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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.
* *
* * 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
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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.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.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.EmptyParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonIgnore;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class SubsamplingLayer extends NoParamLayer {
protected ConvolutionMode convolutionMode = ConvolutionMode.Truncate; //Default to truncate here - default for 0.6.0 and earlier networks on JSON deserialization
protected org.deeplearning4j.nn.conf.layers.PoolingType poolingType;
protected int[] kernelSize; // Same as filter size from the last conv layer
protected int[] stride; // Default is 2. Down-sample by a factor of 2
protected int[] padding;
protected int[] dilation = new int[] {1, 1};
protected int pnorm;
protected double eps;
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat cnn2dDataFormat = CNN2DFormat.NCHW; //default value for legacy reasons
public final static CNN2DFormat DEFAULT_FORMAT = CNN2DFormat.NCHW;
@JsonIgnore
@EqualsAndHashCode.Exclude
private boolean defaultValueOverridden = false;
/*
Default here for JSON deserialization of 1.0.0-beta4 and earlier models. New models default to false via builder.
This impacts average pooling only - whether the divisor should include or exclude padding along image edges.
DL4J originally included padding in the count, versions after 1.0.0-beta4 will exclude it by default.
*/
protected boolean avgPoolIncludePadInDivisor = true;
public enum PoolingType {
MAX, AVG, SUM, PNORM;
public org.deeplearning4j.nn.conf.layers.PoolingType toPoolingType() {
switch (this) {
case MAX:
return org.deeplearning4j.nn.conf.layers.PoolingType.MAX;
case AVG:
return org.deeplearning4j.nn.conf.layers.PoolingType.AVG;
case SUM:
return org.deeplearning4j.nn.conf.layers.PoolingType.SUM;
case PNORM:
return org.deeplearning4j.nn.conf.layers.PoolingType.PNORM;
}
throw new UnsupportedOperationException("Unknown/not supported pooling type: " + this);
}
}
protected SubsamplingLayer(BaseSubsamplingBuilder builder) {
super(builder);
this.poolingType = builder.poolingType;
if (builder.kernelSize.length != 2) {
throw new IllegalArgumentException("Kernel size of should be rows x columns (a 2d array)");
}
this.kernelSize = builder.kernelSize;
if (builder.stride.length != 2) {
throw new IllegalArgumentException("Invalid stride, must be length 2");
}
this.stride = builder.stride;
this.padding = builder.padding;
this.convolutionMode = builder.convolutionMode;
this.cnn2dDataFormat = builder.cnn2DFormat;
if (builder instanceof Builder) {
this.dilation = ((Builder) builder).dilation;
}
this.pnorm = builder.pnorm;
this.eps = builder.eps;
this.cudnnAllowFallback = builder.cudnnAllowFallback;
this.avgPoolIncludePadInDivisor = builder.avgPoolIncludePadInDivisor;
}
@Override
public SubsamplingLayer clone() {
SubsamplingLayer clone = (SubsamplingLayer) super.clone();
if (clone.kernelSize != null) {
clone.kernelSize = clone.kernelSize.clone();
}
if (clone.stride != null) {
clone.stride = clone.stride.clone();
}
if (clone.padding != null) {
clone.padding = clone.padding.clone();
}
if (clone.dilation != null) {
clone.dilation = clone.dilation.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.convolution.subsampling.SubsamplingLayer ret =
new org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer(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 for Subsampling layer (layer name=\"" + getLayerName()
+ "\"): Expected CNN input, got " + inputType);
}
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
((InputType.InputTypeConvolutional) inputType).getChannels(), layerIndex, getLayerName(),
cnn2dDataFormat, SubsamplingLayer.class);
}
@Override
public void setNIn(InputType inputType, boolean override) {
//No op: subsampling layer doesn't have nIn value
if(!defaultValueOverridden || override) {
this.cnn2dDataFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
defaultValueOverridden = true;
}
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for Subsampling layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public boolean isPretrainParam(String paramName) {
throw new UnsupportedOperationException("SubsamplingLayer does not contain parameters");
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
InputType.InputTypeConvolutional outputType = (InputType.InputTypeConvolutional) getOutputType(-1, inputType);
val actElementsPerEx = outputType.arrayElementsPerExample();
//TODO Subsampling helper memory use... (CuDNN etc)
//During forward pass: im2col array + reduce. Reduce is counted as activations, so only im2col is working mem
val im2colSizePerEx = c.getChannels() * outputType.getHeight() * outputType.getWidth() * kernelSize[0]
* kernelSize[1];
//Current implementation does NOT cache im2col etc... which means: it's recalculated on each backward pass
long trainingWorkingSizePerEx = im2colSizePerEx;
if (getIDropout() != null) {
//Dup on the input before dropout, but only for training
trainingWorkingSizePerEx += inputType.arrayElementsPerExample();
}
return new LayerMemoryReport.Builder(layerName, SubsamplingLayer.class, inputType, outputType)
.standardMemory(0, 0) //No params
.workingMemory(0, im2colSizePerEx, 0, trainingWorkingSizePerEx)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching
.build();
}
public int getPnorm() {
return pnorm;
}
public double getEps() {
return eps;
}
@NoArgsConstructor
@Getter
@Setter
public static class Builder extends BaseSubsamplingBuilder {
/**
* Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions,
* which are also known as atrous convolutions. NOTE: Kernel dilation is less common in practice for
* subsampling layers, compared to convolutional layers.
