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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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.convolutional;
import lombok.*;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.layers.NoParamLayer;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.layers.convolution.Cropping3DLayer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Collection;
import java.util.Map;
/**
* Cropping layer for convolutional (3d) neural networks. Allows cropping to be done separately for upper and lower
* bounds of depth, height and width dimensions.
*
* @author Max Pumperla
*/
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
public class Cropping3D extends NoParamLayer {
private int[] cropping;
/**
* @param cropDepth Amount of cropping to apply to both depth boundaries of the input activations
* @param cropHeight Amount of cropping to apply to both height boundaries of the input activations
* @param cropWidth Amount of cropping to apply to both width boundaries of the input activations
*/
public Cropping3D(int cropDepth, int cropHeight, int cropWidth) {
this(cropDepth, cropDepth, cropHeight, cropHeight, cropWidth, cropWidth);
}
/**
* @param cropLeftD Amount of cropping to apply to the left of the depth dimension
* @param cropRightD Amount of cropping to apply to the right of the depth dimension
* @param cropLeftH Amount of cropping to apply to the left of the height dimension
* @param cropRightH Amount of cropping to apply to the right of the height dimension
* @param cropLeftW Amount of cropping to apply to the left of the width dimension
* @param cropRightW Amount of cropping to apply to the right of the width dimension
*/
public Cropping3D(int cropLeftD, int cropRightD, int cropLeftH, int cropRightH, int cropLeftW, int cropRightW) {
this(new Builder(cropLeftD, cropRightD, cropLeftH, cropRightH, cropLeftW, cropRightW));
}
/**
* @param cropping Cropping as either a length 3 array, with values {@code [cropDepth, cropHeight, cropWidth]}, or
* as a length 4 array, with values {@code [cropLeftDepth, cropRightDepth, cropLeftHeight, cropRightHeight,
* cropLeftWidth, cropRightWidth]}
*/
public Cropping3D(int[] cropping) {
this(new Builder(cropping));
}
protected Cropping3D(Builder builder) {
super(builder);
this.cropping = builder.cropping;
}
@Override
public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
Collection iterationListeners, int layerIndex, INDArray layerParamsView,
boolean initializeParams, DataType networkDataType) {
Cropping3DLayer ret = new Cropping3DLayer(conf, networkDataType);
ret.setListeners(iterationListeners);
ret.setIndex(layerIndex);
Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.CNN3D) {
throw new IllegalStateException("Invalid input for 3D cropping layer (layer index = " + layerIndex
+ ", layer name = \"" + getLayerName() + "\"): expect CNN3D input type with size > 0. Got: "
+ inputType);
}
InputType.InputTypeConvolutional3D c = (InputType.InputTypeConvolutional3D) inputType;
return InputType.convolutional3D(c.getDepth() - cropping[0] - cropping[1],
c.getHeight() - cropping[2] - cropping[3], c.getWidth() - cropping[4] - cropping[5],
c.getChannels());
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
Preconditions.checkArgument(inputType != null, "Invalid input for Cropping3D " + "layer (layer name=\""
+ getLayerName() + "\"): InputType is null");
return InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(inputType, getLayerName());
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
return null;
}
@Getter
@Setter
public static class Builder extends Layer.Builder {
/**
* Cropping amount, a length 6 array, i.e. crop left depth, crop right depth, crop left height, crop right height, crop left width, crop right width
*/
@Setter(AccessLevel.NONE)
private int[] cropping = new int[] {0, 0, 0, 0, 0, 0};
/**
* @param cropping Cropping amount, must be length 1, 3, or 6 array, i.e. either all values, crop depth, crop height, crop width
* or crop left depth, crop right depth, crop left height, crop right height, crop left width, crop right width
*/
public void setCropping(int... cropping) {
this.cropping = ValidationUtils.validate6NonNegative(cropping, "cropping");
}
public Builder() {
}
/**
* @param cropping Cropping amount, must be length 3 or 6 array, i.e. either crop depth, crop height, crop width
* or crop left depth, crop right depth, crop left height, crop right height, crop left width, crop right width
*/
public Builder(@NonNull int[] cropping) {
this.setCropping(cropping);
}
/**
* @param cropDepth Amount of cropping to apply to both depth boundaries of the input activations
* @param cropHeight Amount of cropping to apply to both height boundaries of the input activations
* @param cropWidth Amount of cropping to apply to both width boundaries of the input activations
*/
public Builder(int cropDepth, int cropHeight, int cropWidth) {
this(cropDepth, cropDepth, cropHeight, cropHeight, cropWidth, cropWidth);
}
/**
* @param cropLeftD Amount of cropping to apply to the left of the depth dimension
* @param cropRightD Amount of cropping to apply to the right of the depth dimension
* @param cropLeftH Amount of cropping to apply to the left of the height dimension
* @param cropRightH Amount of cropping to apply to the right of the height dimension
* @param cropLeftW Amount of cropping to apply to the left of the width dimension
* @param cropRightW Amount of cropping to apply to the right of the width dimension
*/
public Builder(int cropLeftD, int cropRightD, int cropLeftH, int cropRightH, int cropLeftW, int cropRightW) {
this.setCropping(new int[] {cropLeftD, cropRightD, cropLeftH, cropRightH, cropLeftW, cropRightW});
}
public Cropping3D build() {
return new Cropping3D(this);
}
}
}
