org.deeplearning4j.nn.conf.layers.ConvolutionLayer Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * information regarding copyright ownership.
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package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.*;
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.ConvolutionParamInitializer;
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.Arrays;
import java.util.Collection;
import java.util.HashMap;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class ConvolutionLayer extends FeedForwardLayer {
protected boolean hasBias = true;
protected ConvolutionMode convolutionMode = ConvolutionMode.Truncate; //Default to truncate here - default for 0.6.0 and earlier networks on JSON deserialization
protected int dilation[] = new int[] {1, 1};
protected int[] kernelSize; // Square filter
protected int[] stride; // Default is 2. Down-sample by a factor of 2
protected int[] padding;
protected boolean cudnnAllowFallback = true;
protected CNN2DFormat cnn2dDataFormat = CNN2DFormat.NCHW; //default value for legacy serialization reasons
@JsonIgnore
@EqualsAndHashCode.Exclude
private boolean defaultValueOverriden = false;
/**
* The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the {@link FwdAlgo},
* {@link BwdFilterAlgo}, and {@link BwdDataAlgo} lists, but they may be very memory intensive, so if weird errors
* occur when using cuDNN, please try the "NO_WORKSPACE" mode. Alternatively, it is possible to specify the
* algorithm manually by setting the "USER_SPECIFIED" mode, but this is not recommended.
*
* Note: Currently only supported with cuDNN.
*/
public enum AlgoMode {
NO_WORKSPACE, PREFER_FASTEST, USER_SPECIFIED
}
/**
* The forward algorithm to use when {@link AlgoMode} is set to "USER_SPECIFIED".
*
* Note: Currently only supported with cuDNN.
*/
public enum FwdAlgo {
IMPLICIT_GEMM, IMPLICIT_PRECOMP_GEMM, GEMM, DIRECT, FFT, FFT_TILING, WINOGRAD, WINOGRAD_NONFUSED, COUNT
}
/**
* The backward filter algorithm to use when {@link AlgoMode} is set to "USER_SPECIFIED".
*
* Note: Currently only supported with cuDNN.
*/
public enum BwdFilterAlgo {
ALGO_0, ALGO_1, FFT, ALGO_3, WINOGRAD, WINOGRAD_NONFUSED, FFT_TILING, COUNT
}
/**
* The backward data algorithm to use when {@link AlgoMode} is set to "USER_SPECIFIED".
*
* Note: Currently only supported with cuDNN.
*/
public enum BwdDataAlgo {
ALGO_0, ALGO_1, FFT, FFT_TILING, WINOGRAD, WINOGRAD_NONFUSED, COUNT
}
/**
* Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
*/
protected AlgoMode cudnnAlgoMode = AlgoMode.PREFER_FASTEST;
protected FwdAlgo cudnnFwdAlgo;
protected BwdFilterAlgo cudnnBwdFilterAlgo;
protected BwdDataAlgo cudnnBwdDataAlgo;
/**
* ConvolutionLayer nIn in the input layer is the number of channels nOut is the number of filters to be used in the
* net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The
* pooling layer takes the kernel size
*/
protected ConvolutionLayer(BaseConvBuilder builder) {
super(builder);
int dim = builder.convolutionDim;
this.hasBias = builder.hasBias;
this.convolutionMode = builder.convolutionMode;
this.dilation = builder.dilation;
if (builder.kernelSize.length != dim) {
throw new IllegalArgumentException("Kernel argument should be a " + dim + "d array, got " + Arrays.toString(builder.kernelSize));
}
this.kernelSize = builder.kernelSize;
if (builder.stride.length != dim) {
throw new IllegalArgumentException("Strides argument should be a " + dim + "d array, got " + Arrays.toString(builder.stride));
}
this.stride = builder.stride;
if (builder.padding.length != dim) {
throw new IllegalArgumentException("Padding argument should be a " + dim + "d array, got " + Arrays.toString(builder.padding));
}
this.padding = builder.padding;
if (builder.dilation.length != dim) {
throw new IllegalArgumentException("Dilation argument should be a " + dim + "d array, got " + Arrays.toString(builder.dilation));
}
this.dilation = builder.dilation;
this.cudnnAlgoMode = builder.cudnnAlgoMode;
this.cudnnFwdAlgo = builder.cudnnFwdAlgo;
this.cudnnBwdFilterAlgo = builder.cudnnBwdFilterAlgo;
this.cudnnBwdDataAlgo = builder.cudnnBwdDataAlgo;
this.cudnnAllowFallback = builder.cudnnAllowFallback;
if(builder instanceof Builder) {
this.cnn2dDataFormat = ((Builder)builder).dataFormat;
}
initializeConstraints(builder);
}
public boolean hasBias() {
return hasBias;
}
@Override
public ConvolutionLayer clone() {
ConvolutionLayer clone = (ConvolutionLayer) 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();
}
return clone;
}
@Override
public Layer instantiate(NeuralNetConfiguration conf, Collection trainingListeners,
int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
LayerValidation.assertNInNOutSet("ConvolutionLayer", getLayerName(), layerIndex, getNIn(), getNOut());
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer ret =
new org.deeplearning4j.nn.layers.convolution.ConvolutionLayer(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 ConvolutionParamInitializer.getInstance();
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.CNN) {
