling2D extends BaseUpsamplingLayer {
val inWidth = i.getWidth();
val inDepth = i.getChannels();
return InputType.convolutional(size[0] * inHeight, size[1] * inWidth, inDepth);
return InputType.convolutional(size[0] * inHeight, size[1] * inWidth, inDepth, i.getFormat());
}
@Override
@ -131,14 +134,35 @@ public class Upsampling2D extends BaseUpsamplingLayer {
.build();
}
@Override
public void setNIn(InputType inputType, boolean override) {
if (inputType == null || inputType.getType() != InputType.Type.CNN) {
throw new IllegalStateException("Invalid input for Upsampling 2D layer (layer name=\"" + getLayerName()
+ "\"): Expected CNN input, got " + inputType);
}
this.format = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@NoArgsConstructor
public static class Builder extends UpsamplingBuilder<Builder> {
protected CNN2DFormat format = CNN2DFormat.NCHW;
public Builder(int size) {
super(new int[] {size, size});
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.format = format;
return this;
}
/**
* Upsampling size int, used for both height and width
*
@ -146,7 +170,7 @@ public class Upsampling2D extends BaseUpsamplingLayer {
*/
public Builder size(int size) {
this.setSize(new int[] {size, size});
this.setSize(size, size);
return this;
}

26
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ZeroPaddingLayer.java
View File

@ -17,6 +17,7 @@
package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -45,6 +46,7 @@ import java.util.Map;
public class ZeroPaddingLayer extends NoParamLayer {
private int[] padding;
private CNN2DFormat dataFormat = CNN2DFormat.NCHW;
public ZeroPaddingLayer(int padTopBottom, int padLeftRight) {
this(new Builder(padTopBottom, padLeftRight));
@ -63,6 +65,7 @@ public class ZeroPaddingLayer extends NoParamLayer {
}
this.padding = builder.padding;
this.dataFormat = builder.cnn2DFormat;
}
@Override
@ -85,7 +88,9 @@ public class ZeroPaddingLayer extends NoParamLayer {
int outH = hwd[0] + padding[0] + padding[1];
int outW = hwd[1] + padding[2] + padding[3];
return InputType.convolutional(outH, outW, hwd[2]);
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional)inputType;
return InputType.convolutional(outH, outW, hwd[2], c.getFormat());
}
@Override
@ -107,6 +112,12 @@ public class ZeroPaddingLayer extends NoParamLayer {
.build();
}
@Override
public void setNIn(InputType inputType, boolean override) {
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional)inputType;
this.dataFormat = c.getFormat();
}
@Getter
@Setter
public static class Builder extends Layer.Builder<Builder> {
@ -117,6 +128,19 @@ public class ZeroPaddingLayer extends NoParamLayer {
@Setter(AccessLevel.NONE)
private int[] padding = new int[] {0, 0, 0, 0}; //Padding: top, bottom, left, right
private CNN2DFormat cnn2DFormat = CNN2DFormat.NCHW;
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.cnn2DFormat = format;
return this;
}
/**
* @param padding Padding value for top, bottom, left, and right. Must be length 4 array
*/

35
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping2D.java
View File

@ -17,12 +17,14 @@
package org.deeplearning4j.nn.conf.layers.convolutional;
import lombok.*;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.layers.NoParamLayer;
import org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.layers.convolution.Cropping2DLayer;
import org.deeplearning4j.optimize.api.TrainingListener;
@ -47,6 +49,7 @@ import java.util.Map;
public class Cropping2D extends NoParamLayer {
private int[] cropping;
private CNN2DFormat dataFormat = CNN2DFormat.NCHW;
/**
* @param cropTopBottom Amount of cropping to apply to both the top and the bottom of the input activations
@ -56,6 +59,10 @@ public class Cropping2D extends NoParamLayer {
this(cropTopBottom, cropTopBottom, cropLeftRight, cropLeftRight);
}
public Cropping2D(CNN2DFormat dataFormat, int cropTopBottom, int cropLeftRight) {
this(dataFormat, cropTopBottom, cropTopBottom, cropLeftRight, cropLeftRight);
}
/**
* @param cropTop Amount of cropping to apply to the top of the input activations
* @param cropBottom Amount of cropping to apply to the bottom of the input activations
@ -63,7 +70,11 @@ public class Cropping2D extends NoParamLayer {
* @param cropRight Amount of cropping to apply to the right of the input activations
*/
public Cropping2D(int cropTop, int cropBottom, int cropLeft, int cropRight) {
this(new Builder(cropTop, cropBottom, cropLeft, cropRight));
this(CNN2DFormat.NCHW, cropTop, cropBottom, cropLeft, cropRight);
}
public Cropping2D(CNN2DFormat format, int cropTop, int cropBottom, int cropLeft, int cropRight) {
this(new Builder(cropTop, cropBottom, cropLeft, cropRight).dataFormat(format));
}
/**
@ -77,6 +88,7 @@ public class Cropping2D extends NoParamLayer {
protected Cropping2D(Builder builder) {
super(builder);
this.cropping = builder.cropping;
this.dataFormat = builder.cnn2DFormat;
}
@Override
@ -98,7 +110,9 @@ public class Cropping2D extends NoParamLayer {
int outH = hwd[0] - cropping[0] - cropping[1];
int outW = hwd[1] - cropping[2] - cropping[3];
return InputType.convolutional(outH, outW, hwd[2]);
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional)inputType;
return InputType.convolutional(outH, outW, hwd[2], c.getFormat());
}
@Override
@ -113,6 +127,10 @@ public class Cropping2D extends NoParamLayer {
return null;
}
@Override
public void setNIn(InputType inputType, boolean override) {
this.dataFormat = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Getter
@Setter
@ -124,6 +142,19 @@ public class Cropping2D extends NoParamLayer {
@Setter(AccessLevel.NONE)
private int[] cropping = new int[] {0, 0, 0, 0};
private CNN2DFormat cnn2DFormat = CNN2DFormat.NCHW;
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.<br>
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat format){
this.cnn2DFormat = format;
return this;
}
/**
* @param cropping Cropping amount for top/bottom/left/right (in that order). Must be length 1, 2, or 4 array.
*/

38
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToFeedForwardPreProcessor.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.conf.preprocessor;
import lombok.Data;
import lombok.val;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.nd4j.linalg.api.ndarray.INDArray;
@ -52,6 +53,7 @@ public class CnnToFeedForwardPreProcessor implements InputPreProcessor {
protected long inputHeight;
protected long inputWidth;
protected long numChannels;
protected CNN2DFormat format = CNN2DFormat.NCHW; //Default for legacy JSON deserialization
/**
* @param inputHeight the columns
@ -61,16 +63,20 @@ public class CnnToFeedForwardPreProcessor implements InputPreProcessor {
@JsonCreator
public CnnToFeedForwardPreProcessor(@JsonProperty("inputHeight") long inputHeight,
@JsonProperty("inputWidth") long inputWidth, @JsonProperty("numChannels") long numChannels) {
@JsonProperty("inputWidth") long inputWidth, @JsonProperty("numChannels") long numChannels,
@JsonProperty("format") CNN2DFormat format) {
this.inputHeight = inputHeight;
this.inputWidth = inputWidth;
this.numChannels = numChannels;
this.format = format;
}
public CnnToFeedForwardPreProcessor(long inputHeight, long inputWidth) {
this.inputHeight = inputHeight;
this.inputWidth = inputWidth;
this.numChannels = 1;
this(inputHeight, inputWidth, 1, CNN2DFormat.NCHW);
}
public CnnToFeedForwardPreProcessor(long inputHeight, long inputWidth, long numChannels) {
this(inputHeight, inputWidth, numChannels, CNN2DFormat.NCHW);
}
public CnnToFeedForwardPreProcessor() {}
@ -80,20 +86,34 @@ public class CnnToFeedForwardPreProcessor implements InputPreProcessor {
public INDArray preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr) {
if (input.rank() == 2)
return input; //Should usually never happen
if(input.size(1) != numChannels || input.size(2) != inputHeight || input.size(3) != inputWidth){
int chDim = 1;
int hDim = 2;
int wDim = 3;
if(format == CNN2DFormat.NHWC){
chDim = 3;
hDim = 1;
wDim = 2;
}
if(input.size(chDim) != numChannels || input.size(hDim) != inputHeight || input.size(wDim) != inputWidth){
throw new IllegalStateException("Invalid input, does not match configuration: expected [minibatch, numChannels="
+ numChannels + ", inputHeight=" + inputHeight + ", inputWidth=" + inputWidth + "] but got input array of" +
"shape " + Arrays.toString(input.shape()));
}
//Check input: nchw format
if(input.size(1) != numChannels || input.size(2) != inputHeight ||
input.size(3) != inputWidth){
if(input.size(chDim) != numChannels || input.size(hDim) != inputHeight ||
input.size(wDim) != inputWidth){
throw new IllegalStateException("Invalid input array: expected shape [minibatch, channels, height, width] = "
+ "[minibatch, " + numChannels + ", " + inputHeight + ", " + inputWidth + "] - got "
+ Arrays.toString(input.shape()));
}
if(format == CNN2DFormat.NHWC) {
input = input.permute(0, 3, 1, 2); //NHWC to NCHW
}
//Assume input is standard rank 4 activations out of CNN layer
//First: we require input to be in c order. But c order (as declared in array order) isn't enough; also need strides to be correct
if (input.ordering() != 'c' || !Shape.hasDefaultStridesForShape(input))
@ -120,6 +140,10 @@ public class CnnToFeedForwardPreProcessor implements InputPreProcessor {
+ Arrays.toString(epsilons.shape()));
INDArray ret = epsilons.reshape('c', epsilons.size(0), numChannels, inputHeight, inputWidth);
if(format == CNN2DFormat.NHWC){
ret = ret.permute(0,2,3,1); //NCHW to NHWC
}
return workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, ret); //Move if required to specified workspace
}

33
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/CnnLossLayer.java
View File

