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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
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
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package org.deeplearning4j.nn.conf.layers;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer;
import org.deeplearning4j.nn.params.DeconvolutionParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Deconvolution2D extends ConvolutionLayer {
/**
* Deconvolution2D 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 Deconvolution2D(BaseConvBuilder builder) {
super(builder);
initializeConstraints(builder);
if(builder instanceof Builder){
this.cnn2dDataFormat = ((Builder) builder).format;
}
}
public boolean hasBias() {
return hasBias;
}
@Override
public Deconvolution2D clone() {
Deconvolution2D clone = (Deconvolution2D) 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("Deconvolution2D", getLayerName(), layerIndex, getNIn(), getNOut());
Deconvolution2DLayer ret =
new Deconvolution2DLayer(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 DeconvolutionParamInitializer.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.getOutputTypeDeconvLayer(inputType, kernelSize, stride, padding, dilation, convolutionMode,
nOut, layerIndex, getLayerName(), Deconvolution2DLayer.class);
}
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();
}
private CNN2DFormat format = CNN2DFormat.NCHW;
public Builder dataFormat(CNN2DFormat format){
this.format = format;
return this;
}
@Override
protected boolean allowCausal() {
//Causal convolution - allowed for 1D only
return false;
}
/**
* Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details
*
* @param convolutionMode Convolution mode for layer
*/
public Builder convolutionMode(ConvolutionMode convolutionMode) {
return super.convolutionMode(convolutionMode);
}
/**
* 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;
}
@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
public void setDilation(int... dilation) {
this.dilation = ValidationUtils.validate2NonNegative(dilation, false,"dilation");
}
@Override
public Deconvolution2D build() {
return new Deconvolution2D(this);
}
}
}