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org.deeplearning4j.nn.conf.layers.PrimaryCapsules 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional;
import org.deeplearning4j.nn.conf.inputs.InputType.Type;
import org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.deeplearning4j.util.CapsuleUtils;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Map;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
public class PrimaryCapsules extends SameDiffLayer {
private int[] kernelSize;
private int[] stride;
private int[] padding;
private int[] dilation;
private int inputChannels;
private int channels;
private boolean hasBias;
private int capsules;
private int capsuleDimensions;
private ConvolutionMode convolutionMode = ConvolutionMode.Truncate;
private boolean useRelu = false;
private double leak = 0;
private static final String WEIGHT_PARAM = "weight";
private static final String BIAS_PARAM = "bias";
public PrimaryCapsules(Builder builder){
super(builder);
this.kernelSize = builder.kernelSize;
this.stride = builder.stride;
this.padding = builder.padding;
this.dilation = builder.dilation;
this.channels = builder.channels;
this.hasBias = builder.hasBias;
this.capsules = builder.capsules;
this.capsuleDimensions = builder.capsuleDimensions;
this.convolutionMode = builder.convolutionMode;
this.useRelu = builder.useRelu;
this.leak = builder.leak;
if(capsuleDimensions <= 0 || channels <= 0){
throw new IllegalArgumentException("Invalid configuration for Primary Capsules (layer name = \""
+ layerName + "\"):"
+ " capsuleDimensions and channels must be > 0. Got: "
+ capsuleDimensions + ", " + channels);
}
if(capsules < 0){
throw new IllegalArgumentException("Invalid configuration for Capsule Layer (layer name = \""
+ layerName + "\"):"
+ " capsules must be >= 0 if set. Got: "
+ capsules);
}
}
@Override
public SDVariable defineLayer(SameDiff SD, SDVariable input, Map paramTable, SDVariable mask) {
Conv2DConfig conf = Conv2DConfig.builder()
.kH(kernelSize[0]).kW(kernelSize[1])
.sH(stride[0]).sW(stride[1])
.pH(padding[0]).pW(padding[1])
.dH(dilation[0]).dW(dilation[1])
.paddingMode(ConvolutionMode.mapToMode(convolutionMode))
.build();
SDVariable conved;
if(hasBias){
conved = SD.cnn.conv2d(input, paramTable.get(WEIGHT_PARAM), paramTable.get(BIAS_PARAM), conf);
} else {
conved = SD.cnn.conv2d(input, paramTable.get(WEIGHT_PARAM), conf);
}
if(useRelu){
if(leak == 0) {
conved = SD.nn.relu(conved, 0);
} else {
conved = SD.nn.leakyRelu(conved, leak);
}
}
SDVariable reshaped = conved.reshape(-1, capsules, capsuleDimensions);
return CapsuleUtils.squash(SD, reshaped, 2);
}
@Override
public void defineParameters(SDLayerParams params) {
params.clear();
params.addWeightParam(WEIGHT_PARAM,
kernelSize[0], kernelSize[1], inputChannels, capsuleDimensions * channels);
if(hasBias){
params.addBiasParam(BIAS_PARAM, capsuleDimensions * channels);
}
}
@Override
public void initializeParameters(Map params) {
try (MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
for (Map.Entry e : params.entrySet()) {
if (BIAS_PARAM.equals(e.getKey())) {
e.getValue().assign(0);
} else if(WEIGHT_PARAM.equals(e.getKey())){
double fanIn = inputChannels * kernelSize[0] * kernelSize[1];
double fanOut = capsuleDimensions * channels * kernelSize[0] * kernelSize[1] / ((double) stride[0] * stride[1]);
WeightInitUtil.initWeights(fanIn, fanOut, e.getValue().shape(), weightInit, null, 'c',
e.getValue());
}
}
}
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != Type.CNN) {
throw new IllegalStateException("Invalid input for Primary Capsules layer (layer name = \""
+ layerName + "\"): expect CNN input. Got: " + inputType);
}
if(capsules > 0){
return InputType.recurrent(capsules, capsuleDimensions);
} else {
InputTypeConvolutional out = (InputTypeConvolutional) InputTypeUtil
.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
capsuleDimensions * channels, -1, getLayerName(), PrimaryCapsules.class);
return InputType.recurrent((int) (out.getChannels() * out.getHeight() * out.getWidth() / capsuleDimensions),
capsuleDimensions);
}
}
@Override
public void setNIn(InputType inputType, boolean override) {
if (inputType == null || inputType.getType() != Type.CNN) {
throw new IllegalStateException("Invalid input for Primary Capsules layer (layer name = \""
+ layerName + "\"): expect CNN input. Got: " + inputType);
}
InputTypeConvolutional ci = (InputTypeConvolutional) inputType;
this.inputChannels = (int) ci.getChannels();
if(capsules <= 0 || override) {
InputTypeConvolutional out = (InputTypeConvolutional) InputTypeUtil
.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
capsuleDimensions * channels, -1, getLayerName(), PrimaryCapsules.class);
this.capsules = (int) (out.getChannels() * out.getHeight() * out.getWidth() / capsuleDimensions);
}
}
@Getter
@Setter
public static class Builder extends SameDiffLayer.Builder{
@Setter(AccessLevel.NONE)
private int[] kernelSize = new int[]{9, 9};