@ -22,6 +22,7 @@ import lombok.val;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.MaskState;
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;
@ -73,22 +74,23 @@ public class CnnLossLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.Cn
if (input.rank() != 4)
throw new UnsupportedOperationException(
"Input is not rank 4. Got input with rank " + input.rank() + " " + layerId() + " with shape "
+ Arrays.toString(input.shape()) + " - expected shape [minibatch,channels,height,width]");
+ Arrays.toString(input.shape()) + " - expected shape " + layerConf().getFormat().dimensionNames());
if (labels == null)
throw new IllegalStateException("Labels are not set (null)");
Preconditions.checkState(input.equalShapes(labels), "Input and label arrays do not have same shape: %ndShape vs. %ndShape",input, labels);
INDArray input2d = ConvolutionUtils.reshape4dTo2d(input, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray labels2d = ConvolutionUtils.reshape4dTo2d(labels, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray maskReshaped = ConvolutionUtils.reshapeMaskIfRequired(maskArray, input, workspaceMgr, ArrayType.FF_WORKING_MEM);
CNN2DFormat format = layerConf().getFormat();
INDArray input2d = ConvolutionUtils.reshape4dTo2d(input, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray labels2d = ConvolutionUtils.reshape4dTo2d(labels, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray maskReshaped = ConvolutionUtils.reshapeMaskIfRequired(maskArray, input, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
// delta calculation
ILossFunction lossFunction = layerConf().getLossFn();
INDArray delta2d = lossFunction.computeGradient(labels2d, input2d.dup(input2d.ordering()), layerConf().getActivationFn(), maskReshaped);
delta2d = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, delta2d);
INDArray delta4d = ConvolutionUtils.reshape2dTo4d(delta2d, input.shape(), workspaceMgr, ArrayType.ACTIVATION_GRAD);
INDArray delta4d = ConvolutionUtils.reshape2dTo4d(delta2d, input.shape(), format, workspaceMgr, ArrayType.ACTIVATION_GRAD);
// grab the empty gradient
Gradient gradient = new DefaultGradient();
@ -161,13 +163,16 @@ public class CnnLossLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.Cn
assertInputSet(false);
if (input.rank() != 4)
throw new UnsupportedOperationException(
"Input must be rank 4. Got input with rank " + input.rank() + " " + layerId());
"Input must be rank 4 with shape " + layerConf().getFormat().dimensionNames() +
". Got input with rank " + input.rank() + " " + layerId());
CNN2DFormat format = layerConf().getFormat();
INDArray in = workspaceMgr.dup(ArrayType.ACTIVATIONS, input, input.ordering());
INDArray input2d = ConvolutionUtils.reshape4dTo2d(in, workspaceMgr, ArrayType.ACTIVATIONS);
INDArray input2d = ConvolutionUtils.reshape4dTo2d(in, format, workspaceMgr, ArrayType.ACTIVATIONS);
INDArray out2d = layerConf().getActivationFn().getActivation(input2d, training);
return ConvolutionUtils.reshape2dTo4d(out2d, input.shape(), workspaceMgr, ArrayType.ACTIVATIONS);
return ConvolutionUtils.reshape2dTo4d(out2d, input.shape(), format, workspaceMgr, ArrayType.ACTIVATIONS);
}
@Override
@ -196,7 +201,7 @@ public class CnnLossLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.Cn
public double computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr) {
INDArray input2d = ConvolutionUtils.reshape4dTo2d(input, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray labels2d = ConvolutionUtils.reshape4dTo2d(labels, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray maskReshaped = ConvolutionUtils.reshapeMaskIfRequired(maskArray, input, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray maskReshaped = ConvolutionUtils.reshapeMaskIfRequired(maskArray, input, layerConf().getFormat(), workspaceMgr, ArrayType.FF_WORKING_MEM);
ILossFunction lossFunction = layerConf().getLossFn();
@ -220,9 +225,11 @@ public class CnnLossLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.Cn
if (input == null || labels == null)
throw new IllegalStateException("Cannot calculate score without input and labels " + layerId());
INDArray input2d = ConvolutionUtils.reshape4dTo2d(input, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray labels2d = ConvolutionUtils.reshape4dTo2d(labels, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray maskReshaped = ConvolutionUtils.reshapeMaskIfRequired(maskArray, input, workspaceMgr, ArrayType.FF_WORKING_MEM);
CNN2DFormat format = layerConf().getFormat();
INDArray input2d = ConvolutionUtils.reshape4dTo2d(input, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray labels2d = ConvolutionUtils.reshape4dTo2d(labels, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray maskReshaped = ConvolutionUtils.reshapeMaskIfRequired(maskArray, input, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
ILossFunction lossFunction = layerConf().getLossFn();
INDArray scoreArray =
@ -233,7 +240,7 @@ public class CnnLossLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.Cn
val newShape = input.shape().clone();
newShape[1] = 1;
INDArray scoreArrayTs = ConvolutionUtils.reshape2dTo4d(scoreArray, newShape, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray scoreArrayTs = ConvolutionUtils.reshape2dTo4d(scoreArray, newShape, format, workspaceMgr, ArrayType.FF_WORKING_MEM);
INDArray summedScores = scoreArrayTs.sum(1,2,3).reshape(scoreArrayTs.size(0), 1);
if (fullNetRegTerm != 0.0) {

5
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ConvolutionHelper.java
View File

@ -16,6 +16,7 @@
package org.deeplearning4j.nn.layers.convolution;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo;
@ -39,10 +40,10 @@ public interface ConvolutionHelper extends LayerHelper {
Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel,
int[] strides, int[] pad, INDArray biasGradView, INDArray weightGradView, IActivation afn,
AlgoMode mode, BwdFilterAlgo bwdFilterAlgo, BwdDataAlgo bwdDataAlgo,
ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr);
ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);
INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad,
AlgoMode mode, FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr);
AlgoMode mode, FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);
INDArray activate(INDArray z, IActivation afn, boolean training);
}

59
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ConvolutionLayer.java
View File

@ -20,6 +20,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -43,8 +44,6 @@ import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.nd4j.util.OneTimeLogger;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Arrays;
@ -115,6 +114,14 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
if(epsilon.dataType() != dataType)
epsilon = epsilon.castTo(dataType);
INDArray origInput = input;
INDArray origEps = epsilon;
if(layerConf().getCnn2dDataFormat() != CNN2DFormat.NCHW){
input = input.permute(0,3,1,2); //NHWC to NCHW
epsilon = epsilon.permute(0,3,1,2); //NHWC to NCHW
}
long miniBatch = input.size(0);
int inH = (int) input.size(2);
int inW = (int) input.size(3);
@ -130,11 +137,11 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, CNN2DFormat.NCHW); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {inH, inW}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, CNN2DFormat.NCHW); //Also performs validation
}
int outH = outSize[0];
@ -152,9 +159,16 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
IActivation afn = layerConf().getActivationFn();
Pair<INDArray, INDArray> p = preOutput4d(true, true, workspaceMgr);
delta = afn.backprop(p.getFirst(), epsilon).getFirst(); //TODO handle activation function params
INDArray z = p.getFirst();
if(layerConf().getCnn2dDataFormat() != CNN2DFormat.NCHW){
z = z.permute(0,3,1,2); //NHWC to NCHW
}
delta = afn.backprop(z, epsilon).getFirst(); //TODO handle activation function params
if (helper != null && (helperCountFail == 0 || !layerConf().isCudnnAllowFallback())) {
INDArray helperDelta = delta;
if(layerConf().getCnn2dDataFormat() == CNN2DFormat.NHWC)
helperDelta = delta.permute(0,2,3,1); //NCHW to NHWC
if(!hasBias() && !(helper instanceof MKLDNNConvHelper)){
//MKL-DNN supports no bias, CuDNN doesn't
@ -168,10 +182,10 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
Pair<Gradient, INDArray> ret = null;
try {
ret = helper.backpropGradient(input, weights, bias, delta, kernel, strides,
ret = helper.backpropGradient(origInput, weights, bias, helperDelta, kernel, strides,
pad, biasGradView, weightGradView, afn,
layerConf().getCudnnAlgoMode(), layerConf().getCudnnBwdFilterAlgo(), layerConf().getCudnnBwdDataAlgo(),
convolutionMode, dilation, workspaceMgr);
convolutionMode, dilation, layerConf().getCnn2dDataFormat(), workspaceMgr);
} catch (ND4JOpProfilerException e){
throw e; //NaN panic etc for debugging
} catch (Exception e){
@ -254,6 +268,11 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
weightNoiseParams.clear();
epsNext = backpropDropOutIfPresent(epsNext);
if(layerConf().getCnn2dDataFormat() != CNN2DFormat.NCHW){
epsNext = epsNext.permute(0,2,3,1); //NCHW to NHWC
}
return new Pair<>(retGradient, epsNext);
}
@ -284,14 +303,16 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
}
protected void validateInputDepth(long inDepth) {
if (input.size(1) != inDepth) {
CNN2DFormat format = layerConf().getCnn2dDataFormat();
int dim = format == CNN2DFormat.NHWC ? 3 : 1;
if (input.size(dim) != inDepth) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
throw new DL4JInvalidInputException("Cannot do forward pass in Convolution layer (layer name = " + layerName
+ ", layer index = " + index + "): input array channels does not match CNN layer configuration"
+ " (data input channels = " + input.size(1) + ", [minibatch,inputDepth,height,width]="
+ Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
+ " (data input channels = " + input.size(dim) + ", " + layerConf().getCnn2dDataFormat().dimensionNames()
+ "=" + Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
+ layerId());
}
}
@ -313,6 +334,11 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
validateInputRank();
INDArray input = this.input.castTo(dataType);
INDArray inputOrig = input;
if(layerConf().getCnn2dDataFormat() == CNN2DFormat.NHWC){
input = input.permute(0,3,1,2).dup(); //NHWC to NCHW
}
long miniBatch = input.size(0);
long outDepth = weights.size(0);
@ -329,7 +355,7 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, CNN2DFormat.NCHW); //Note: hardcoded to NCHW due to permute earlier in this method
if (input.size(2) > Integer.MAX_VALUE || input.size(3) > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
@ -337,7 +363,7 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
strides, dilation );
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, CNN2DFormat.NCHW); //Note: hardcoded to NCHW due to permute earlier in this method
}
int outH = outSize[0];
@ -361,8 +387,8 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
INDArray ret = null;
try {
ret = helper.preOutput(input, weights, bias, kernel, strides, pad, layerConf().getCudnnAlgoMode(),
layerConf().getCudnnFwdAlgo(), convolutionMode, dilation, workspaceMgr);
ret = helper.preOutput(inputOrig, weights, bias, kernel, strides, pad, layerConf().getCudnnAlgoMode(),
layerConf().getCudnnFwdAlgo(), convolutionMode, dilation, layerConf().getCnn2dDataFormat(), workspaceMgr);
} catch (ND4JOpProfilerException e){
throw e; //NaN panic etc for debugging
} catch (Exception e){
@ -430,6 +456,11 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
}
}
if(layerConf().getCnn2dDataFormat() == CNN2DFormat.NHWC){
z = z.permute(0,2,3,1); //NCHW to NHWC
z = workspaceMgr.dup(ArrayType.ACTIVATIONS, z);
}
return new Pair<>(z, forBackprop ? im2col2d : null);
}

15
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/Cropping2DLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
@ -91,9 +92,19 @@ public class Cropping2DLayer extends AbstractLayer<org.deeplearning4j.nn.conf.la
}
private INDArray inputSubset(INDArray from){
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
if(nchw) {
//NCHW format
return from.get(all(), all(),
interval(cropping[0], from.size(2)-cropping[1]),
interval(cropping[2], from.size(3)-cropping[3]));
interval(cropping[0], from.size(2) - cropping[1]),
interval(cropping[2], from.size(3) - cropping[3]));
} else {
//NHWC
return from.get(all(),
interval(cropping[0], from.size(1) - cropping[1]),
interval(cropping[2], from.size(2) - cropping[3]),
all());
}
}
}

