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org.deeplearning4j.nn.conf.layers.Upsampling1D Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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
* *****************************************************************************
*/
package org.deeplearning4j.nn.conf.layers;
import java.util.Arrays;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
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.optimize.api.TrainingListener;
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 java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Upsampling1D extends BaseUpsamplingLayer {
protected int[] size;
protected Upsampling1D(UpsamplingBuilder builder) {
super(builder);
this.size = builder.size;
}
@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.upsampling.Upsampling1D ret =
new org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D(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 Upsampling1D clone() {
Upsampling1D clone = (Upsampling1D) super.clone();
return clone;
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.RNN) {
throw new IllegalStateException("Invalid input for 1D Upsampling layer (layer index = " + layerIndex
+ ", layer name = \"" + getLayerName() + "\"): expect RNN input type with size > 0. Got: "
+ inputType);
}
InputType.InputTypeRecurrent recurrent = (InputType.InputTypeRecurrent) inputType;
long outLength = recurrent.getTimeSeriesLength();
if (outLength > 0) {
outLength *= size[0];
}
return InputType.recurrent(recurrent.getSize(), outLength);
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for Upsampling layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
InputType.InputTypeRecurrent recurrent = (InputType.InputTypeRecurrent) inputType;
InputType.InputTypeRecurrent outputType = (InputType.InputTypeRecurrent) getOutputType(-1, inputType);
long im2colSizePerEx = recurrent.getSize() * outputType.getTimeSeriesLength() * size[0];
long trainingWorkingSizePerEx = im2colSizePerEx;
if (getIDropout() != null) {
trainingWorkingSizePerEx += inputType.arrayElementsPerExample();
}
return new LayerMemoryReport.Builder(layerName, Upsampling1D.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();
}
@NoArgsConstructor
public static class Builder extends UpsamplingBuilder {
public Builder(int size) {
super(new int[] {size, size});
}
/**
* Upsampling size
*
* @param size upsampling size in single spatial dimension of this 1D layer
*/
public Builder size(int size) {
this.setSize(new int[] {size});
return this;
}
/**
* Upsampling size int array with a single element. Array must be length 1
*
* @param size upsampling size in single spatial dimension of this 1D layer
*/
public Builder size(int[] size) {
this.setSize(size);
return this;
}
@Override
@SuppressWarnings("unchecked")
public Upsampling1D build() {
return new Upsampling1D(this);
}
@Override
public void setSize(int... size) {
if(size.length == 2){
if(size[0] == size[1]) {
setSize(new int[]{size[0]});
return;
} else {
Preconditions.checkArgument(false,
"When given a length 2 array for size, "
+ "the values must be equal. Got: " + Arrays.toString(size));
}
}
int[] temp = ValidationUtils.validate1NonNegative(size, "size");
this.size = new int[]{temp[0], temp[0]};
}
}
}