@Setter(AccessLevel.NONE)
private int[] stride = new int[]{2, 2};
@Setter(AccessLevel.NONE)
private int[] padding = new int[]{0, 0};
@Setter(AccessLevel.NONE)
private int[] dilation = new int[]{1, 1};
private int channels = 32;
private boolean hasBias = true;
private int capsules;
private int capsuleDimensions;
private ConvolutionMode convolutionMode = ConvolutionMode.Truncate;
private boolean useRelu = false;
private double leak = 0;
public void setKernelSize(int... kernelSize){
this.kernelSize = ValidationUtils.validate2NonNegative(kernelSize, true, "kernelSize");
}
public void setStride(int... stride){
this.stride = ValidationUtils.validate2NonNegative(stride, true, "stride");
}
public void setPadding(int... padding){
this.padding = ValidationUtils.validate2NonNegative(padding, true, "padding");
}
public void setDilation(int... dilation){
this.dilation = ValidationUtils.validate2NonNegative(dilation, true, "dilation");
}
public Builder(int capsuleDimensions, int channels,
int[] kernelSize, int[] stride, int[] padding, int[] dilation,
ConvolutionMode convolutionMode){
this.capsuleDimensions = capsuleDimensions;
this.channels = channels;
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
this.setDilation(dilation);
this.convolutionMode = convolutionMode;
}
public Builder(int capsuleDimensions, int channels,
int[] kernelSize, int[] stride, int[] padding, int[] dilation){
this(capsuleDimensions, channels, kernelSize, stride, padding, dilation, ConvolutionMode.Truncate);
}
public Builder(int capsuleDimensions, int channels,
int[] kernelSize, int[] stride, int[] padding){
this(capsuleDimensions, channels, kernelSize, stride, padding, new int[]{1, 1}, ConvolutionMode.Truncate);
}
public Builder(int capsuleDimensions, int channels,
int[] kernelSize, int[] stride){
this(capsuleDimensions, channels, kernelSize, stride, new int[]{0, 0}, new int[]{1, 1}, ConvolutionMode.Truncate);
}
public Builder(int capsuleDimensions, int channels,
int[] kernelSize){
this(capsuleDimensions, channels, kernelSize, new int[]{2, 2}, new int[]{0, 0}, new int[]{1, 1}, ConvolutionMode.Truncate);
}
public Builder(int capsuleDimensions, int channels){
this(capsuleDimensions, channels, new int[]{9, 9}, new int[]{2, 2}, new int[]{0, 0}, new int[]{1, 1}, ConvolutionMode.Truncate);
}
/**
* Sets the kernel size of the 2d convolution
*
* @see ConvolutionLayer.Builder#kernelSize(int...)
* @param kernelSize
* @return
*/
public Builder kernelSize(int... kernelSize){
this.setKernelSize(kernelSize);
return this;
}
/**
* Sets the stride of the 2d convolution
*
* @see ConvolutionLayer.Builder#stride(int...)
* @param stride
* @return
*/
public Builder stride(int... stride){
this.setStride(stride);
return this;
}
/**
* Sets the padding of the 2d convolution
*
* @see ConvolutionLayer.Builder#padding(int...)
* @param padding
* @return
*/
public Builder padding(int... padding){
this.setPadding(padding);
return this;
}
/**
* Sets the dilation of the 2d convolution
*
* @see ConvolutionLayer.Builder#dilation(int...)
* @param dilation
* @return
*/
public Builder dilation(int... dilation){
this.setDilation(dilation);
return this;
}
/**
* Sets the number of channels to use in the 2d convolution.
*
* Note that the actual number of channels is channels * capsuleDimensions
*
* Does the same thing as nOut()
*
* @param channels
* @return
*/
public Builder channels(int channels){
this.channels = channels;
return this;
}
/**
* Sets the number of channels to use in the 2d convolution.
*
* Note that the actual number of channels is channels * capsuleDimensions
*
* Does the same thing as channels()
*
* @param nOut
* @return
*/
public Builder nOut(int nOut){
return channels(nOut);
}
/**
* Sets the number of dimensions to use in the capsules.
* @param capsuleDimensions
* @return
*/
public Builder capsuleDimensions(int capsuleDimensions){
this.capsuleDimensions = capsuleDimensions;
return this;
}
/**
* Usually inferred automatically.
* @param capsules
* @return
*/
public Builder capsules(int capsules){
this.capsules = capsules;
return this;
}
public Builder hasBias(boolean hasBias){
this.hasBias = hasBias;
return this;
}
/**
* The convolution mode to use in the 2d convolution
* @param convolutionMode
* @return
*/
public Builder convolutionMode(ConvolutionMode convolutionMode){
this.convolutionMode = convolutionMode;
return this;
}
/**
* Whether to use a ReLU activation on the 2d convolution
* @param useRelu
* @return
*/
public Builder useReLU(boolean useRelu){
this.useRelu = useRelu;
return this;
}
/**
* Use a ReLU activation on the 2d convolution
* @return
*/
public Builder useReLU(){
return useReLU(true);
}
/**
* Use a LeakyReLU activation on the 2d convolution
* @param leak the alpha value for the LeakyReLU activation.
* @return
*/
public Builder useLeakyReLU(double leak){
this.useRelu = true;
this.leak = leak;
return this;
}
@Override
public E build() {
return (E) new PrimaryCapsules(this);
}
}
}