throw new IllegalStateException("Invalid input for Convolution layer (layer name=\"" + getLayerName()
+ "\"): Expected CNN input, got " + inputType);
}
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), cnn2dDataFormat, ConvolutionLayer.class);
}
@Override
public void setNIn(InputType inputType, boolean override) {
if (inputType == null || inputType.getType() != InputType.Type.CNN) {
throw new IllegalStateException("Invalid input for Convolution layer (layer name=\"" + getLayerName()
+ "\"): Expected CNN input, got " + inputType);
}
if (!defaultValueOverriden || nIn <= 0 || override) {
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
this.nIn = c.getChannels();
this.cnn2dDataFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
}
if(cnn2dDataFormat == null || override)
this.cnn2dDataFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for Convolution layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
val paramSize = initializer().numParams(this);
val updaterStateSize = (int) getIUpdater().stateSize(paramSize);
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
InputType.InputTypeConvolutional outputType = (InputType.InputTypeConvolutional) getOutputType(-1, inputType);
//TODO convolution helper memory use... (CuDNN etc)
//During forward pass: im2col array, mmul (result activations), in-place broadcast add
val im2colSizePerEx = c.getChannels() * outputType.getHeight() * outputType.getWidth() * kernelSize[0]
* kernelSize[1];
//During training: have im2col array, in-place gradient calculation, then epsilons...
//But: im2col array may be cached...
Map trainWorkingMemoryPerEx = new HashMap<>();
Map cachedPerEx = new HashMap<>();
//During backprop: im2col array for forward pass (possibly cached) + the epsilon6d array required to calculate
// the 4d epsilons (equal size to input)
//Note that the eps6d array is same size as im2col
for (CacheMode cm : CacheMode.values()) {
long trainWorkingSizePerEx;
long cacheMemSizePerEx = 0;
if (cm == CacheMode.NONE) {
trainWorkingSizePerEx = 2 * im2colSizePerEx;
} else {
//im2col is cached, but epsNext2d/eps6d is not
cacheMemSizePerEx = im2colSizePerEx;
trainWorkingSizePerEx = im2colSizePerEx;
}
if (getIDropout() != null) {
//Dup on the input before dropout, but only for training
trainWorkingSizePerEx += inputType.arrayElementsPerExample();
}
trainWorkingMemoryPerEx.put(cm, trainWorkingSizePerEx);
cachedPerEx.put(cm, cacheMemSizePerEx);
}
return new LayerMemoryReport.Builder(layerName, ConvolutionLayer.class, inputType, outputType)
.standardMemory(paramSize, updaterStateSize)
//im2col caching -> only variable size caching
.workingMemory(0, im2colSizePerEx, MemoryReport.CACHE_MODE_ALL_ZEROS, trainWorkingMemoryPerEx)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, cachedPerEx).build();
}
public static class Builder extends BaseConvBuilder {
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() {
super();
}
protected CNN2DFormat dataFormat = CNN2DFormat.NCHW;
@Override
protected boolean allowCausal() {
//Causal convolution - allowed for 1D only
return false;
}
/**
* Size of the convolution rows/columns
*
* @param kernelSize the height and width of the kernel
*/
public Builder kernelSize(int... kernelSize) {
this.setKernelSize(kernelSize);
return this;
}
public Builder stride(int... stride) {
this.setStride(stride);
return this;
}
public Builder padding(int... padding) {
this.setPadding(padding);
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 format){
this.dataFormat = format;
return this;
}
@Override
@SuppressWarnings("unchecked")
public ConvolutionLayer build() {
ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding);
ConvolutionUtils.validateCnnKernelStridePadding(kernelSize, stride, padding);
return new ConvolutionLayer(this);
}
/**
* Set kernel size for 3D convolutions in (depth, height, width) order
*
* @param kernelSize kernel size
*/
@Override
public void setKernelSize(int... kernelSize) {
this.kernelSize = ValidationUtils.validate2NonNegative(kernelSize, false, "kernelSize");
}
/**
* Set stride size for 3D convolutions in (depth, height, width) order
*
* @param stride kernel size
*/
@Override
public void setStride(int... stride) {
this.stride = ValidationUtils.validate2NonNegative(stride, false, "stride");
}
/**
* Set padding size for 3D convolutions in (depth, height, width) order
*
* @param padding kernel size
*/
@Override
public void setPadding(int... padding) {
this.padding = ValidationUtils.validate2NonNegative(padding, false, "padding");
}
/**
* Set dilation size for 3D convolutions in (depth, height, width) order
*
* @param dilation kernel size
*/
@Override
public void setDilation(int... dilation) {
this.dilation = ValidationUtils.validate2NonNegative(dilation, false, "dilation");
}
public void setDataFormat(CNN2DFormat dataFormat){
this.dataFormat = dataFormat;
}
}
@Getter
@Setter
public static abstract class BaseConvBuilder> extends FeedForwardLayer.Builder {
protected int convolutionDim = 2; // 2D convolution by default
/**
* If true (default): include bias parameters in the model. False: no bias.
