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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
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* * 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.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer;
import org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.*;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class SeparableConvolution2D extends ConvolutionLayer {
int depthMultiplier;
/**
* SeparableConvolution2D layer 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 SeparableConvolution2D(Builder builder) {
super(builder);
this.hasBias = builder.hasBias;
this.depthMultiplier = builder.depthMultiplier;
this.convolutionMode = builder.convolutionMode;
this.dilation = builder.dilation;
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("Stride should include stride for rows and columns (a 2d array)");
}
this.stride = builder.stride;
if (builder.padding.length != 2) {
throw new IllegalArgumentException("Padding should include padding for rows and columns (a 2d array)");
}
this.padding = builder.padding;
this.cudnnAlgoMode = builder.cudnnAlgoMode;
this.cudnnFwdAlgo = builder.cudnnFwdAlgo;
this.cudnnBwdFilterAlgo = builder.cudnnBwdFilterAlgo;
this.cudnnBwdDataAlgo = builder.cudnnBwdDataAlgo;
this.cnn2dDataFormat = builder.dataFormat;
initializeConstraints(builder);
}
@Override
protected void initializeConstraints(org.deeplearning4j.nn.conf.layers.Layer.Builder builder) {
super.initializeConstraints(builder);
if (((Builder) builder).pointWiseConstraints != null) {
if (constraints == null) {
constraints = new ArrayList<>();
}
for (LayerConstraint constraint : ((Builder) builder).pointWiseConstraints) {
LayerConstraint clonedConstraint = constraint.clone();
clonedConstraint.setParams(
Collections.singleton(SeparableConvolutionParamInitializer.POINT_WISE_WEIGHT_KEY));
constraints.add(clonedConstraint);
}
}
}
public boolean hasBias() {
return hasBias;
}
@Override
public SeparableConvolution2D clone() {
SeparableConvolution2D clone = (SeparableConvolution2D) 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("SeparableConvolution2D", getLayerName(), layerIndex, getNIn(), getNOut());
SeparableConvolution2DLayer ret =
new SeparableConvolution2DLayer(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 SeparableConvolutionParamInitializer.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);
}
CNN2DFormat format = ((InputType.InputTypeConvolutional)inputType).getFormat();
return InputTypeUtil.getOutputTypeCnnLayers(inputType, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), format, SeparableConvolution2DLayer.class);
}
@Getter
@Setter
public static class Builder extends BaseConvBuilder {
/**
* Set channels multiplier of channels-wise step in separable convolution
*
*/
protected int depthMultiplier = 1;
protected CNN2DFormat dataFormat;
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();
}
@Override
protected boolean allowCausal() {
//Causal convolution - allowed for 1D only
return false;
}
/**
* 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;
}
/**
* Set channels multiplier of channels-wise step in separable convolution
*
* @param depthMultiplier integer value, for each input map we get depthMultipler outputs in channels-wise
* step.
* @return Builder
*/
public Builder depthMultiplier(int depthMultiplier) {
this.setDepthMultiplier(depthMultiplier);
return this;
}
/**
* Set constraints to be applied to the point-wise convolution weight parameters of this layer. Default: no
* constraints. Constraints can be used to enforce certain conditions (non-negativity of parameters,
* max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have
* been updated.
*/
protected List pointWiseConstraints;
/**
* Set constraints to be applied to the point-wise convolution weight parameters of this layer. Default: no
* constraints. Constraints can be used to enforce certain conditions (non-negativity of parameters,
* max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have
* been updated.
*
* @param constraints Constraints to apply to the point-wise convolution parameters of this layer
*/
public Builder constrainPointWise(LayerConstraint... constraints) {
this.setPointWiseConstraints(Arrays.asList(constraints));
return this;
}
/**
* Size of the convolution rows/columns (height/width)
*
* @param kernelSize the height and width of the kernel
*/
public Builder kernelSize(int... kernelSize) {
this.setKernelSize(kernelSize);
return this;
}
/**
* Stride of the convolution rows/columns (height/width)
*
* @param stride the stride of the kernel (in h/w dimensions)
*/
public Builder stride(int... stride) {
this.setStride(stride);
return this;
}
/**
* Padding - rows/columns (height/width)
*
* @param padding the padding in h/w dimensions
*/
public Builder padding(int... padding) {
this.setPadding(padding);
return 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");
}
@Override
@SuppressWarnings("unchecked")
public SeparableConvolution2D build() {
ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding);
ConvolutionUtils.validateCnnKernelStridePadding(kernelSize, stride, padding);
return new SeparableConvolution2D(this);
}
}
}
