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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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* * 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
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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.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.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 SpaceToBatchLayer extends NoParamLayer {
// TODO: throw error when block and padding dims don't match
protected int[] blocks;
protected int[][] padding;
protected CNN2DFormat format = CNN2DFormat.NCHW;
protected SpaceToBatchLayer(Builder builder) {
super(builder);
this.blocks = builder.blocks;
this.padding = builder.padding;
this.format = builder.format;
}
@Override
public SpaceToBatchLayer clone() {
return (SpaceToBatchLayer) super.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.SpaceToBatch ret =
new org.deeplearning4j.nn.layers.convolution.SpaceToBatch(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 LayerMemoryReport getMemoryReport(InputType inputType) {
InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
InputType.InputTypeConvolutional outputType = (InputType.InputTypeConvolutional) getOutputType(-1, inputType);
return new LayerMemoryReport.Builder(layerName, SpaceToBatchLayer.class, inputType, outputType)
.standardMemory(0, 0) //No params
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching
.build();
}
@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);
}
InputType.InputTypeConvolutional i = (InputType.InputTypeConvolutional) inputType;
return InputType.convolutional((i.getHeight() + padding[0][0] + padding[0][1]) / blocks[0],
(i.getWidth() + padding[1][0] + padding[1][1]) / blocks[1], i.getChannels(), i.getFormat());
}
@Override
public ParamInitializer initializer() {
return EmptyParamInitializer.getInstance();
}
@Override
public void setNIn(InputType inputType, boolean override) {
Preconditions.checkState(inputType.getType() == InputType.Type.CNN, "Only CNN input types can be used with SpaceToBatchLayer, got %s", inputType);
this.format = ((InputType.InputTypeConvolutional)inputType).getFormat();
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for space to batch layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public boolean isPretrainParam(String paramName) {
throw new UnsupportedOperationException("SpaceToBatchLayer does not contain parameters");
}
@NoArgsConstructor
@Getter
@Setter
public static class Builder> extends Layer.Builder {
/**
* Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions
*/
@Setter(AccessLevel.NONE)
protected int[] blocks;
/**
* A 2d array, with format [[padTop, padBottom], [padLeft, padRight]]
*/
protected int[][] padding;
protected CNN2DFormat format = CNN2DFormat.NCHW;
/**
* @param blocks Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions
*/
public void setBlocks(int... blocks) {
this.blocks = ValidationUtils.validate2NonNegative(blocks, false, "blocks");
}
/**
* @param padding Padding - should be a 2d array, with format [[padTop, padBottom], [padLeft, padRight]]
*/
public void setPadding(int[][] padding) {
this.padding = ValidationUtils.validate2x2NonNegative(padding, "padding");
}
/**
* @param blocks Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions
*/
public Builder(int[] blocks) {
this.setBlocks(blocks);
this.setPadding(new int[][] {{0, 0}, {0, 0}});
}
/**
* @param blocks Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions
* @param padding Padding - should be a 2d array, with format [[padTop, padBottom], [padLeft, padRight]]
*/
public Builder(int[] blocks, int[][] padding) {
this.setBlocks(blocks);
this.setPadding(padding);
}
/**
* 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 T dataFormat(CNN2DFormat format){
this.format = format;
return (T)this;
}
/**
* @param blocks Block size for SpaceToBatch layer. Should be a length 2 array for the height and width
* dimensions
*/
public T blocks(int... blocks) {
this.setBlocks(blocks);
return (T) this;
}
/**
* @param padding Padding - should be a 2d array, with format [[padTop, padBottom], [padLeft, padRight]]
*/
public T padding(int[][] padding) {
this.setPadding(padding);
return (T) this;
}
@Override
public T name(String layerName) {
this.setLayerName(layerName);
return (T) this;
}
@Override
@SuppressWarnings("unchecked")
public SpaceToBatchLayer build() {
if(padding == null)
setPadding(new int[][] {{0, 0}, {0, 0}});
return new SpaceToBatchLayer(this);
}
}
}