*
*/
protected boolean hasBias = true;
/**
* Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details
*
*/
protected ConvolutionMode convolutionMode;
/**
* Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions,
* which are also known as atrous convolutions.
*
* 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
*
*/
protected int[] dilation = new int[] {1, 1};
public int[] kernelSize = new int[] {5, 5};
protected int[] stride = new int[] {1, 1};
protected int[] padding = new int[] {0, 0};
/**
* Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
*/
protected AlgoMode cudnnAlgoMode = null;
protected FwdAlgo cudnnFwdAlgo;
protected BwdFilterAlgo cudnnBwdFilterAlgo;
protected BwdDataAlgo cudnnBwdDataAlgo;
/**
* 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
*
*/
protected boolean cudnnAllowFallback = true;
protected BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding, int[] dilation, int dim) {
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
this.setDilation(dilation);
this.setConvolutionDim(dim);
}
protected BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding, int[] dilation) {
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
this.setDilation(dilation);
}
protected BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding, int dim) {
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
this.setConvolutionDim(dim);
}
protected BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding) {
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setPadding(padding);
}
protected BaseConvBuilder(int[] kernelSize, int[] stride, int dim) {
this.setKernelSize(kernelSize);
this.setStride(stride);
this.setConvolutionDim(dim);
}
protected BaseConvBuilder(int[] kernelSize, int[] stride) {
this.setKernelSize(kernelSize);
this.setStride(stride);
}
protected BaseConvBuilder(int dim, int... kernelSize) {
this.setKernelSize(kernelSize);
this.setConvolutionDim(dim);
}
protected BaseConvBuilder(int... kernelSize) {
this.setKernelSize(kernelSize);
}
protected BaseConvBuilder() {}
protected abstract boolean allowCausal();
protected 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;
}
/**
* If true (default): include bias parameters in the model. False: no bias.
*
* @param hasBias If true: include bias parameters in this model
*/
public T hasBias(boolean hasBias) {
this.setHasBias(hasBias);
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;
}
/**
* Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions,
* which are also known as atrous convolutions.
*
* 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 T dilation(int... dilation) {
this.setDilation(dilation);
return (T) this;
}
public T kernelSize(int... kernelSize) {
this.setKernelSize(kernelSize);
return (T) this;
}
public T stride(int... stride) {
this.setStride(stride);
return (T) this;
}
public T padding(int... padding) {
this.setPadding(padding);
return (T) this;
}
/**
* Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
*/
public T cudnnAlgoMode(AlgoMode cudnnAlgoMode) {
this.setCudnnAlgoMode(cudnnAlgoMode);
return (T) this;
}
public T cudnnFwdMode(FwdAlgo cudnnFwdAlgo) {
this.setCudnnFwdAlgo(cudnnFwdAlgo);
return (T) this;
}
public T cudnnBwdFilterMode(BwdFilterAlgo cudnnBwdFilterAlgo) {
this.setCudnnBwdFilterAlgo(cudnnBwdFilterAlgo);
return (T) this;
}
public T cudnnBwdDataMode(BwdDataAlgo cudnnBwdDataAlgo) {
this.setCudnnBwdDataAlgo(cudnnBwdDataAlgo);
return (T) 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 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.setCudnnAllowFallback(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 ConvolutionLayer 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;
}
}
}