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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.*;
import org.deeplearning4j.nn.api.ParamInitializer;
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.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Arrays;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
public class ZeroPadding3DLayer extends NoParamLayer {
private int[] padding; // [padLeftD, padRightD, padLeftH, padRightH, padLeftW, padRightW]
private ZeroPadding3DLayer(Builder builder) {
super(builder);
this.padding = builder.padding;
}
@Override
public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
Collection iterationListeners, int layerIndex, INDArray layerParamsView,
boolean initializeParams, DataType networkDataType) {
org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer ret =
new org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer(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 ParamInitializer initializer() {
return EmptyParamInitializer.getInstance();
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null || inputType.getType() != InputType.Type.CNN3D) {
throw new IllegalStateException("Invalid input for 3D CNN 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() + padding[0] + padding[1],
c.getHeight() + padding[2] + padding[3], c.getWidth() + padding[4] + padding[5],
c.getChannels());
}
@Override
public void setNIn(InputType inputType, boolean override) {
//No op
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input for ZeroPadding3DLayer layer (layer name=\"" + getLayerName()
+ "\"): input is null");
}
return InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(inputType, getLayerName());
}
@Override
public boolean isPretrainParam(String paramName) {
throw new UnsupportedOperationException("ZeroPadding3DLayer does not contain parameters");
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
InputType outputType = getOutputType(-1, inputType);
return new LayerMemoryReport.Builder(layerName, ZeroPadding3DLayer.class, inputType, outputType)
.standardMemory(0, 0) //No params
.workingMemory(0, 0, MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching
.build();
}
@Getter
@Setter
public static class Builder extends Layer.Builder {
/**
* [padLeftD, padRightD, padLeftH, padRightH, padLeftW, padRightW]
*/
@Setter(AccessLevel.NONE)
private int[] padding = new int[] {0, 0, 0, 0, 0, 0};
/**
* [padLeftD, padRightD, padLeftH, padRightH, padLeftW, padRightW]
*/
public void setPadding(int... padding) {
this.padding = ValidationUtils.validate6NonNegative(padding, "padding");
}
/**
* @param padding Padding for both the left and right in all three spatial dimensions
*/
public Builder(int padding) {
this(padding, padding, padding, padding, padding, padding);
}
/**
* Use same padding for left and right boundaries in depth, height and width.
*
* @param padDepth padding used for both depth boundaries
* @param padHeight padding used for both height boundaries
* @param padWidth padding used for both width boudaries
*/
public Builder(int padDepth, int padHeight, int padWidth) {
this(padDepth, padDepth, padHeight, padHeight, padWidth, padWidth);
}
/**
* Explicit padding of left and right boundaries in depth, height and width dimensions
*
* @param padLeftD Depth padding left
* @param padRightD Depth padding right
* @param padLeftH Height padding left
* @param padRightH Height padding right
* @param padLeftW Width padding left
* @param padRightW Width padding right
*/
public Builder(int padLeftD, int padRightD, int padLeftH, int padRightH, int padLeftW, int padRightW) {
this(new int[] {padLeftD, padRightD, padLeftH, padRightH, padLeftW, padRightW});
}
public Builder(int[] padding) {
this.setPadding(padding);
}
@Override
@SuppressWarnings("unchecked")
public ZeroPadding3DLayer build() {
for (int p : padding) {
if (p < 0) {
throw new IllegalStateException("Invalid zero padding layer config: padding [left, right]"
+ " must be > 0 for all elements. Got: " + Arrays.toString(padding));
}
}
return new ZeroPadding3DLayer(this);
}
}
}