49
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/Deconvolution2DLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.val;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -71,15 +72,20 @@ public class Deconvolution2DLayer extends ConvolutionLayer {
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to Deconvolution2DLayer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [minibatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getCnn2dDataFormat().dimensionNames() + ". "
+ layerId());
}
INDArray weights = getParamWithNoise(DeconvolutionParamInitializer.WEIGHT_KEY, true, workspaceMgr);
CNN2DFormat format = layerConf().getCnn2dDataFormat();
boolean nchw = format == CNN2DFormat.NCHW;
int hDim = nchw ? 2 : 1;
int wDim = nchw ? 3 : 2;
long miniBatch = input.size(0);
long inH = input.size(2);
long inW = input.size(3);
long inH = input.size(hDim);
long inW = input.size(wDim);
long inDepth = weights.size(0);
@ -90,25 +96,25 @@ public class Deconvolution2DLayer extends ConvolutionLayer {
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getDeconvolutionOutputSize(input, kernel, strides, null, convolutionMode, dilation);
int[] outSize = new int[]{(int)epsilon.size(hDim), (int)epsilon.size(wDim)};
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int)inH, (int)inW}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getDeconvolutionOutputSize(input, kernel, strides, pad, convolutionMode, dilation);
}
INDArray biasGradView = gradientViews.get(DeconvolutionParamInitializer.BIAS_KEY);
INDArray weightGradView = gradientViews.get(DeconvolutionParamInitializer.WEIGHT_KEY);
INDArray outEps = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, weights.dataType(), new long[]{miniBatch, inDepth, inH, inW}, 'c');
long[] epsShape = nchw ? new long[]{miniBatch, inDepth, inH, inW} : new long[]{miniBatch, inH, inW, inDepth};
INDArray outEps = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, weights.dataType(), epsShape, 'c');
Integer sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[] {
(int)kH, (int)kW, strides[0], strides[1],
pad[0], pad[1], dilation[0], dilation[1], sameMode
pad[0], pad[1], dilation[0], dilation[1], sameMode,
nchw ? 0 : 1 //0 = NCHW; 1 = NHWC
};
INDArray delta;
@ -171,20 +177,23 @@ public class Deconvolution2DLayer extends ConvolutionLayer {
+ " " + layerId());
}
CNN2DFormat format = layerConf().getCnn2dDataFormat();
boolean nchw = format == CNN2DFormat.NCHW;
int cDim = nchw ? 1 : 3;
int hDim = nchw ? 2 : 1;
int wDim = nchw ? 3 : 2;
long inDepth = weights.size(0);
long outDepth = weights.size(1);
if (input.size(1) != inDepth && input.size(3) == inDepth) {
//TODO AB 2019/10/25 this is an ugly "pseudo-NHWC support" hack that needs to be removed ASAD
//https://github.com/eclipse/deeplearning4j/issues/8315
input = input.permute(0, 3, 1, 2);
} else if (input.size(1) != inDepth ) {
if (input.size(cDim) != inDepth ) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
throw new DL4JInvalidInputException("Cannot do forward pass in Deconvolution2D layer (layer name = " + layerName
+ ", layer index = " + index + "): input array channels does not match CNN layer configuration"
+ " (data input channels = " + input.size(1) + ", [minibatch,inputDepth,height,width]="
+ " (data input channels = " + input.size(cDim) + ", "
+ (nchw ? "[minibatch,inputDepth,height,width]" : "[minibatch,height,width,inputDepth]") + "="
+ Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
+ layerId());
}
@ -198,12 +207,12 @@ public class Deconvolution2DLayer extends ConvolutionLayer {
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getDeconvolutionOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) input.size(2), (int) input.size(3)}, kernel,
outSize = ConvolutionUtils.getDeconvolutionOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) input.size(hDim), (int) input.size(wDim)}, kernel,
strides, dilation );
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getDeconvolutionOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getDeconvolutionOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
}
long outH = outSize[0];
@ -211,13 +220,15 @@ public class Deconvolution2DLayer extends ConvolutionLayer {
val miniBatch = input.size(0);
INDArray output = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), new long[]{miniBatch, outDepth, outH, outW}, 'c');
long[] outShape = nchw ? new long[]{miniBatch, outDepth, outH, outW} : new long[]{miniBatch, outH, outW, outDepth};
INDArray output = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
int sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[] {
kH, kW, strides[0], strides[1],
pad[0], pad[1], dilation[0], dilation[1], sameMode, 0 //Last arg: 0 for nchw
pad[0], pad[1], dilation[0], dilation[1], sameMode,
nchw ? 0 : 1 //0 = NCHW; 1 = NHWC
};
//DL4J Deconv weights: [inputDepth, outputDepth, kH, kW]

45
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/DepthwiseConvolution2DLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.val;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -64,10 +65,12 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
CNN2DFormat format = layerConf().getCnn2dDataFormat();
boolean nchw = format == CNN2DFormat.NCHW;
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to Convolution layer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [miniBatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getCnn2dDataFormat().dimensionNames() + ". "
+ layerId());
}
INDArray bias;
@ -77,8 +80,8 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
INDArray input = this.input.castTo(dataType); //No-op if correct type
long miniBatch = input.size(0);
int inH = (int)input.size(2);
int inW = (int)input.size(3);
int inH = (int)input.size(nchw ? 2 : 1);
int inW = (int)input.size(nchw ? 3 : 2);
long inDepth = depthWiseWeights.size(2);
int kH = (int) depthWiseWeights.size(0);
@ -90,25 +93,25 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
int[] pad;
if (convolutionMode == ConvolutionMode.Same) {
int[] outSize = ConvolutionUtils.getOutputSize(
input, kernel, strides, null, convolutionMode, dilation);
input, kernel, strides, null, convolutionMode, dilation, format);
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[]{inH, inW}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation);
ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format);
}
INDArray biasGradView = gradientViews.get(DepthwiseConvolutionParamInitializer.BIAS_KEY);
INDArray weightGradView = gradientViews.get(DepthwiseConvolutionParamInitializer.WEIGHT_KEY);
INDArray outEpsilon = workspaceMgr.create(
ArrayType.ACTIVATION_GRAD, depthWiseWeights.dataType(), new long[]{miniBatch, inDepth, inH, inW}, 'c');
long[] epsShape = nchw ? new long[]{miniBatch, inDepth, inH, inW} : new long[]{miniBatch, inH, inW, inDepth};
INDArray outEpsilon = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, depthWiseWeights.dataType(), epsShape, 'c');
Integer sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[]{
kH, kW, strides[0], strides[1],
pad[0], pad[1], dilation[0], dilation[1],
sameMode
sameMode, (nchw ? 0 : 1)
};
INDArray delta;
@ -161,7 +164,7 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to DepthwiseConvolution2D (layer name = " + layerName + ", layer index = "
+ index + ") with shape " + Arrays.toString(input.shape()) + ". "
+ "Expected rank 4 array with shape [miniBatchSize, layerInputDepth, inputHeight, inputWidth]."
+ "Expected rank 4 array with shape " + layerConf().getCnn2dDataFormat().dimensionNames() + "."
+ (input.rank() == 2
? " (Wrong input type (see InputType.convolutionalFlat()) or wrong data type?)"
: "") + " " + layerId());
@ -169,18 +172,22 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
INDArray input = this.input.castTo(dataType); //no-op if correct dtype
CNN2DFormat format = layerConf().getCnn2dDataFormat();
boolean nchw = format == CNN2DFormat.NCHW;
long inDepth = depthWiseWeights.size(2);
long depthMultiplier = depthWiseWeights.size(3);
long outDepth = depthMultiplier * inDepth;
if (input.size(1) != inDepth) {
if (input.size(nchw ? 1 : 3) != inDepth) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
throw new DL4JInvalidInputException("Cannot do forward pass in DepthwiseConvolution2D layer " +
"(layer name = " + layerName
+ ", layer index = " + index + "): input array channels does not match CNN layer configuration"
+ " (data input channels = " + input.size(1) + ", [minibatch,inputDepth,height,width]="
+ " (data input channels = " + input.size(1) + ", "
+ (nchw ? "[minibatch,inputDepth,height,width]=" : "[minibatch,height,width,inputDepth]=")
+ Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
+ layerId());
}
@ -194,30 +201,30 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation);
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format);
if (input.size(2) > Integer.MAX_VALUE || input.size(3) > Integer.MAX_VALUE) {
throw new ND4JArraySizeException();
}
pad = ConvolutionUtils.getSameModeTopLeftPadding(
outSize, new int[]{(int) input.size(2), (int) input.size(3)}, kernel, strides, dilation);
outSize, new int[]{(int) input.size(nchw ? 2 : 1), (int) input.size(nchw ? 3 : 2)}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation);
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format);
}
long outH = outSize[0];
long outW = outSize[1];
val miniBatch = input.size(0);
INDArray output = workspaceMgr.create(
ArrayType.ACTIVATIONS, depthWiseWeights.dataType(), new long[]{miniBatch, outDepth, outH, outW}, 'c');
long[] outShape = nchw ? new long[]{miniBatch, outDepth, outH, outW} : new long[]{miniBatch, outH, outW, outDepth};
INDArray output = workspaceMgr.create(ArrayType.ACTIVATIONS, depthWiseWeights.dataType(), outShape, 'c');
Integer sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[]{
kH, kW, strides[0], strides[1],
pad[0], pad[1], dilation[0], dilation[1], sameMode
pad[0], pad[1], dilation[0], dilation[1], sameMode, (nchw ? 0 : 1)
};
INDArray[] inputs;

44
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/SeparableConvolution2DLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.val;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@ -80,7 +81,7 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to SubsamplingLayer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [minibatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getCnn2dDataFormat().dimensionNames() + ". "
+ layerId());
}
INDArray bias;
@ -91,9 +92,12 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
INDArray input = this.input.castTo(dataType);
CNN2DFormat format = layerConf().getCnn2dDataFormat();
boolean nchw = format == CNN2DFormat.NCHW;
long miniBatch = input.size(0);
int inH = (int)input.size(2);
int inW = (int)input.size(3);
int inH = (int)input.size(nchw ? 2 : 1);
int inW = (int)input.size(nchw ? 3 : 2);
int inDepth = (int) depthWiseWeights.size(1);
int kH = (int) depthWiseWeights.size(2);
@ -104,24 +108,26 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
int[] strides = layerConf().getStride();
int[] pad;
if (convolutionMode == ConvolutionMode.Same) {
int[] outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
int[] outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {inH, inW}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
}
INDArray biasGradView = gradientViews.get(SeparableConvolutionParamInitializer.BIAS_KEY);
INDArray depthWiseWeightGradView = gradientViews.get(SeparableConvolutionParamInitializer.DEPTH_WISE_WEIGHT_KEY);
INDArray pointWiseWeightGradView = gradientViews.get(SeparableConvolutionParamInitializer.POINT_WISE_WEIGHT_KEY);
INDArray outEpsilon = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, depthWiseWeights.dataType(), new long[]{miniBatch, inDepth, inH, inW}, 'c');
long[] epsShape = nchw ? new long[]{miniBatch, inDepth, inH, inW} : new long[]{miniBatch, inH, inW, inDepth};
INDArray outEpsilon = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, depthWiseWeights.dataType(), epsShape, 'c');
Integer sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[] {
kH, kW, strides[0], strides[1],
pad[0], pad[1], dilation[0], dilation[1], sameMode
pad[0], pad[1], dilation[0], dilation[1], sameMode,
nchw ? 0 : 1
};
INDArray delta;
@ -180,6 +186,12 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
INDArray input = this.input.castTo(dataType);
CNN2DFormat format = layerConf().getCnn2dDataFormat();
boolean nchw = format == CNN2DFormat.NCHW;
int chIdx = nchw ? 1 : 3;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
if (input.rank() != 4) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
@ -187,7 +199,7 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to SeparableConvolution2D (layer name = " + layerName + ", layer index = "
+ index + ") with shape " + Arrays.toString(input.shape()) + ". "
+ "Expected rank 4 array with shape [minibatchSize, layerInputDepth, inputHeight, inputWidth]."
+ "Expected rank 4 array with shape " + format.dimensionNames() + "."
+ (input.rank() == 2
? " (Wrong input type (see InputType.convolutionalFlat()) or wrong data type?)"
: "")
@ -197,7 +209,7 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
long inDepth = depthWiseWeights.size(1);
long outDepth = pointWiseWeights.size(0);
if (input.size(1) != inDepth) {
if (input.size(nchw ? 1 : 3) != inDepth) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
@ -217,29 +229,31 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
if (input.size(2) > Integer.MAX_VALUE || input.size(3) > Integer.MAX_VALUE) {
throw new ND4JArraySizeException();
}
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) input.size(2), (int) input.size(3)}, kernel,
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) input.size(hIdx), (int) input.size(wIdx)}, kernel,
strides, dilation );
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
}
int outH = outSize[0];
int outW = outSize[1];
val miniBatch = input.size(0);
INDArray output = workspaceMgr.create(ArrayType.ACTIVATIONS, depthWiseWeights.dataType(), new long[]{miniBatch, outDepth, outH, outW}, 'c');
long[] outShape = nchw ? new long[]{miniBatch, outDepth, outH, outW} : new long[]{miniBatch, outH, outW, outDepth};
INDArray output = workspaceMgr.create(ArrayType.ACTIVATIONS, depthWiseWeights.dataType(), outShape, 'c');
Integer sameMode = (convolutionMode == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[] {
kH, kW, strides[0], strides[1],
pad[0], pad[1], dilation[0], dilation[1], sameMode
pad[0], pad[1], dilation[0], dilation[1], sameMode,
nchw ? 0 : 1
};
//dl4j weights: depth [depthMultiplier, nIn, kH, kW], point [nOut, nIn * depthMultiplier, 1, 1]

