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org.deeplearning4j.nn.layers.convolution.Deconvolution3DLayer Maven / Gradle / Ivy
/*
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* * 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.
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* * information regarding copyright ownership.
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.layers.convolution;
import lombok.val;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.Convolution3D;
import org.deeplearning4j.nn.conf.layers.Deconvolution3D;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseLayer;
import org.deeplearning4j.nn.params.DeconvolutionParamInitializer;
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.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import org.nd4j.common.util.ArrayUtil;
import java.util.Arrays;
public class Deconvolution3DLayer extends BaseLayer {
public Deconvolution3DLayer(NeuralNetConfiguration conf, DataType dataType) {
super(conf, dataType);
}
@Override
public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
if (input.rank() != 5) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to Deconvolution3DLayer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 5 array with shape [minibatchSize, channels, inputHeight, inputWidth, inputDepth] or" +
" [minibatchSize, inputHeight, inputWidth, inputDepth, channels]. " + layerId());
}
INDArray weights = getParamWithNoise(DeconvolutionParamInitializer.WEIGHT_KEY, true, workspaceMgr);
Convolution3D.DataFormat df = layerConf().getDataFormat();
ConvolutionMode cm = layerConf().getConvolutionMode();
int[] dilation = layerConf().getDilation();
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
int[] pad = layerConf().getPadding();
INDArray biasGradView = gradientViews.get(DeconvolutionParamInitializer.BIAS_KEY);
INDArray weightGradView = gradientViews.get(DeconvolutionParamInitializer.WEIGHT_KEY);
INDArray outEps = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, weights.dataType(), input.shape(), 'c');
Integer sameMode = (layerConf().getConvolutionMode() == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[] {
kernel[0], kernel[1], kernel[2], strides[0], strides[1], strides[2],
pad[0], pad[1], pad[2], dilation[0], dilation[1], dilation[2], sameMode,
df == Convolution3D.DataFormat.NCDHW ? 0 : 1
};
INDArray delta;
IActivation afn = layerConf().getActivationFn();
INDArray preOutput = preOutput(true, workspaceMgr);
delta = afn.backprop(preOutput, epsilon).getFirst();
INDArray[] opInputs;
INDArray[] opOutputs;
if(layerConf().hasBias()){
INDArray bias = getParamWithNoise(DeconvolutionParamInitializer.BIAS_KEY, true, workspaceMgr);
opInputs = new INDArray[]{input, weights, bias, delta};
opOutputs = new INDArray[]{outEps, weightGradView, biasGradView};
} else {
opInputs = new INDArray[]{input, weights, delta};
opOutputs = new INDArray[]{outEps, weightGradView};
}
CustomOp op = DynamicCustomOp.builder("deconv3d_bp")
.addInputs(opInputs)
.addIntegerArguments(args)
.addOutputs(opOutputs)
.callInplace(false)
.build();
Nd4j.getExecutioner().exec(op);
Gradient retGradient = new DefaultGradient();
if(layerConf().hasBias()){
retGradient.setGradientFor(DeconvolutionParamInitializer.BIAS_KEY, biasGradView);
}
retGradient.setGradientFor(DeconvolutionParamInitializer.WEIGHT_KEY, weightGradView, 'c');
weightNoiseParams.clear();
return new Pair<>(retGradient, outEps);
}
protected INDArray preOutput(boolean training , LayerWorkspaceMgr workspaceMgr) {
INDArray bias = getParamWithNoise(DeconvolutionParamInitializer.BIAS_KEY, training, workspaceMgr);
INDArray weights = getParamWithNoise(DeconvolutionParamInitializer.WEIGHT_KEY, training, workspaceMgr);
//Input validation: expect rank 5 matrix
if (input.rank() != 5) {
throw new DL4JInvalidInputException("Got rank " + input.rank()
+ " array as input to Deconvolution3DLayer with shape " + Arrays.toString(input.shape())
+ ". Expected rank 5 array with shape [minibatchSize, channels, inputHeight, inputWidth, inputDepth] or" +
" [minibatchSize, inputHeight, inputWidth, inputDepth, channels]. " + layerId());
}
Convolution3D.DataFormat df = layerConf().getDataFormat();
boolean ncdhw = layerConf().getDataFormat() == Convolution3D.DataFormat.NCDHW;
int chDim = ncdhw ? 1 : 4;
if (input.size(chDim) != layerConf().getNIn() ) {
String layerName = conf.getLayer().getLayerName();
if (layerName == null)
layerName = "(not named)";
throw new DL4JInvalidInputException("Cannot do forward pass in Deconvolution3D layer (layer name = " + layerName
+ ", layer index = " + index + "): input array channels does not match CNN layer configuration"
+ " (data input channels = " + input.size(chDim) + ", " + (ncdhw ? "[minibatch,channels,height,width,depth]=" : "[minibatch,height,width,depth,channels]=")
+ Arrays.toString(input.shape()) + "; expected" + " input channels = " + layerConf().getNIn() + ") "
+ layerId());
}
int[] dilation = layerConf().getDilation();
int[] kernel = layerConf().getKernelSize();
int[] strides = layerConf().getStride();
int[] pad;
ConvolutionMode cm = layerConf().getConvolutionMode();
long[] outSize;
int[] inSize = df == Convolution3D.DataFormat.NCDHW ? new int[]{(int)input.size(2), (int)input.size(3), (int)input.size(4)} : new int[]{(int)input.size(1), (int)input.size(2), (int)input.size(3)};
if (cm == ConvolutionMode.Same) {
outSize = ConvolutionUtils.getDeconvolution3DOutputSize(input, kernel, strides, null, dilation, cm, layerConf().getDataFormat()); //Also performs validation
pad = ConvolutionUtils.getSameModeTopLeftPadding(ArrayUtil.toInts(outSize), inSize, kernel, strides, dilation );
} else {
pad = layerConf().getPadding();
outSize = ConvolutionUtils.getDeconvolution3DOutputSize(input, kernel, strides, pad, dilation, cm, layerConf().getDataFormat()); //Also performs validation
}
long outH = outSize[0];
long outW = outSize[1];
long outD = outSize[2];
val miniBatch = input.size(0);
long[] outShape = df == Convolution3D.DataFormat.NCDHW ? new long[]{miniBatch, layerConf().getNOut(), outH, outW, outD} : new long[]{miniBatch, outH, outW, outD, layerConf().getNOut()};
INDArray output = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');
int sameMode = (cm == ConvolutionMode.Same) ? 1 : 0;
int[] args = new int[] {
kernel[0], kernel[1], kernel[2], strides[0], strides[1], strides[2],
pad[0], pad[1], pad[2], dilation[0], dilation[1], dilation[2], sameMode,
df == Convolution3D.DataFormat.NCDHW ? 0 : 1
};
INDArray[] opInputs;
if (layerConf().hasBias()) {
opInputs = new INDArray[]{input, weights, bias};
} else {
opInputs = new INDArray[]{input, weights};
}
CustomOp op = DynamicCustomOp.builder("deconv3d")
.addInputs(opInputs)
.addIntegerArguments(args)
.addOutputs(output)
.callInplace(false)
.build();
Nd4j.getExecutioner().exec(op);
return output;
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
if (cacheMode == null)
cacheMode = CacheMode.NONE;
applyDropOutIfNecessary(training, workspaceMgr);
INDArray z = preOutput(training, workspaceMgr);
IActivation afn = layerConf().getActivationFn();
INDArray activation = afn.getActivation(z, training);
return activation;
}
@Override
public boolean isPretrainLayer() {
return false;
}
}