*
* For more details, see:
* Yu and Koltun (2014) and
* Chen et al. (2014), as well as
*
* http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#dilated-convolutions
*
* Dilation for kernel
*/
private int[] dilation = new int[] {1, 1};
public Builder(PoolingType poolingType, int[] kernelSize, int[] stride) {
super(poolingType, kernelSize, stride);
}
public Builder(PoolingType poolingType, int[] kernelSize) {
super(poolingType, kernelSize);
}
public Builder(PoolingType poolingType, int[] kernelSize, int[] stride, int[] padding) {
super(poolingType, kernelSize, stride, padding);
}
public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize) {
super(poolingType, kernelSize);
}
public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize, int[] stride,
int[] padding) {
super(poolingType, kernelSize, stride, padding);
}
public Builder(int[] kernelSize, int[] stride, int[] padding) {
super(kernelSize, stride, padding);
}
public Builder(int[] kernelSize, int[] stride) {
super(kernelSize, stride);
}
public Builder(int... kernelSize) {
super(kernelSize);
}
public Builder(PoolingType poolingType) {
super(poolingType);
}
public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType) {
super(poolingType);
}
@Override
protected boolean allowCausal() {
//Only conv1d/subsampling1d can use causal mode
return false;
}
/**
* Kernel size
*
* @param kernelSize kernel size in height and width dimensions
*/
public Builder kernelSize(int... kernelSize) {
this.setKernelSize(kernelSize);
return this;
}
/**
* Stride
*
* @param stride stride in height and width dimensions
*/
public Builder stride(int... stride) {
this.setStride(stride);
return this;
}
/**
* Padding
*
* @param padding padding in the height and width dimensions
*/
public Builder padding(int... padding) {
this.setPadding(padding);
return this;
}
/**
* Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions,
* which are also known as atrous convolutions. NOTE: Kernel dilation is less common in practice for
* subsampling layers, compared to convolutional layers.
*
* For more details, see:
* Yu and Koltun (2014) and
* Chen et al. (2014), as well as
*
* http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#dilated-convolutions
*
* @param dilation Dilation for kernel
*/
public Builder dilation(int... dilation) {
this.setDilation(dilation);
return this;
}
@Override
@SuppressWarnings("unchecked")
public SubsamplingLayer build() {
if (poolingType == org.deeplearning4j.nn.conf.layers.PoolingType.PNORM && pnorm <= 0) {
throw new IllegalStateException(
"Incorrect Subsampling config: p-norm must be set when using PoolingType.PNORM");
}
ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding);
ConvolutionUtils.validateCnnKernelStridePadding(kernelSize, stride, padding);
return new SubsamplingLayer(this);
}
@Override
public void setKernelSize(int... kernelSize) {
this.kernelSize = ValidationUtils.validate2NonNegative(kernelSize,false, "kernelSize");
}
@Override
public void setStride(int... stride) {
this.stride = ValidationUtils.validate2NonNegative(stride, false, "stride");
}
@Override
public void setPadding(int... padding) {
this.padding = ValidationUtils.validate2NonNegative(padding,false, "padding");
}
public void setDilation(int[] dilation) {
this.dilation = ValidationUtils.validate2NonNegative(dilation, false, "dilation");
}
public void setDataFormat(CNN2DFormat format){
this.cnn2DFormat = format;
}
}
@NoArgsConstructor
@Getter
@Setter
protected static abstract class BaseSubsamplingBuilder>
extends Layer.Builder {
protected org.deeplearning4j.nn.conf.layers.PoolingType poolingType =
org.deeplearning4j.nn.conf.layers.PoolingType.MAX;
protected int[] kernelSize = new int[] {1, 1}; // Same as filter size from the last conv layer
protected int[] stride = new int[] {2, 2}; // Default is 2. Down-sample by a factor of 2
protected int[] padding = new int[] {0, 0};
/**
* Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details
*
* Convolution mode for layer
*/
protected ConvolutionMode convolutionMode = null;
protected int pnorm;
protected double eps = 1e-8;
/**
* 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 ConvolutionLayer will be used
*
* Whether fallback to non-CuDNN implementation should be used
*/
protected boolean cudnnAllowFallback = true;
protected boolean avgPoolIncludePadInDivisor = false;
/**
* Configure the 2d data format
*/
protected CNN2DFormat cnn2DFormat = CNN2DFormat.NCHW;
protected BaseSubsamplingBuilder(PoolingType poolingType, int[] kernelSize, int[] stride) {