30
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/SpaceToBatch.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
@ -91,17 +92,14 @@ public class SpaceToBatch extends AbstractLayer<org.deeplearning4j.nn.conf.layer
INDArray input = this.input.castTo(dataType); //Cast to network dtype if required (no-op if already correct type)
long miniBatch = input.size(0);
long inDepth = input.size(1);
long inH = input.size(2);
long inW = input.size(3);
boolean nchw = layerConf().getFormat() == CNN2DFormat.NCHW;
INDArray outEpsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), new long[]{miniBatch, inDepth, inH, inW}, 'c');
INDArray outEpsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), input.shape(), 'c');
Gradient gradient = new DefaultGradient();
INDArray epsilonNHWC = epsilon.permute(0, 2, 3, 1);
INDArray outEpsilonNHWC = outEpsilon.permute(0, 2, 3, 1);
INDArray epsilonNHWC = nchw ? epsilon.permute(0, 2, 3, 1) : epsilon;
INDArray outEpsilonNHWC = nchw ? outEpsilon.permute(0, 2, 3, 1) : outEpsilon;
CustomOp op = DynamicCustomOp.builder("batch_to_space_nd")
.addInputs(epsilonNHWC, getBlocksArray(), getPaddingArray())
@ -121,7 +119,7 @@ public class SpaceToBatch extends AbstractLayer<org.deeplearning4j.nn.conf.layer
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to space to batch with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [minibatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getFormat().dimensionNames() + ". "
+ layerId());
}
@ -129,10 +127,12 @@ public class SpaceToBatch extends AbstractLayer<org.deeplearning4j.nn.conf.layer
return preOutput;
}
boolean nchw = layerConf().getFormat() == CNN2DFormat.NCHW;
long inMiniBatch = input.size(0);
long depth = input.size(1);
long inH = input.size(2);
long inW = input.size(3);
long depth = input.size(nchw ? 1 : 3);
long inH = input.size(nchw ? 2 : 1);
long inW = input.size(nchw ? 3 : 2);
int[] blocks = getBlocks();
int[][] padding = getPadding();
@ -144,10 +144,12 @@ public class SpaceToBatch extends AbstractLayer<org.deeplearning4j.nn.conf.layer
long outW = paddedW / blocks[1];
long outMiniBatch = inMiniBatch * blocks[0] * blocks[1];
INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), new long[]{outMiniBatch, depth, outH, outW}, 'c');
long[] outShape = nchw ? new long[]{outMiniBatch, depth, outH, outW} : new long[]{outMiniBatch, outH, outW, depth};
INDArray inNHWC = input.permute(0, 2, 3, 1);
INDArray outNHWC = out.permute(0, 2, 3, 1);
INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
INDArray inNHWC = nchw ? input.permute(0, 2, 3, 1) : input;
INDArray outNHWC = nchw ? out.permute(0, 2, 3, 1) : out;
CustomOp op = DynamicCustomOp.builder("space_to_batch_nd")
.addInputs(inNHWC, getBlocksArray(), getPaddingArray())

66
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/SpaceToDepth.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -28,6 +29,7 @@ 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.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
@ -63,8 +65,6 @@ public class SpaceToDepth extends AbstractLayer<org.deeplearning4j.nn.conf.layer
return layerConf().getBlockSize();
}
private int isNHWC() {return layerConf().getDataFormat().equals(SpaceToDepthLayer.DataFormat.NHWC)? 1: 0;}
@Override
public Type type() {
return Type.CONVOLUTIONAL;
@ -75,35 +75,33 @@ public class SpaceToDepth extends AbstractLayer<org.deeplearning4j.nn.conf.layer
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
INDArray input = this.input.castTo(epsilon.dataType());
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
long miniBatch = input.size(0);
long inDepth = input.size(1);
long inH = input.size(2);
long inW = input.size(3);
long inDepth = input.size(nchw ? 1 : 3);
long inH = input.size(nchw ? 2 : 1);
long inW = input.size(nchw ? 3 : 2);
INDArray input = this.input.castTo(dataType); //No-op if already correct type
INDArray outEpsilon = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, input.dataType(), new long[]{1, miniBatch * inDepth * inH * inW}, 'c');
INDArray reshapedEpsilon;
if (isNHWC() == 1) {
reshapedEpsilon = outEpsilon.reshape('c', miniBatch, inH, inW, inDepth);
} else {
reshapedEpsilon = outEpsilon.reshape('c', miniBatch, inDepth, inH, inW);
}
long[] epsShape = nchw ? new long[]{miniBatch, inDepth, inH, inW} : new long[]{miniBatch, inH, inW, inDepth};
INDArray outEpsilon = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, input.dataType(), epsShape, 'c');
Gradient gradient = new DefaultGradient();
int blockSize = getBlockSize();
//Workaround for issue: https://github.com/eclipse/deeplearning4j/issues/8859
if(!Shape.hasDefaultStridesForShape(epsilon))
epsilon = epsilon.dup('c');
CustomOp op = DynamicCustomOp.builder("depth_to_space")
.addInputs(epsilon)
.addIntegerArguments(blockSize, isNHWC())
.addOutputs(reshapedEpsilon)
.addIntegerArguments(blockSize, nchw ? 0 : 1) //nchw = 0, nhwc = 1
.addOutputs(outEpsilon)
.build();
Nd4j.getExecutioner().exec(op);
reshapedEpsilon = backpropDropOutIfPresent(reshapedEpsilon);
return new Pair<>(gradient, reshapedEpsilon);
return new Pair<>(gradient, outEpsilon);
}
protected INDArray preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr) {
@ -113,7 +111,7 @@ public class SpaceToDepth extends AbstractLayer<org.deeplearning4j.nn.conf.layer
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to space to channels with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [minibatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getDataFormat().dimensionNames() + ". "
+ layerId());
}
@ -121,10 +119,12 @@ public class SpaceToDepth extends AbstractLayer<org.deeplearning4j.nn.conf.layer
return preOutput;
}
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
long miniBatch = input.size(0);
long depth = input.size(1);
long inH = input.size(2);
long inW = input.size(3);
long depth = input.size(nchw ? 1 : 3);
long inH = input.size(nchw ? 2 : 1);
long inW = input.size(nchw ? 3 : 2);
int blockSize = getBlockSize();
@ -132,22 +132,22 @@ public class SpaceToDepth extends AbstractLayer<org.deeplearning4j.nn.conf.layer
long outW = inW / blockSize;
long outDepth = depth * blockSize * blockSize;
INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), new long[]{1, miniBatch * outDepth * outH * outW}, 'c');
INDArray reshapedOut;
if (isNHWC() == 1) {
reshapedOut = out.reshape('c', miniBatch, outH, outW, outDepth);
} else {
reshapedOut = out.reshape('c', miniBatch, outDepth, outH, outW);
}
long[] outShape = nchw ? new long[]{miniBatch, outDepth, outH, outW} : new long[]{miniBatch, outH, outW, outDepth};
INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
//Workaround for issue: https://github.com/eclipse/deeplearning4j/issues/8859
INDArray input = this.input;
if(!Shape.hasDefaultStridesForShape(input))
input = input.dup('c');
CustomOp op = DynamicCustomOp.builder("space_to_depth")
.addInputs(input)
.addIntegerArguments(blockSize, isNHWC())
.addOutputs(reshapedOut)
.addIntegerArguments(blockSize, nchw ? 0 : 1) //nchw = 0, nhwc = 1
.addOutputs(out)
.build();
Nd4j.getExecutioner().exec(op);
return reshapedOut;
return out;
}
@Override

48
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ZeroPaddingLayer.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.convolution;
import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
@ -38,11 +39,8 @@ import org.deeplearning4j.nn.workspace.ArrayType;
*/
public class ZeroPaddingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer> {
private int[] padding; //[padTop, padBottom, padLeft, padRight]
public ZeroPaddingLayer(NeuralNetConfiguration conf, DataType dataType) {
super(conf, dataType);
this.padding = ((org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer) conf.getLayer()).getPadding();
}
@Override
@ -65,9 +63,23 @@ public class ZeroPaddingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
assertInputSet(true);
val inShape = input.shape();
INDArray epsNext = epsilon.get(NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.interval(padding[0], padding[0] + inShape[2]),
NDArrayIndex.interval(padding[2], padding[2] + inShape[3]));
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
INDArray epsNext;
int[] padding = layerConf().getPadding();
if(layerConf().getDataFormat() == CNN2DFormat.NCHW){
epsNext = epsilon.get(NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.interval(padding[0], padding[0] + inShape[hIdx]),
NDArrayIndex.interval(padding[2], padding[2] + inShape[wIdx]));
} else {
//NHWC
epsNext = epsilon.get(NDArrayIndex.all(),
NDArrayIndex.interval(padding[0], padding[0] + inShape[hIdx]),
NDArrayIndex.interval(padding[2], padding[2] + inShape[wIdx]),
NDArrayIndex.all());
}
epsNext = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, epsNext);
return new Pair<>((Gradient) new DefaultGradient(), epsNext);
@ -77,16 +89,28 @@ public class ZeroPaddingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
int[] padding = layerConf().getPadding();
val inShape = input.shape();
val outH = inShape[2] + padding[0] + padding[1];
val outW = inShape[3] + padding[2] + padding[3];
val outShape = new long[] {inShape[0], inShape[1], outH, outW};
val outH = inShape[hIdx] + padding[0] + padding[1];
val outW = inShape[wIdx] + padding[2] + padding[3];
val outShape = nchw ? new long[] {inShape[0], inShape[1], outH, outW} : new long[] {inShape[0], outH, outW, inShape[3]};
INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
out.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.interval(padding[0], padding[0] + inShape[2]),
NDArrayIndex.interval(padding[2], padding[2] + inShape[3])}, input);
if(nchw) {
out.put(new INDArrayIndex[]{NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.interval(padding[0], padding[0] + inShape[hIdx]),
NDArrayIndex.interval(padding[2], padding[2] + inShape[wIdx])}, input);
} else {
out.put(new INDArrayIndex[]{NDArrayIndex.all(),
NDArrayIndex.interval(padding[0], padding[0] + inShape[hIdx]),
NDArrayIndex.interval(padding[2], padding[2] + inShape[wIdx]),
NDArrayIndex.all()}, input);
}
return out;
}

6
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/SubsamplingHelper.java
View File

@ -16,6 +16,7 @@
package org.deeplearning4j.nn.layers.convolution.subsampling;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.gradient.Gradient;
@ -33,8 +34,9 @@ public interface SubsamplingHelper extends LayerHelper {
boolean checkSupported();
Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad,
PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr);
PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation,
CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);
INDArray activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad, PoolingType poolingType,
ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr);
ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);
}

64
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/SubsamplingLayer.java
View File

