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org.deeplearning4j.nn.conf.layers.LocallyConnected1D Maven / Gradle / Ivy
/*******************************************************************************
* 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.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.conf.ConvolutionMode;
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.samediff.SDLayerParams;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayerUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.deeplearning4j.util.Convolution1DUtils;
import org.nd4j.autodiff.samediff.SDIndex;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.shade.jackson.annotation.JsonIgnoreProperties;
import java.util.*;
/**
* SameDiff version of a 1D locally connected layer.
*
* @author Max Pumperla
*/
@Data
@EqualsAndHashCode(callSuper = true)
@JsonIgnoreProperties({"paramShapes"})
public class LocallyConnected1D extends SameDiffLayer {
private static final List WEIGHT_KEYS = Collections.singletonList(ConvolutionParamInitializer.WEIGHT_KEY);
private static final List BIAS_KEYS = Collections.singletonList(ConvolutionParamInitializer.BIAS_KEY);
private static final List PARAM_KEYS =
Arrays.asList(ConvolutionParamInitializer.BIAS_KEY, ConvolutionParamInitializer.WEIGHT_KEY);
private long nIn;
private long nOut;
private Activation activation;
private int kernel;
private int stride;
private int padding;
private int paddingR; //Right/bottom padding
private ConvolutionMode cm;
private int dilation;
private boolean hasBias;
private int inputSize;
private int outputSize;
private int featureDim;
protected LocallyConnected1D(Builder builder) {
super(builder);
this.nIn = builder.nIn;
this.nOut = builder.nOut;
this.activation = builder.activation;
this.kernel = builder.kernel;
this.stride = builder.stride;
this.padding = builder.padding;
this.cm = builder.cm;
this.dilation = builder.dilation;
this.hasBias = builder.hasBias;
this.inputSize = builder.inputSize;
this.featureDim = kernel * (int) nIn;
}
private LocallyConnected1D() {
//No arg constructor for Jackson/JSON serialization
}
public void computeOutputSize() {
int nIn = (int) getNIn();
if (inputSize == 0) {
throw new IllegalArgumentException("Input size has to be set for Locally connected layers");
}
int[] inputShape = new int[] {1, nIn, inputSize};
INDArray dummyInputForShapeInference = Nd4j.ones(inputShape);
if (cm == ConvolutionMode.Same) {
this.outputSize = Convolution1DUtils.getOutputSize(dummyInputForShapeInference, kernel, stride, 0, cm,
dilation);
this.padding = Convolution1DUtils.getSameModeTopLeftPadding(outputSize, inputSize, kernel, stride, dilation);
this.paddingR = Convolution1DUtils.getSameModeBottomRightPadding(outputSize, inputSize, kernel, stride, dilation);
} else {
this.outputSize = Convolution1DUtils.getOutputSize(dummyInputForShapeInference, kernel, stride, padding, cm,
dilation);
}
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.RNN) {
throw new IllegalArgumentException("Provided input type for locally connected 1D layers has to be "
+ "of CNN1D/RNN type, got: " + inputType);
}
// dynamically compute input size from input type
InputType.InputTypeRecurrent rnnType = (InputType.InputTypeRecurrent) inputType;
this.inputSize = (int) rnnType.getTimeSeriesLength();
computeOutputSize();
return InputTypeUtil.getOutputTypeCnn1DLayers(inputType, kernel, stride, padding, 1, cm, nOut, layerIndex,
getLayerName(), LocallyConnected1D.class);
}
@Override
public void setNIn(InputType inputType, boolean override) {
if (nIn <= 0 || override) {
InputType.InputTypeRecurrent c = (InputType.InputTypeRecurrent) inputType;
this.nIn = c.getSize();
}
if(featureDim <= 0 || override){
InputType.InputTypeRecurrent c = (InputType.InputTypeRecurrent) inputType;
this.featureDim = kernel * (int) c.getSize();
}
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
return InputTypeUtil.getPreprocessorForInputTypeRnnLayers(inputType, getLayerName());
}
@Override
public void defineParameters(SDLayerParams params) {
Preconditions.checkState(featureDim > 0, "Cannot initialize layer: Feature dimension is set to %s", featureDim);
params.clear();
val weightsShape = new long[] {outputSize, featureDim, nOut};
params.addWeightParam(ConvolutionParamInitializer.WEIGHT_KEY, weightsShape);
if (hasBias) {
val biasShape = new long[] {1, nOut};
params.addBiasParam(ConvolutionParamInitializer.BIAS_KEY, biasShape);
}
}
@Override
public void initializeParameters(Map params) {
try (MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
for (Map.Entry e : params.entrySet()) {
if (ConvolutionParamInitializer.BIAS_KEY.equals(e.getKey())) {
e.getValue().assign(0);
} else {
double fanIn = nIn * kernel;
double fanOut = nOut * kernel / ((double) stride);
WeightInitUtil.initWeights(fanIn, fanOut, e.getValue().shape(), weightInit, null, 'c',
e.getValue());
}
}
}
}
@Override
public SDVariable defineLayer(SameDiff sameDiff, SDVariable layerInput, Map paramTable, SDVariable mask) {
SDVariable w = paramTable.get(ConvolutionParamInitializer.WEIGHT_KEY); // (outH, featureDim, nOut)
int outH = outputSize;
int sH = stride;
int kH = kernel;
if(padding > 0 || (cm == ConvolutionMode.Same && paddingR > 0)){
//Note: for same mode, bottom/right padding can be 1 more than top/left padding
//NCW format.
