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org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer Maven / Gradle / Ivy
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* * information regarding copyright ownership.
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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;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.util.ConvolutionUtils;
import org.nd4j.linalg.activations.IActivation;
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.exception.ND4JArraySizeException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import java.util.Arrays;
public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
public DepthwiseConvolution2DLayer(NeuralNetConfiguration conf, DataType dataType) {
super(conf, dataType);
}
@Override
void initializeHelper() {
//No op - no separable conv implementation in cudnn
}
@Override
public Pair 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 " + layerConf().getCnn2dDataFormat().dimensionNames() + ". "
+ layerId());
}
INDArray bias;
INDArray depthWiseWeights =
getParamWithNoise(DepthwiseConvolutionParamInitializer.WEIGHT_KEY, true, workspaceMgr);
INDArray input = this.input.castTo(dataType); //No-op if correct type
long miniBatch = input.size(0);
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);
int kW = (int) depthWiseWeights.size(1);
int[] dilation = layerConf().getDilation();
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
int[] pad;
if (convolutionMode == ConvolutionMode.Same) {
int[] outSize = ConvolutionUtils.getOutputSize(
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, format);
}
INDArray biasGradView = gradientViews.get(DepthwiseConvolutionParamInitializer.BIAS_KEY);
INDArray weightGradView = gradientViews.get(DepthwiseConvolutionParamInitializer.WEIGHT_KEY);
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');
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, (nchw ? 0 : 1)
};
INDArray delta;
IActivation afn = layerConf().getActivationFn();
Pair p = preOutput4d(true, true, workspaceMgr);
delta = afn.backprop(p.getFirst(), epsilon).getFirst();
INDArray[] inputs;
INDArray[] outputs;
if (layerConf().hasBias()) {
bias = getParamWithNoise(DepthwiseConvolutionParamInitializer.BIAS_KEY, true, workspaceMgr);
inputs = new INDArray[]{input, depthWiseWeights, bias, delta};
outputs = new INDArray[]{outEpsilon, weightGradView, biasGradView};
} else {
inputs = new INDArray[]{input, depthWiseWeights, delta};
outputs = new INDArray[]{outEpsilon, weightGradView};
}
CustomOp op = DynamicCustomOp.builder("depthwise_conv2d_bp")
.addInputs(inputs)
.addIntegerArguments(args)
.addOutputs(outputs)
.callInplace(false)
.build();
Nd4j.getExecutioner().exec(op);
Gradient retGradient = new DefaultGradient();
if (layerConf().hasBias()) {
retGradient.setGradientFor(DepthwiseConvolutionParamInitializer.BIAS_KEY, biasGradView);
}
retGradient.setGradientFor(DepthwiseConvolutionParamInitializer.WEIGHT_KEY, weightGradView, 'c');
weightNoiseParams.clear();
outEpsilon = backpropDropOutIfPresent(outEpsilon);
return new Pair<>(retGradient, outEpsilon);
}
@Override
protected Pair preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
INDArray bias = getParamWithNoise(DepthwiseConvolutionParamInitializer.BIAS_KEY, training, workspaceMgr);
INDArray depthWiseWeights =
getParamWithNoise(DepthwiseConvolutionParamInitializer.WEIGHT_KEY, training, workspaceMgr);
if (input.rank() != 4) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
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 " + layerConf().getCnn2dDataFormat().dimensionNames() + "."
+ (input.rank() == 2
? " (Wrong input type (see InputType.convolutionalFlat()) or wrong data type?)"
: "") + " " + layerId());
}
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(nchw ? 1 : 3) != inDepth) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
String s = "Cannot do forward pass in DepthwiseConvolution2D layer " +
"(layer name = " + layerName
+ ", layer index = " + index + "): input array channels does not match CNN layer configuration"
+ " (data format = " + format + ", data input channels = " + input.size(1) + ", "
+ (nchw ? "[minibatch,inputDepth,height,width]=" : "[minibatch,height,width,inputDepth]=")
+ Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
+ layerId();
int dimIfWrongFormat = format == CNN2DFormat.NHWC ? 1 : 3;
if(input.size(dimIfWrongFormat) == inDepth){
//User might have passed NCHW data to a NHWC net, or vice versa?
s += "\n" + ConvolutionUtils.NCHW_NHWC_ERROR_MSG;
}
throw new DL4JInvalidInputException(s);
}
int kH = (int) depthWiseWeights.size(0);
int kW = (int) depthWiseWeights.size(1);
int[] dilation = layerConf().getDilation();
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
int[] pad;
int[] outSize;
if (convolutionMode == ConvolutionMode.Same) {
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(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, format);
}
long outH = outSize[0];
long outW = outSize[1];
val miniBatch = input.size(0);
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');
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, (nchw ? 0 : 1)
};
INDArray[] inputs;
if (layerConf().hasBias()) {
inputs = new INDArray[]{input, depthWiseWeights, bias};
} else {
inputs = new INDArray[]{input, depthWiseWeights};
}
CustomOp op = DynamicCustomOp.builder("depthwise_conv2d")
.addInputs(inputs)
.addIntegerArguments(args)
.addOutputs(output)
.callInplace(false)
.build();
Nd4j.getExecutioner().exec(op);
return new Pair<>(output, null);
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
if (cacheMode == null)
cacheMode = CacheMode.NONE;
applyDropOutIfNecessary(training, workspaceMgr);
INDArray z = preOutput(training, false, workspaceMgr).getFirst();
//String afn = conf.getLayer().getActivationFunction();
IActivation afn = layerConf().getActivationFn();
INDArray activation = afn.getActivation(z, training);
return activation;
}
}