@ -19,6 +19,7 @@ package org.deeplearning4j.nn.layers.convolution.subsampling;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -108,15 +109,23 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
if(epsilon.dataType() != dataType)
epsilon = epsilon.castTo(dataType);
int inH = (int)input.size(2);
int inW = (int)input.size(3);
CNN2DFormat dataFormat = layerConf().getCnn2dDataFormat();
int hIdx = 2;
int wIdx = 3;
if(dataFormat == CNN2DFormat.NHWC){
hIdx = 1;
wIdx = 2;
}
int inH = (int)input.size(hIdx);
int inW = (int)input.size(wIdx);
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
int[] dilation = layerConf().getDilation();
int[] pad;
int[] outSizeFwd = new int[]{(int)epsilon.size(2), (int)epsilon.size(3)}; //NCHW
int[] outSizeFwd = new int[]{(int)epsilon.size(hIdx), (int)epsilon.size(wIdx)}; //NCHW
boolean same = convolutionMode == ConvolutionMode.Same;
if (same) {
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSizeFwd, new int[] {inH, inW}, kernel, strides, dilation);
@ -128,7 +137,7 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
Pair<Gradient, INDArray> ret = null;
try{
ret = helper.backpropGradient(input, epsilon, kernel, strides, pad,
layerConf().getPoolingType(), convolutionMode, dilation, workspaceMgr);
layerConf().getPoolingType(), convolutionMode, dilation, dataFormat, workspaceMgr);
} catch (ND4JOpProfilerException e){
throw e; //NaN panic etc for debugging
} catch (Exception e){
@ -188,26 +197,14 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
b.addInputs(input, epsilon)
.addOutputs(epsAtInput)
.addIntegerArguments(kernel[0], kernel[1], strides[0], strides[1], pad[0], pad[1], dilation[0], dilation[1],
(same ? 1 : 0), extra, 0); //last 0 = NCHW
(same ? 1 : 0), extra,
dataFormat == CNN2DFormat.NCHW ? 0 : 1); //0 = NCHW, 1=NHWC
Nd4j.exec(b.build());
return new Pair<>(retGradient, epsAtInput);
}
private static double minValue(){
switch (Nd4j.dataType()){
case DOUBLE:
return -Double.MAX_VALUE;
case FLOAT:
return -Float.MAX_VALUE;
case HALF:
return -65504.0;
default:
throw new IllegalStateException("Unexpected data type: " + Nd4j.dataType());
}
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
@ -219,16 +216,26 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to SubsamplingLayer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [minibatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getCnn2dDataFormat().dimensionNames() + ". "
+ layerId());
}
INDArray input = this.input.castTo(dataType);
int chIdx = 1;
int hIdx = 2;
int wIdx = 3;
if(layerConf().getCnn2dDataFormat() == CNN2DFormat.NHWC){
chIdx = 3;
hIdx = 1;
wIdx = 2;
}
CNN2DFormat dataFormat = layerConf().getCnn2dDataFormat();
long miniBatch = input.size(0);
long inDepth = input.size(1);
int inH = (int)input.size(2);
int inW = (int)input.size(3);
long inDepth = input.size(chIdx);
int inH = (int)input.size(hIdx);
int inW = (int)input.size(wIdx);
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
@ -237,11 +244,11 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
int[] outSize;
boolean same = convolutionMode == ConvolutionMode.Same;
if (same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, layerConf().getCnn2dDataFormat()); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {inH, inW}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, layerConf().getCnn2dDataFormat()); //Also performs validation
}
long outH = outSize[0];
long outW = outSize[1];
@ -251,7 +258,7 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
INDArray ret = null;
try {
ret = helper.activate(input, training, kernel, strides, pad, layerConf().getPoolingType(),
convolutionMode, dilation, workspaceMgr);
convolutionMode, dilation, dataFormat, workspaceMgr);
} catch (ND4JOpProfilerException e){
throw e; //NaN panic etc for debugging
} catch (Exception e){
@ -271,7 +278,9 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
}
}
INDArray output = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), new long[]{miniBatch, inDepth, outH, outW}, 'c');
long[] outShape = (layerConf().getCnn2dDataFormat() == CNN2DFormat.NCHW) ? new long[]{miniBatch, inDepth, outH, outW} : new long[]{miniBatch, outH, outW, inDepth};
INDArray output = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
DynamicCustomOp.DynamicCustomOpsBuilder b;
int extra = 0;
switch (layerConf().getPoolingType()){
@ -299,7 +308,8 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
b.addInputs(input)
.addOutputs(output)
.addIntegerArguments(kernel[0], kernel[1], strides[0], strides[1], pad[0], pad[1], dilation[0], dilation[1],
(same ? 1 : 0), extra, 0); //Last 0: NCHW
(same ? 1 : 0), extra,
layerConf().getCnn2dDataFormat() == CNN2DFormat.NCHW ? 0 : 1); //0: NCHW, 1=NHWC
Nd4j.exec(b.build());

5
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling1D.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.convolution.upsampling;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -52,6 +53,10 @@ public class Upsampling1D extends Upsampling2D {
super(conf, dataType);
}
@Override
protected CNN2DFormat getFormat(){
return CNN2DFormat.NCHW;
}
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {

50
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling2D.java
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2020 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
@ -18,7 +19,7 @@ package org.deeplearning4j.nn.layers.convolution.upsampling;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -62,34 +63,41 @@ public class Upsampling2D extends AbstractLayer<org.deeplearning4j.nn.conf.layer
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
long miniBatch = (int) input.size(0);
long inDepth = (int) input.size(1);
long inH = (int) input.size(2);
long inW = (int) input.size(3);
CNN2DFormat format = getFormat();
boolean nchw = format == CNN2DFormat.NCHW;
INDArray reshapedEpsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, epsilon.dataType(), new long[]{miniBatch, inDepth, inH, inW}, 'c');
long miniBatch = (int) input.size(0);
long inDepth = (int) input.size(nchw ? 1 : 3);
long inH = (int) input.size(nchw ? 2 : 1);
long inW = (int) input.size(nchw ? 3 : 2);
long[] epsShape = nchw ? new long[]{miniBatch, inDepth, inH, inW} : new long[]{miniBatch, inH, inW, inDepth};
INDArray epsOut = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, epsilon.dataType(), epsShape, 'c');
Gradient gradient = new DefaultGradient();
int[] intArgs = new int[] {1}; // 1 is for NCHW
CustomOp op = DynamicCustomOp.builder("upsampling_bp")
.addIntegerArguments(intArgs)
.addIntegerArguments(nchw ? 1 : 0) //1=NCHW, 0=NHWC
.addInputs(input, epsilon)
.addOutputs(reshapedEpsilon)
.addOutputs(epsOut)
.callInplace(false)
.build();
Nd4j.getExecutioner().exec(op);
reshapedEpsilon = backpropDropOutIfPresent(reshapedEpsilon);
return new Pair<>(gradient, reshapedEpsilon);
epsOut = backpropDropOutIfPresent(epsOut);
return new Pair<>(gradient, epsOut);
}
protected int[] getSize(){
return layerConf().getSize();
}
protected CNN2DFormat getFormat(){
//Here so it can be overridden by Upsampling1D
return layerConf().getFormat();
}
protected INDArray preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
applyDropOutIfNecessary(training, workspaceMgr);
@ -97,7 +105,7 @@ public class Upsampling2D extends AbstractLayer<org.deeplearning4j.nn.conf.layer
if (input.rank() != 4) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to SubsamplingLayer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 4 array with shape [minibatchSize, channels, inputHeight, inputWidth]. "
+ ". Expected rank 4 array with shape " + layerConf().getFormat().dimensionNames() + ". "
+ layerId());
}
@ -105,18 +113,22 @@ public class Upsampling2D extends AbstractLayer<org.deeplearning4j.nn.conf.layer
return preOutput;
}
CNN2DFormat format = getFormat();
boolean nchw = format == CNN2DFormat.NCHW;
long miniBatch = (int) input.size(0);
long inDepth = (int) input.size(1);
long inH = (int) input.size(2);
long inW = (int) input.size(3);
long inDepth = (int) input.size(nchw ? 1 : 3);
long inH = (int) input.size(nchw ? 2 : 1);
long inW = (int) input.size(nchw ? 3 : 2);
int[] size = getSize();
int outH = (int)inH * size[0];
int outW = (int)inW * size[1];
INDArray reshapedOutput = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), new long[]{miniBatch, inDepth, outH, outW}, 'c');
long[] outShape = nchw ? new long[]{miniBatch, inDepth, outH, outW} : new long[]{miniBatch, outH, outW, inDepth};
INDArray reshapedOutput = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
int[] intArgs = new int[] {size[0], size[1], 1}; // 1 is for NCHW
int[] intArgs = new int[] {size[0], size[1], nchw ? 1 : 0}; // 1 = NCHW, 0 = NHWC
CustomOp upsampling = DynamicCustomOp.builder("upsampling2d")
.addIntegerArguments(intArgs)

5
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/mkldnn/BaseMKLDNNHelper.java
View File

@ -41,6 +41,11 @@ public class BaseMKLDNNHelper {
return false;
}
if(!Nd4j.getEnvironment().helpersAllowed()){
//C++ helpers not allowed
return false;
}
try{
Class<?> c = Class.forName("org.nd4j.nativeblas.Nd4jCpu$Environment");
Method m = c.getMethod("getInstance");

44
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/mkldnn/MKLDNNBatchNormHelper.java
View File

@ -16,6 +16,7 @@
package org.deeplearning4j.nn.layers.mkldnn;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper;
@ -28,9 +29,8 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.OpContext;
import org.nd4j.linalg.api.ops.impl.layers.convolution.BatchNorm;
import org.nd4j.linalg.api.ops.impl.layers.convolution.BatchNormDerivative;
import org.nd4j.linalg.api.ops.impl.summarystats.Variance;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.linalg.util.ArrayUtil;
@ -47,7 +47,8 @@ import java.util.Map;
*/
public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
private static final int[] RANK2_DIMS = {0};
private static final int[] RANK4_DIMS = {0,2,3};
private static final int[] RANK4_DIMS_NCHW = {0,2,3};
private static final int[] RANK4_DIMS_NHWC = {0,1,2};
protected OpContext context;
private INDArray meanCache;
@ -64,11 +65,18 @@ public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, long[] shape, INDArray gamma,
INDArray beta, INDArray dGammaView, INDArray dBetaView, double eps, LayerWorkspaceMgr workspaceMgr) {
INDArray beta, INDArray dGammaView, INDArray dBetaView, double eps,
CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
//Workaround for: https://github.com/eclipse/deeplearning4j/issues/8860
if(!Shape.hasDefaultStridesForShape(epsilon))
epsilon = epsilon.dup('c');
if(input.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports float
//TODO FIXME - AB 2019/11/01 - https://github.com/eclipse/deeplearning4j/issues/8335
int axis = (input.rank() != 4 || format == CNN2DFormat.NCHW) ? 1 : 3;
List<INDArray> args = new ArrayList<>();
args.add(input);
args.add(meanCache);
@ -85,7 +93,7 @@ public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
.addIntegerArguments(
gamma == null ? 0 : 1, //Apply scale
beta == null ? 0 : 1, //Apply beta
1) //Axis (NCHW)
axis) //Axis (NCHW) - 1=NCHW, 3=NHWC
.addFloatingPointArguments(eps)
.build();
@ -114,16 +122,18 @@ public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
@Override
public INDArray preOutput(INDArray x, boolean training, long[] shape, INDArray gamma, INDArray beta, INDArray mean, INDArray var,
double decay, double eps, LayerWorkspaceMgr workspaceMgr) {
double decay, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(x.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports float
int axis = (x.rank() != 4 || format == CNN2DFormat.NCHW) ? 1 : 3;
if(context == null){
context = Nd4j.getExecutioner().buildContext();
context.setIArguments(
ArrayUtil.fromBoolean(gamma != null),
ArrayUtil.fromBoolean(beta != null),
1); //Axis
axis); //Axis - 1 = NCHW, 3 = NHWC
context.setTArguments(eps);
}
@ -132,12 +142,22 @@ public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
if(training){
if(meanCache == null){
try(MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
meanCache = Nd4j.createUninitialized(x.dataType(), x.size(1));
varCache = Nd4j.createUninitialized(x.dataType(), x.size(1));
meanCache = Nd4j.createUninitialized(x.dataType(), x.size(axis));
varCache = Nd4j.createUninitialized(x.dataType(), x.size(axis));
}
}
x.mean(meanCache, x.rank() == 2 ? RANK2_DIMS : RANK4_DIMS);
Nd4j.exec(new Variance(x, varCache, false, x.rank() == 2 ? RANK2_DIMS : RANK4_DIMS));
int[] dims;
if(x.rank() == 2){
dims = RANK2_DIMS;
} else if(format == CNN2DFormat.NCHW){
dims = RANK4_DIMS_NCHW;
} else {
dims = RANK4_DIMS_NHWC;
}
x.mean(meanCache, dims);
Nd4j.exec(new Variance(x, varCache, false, dims));
m = meanCache;
v = varCache;