if(cm == ConvolutionMode.Same) {
layerInput = sameDiff.nn().pad(layerInput, new int[][]{{0, 0}, {0, 0}, {padding, paddingR}}, 0);
} else {
layerInput = sameDiff.nn().pad(layerInput, new int[][]{{0, 0}, {0, 0}, {padding, padding}}, 0);
}
}
SDVariable[] inputArray = new SDVariable[outH];
for (int i = 0; i < outH; i++) {
SDVariable slice = layerInput.get(SDIndex.all(), // miniBatch
SDIndex.all(), // nIn
SDIndex.interval(i * sH, i * sH + kH) // kernel
);
inputArray[i] = sameDiff.reshape(slice, 1, -1, featureDim);
}
SDVariable concatOutput = sameDiff.concat(0, inputArray); // (outH, miniBatch, featureDim)
SDVariable mmulResult = sameDiff.mmul(concatOutput, w); // (outH, miniBatch, nOut)
SDVariable result = sameDiff.permute(mmulResult, 1, 2, 0); // (miniBatch, nOut, outH)
if (hasBias) {
SDVariable b = paramTable.get(ConvolutionParamInitializer.BIAS_KEY);
SDVariable biasAddedResult = sameDiff.nn().biasAdd(result, b);
return activation.asSameDiff("out", sameDiff, biasAddedResult);
} else {
return activation.asSameDiff("out", sameDiff, result);
}
}
@Override
public void applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig) {
if (activation == null) {
activation = SameDiffLayerUtils.fromIActivation(globalConfig.getActivationFn());
}
if (cm == null) {
cm = globalConfig.getConvolutionMode();
}
}
@Getter
@Setter
public static class Builder extends SameDiffLayer.Builder {
/**
* Number of inputs to the layer (input size)
*/
private int nIn;
/**
* Number of outputs (output size)
*/
private int nOut;
/**
* Activation function for the layer
*/
private Activation activation = Activation.TANH;
/**
* Kernel size for the layer
*/
private int kernel = 2;
/**
* Stride for the layer
*/
private int stride = 1;
/**
* Padding for the layer. Not used if {@link ConvolutionMode#Same} is set
*/
private int padding = 0;
/**
* Dilation for the layer
*/
private int dilation = 1;
/**
* Input filter size for this locally connected 1D layer
*
*/
@Setter(AccessLevel.NONE)
private int inputSize;
/**
* Convolution mode for the layer. See {@link ConvolutionMode} for details
*/
private ConvolutionMode cm = ConvolutionMode.Same;
/**
* If true (default is false) the layer will have a bias
*/
private boolean hasBias = true;
/**
* @param nIn Number of inputs to the layer (input size)
*/
public Builder nIn(int nIn) {
this.setNIn(nIn);
return this;
}
/**
* @param nOut Number of outputs (output size)
*/
public Builder nOut(int nOut) {
this.setNOut(nOut);
return this;
}
/**
* @param activation Activation function for the layer
*/
public Builder activation(Activation activation) {
this.setActivation(activation);
return this;
}
/**
* @param k Kernel size for the layer
*/
public Builder kernelSize(int k) {
this.setKernel(k);
return this;
}
/**
* @param s Stride for the layer
*/
public Builder stride(int s) {
this.setStride(s);
return this;
}
/**
* @param p Padding for the layer. Not used if {@link ConvolutionMode#Same} is set
*/
public Builder padding(int p) {
this.setPadding(p);
return this;
}
/**
* @param cm Convolution mode for the layer. See {@link ConvolutionMode} for details
*/
public Builder convolutionMode(ConvolutionMode cm) {
this.setCm(cm);
return this;
}
/**
* @param d Dilation for the layer
*/
public Builder dilation(int d) {
this.setDilation(d);
return this;
}
/**
* @param hasBias If true (default is false) the layer will have a bias
*/
public Builder hasBias(boolean hasBias) {
this.setHasBias(hasBias);
return this;
}
/**
* Set input filter size for this locally connected 1D layer
*
* @param inputSize height of the input filters
* @return Builder
*/
public Builder setInputSize(int inputSize) {
this.inputSize = inputSize;
return this;
}
@Override
@SuppressWarnings("unchecked")
public LocallyConnected1D build() {
Convolution1DUtils.validateConvolutionModePadding(cm, padding);
Convolution1DUtils.validateCnn1DKernelStridePadding(kernel, stride, padding);
return new LocallyConnected1D(this);
}
}
}
