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* ******************************************************************************
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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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* * 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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.EmptyParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.Collection;
import java.util.List;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class LocalResponseNormalization extends Layer {
// Defaults as per http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
//Set defaults here as well as in builder, in case users use no-arg constructor instead of builder
protected double n = 5; // # adjacent kernal maps
protected double k = 2; // constant (e.g. scale)
protected double beta = 0.75; // decay rate
protected double alpha = 1e-4; // decay rate
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
private LocalResponseNormalization(Builder builder) {
super(builder);
this.k = builder.k;
this.n = builder.n;
this.alpha = builder.alpha;
this.beta = builder.beta;
this.cudnnAllowFallback = builder.cudnnAllowFallback;
this.dataFormat = builder.dataFormat;
}
@Override
public LocalResponseNormalization clone() {
LocalResponseNormalization clone = (LocalResponseNormalization) super.clone();
return clone;
}
@Override
public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
Collection trainingListeners, int layerIndex, INDArray layerParamsView,
boolean initializeParams, DataType networkDataType) {
org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization ret =
new org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization(conf, networkDataType);
ret.setListeners(trainingListeners);
ret.setIndex(layerIndex);
ret.setParamsViewArray(layerParamsView);
Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public ParamInitializer initializer() {
return EmptyParamInitializer.getInstance();
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.CNN) {
throw new IllegalStateException(
"Invalid input type for LRN layer (layer index = " + layerIndex + ", layer name = \""
+ getLayerName() + "\"): Expected input of type CNN, got " + inputType);
}
return inputType;
}
@Override
public void setNIn(InputType inputType, boolean override) {
Preconditions.checkState(inputType.getType() == InputType.Type.CNN, "Only CNN input types can be used with LocalResponseNormalisation, got %s", inputType);
this.dataFormat = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException(
"Invalid input type for LRN layer (layer name = \"" + getLayerName() + "\"): null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public List getRegularizationByParam(String paramName) {
return null;
}
@Override
public boolean isPretrainParam(String paramName) {
return false; //No params in LRN
}
@Override
public GradientNormalization getGradientNormalization() {
return GradientNormalization.None;
}
@Override
public double getGradientNormalizationThreshold() {
return 0;
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
val actElementsPerEx = inputType.arrayElementsPerExample();
//Forward pass: 3x input size as working memory, in addition to output activations
//Backward pass: 2x input size as working memory, in addition to epsilons
return new LayerMemoryReport.Builder(layerName, DenseLayer.class, inputType, inputType).standardMemory(0, 0)
.workingMemory(0, 2 * actElementsPerEx, 0, 3 * actElementsPerEx)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayer
.build();
}
@AllArgsConstructor
@Getter
@Setter
public static class Builder extends Layer.Builder {
// defaults based on AlexNet model
/**
* LRN scaling constant k. Default: 2
*
*/
private double k = 2;
/**
* Number of adjacent kernel maps to use when doing LRN. default: 5
*
*/
private double n = 5;
/**
* LRN scaling constant alpha. Default: 1e-4
*
*/
private double alpha = 1e-4;
/**
* Scaling constant beta. Default: 0.75
*
*/
private double beta = 0.75;
/**
* When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed?
* If set to false, an exception in CuDNN will be propagated back to the user. If false, the built-in
* (non-CuDNN) implementation for BatchNormalization will be used
*
*/
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
public Builder(double k, double n, double alpha, double beta) {
this(k, n, alpha, beta, true, CNN2DFormat.NCHW);
}
public Builder(double k, double alpha, double beta) {
this.setK(k);
this.setAlpha(alpha);
this.setBeta(beta);
}
public Builder() {}
/**
* LRN scaling constant k. Default: 2
*
* @param k Scaling constant
*/
public Builder k(double k) {
this.setK(k);
return this;
}
/**
* Number of adjacent kernel maps to use when doing LRN. default: 5
*
* @param n Number of adjacent kernel maps
*/
public Builder n(double n) {
this.setN(n);
return this;
}
/**
* LRN scaling constant alpha. Default: 1e-4
*
* @param alpha Scaling constant
*/
public Builder alpha(double alpha) {
this.setAlpha(alpha);
return this;
}
/**
* Scaling constant beta. Default: 0.75
*
* @param beta Scaling constant
*/
public Builder beta(double beta) {
this.setBeta(beta);
return this;
}
/**
* When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed?
* If set to false, an exception in CuDNN will be propagated back to the user. If true, the built-in
* (non-CuDNN) implementation for BatchNormalization will be used
*
* @deprecated Use {@link #helperAllowFallback(boolean)}
*
* @param allowFallback Whether fallback to non-CuDNN implementation should be used
*/
@Deprecated
public Builder cudnnAllowFallback(boolean allowFallback) {
this.setCudnnAllowFallback(allowFallback);
return this;
}
/**
* When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed?
* If set to false, an exception in the helper will be propagated back to the user. If true, the built-in
* (non-MKL/CuDNN) implementation for LocalResponseNormalizationLayer will be used
*
* @param allowFallback Whether fallback to non-CuDNN implementation should be used
*/
public Builder helperAllowFallback(boolean allowFallback) {
this.cudnnAllowFallback = allowFallback;
return this;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.
* Default: NCHW
* @param format Format for activations (in and out)
*/
public Builder dataFormat(CNN2DFormat dataFormat){
this.dataFormat = dataFormat;
return this;
}
@Override
public LocalResponseNormalization build() {
return new LocalResponseNormalization(this);
}
}
}