39
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/mkldnn/MKLDNNConvHelper.java
View File

@ -16,6 +16,7 @@
package org.deeplearning4j.nn.layers.mkldnn;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -61,7 +62,7 @@ public class MKLDNNConvHelper implements ConvolutionHelper {
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel, int[] strides, int[] pad,
INDArray biasGradView, INDArray weightGradView, IActivation afn, ConvolutionLayer.AlgoMode mode,
ConvolutionLayer.BwdFilterAlgo bwdFilterAlgo, ConvolutionLayer.BwdDataAlgo bwdDataAlgo, ConvolutionMode convolutionMode,
int[] dilation, LayerWorkspaceMgr workspaceMgr) {
int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(input.dataType() != DataType.FLOAT || weights.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports floating point dtype
@ -69,8 +70,15 @@ public class MKLDNNConvHelper implements ConvolutionHelper {
INDArray weightsPermute = weights.permute(2,3,1,0);
INDArray weightGradViewPermute = weightGradView.permute(2,3,1,0);
int hDim = 2;
int wDim = 3;
if(format == CNN2DFormat.NHWC){
hDim = 1;
wDim = 2;
}
if (convolutionMode == ConvolutionMode.Same) {
pad = ConvolutionUtils.getSameModeTopLeftPadding(new int[]{(int)delta.size(2), (int)delta.size(3)}, new int[] {(int) input.size(2), (int) input.size(3)},
pad = ConvolutionUtils.getSameModeTopLeftPadding(new int[]{(int)delta.size(hDim), (int)delta.size(wDim)}, new int[] {(int) input.size(hDim), (int) input.size(wDim)},
kernel, strides, dilation);
}
@ -81,7 +89,7 @@ public class MKLDNNConvHelper implements ConvolutionHelper {
pad[0], pad[1],
dilation[0], dilation[1],
ArrayUtil.fromBoolean(convolutionMode == ConvolutionMode.Same),
0 //0=NCHW
format == CNN2DFormat.NCHW ? 0 : 1 //0=NCHW, 1=NHWC
);
};
@ -110,18 +118,28 @@ public class MKLDNNConvHelper implements ConvolutionHelper {
}
@Override
public INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad, ConvolutionLayer.AlgoMode mode, ConvolutionLayer.FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
public INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad,
ConvolutionLayer.AlgoMode mode, ConvolutionLayer.FwdAlgo fwdAlgo, ConvolutionMode convolutionMode,
int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(input.dataType() != DataType.FLOAT || weights.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports floating point dtype
int inH = (int)input.size(2);
int inW = (int)input.size(3);
int hDim = 2;
int wDim = 3;
if(format == CNN2DFormat.NHWC){
hDim = 1;
wDim = 2;
}
int inH = (int)input.size(hDim);
int inW = (int)input.size(wDim);
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {inH, inW}, kernel, strides, dilation);
} else {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
}
if(context == null ){
@ -131,12 +149,13 @@ public class MKLDNNConvHelper implements ConvolutionHelper {
pad[0], pad[1],
dilation[0], dilation[1],
ArrayUtil.fromBoolean(convolutionMode == ConvolutionMode.Same),
0 //0=NCHW
format == CNN2DFormat.NCHW ? 0 : 1 //0=NCHW, 1=NHWC
);
};
int outDepth = (int) weights.size(0);
INDArray out = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), input.size(0), outDepth, outSize[0], outSize[1]);
long[] outShape = (format == CNN2DFormat.NCHW) ? new long[]{input.size(0), outDepth, outSize[0], outSize[1]} : new long[]{input.size(0), outSize[0], outSize[1], outDepth};
INDArray out = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape);
//Note: conv2d op expects [kH, kW, iC, oC] weights... DL4J conv uses [oC, iC, kH, kW]
weights = weights.permute(2,3,1,0);

44
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/mkldnn/MKLDNNSubsamplingHelper.java
View File

@ -16,6 +16,7 @@
package org.deeplearning4j.nn.layers.mkldnn;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.gradient.DefaultGradient;
@ -59,14 +60,23 @@ public class MKLDNNSubsamplingHelper implements SubsamplingHelper {
}
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad,
PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation,
CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
if(poolingType == PoolingType.SUM || poolingType == PoolingType.PNORM)
return null;
INDArray gradAtInput = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), input.shape());
int hIdx = 2;
int wIdx = 3;
if(format == CNN2DFormat.NHWC){
hIdx = 1;
wIdx = 2;
}
if (convolutionMode == ConvolutionMode.Same) {
pad = ConvolutionUtils.getSameModeTopLeftPadding(new int[]{(int)epsilon.size(2), (int)epsilon.size(3)}, new int[] {(int)input.size(2), (int)input.size(3)}, kernel, strides, dilation);
pad = ConvolutionUtils.getSameModeTopLeftPadding(new int[]{(int)epsilon.size(hIdx), (int)epsilon.size(wIdx)}, new int[] {(int)input.size(hIdx), (int)input.size(wIdx)}, kernel, strides, dilation);
}
Pooling2DConfig conf = Pooling2DConfig.builder()
@ -75,7 +85,7 @@ public class MKLDNNSubsamplingHelper implements SubsamplingHelper {
.sH(strides[0]).sW(strides[1])
.dH(dilation[0]).dW(dilation[1])
.pH(pad[0]).pW(pad[1])
.isNHWC(false)
.isNHWC(format == CNN2DFormat.NHWC)
.build();
switch (poolingType){
@ -94,16 +104,26 @@ public class MKLDNNSubsamplingHelper implements SubsamplingHelper {
}
@Override
public INDArray activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, LayerWorkspaceMgr workspaceMgr) {
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int)input.size(2), (int)input.size(3)}, kernel, strides, dilation);
} else {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation); //Also performs validation
public INDArray activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad, PoolingType poolingType,
ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr) {
int hIdx = 2;
int wIdx = 3;
if(format == CNN2DFormat.NHWC){
hIdx = 1;
wIdx = 2;
}
long[] outShape = new long[]{input.size(0), input.size(1), outSize[0], outSize[1]};
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int)input.size(hIdx), (int)input.size(wIdx)}, kernel, strides, dilation);
} else {
outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode, dilation, format); //Also performs validation
}
long[] outShape = format == CNN2DFormat.NCHW ? new long[]{input.size(0), input.size(1), outSize[0], outSize[1]} :
new long[]{input.size(0), outSize[0], outSize[1], input.size(3)};
INDArray output = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), outShape);
if(context == null){
@ -115,7 +135,7 @@ public class MKLDNNSubsamplingHelper implements SubsamplingHelper {
dilation[0], dilation[1],
ArrayUtil.fromBoolean(convolutionMode == ConvolutionMode.Same),
0, //Extra - not used?
0); //0 = NCHW
format == CNN2DFormat.NCHW ? 0 : 1); //0 = NCHW, 1=NHWC
}
DynamicCustomOp op;