this.setPoolingType(poolingType.toPoolingType());
this.setKernelSize(kernelSize);
this.setStride(stride);
}
protected BaseSubsamplingBuilder(PoolingType poolingType, int[] kernelSize) {
this.setPoolingType(poolingType.toPoolingType());
this.setKernelSize(kernelSize);
}
protected BaseSubsamplingBuilder(PoolingType poolingType, int[] kernelSize, int[] stride, int[] padding) {
this.setPoolingType(poolingType.toPoolingType());
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
}
protected BaseSubsamplingBuilder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize) {
this.setPoolingType(poolingType);
this.setKernelSize(kernelSize);
}
protected BaseSubsamplingBuilder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int[] kernelSize,
int[] stride, int[] padding) {
this.setPoolingType(poolingType);
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
}
protected BaseSubsamplingBuilder(int[] kernelSize, int[] stride, int[] padding) {
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
}
protected BaseSubsamplingBuilder(int[] kernelSize, int[] stride) {
this.setKernelSize(kernelSize);
this.setStride(stride);
}
protected BaseSubsamplingBuilder(int... kernelSize) {
this.setKernelSize(kernelSize);
}
protected BaseSubsamplingBuilder(PoolingType poolingType) {
this.setPoolingType(poolingType.toPoolingType());
}
protected BaseSubsamplingBuilder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType) {
this.setPoolingType(poolingType);
}
public void setPnorm(int pnorm){
ValidationUtils.validateNonNegative(pnorm, "pnorm");
this.pnorm = pnorm;
}
public void setEps(double eps) {
ValidationUtils.validateNonNegative(eps, "eps");
this.eps = eps;
}
protected abstract boolean allowCausal();
public void setConvolutionMode(ConvolutionMode convolutionMode){
Preconditions.checkState(allowCausal() || convolutionMode != ConvolutionMode.Causal, "Causal convolution mode can only be used with 1D" +
" convolutional neural network layers");
this.convolutionMode = convolutionMode;
}
/**
* Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
* See {@link CNN2DFormat} for more details.
* Default: NCHW
* @param cnn2DFormat Format for activations (in and out)
*/
public T dataFormat(CNN2DFormat cnn2DFormat) {
this.cnn2DFormat = cnn2DFormat;
return (T) this;
}
/**
* Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details
*
* @param convolutionMode Convolution mode for layer
*/
public T convolutionMode(ConvolutionMode convolutionMode) {
this.setConvolutionMode(convolutionMode);
return (T) this;
}
public T poolingType(PoolingType poolingType) {
this.setPoolingType(poolingType.toPoolingType());
return (T) this;
}
public T poolingType(org.deeplearning4j.nn.conf.layers.PoolingType poolingType){
this.setPoolingType(poolingType);
return (T) this;
}
public T pnorm(int pnorm) {
this.setPnorm(pnorm);
return (T) this;
}
public T eps(double eps) {
this.setEps(eps);
return (T) 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 ConvolutionLayer will be used
*
* @deprecated Use {@link #helperAllowFallback(boolean)}
*
* @param allowFallback Whether fallback to non-CuDNN implementation should be used
*/
@Deprecated
public T cudnnAllowFallback(boolean allowFallback) {
this.cudnnAllowFallback = allowFallback;
return (T) 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 SubsamplingLayer will be used
*
* @param allowFallback Whether fallback to non-CuDNN implementation should be used
*/
public T helperAllowFallback(boolean allowFallback) {
this.cudnnAllowFallback = allowFallback;
return (T) this;
}
/**
* When doing average pooling, should the padding values be included in the divisor or not?
* Not applicable for max and p-norm pooling.
* Users should not usually set this - instead, leave it as the default (false). It is included mainly for backward
* compatibility of older models
* Consider the following 2x2 segment along the right side of the image:
*
* [A, P]
* [B, P]
*
* Where A and B are actual values, and P is padding (0).
* With avgPoolIncludePadInDivisor = true, we have: out = (A+B+0+0)/4
* With avgPoolIncludePadInDivisor = false, we have: out = (A+B+0+0)/2
*
* Earlier versions of DL4J originally included padding in the count, newer versions exclude it.
*
* @param avgPoolIncludePadInDivisor Whether the divisor should include or exclude padding for average pooling
*/
public T avgPoolIncludePadInDivisor(boolean avgPoolIncludePadInDivisor){
this.avgPoolIncludePadInDivisor = avgPoolIncludePadInDivisor;
return (T) this;
}
}
}
