Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer 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.params;
import lombok.val;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.layers.SeparableConvolution2D;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;
import java.util.*;
/**
* Initialize separable convolution params.
*
* @author Max Pumperla
*/
public class SeparableConvolutionParamInitializer implements ParamInitializer {
private static final SeparableConvolutionParamInitializer INSTANCE = new SeparableConvolutionParamInitializer();
public static SeparableConvolutionParamInitializer getInstance() {
return INSTANCE;
}
public final static String DEPTH_WISE_WEIGHT_KEY = DefaultParamInitializer.WEIGHT_KEY;
public final static String POINT_WISE_WEIGHT_KEY = "pW";
public final static String BIAS_KEY = DefaultParamInitializer.BIAS_KEY;
@Override
public long numParams(NeuralNetConfiguration conf) {
return numParams(conf.getLayer());
}
@Override
public long numParams(Layer l) {
SeparableConvolution2D layerConf = (SeparableConvolution2D) l;
val depthWiseParams = numDepthWiseParams(layerConf);
val pointWiseParams = numPointWiseParams(layerConf);
val biasParams = numBiasParams(layerConf);
return depthWiseParams + pointWiseParams + biasParams;
}
private long numBiasParams(SeparableConvolution2D layerConf) {
val nOut = layerConf.getNOut();
return (layerConf.hasBias() ? nOut : 0);
}
/**
* For each input feature we separately compute depthMultiplier many
* output maps for the given kernel size
*
* @param layerConf layer configuration of the separable conv2d layer
* @return number of parameters of the channels-wise convolution operation
*/
private long numDepthWiseParams(SeparableConvolution2D layerConf) {
int[] kernel = layerConf.getKernelSize();
val nIn = layerConf.getNIn();
val depthMultiplier = layerConf.getDepthMultiplier();
return nIn * depthMultiplier * kernel[0] * kernel[1];
}
/**
* For the point-wise convolution part we have (nIn * depthMultiplier) many
* input maps and nOut output maps. Kernel size is (1, 1) for this operation.
*
* @param layerConf layer configuration of the separable conv2d layer
* @return number of parameters of the point-wise convolution operation
*/
private long numPointWiseParams(SeparableConvolution2D layerConf) {
val nIn = layerConf.getNIn();
val nOut = layerConf.getNOut();
val depthMultiplier = layerConf.getDepthMultiplier();
return (nIn * depthMultiplier) * nOut;
}
@Override
public List paramKeys(Layer layer) {
SeparableConvolution2D layerConf =
(SeparableConvolution2D) layer;
if(layerConf.hasBias()){
return Arrays.asList(DEPTH_WISE_WEIGHT_KEY, POINT_WISE_WEIGHT_KEY, BIAS_KEY);
} else {
return weightKeys(layer);
}
}
@Override
public List weightKeys(Layer layer) {
return Arrays.asList(DEPTH_WISE_WEIGHT_KEY, POINT_WISE_WEIGHT_KEY);
}
@Override
public List biasKeys(Layer layer) {
SeparableConvolution2D layerConf =
(SeparableConvolution2D) layer;
if(layerConf.hasBias()){
return Collections.singletonList(BIAS_KEY);
} else {
return Collections.emptyList();
}
}
@Override
public boolean isWeightParam(Layer layer, String key) {
return DEPTH_WISE_WEIGHT_KEY.equals(key) || POINT_WISE_WEIGHT_KEY.equals(key);
}
@Override
public boolean isBiasParam(Layer layer, String key) {
return BIAS_KEY.equals(key);
}
@Override
public Map init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
SeparableConvolution2D layer = (SeparableConvolution2D) conf.getLayer();
if (layer.getKernelSize().length != 2) throw new IllegalArgumentException("Filter size must be == 2");
Map params = Collections.synchronizedMap(new LinkedHashMap());
SeparableConvolution2D layerConf = (SeparableConvolution2D) conf.getLayer();
val depthWiseParams = numDepthWiseParams(layerConf);
val biasParams = numBiasParams(layerConf);
INDArray depthWiseWeightView = paramsView.get(
NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(biasParams, biasParams + depthWiseParams));
INDArray pointWiseWeightView = paramsView.get(
NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(biasParams + depthWiseParams, numParams(conf)));
params.put(DEPTH_WISE_WEIGHT_KEY, createDepthWiseWeightMatrix(conf, depthWiseWeightView, initializeParams));
conf.addVariable(DEPTH_WISE_WEIGHT_KEY);
params.put(POINT_WISE_WEIGHT_KEY, createPointWiseWeightMatrix(conf, pointWiseWeightView, initializeParams));
conf.addVariable(POINT_WISE_WEIGHT_KEY);
if(layer.hasBias()){
INDArray biasView = paramsView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, biasParams));
params.put(BIAS_KEY, createBias(conf, biasView, initializeParams));
conf.addVariable(BIAS_KEY);
}
return params;
}
@Override
public Map getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
SeparableConvolution2D layerConf =
(SeparableConvolution2D) conf.getLayer();
int[] kernel = layerConf.getKernelSize();
val nIn = layerConf.getNIn();
val depthMultiplier = layerConf.getDepthMultiplier();
val nOut = layerConf.getNOut();
Map out = new LinkedHashMap<>();
val depthWiseParams = numDepthWiseParams(layerConf);
val biasParams = numBiasParams(layerConf);
INDArray depthWiseWeightGradientView = gradientView.get(
NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(biasParams, biasParams + depthWiseParams))
.reshape('c', depthMultiplier, nIn, kernel[0], kernel[1]);
INDArray pointWiseWeightGradientView = gradientView.get(
NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(biasParams + depthWiseParams, numParams(conf)))
.reshape('c', nOut, nIn * depthMultiplier, 1, 1);
out.put(DEPTH_WISE_WEIGHT_KEY, depthWiseWeightGradientView);
out.put(POINT_WISE_WEIGHT_KEY, pointWiseWeightGradientView);
if(layerConf.hasBias()){
INDArray biasGradientView = gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, nOut));
out.put(BIAS_KEY, biasGradientView);
}
return out;
}
protected INDArray createBias(NeuralNetConfiguration conf, INDArray biasView, boolean initializeParams) {
SeparableConvolution2D layerConf =
(SeparableConvolution2D) conf.getLayer();
if (initializeParams)
biasView.assign(layerConf.getBiasInit());
return biasView;
}
protected INDArray createDepthWiseWeightMatrix(NeuralNetConfiguration conf, INDArray weightView, boolean initializeParams) {
/*
Create a 4d weight matrix of: (channels multiplier, num input channels, kernel height, kernel width)
Inputs to the convolution layer are: (batch size, num input feature maps, image height, image width)
*/
SeparableConvolution2D layerConf =
(SeparableConvolution2D) conf.getLayer();
int depthMultiplier = layerConf.getDepthMultiplier();
if (initializeParams) {
int[] kernel = layerConf.getKernelSize();
int[] stride = layerConf.getStride();
val inputDepth = layerConf.getNIn();
double fanIn = inputDepth * kernel[0] * kernel[1];
double fanOut = depthMultiplier * kernel[0] * kernel[1] / ((double) stride[0] * stride[1]);
val weightsShape = new long[] {depthMultiplier, inputDepth, kernel[0], kernel[1]};
return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c',
weightView);
} else {
int[] kernel = layerConf.getKernelSize();
return WeightInitUtil.reshapeWeights(
new long[] {depthMultiplier, layerConf.getNIn(), kernel[0], kernel[1]}, weightView, 'c');
}
}
protected INDArray createPointWiseWeightMatrix(NeuralNetConfiguration conf, INDArray weightView,
boolean initializeParams) {
/*
Create a 4d weight matrix of: (num output channels, channels multiplier * num input channels,
kernel height, kernel width)
*/
SeparableConvolution2D layerConf =
(SeparableConvolution2D) conf.getLayer();
int depthMultiplier = layerConf.getDepthMultiplier();
if (initializeParams) {
val inputDepth = layerConf.getNIn();
val outputDepth = layerConf.getNOut();
double fanIn = inputDepth * depthMultiplier;
double fanOut = fanIn;
val weightsShape = new long[] {outputDepth, depthMultiplier * inputDepth, 1, 1};
return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c',
weightView);
} else {
return WeightInitUtil.reshapeWeights(
new long[] {layerConf.getNOut(), depthMultiplier * layerConf.getNIn(), 1, 1}, weightView, 'c');
}
}
}