84
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/BatchNormalization.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.normalization;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
@ -112,6 +113,10 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
val batchSize = epsilon.size(0); // number examples in batch
org.deeplearning4j.nn.conf.layers.BatchNormalization layerConf = layerConf();
CNN2DFormat format = layerConf().getCnn2DFormat();
boolean nchw = format == CNN2DFormat.NCHW;
int chIdx = epsilon.rank() == 2 || nchw ? 1 : 3;
INDArray input = this.input.castTo(dataType); //No-op if correct type
INDArray globalMean = params.get(BatchNormalizationParamInitializer.GLOBAL_MEAN);
@ -125,7 +130,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
INDArray dGlobalVarView = gradientViews.get(BatchNormalizationParamInitializer.GLOBAL_VAR);
INDArray dGlobalLog10StdView = gradientViews.get(BatchNormalizationParamInitializer.GLOBAL_LOG_STD);
if (layerConf.isLockGammaBeta()) {
val tempShape = new long[] {1, shape[1]};
val tempShape = new long[] {1, shape[chIdx]};
dGammaView = Nd4j.createUninitialized(dataType, tempShape, 'c');
dBetaView = Nd4j.createUninitialized(dataType, tempShape, 'c');
} else {
@ -141,14 +146,15 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
if (helper != null && (helperCountFail == 0 || !layerConf().isCudnnAllowFallback())){
//Note that cudnn does not support dense (2d) batch norm case as of v5.1
if (layerConf.isLockGammaBeta()) {
gamma = Nd4j.createUninitialized(dataType, 1, shape[1]).assign(layerConf.getGamma());
gamma = Nd4j.createUninitialized(dataType, 1, shape[chIdx]).assign(layerConf.getGamma());
}
INDArray in;
INDArray eps;
if(input.rank() == 2){
in = input.reshape(input.ordering(), input.size(0), input.size(1), 1, 1);
eps = epsilon.reshape(epsilon.ordering(), epsilon.size(0), epsilon.size(1), 1, 1);
long[] shapeTemp = nchw ? new long[]{input.size(0), input.size(1), 1, 1} : new long[]{input.size(0), 1, 1, input.size(1)};
in = input.reshape(input.ordering(), shapeTemp);
eps = epsilon.reshape(epsilon.ordering(), shapeTemp);
} else {
in = input;
eps = epsilon;
@ -157,7 +163,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
Pair<Gradient,INDArray> ret = null;
try {
ret = helper.backpropGradient(in, eps, shape, gamma, beta, dGammaView, dBetaView,
layerConf.getEps(), workspaceMgr);
layerConf.getEps(), format, workspaceMgr);
} catch (ND4JOpProfilerException e){
throw e; //NaN panic etc for debugging
} catch (Throwable t){
@ -282,39 +288,43 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
batchMean = input.mean(0);
batchVar = input.var(false, 0);
} else if (epsilon.rank() == 4) {
int[] nonChDims = nchw ? new int[]{0, 2, 3} : new int[]{0, 1, 2};
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
if(xHat == null && helper != null){
INDArray mean = helper.getMeanCache(dataType);
std = Transforms.sqrt(helper.getVarCache(dataType).addi(layerConf().getEps()));
xMu = Nd4j.createUninitialized(dataType, input.shape(), input.ordering());
xMu = Nd4j.getExecutioner().exec(new BroadcastSubOp(input, mean, xMu, 1));
xMu = Nd4j.getExecutioner().exec(new BroadcastSubOp(input, mean, xMu, chIdx));
xHat = Nd4j.createUninitialized(dataType, input.shape(), input.ordering());
xHat = Nd4j.getExecutioner().exec(new BroadcastDivOp(xMu, std,xHat, 1));
xHat = Nd4j.getExecutioner().exec(new BroadcastDivOp(xMu, std,xHat, chIdx));
}
INDArray dBeta = epsilon.sum(0, 2, 3);
INDArray dGamma = epsilon.mul(xHat).sum(0, 2, 3);
INDArray dBeta = epsilon.sum(nonChDims);
INDArray dGamma = epsilon.mul(xHat).sum(nonChDims);
INDArray dxhat;
if (layerConf.isLockGammaBeta()) {
dxhat = epsilon.mul(layerConf.getGamma());
} else {
//Standard case
dxhat = Nd4j.getExecutioner().exec(new BroadcastMulOp(epsilon, gamma,
Nd4j.createUninitialized(epsilon.dataType(), epsilon.shape(), epsilon.ordering()), 1));
Nd4j.createUninitialized(epsilon.dataType(), epsilon.shape(), epsilon.ordering()), chIdx));
}
//dL/dVariance
INDArray dLdVar = dxhat.mul(xMu).sum(0, 2, 3).muli(-0.5).muli(Transforms.pow(std, -3.0, true));
INDArray dLdVar = dxhat.mul(xMu).sum(nonChDims).muli(-0.5).muli(Transforms.pow(std, -3.0, true));
//dL/dmu
val effectiveBatchSize = input.size(0) * input.size(2) * input.size(3);
INDArray dxmu1 = dxhat.sum(0, 2, 3).divi(std).negi();
INDArray dxmu2 = xMu.sum(0, 2, 3).muli(-2.0 / effectiveBatchSize).muli(dLdVar);
val effectiveBatchSize = input.size(0) * input.size(hIdx) * input.size(wIdx);
INDArray dxmu1 = dxhat.sum(nonChDims).divi(std).negi();
INDArray dxmu2 = xMu.sum(nonChDims).muli(-2.0 / effectiveBatchSize).muli(dLdVar);
INDArray dLdmu = dxmu1.addi(dxmu2);
INDArray dLdx = Nd4j.getExecutioner().exec(new BroadcastDivOp(dxhat, std, dxhat, 1))
.addi(Nd4j.getExecutioner().exec(new BroadcastMulOp(xMu, dLdVar.muli(2.0 / effectiveBatchSize), xMu, 1)));
INDArray dLdx = Nd4j.getExecutioner().exec(new BroadcastDivOp(dxhat, std, dxhat, chIdx))
.addi(Nd4j.getExecutioner().exec(new BroadcastMulOp(xMu, dLdVar.muli(2.0 / effectiveBatchSize), xMu, chIdx)));
Nd4j.getExecutioner()
.execAndReturn(new BroadcastAddOp(dLdx, dLdmu.muli(1.0 / effectiveBatchSize), dLdx, 1));
.execAndReturn(new BroadcastAddOp(dLdx, dLdmu.muli(1.0 / effectiveBatchSize), dLdx, chIdx));
//TODO rework this to avoid the assign here
dGammaView.assign(dGamma);
@ -324,8 +334,8 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
retGradient.setGradientFor(BatchNormalizationParamInitializer.BETA, dBetaView);
nextEpsilon = dLdx;
batchMean = input.mean(0, 2, 3);
batchVar = input.var(false, 0, 2, 3);
batchMean = input.mean(nonChDims);
batchVar = input.var(false, nonChDims);
} else {
// TODO setup BatchNorm for RNN https://arxiv.org/pdf/1510.01378v1.pdf
throw new IllegalStateException( "The layer prior to BatchNorm in the configuration is not currently supported. " + layerId());
@ -401,15 +411,17 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
}
public INDArray preOutput(INDArray x, TrainingMode training, LayerWorkspaceMgr workspaceMgr) {
if(x.size(1) != layerConf().getNOut()){
throw new IllegalArgumentException("input.size(1) does not match expected input size of " + layerConf().getNIn()
int dim = 1;
if(x.rank() == 4 && layerConf().getCnn2DFormat() == CNN2DFormat.NHWC)
dim = 3;
if(x.size(dim) != layerConf().getNOut()){
throw new IllegalArgumentException("input.size(" + dim + ") does not match expected input size of " + layerConf().getNIn()
+ " - got input array with shape " + Arrays.toString(x.shape()));
}
x = x.castTo(dataType); //No-op if correct type
INDArray activations;
// TODO add this directly in layer or get the layer prior...
// batchnorm true but need to clarify if activation before or after
org.deeplearning4j.nn.conf.layers.BatchNormalization layerConf = layerConf();
val shape = getShape(x);
@ -449,7 +461,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
}
ret = helper.preOutput(in, training == TrainingMode.TRAIN, shape, gamma, beta, globalMeanView,
globalVarView, decay, layerConf.getEps(), workspaceMgr);
globalVarView, decay, layerConf.getEps(), layerConf().getCnn2DFormat(), workspaceMgr);
} catch (ND4JOpProfilerException e){
throw e; //NaN panic etc for debugging
} catch (Throwable t) {
@ -474,6 +486,13 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
}
}
CNN2DFormat format = layerConf().getCnn2DFormat();
boolean nchw = format == CNN2DFormat.NCHW;
int chIdx = nchw ? 1 : 3;
int[] nonChDims = nchw ? new int[]{0, 2, 3} : new int[]{0, 1, 2};
int hIdx = nchw ? 2 : 1;
int wIdx = nchw ? 3 : 2;
// xHat = (x-xmean) / sqrt(var + epsilon)
//Note that for CNNs, mean and variance are calculated per feature map (i.e., per activation) rather than per activation
//Pg5 of https://arxiv.org/pdf/1502.03167v3.pdf
@ -490,8 +509,9 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
break;
case 4:
// mean and variance over samples AND locations
mean = x.mean(0, 2, 3);
var = x.var(false, 0, 2, 3);
mean = x.mean(nonChDims);
var = x.var(false, nonChDims);
break;
default:
throw new IllegalStateException("Batch normalization on activations of rank " + x.rank()
@ -538,9 +558,9 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
if (!Shape.strideDescendingCAscendingF(x))
x = x.dup(); //TODO: temp Workaround for broadcast bug. To be removed when fixed
xMu = workspaceMgr.createUninitialized(ArrayType.INPUT, x.dataType(), x.shape(), x.ordering());
xMu = Nd4j.getExecutioner().exec(new BroadcastSubOp(x, mean,xMu, 1));
xMu = Nd4j.getExecutioner().exec(new BroadcastSubOp(x, mean,xMu, chIdx));
xHat = workspaceMgr.createUninitialized(ArrayType.INPUT, x.dataType(), x.shape(), x.ordering());
xHat = Nd4j.getExecutioner().exec(new BroadcastDivOp(xMu, std,xHat, 1));
xHat = Nd4j.getExecutioner().exec(new BroadcastDivOp(xMu, std,xHat, chIdx));
if (layerConf.isLockGammaBeta()) {
//Special case: gamma/beta have fixed values for all outputs
@ -556,8 +576,8 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
} else {
//Standard case: gamma and beta are learned per parameter
activations = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, x.dataType(), x.shape(), x.ordering());
activations = Nd4j.getExecutioner().exec(new BroadcastMulOp(xHat, gamma, activations, 1));
activations = Nd4j.getExecutioner().exec(new BroadcastAddOp(activations, beta, activations, 1));
activations = Nd4j.getExecutioner().exec(new BroadcastMulOp(xHat, gamma, activations, chIdx));
activations = Nd4j.getExecutioner().exec(new BroadcastAddOp(activations, beta, activations, chIdx));
}
} else {
// TODO setup BatchNorm for RNN https://arxiv.org/pdf/1510.01378v1.pdf
@ -611,8 +631,12 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
}
public long[] getShape(INDArray x) {
if (x.rank() == 2 || x.rank() == 4)
if (x.rank() == 2 )
return new long[] {1, x.size(1)};
if(x.rank() == 4){
int chIdx = layerConf().getCnn2DFormat() == CNN2DFormat.NCHW ? 1 : 3;
return new long[]{1, x.size(chIdx)};
}
if (x.rank() == 3) {
val wDim = x.size(1);
val hdim = x.size(2);

6
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/BatchNormalizationHelper.java
View File

@ -16,6 +16,7 @@
package org.deeplearning4j.nn.layers.normalization;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.LayerHelper;
import org.nd4j.linalg.api.buffer.DataType;
@ -32,10 +33,11 @@ public interface BatchNormalizationHelper extends LayerHelper {
boolean checkSupported(double eps, boolean fixedGammaBeta);
Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, long[] shape, INDArray gamma, INDArray beta,
INDArray dGammaView, INDArray dBetaView, double eps, LayerWorkspaceMgr workspaceMgr);
INDArray dGammaView, INDArray dBetaView, double eps, CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr);
INDArray preOutput(INDArray x, boolean training, long[] shape, INDArray gamma, INDArray beta, INDArray mean,
INDArray var, double decay, double eps, LayerWorkspaceMgr workspaceMgr);
INDArray var, double decay, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);
INDArray getMeanCache(DataType dataType);

42
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/LocalResponseNormalization.java
View File

@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.normalization;
import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
@ -160,12 +161,17 @@ public class LocalResponseNormalization
}
}
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
int chDim = nchw ? 1 : 3;
int hDim = nchw ? 2 : 1;
int wDim = nchw ? 3 : 2;
Triple<INDArray,INDArray,INDArray> triple = activateHelper(true, workspaceMgr, true);
INDArray activations = triple.getFirst();
INDArray unitScale = triple.getSecond();
INDArray scale = triple.getThird();
val channel = input.size(1);
val channel = input.size(chDim);
INDArray tmp, addVal;
Gradient retGradient = new DefaultGradient();
INDArray reverse = activations.mul(epsilon);
@ -173,15 +179,25 @@ public class LocalResponseNormalization
// sumPart = sum(a^j_{x,y} * gb^j_{x,y})
for (int i = 1; i < halfN + 1; i++) {
if(nchw) {
tmp = sumPart.get(NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(), NDArrayIndex.all());
addVal = reverse.get(NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(), NDArrayIndex.all());
sumPart.put(new INDArrayIndex[] {NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(),
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(),
NDArrayIndex.all()}, tmp.addi(addVal));
tmp = sumPart.get(NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(), NDArrayIndex.all());
addVal = reverse.get(NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(), NDArrayIndex.all());
sumPart.put(new INDArrayIndex[] {NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(),
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(),
NDArrayIndex.all()}, tmp.addi(addVal));
} else {
tmp = sumPart.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(i, channel));
addVal = reverse.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(0, channel - i));
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(i, channel)}, tmp.addi(addVal));
tmp = sumPart.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(0, channel - i));
addVal = reverse.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(i, channel));
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(0, channel - i)}, tmp.addi(addVal));
}
}
// gx = gy * unitScale**-beta - 2 * alpha * beta * sumPart/unitScale * a^i_{x,y} - rearranged for more in-place ops
@ -228,7 +244,10 @@ public class LocalResponseNormalization
}
}
val channel = input.size(1);
boolean nchw = layerConf().getDataFormat() == CNN2DFormat.NCHW;
int chDim = nchw ? 1 : 3;
val channel = input.size(chDim);
INDArray tmp, addVal;
// x^2 = (a^j_{x,y})^2
INDArray activitySqr = input.mul(input);
@ -236,16 +255,27 @@ public class LocalResponseNormalization
//sum_{j=max(0, i - n/2)}^{max(N-1, i + n/2)} (a^j_{x,y})^2 )
for (int i = 1; i < halfN + 1; i++) {
if(nchw) {
tmp = sumPart.get(NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(), NDArrayIndex.all());
addVal = activitySqr.get(NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(),
NDArrayIndex.all());
sumPart.put(new INDArrayIndex[] {NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(),
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(),
NDArrayIndex.all()}, tmp.addi(addVal));
tmp = sumPart.get(NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(), NDArrayIndex.all());
addVal = activitySqr.get(NDArrayIndex.all(), interval(i, channel), NDArrayIndex.all(), NDArrayIndex.all());
sumPart.put(new INDArrayIndex[] {NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(),
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), interval(0, channel - i), NDArrayIndex.all(),
NDArrayIndex.all()}, tmp.addi(addVal));
} else {
tmp = sumPart.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(i, channel));
addVal = activitySqr.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(0, channel - i));
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(i, channel)}, tmp.addi(addVal));
tmp = sumPart.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(0, channel - i));
addVal = activitySqr.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(i, channel));
sumPart.put(new INDArrayIndex[]{NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), interval(0, channel - i)}, tmp.addi(addVal));
}
}
INDArray unitScale = null;

100
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ConvolutionUtils.java
View File

@ -22,6 +22,7 @@ import lombok.NonNull;
import lombok.val;
import org.deeplearning4j.exception.DL4JInvalidConfigException;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
@ -56,6 +57,10 @@ public class ConvolutionUtils {
private ConvolutionUtils() {
}
/**
* Use {@link #getOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[], CNN2DFormat)}
*/
@Deprecated
public static int[] getOutputSize(INDArray inputData, int[] kernel, int[] strides, int[] padding,
ConvolutionMode convolutionMode) {
return getOutputSize(inputData, kernel, strides, padding, convolutionMode, ONES);
@ -74,12 +79,15 @@ public class ConvolutionUtils {
* @return Output size: int[2] with output height/width
*/
public static int[] getDeconvolutionOutputSize(INDArray inputData, int[] kernel, int[] strides, int[] padding,
ConvolutionMode convolutionMode, int[] dilation) {
ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format) {
boolean nchw = format == CNN2DFormat.NCHW;
int hDim = nchw ? 2 : 1;
int wDim = nchw ? 3 : 2;
if (inputData.size(2) > Integer.MAX_VALUE || inputData.size(3) > Integer.MAX_VALUE)
if (inputData.size(hDim) > Integer.MAX_VALUE || inputData.size(wDim) > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
int hIn = (int) inputData.size(2);
int wIn = (int) inputData.size(3);
int hIn = (int) inputData.size(hDim);
int wIn = (int) inputData.size(wDim);
int[] eKernel = effectiveKernelSize(kernel, dilation);
if (convolutionMode == ConvolutionMode.Same) {
@ -138,6 +146,15 @@ public class ConvolutionUtils {
}
/**
* @deprecated Use {@link #getOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[], CNN2DFormat)}
*/
@Deprecated
public static int[] getOutputSize(INDArray inputData, int[] kernel, int[] strides, int[] padding,
ConvolutionMode convolutionMode, int[] dilation) {
return getOutputSize(inputData, kernel, strides, padding, convolutionMode, dilation, CNN2DFormat.NCHW);
}
/**
* Get the output size (height/width) for the given input data and CNN configuration
*
@ -147,14 +164,22 @@ public class ConvolutionUtils {
* @param padding Padding (height/width)
* @param convolutionMode Convolution mode (Same, Strict, Truncate)
* @param dilation Kernel dilation (height/width)
* @param format Format for input activations
* @return Output size: int[2] with output height/width
*/
public static int[] getOutputSize(INDArray inputData, int[] kernel, int[] strides, int[] padding,
ConvolutionMode convolutionMode, int[] dilation) {
if (inputData.size(2) > Integer.MAX_VALUE || inputData.size(3) > Integer.MAX_VALUE)
ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format) {
int hDim = 2;
int wDim = 3;
if(format == CNN2DFormat.NHWC){
hDim = 1;
wDim = 2;
}
if (inputData.size(hDim) > Integer.MAX_VALUE || inputData.size(wDim) > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
int inH = (int) inputData.size(2);
int inW = (int) inputData.size(3);
int inH = (int) inputData.size(hDim);
int inW = (int) inputData.size(wDim);
//Determine the effective kernel size, accounting for dilation
//http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#dilated-convolutions
@ -491,18 +516,28 @@ public class ConvolutionUtils {
}
public static INDArray reshape4dTo2d(INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType type){
public static INDArray reshape4dTo2d(INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType type) {
return reshape4dTo2d(in, CNN2DFormat.NCHW, workspaceMgr, type);
}
public static INDArray reshape4dTo2d(INDArray in, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type){
if (in.rank() != 4)
throw new IllegalArgumentException("Invalid input: expect NDArray with rank 4, got rank " + in.rank()
+ " with shape " + Arrays.toString(in.shape()));
val shape = in.shape();
if(format == CNN2DFormat.NCHW){
//Reshape: from [n,c,h,w] to [n*h*w,c]
INDArray out = in.permute(0, 2, 3, 1);
if (out.ordering() != 'c' || !Shape.strideDescendingCAscendingF(out))
out = out.dup('c');
return out.reshape('c', shape[0] * shape[2] * shape[3], shape[1]);
out = workspaceMgr.dup(type, out, 'c');
return workspaceMgr.leverageTo(type, out.reshape('c', shape[0] * shape[2] * shape[3], shape[1]));
} else {
//Reshape: from [n,h,w,c] to [n*h*w,c]
if (in.ordering() != 'c' || !Shape.strideDescendingCAscendingF(in))
in = workspaceMgr.dup(type, in, 'c');
return workspaceMgr.leverageTo(type, in.reshape('c', shape[0] * shape[1] * shape[2], shape[3]));
}
}
public static INDArray reshape5dTo2d(@NonNull Convolution3D.DataFormat format, INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType type){
@ -541,18 +576,23 @@ public class ConvolutionUtils {
}
}
public static INDArray reshape2dTo4d(INDArray in2d, long[] toShape, LayerWorkspaceMgr workspaceMgr, ArrayType type){
public static INDArray reshape2dTo4d(INDArray in2d, long[] toShape, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type){
if(in2d.rank() != 2)
throw new IllegalArgumentException("Invalid input: expect NDArray with rank 2");
if (toShape.length != 4)
throw new IllegalArgumentException("Invalid input: expect toShape with 4 elements: got " + Arrays.toString(toShape));
//Reshape: from [n*h*w,c] to [n,h,w,c] to [n,c,h,w]
if(in2d.ordering() != 'c' || !Shape.hasDefaultStridesForShape(in2d))
if (in2d.ordering() != 'c' || !Shape.hasDefaultStridesForShape(in2d))
in2d = workspaceMgr.dup(type, in2d, 'c');
if(format == CNN2DFormat.NCHW) {
//Reshape: from [n*h*w,c] to [n,h,w,c] to [n,c,h,w]
INDArray out = in2d.reshape('c', toShape[0], toShape[2], toShape[3], toShape[1]);
return workspaceMgr.leverageTo(type, out.permute(0, 3, 1, 2));
} else {
//Reshape: from [n*h*w,c] to [n,h,w,c]
return workspaceMgr.leverageTo(type, in2d.reshape('c', toShape));
}
}
public static INDArray reshape2dTo5d(Convolution3D.DataFormat format, INDArray in2d, long n, long d, long h, long w, long ch, LayerWorkspaceMgr workspaceMgr, ArrayType type){
@ -563,7 +603,6 @@ public class ConvolutionUtils {
if(in2d.ordering() != 'c' || !Shape.hasDefaultStridesForShape(in2d))
in2d = workspaceMgr.dup(type, in2d, 'c');
// INDArray ndhwc = in2d.reshape('c', toShape[0], toShape[2], toShape[3], toShape[4], toShape[1]);
INDArray ndhwc = in2d.reshape('c', n, d, h, w, ch);
if(format == Convolution3D.DataFormat.NDHWC){
return workspaceMgr.leverageTo(type, ndhwc);
@ -572,11 +611,19 @@ public class ConvolutionUtils {
}
}
public static INDArray reshapeMaskIfRequired(INDArray mask, INDArray output, LayerWorkspaceMgr workspaceMgr, ArrayType type){
/**
* @deprecated Use {@link #reshapeMaskIfRequired(INDArray, INDArray, CNN2DFormat, LayerWorkspaceMgr, ArrayType)}
*/
@Deprecated
public static INDArray reshapeMaskIfRequired(INDArray mask, INDArray output, LayerWorkspaceMgr workspaceMgr, ArrayType type) {
return reshapeMaskIfRequired(mask, output, null, workspaceMgr, type);
}
public static INDArray reshapeMaskIfRequired(INDArray mask, INDArray output, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type){
if (mask == null)
return null;
if (mask.rank() == 2) {
return adapt2dMask(mask, output, workspaceMgr, type);
return adapt2dMask(mask, output, format, workspaceMgr, type);
} else if (mask.rank() == 3) {
return reshape3dMask(mask, workspaceMgr, type);
} else {
@ -584,7 +631,9 @@ public class ConvolutionUtils {
}
}
public static INDArray adapt2dMask(INDArray mask, INDArray output, LayerWorkspaceMgr workspaceMgr, ArrayType type){
public static INDArray adapt2dMask(INDArray mask, INDArray output, @NonNull CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type){
if(format == CNN2DFormat.NCHW){
//Input in [n,c,h,w] which is reshaped to [n*h*w,c], mask is [n,1]
//So: We'll broadcast to [n,1,h,w] then reshape to [n*h*w,1] required for the current DL4J loss functions...
@ -597,6 +646,15 @@ public class ConvolutionUtils {
INDArray bMaskPermute = bMask.permute(0, 2, 3, 1).dup('c'); //Not sure if dup is strictly necessary...
return workspaceMgr.leverageTo(type, bMaskPermute.reshape('c', s[0] * s[2] * s[3], 1));
} else {
//Input in [n,h,w,c] which is reshaped to [n*h*w,c], mask is [n,1]
//So: We'll broadcast to [n,h,w,1] then reshape to [n*h*w,1] required for the current DL4J loss functions...
val s = output.shape();
INDArray bMask = workspaceMgr.create(type, mask.dataType(), new long[]{s[0], s[2], s[3], 1}, 'c');
Nd4j.getExecutioner().exec(new BroadcastCopyOp(bMask, mask, bMask, 0, 3));
return workspaceMgr.leverageTo(type, bMask.reshape('c', s[0] * s[2] * s[3], 1));
}
}
public static INDArray reshape3dMask(INDArray mask, LayerWorkspaceMgr workspaceMgr, ArrayType type){
@ -679,10 +737,10 @@ public class ConvolutionUtils {
int[] s = new int[]{stride, 1};
int[] d = new int[]{dilation, 1};
if (cm == ConvolutionMode.Same || cm == ConvolutionMode.Causal) {
outSize = ConvolutionUtils.getOutputSize(reshaped4d, k, s, null, cm, d); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(reshaped4d, k, s, null, cm, d, CNN2DFormat.NCHW); //Also performs validation
} else {
pad = new int[]{padding, 0};
outSize = ConvolutionUtils.getOutputSize(reshaped4d, k, s, pad, cm, d); //Also performs validation
outSize = ConvolutionUtils.getOutputSize(reshaped4d, k, s, pad, cm, d, CNN2DFormat.NCHW); //Also performs validation
}
int outH = outSize[0];