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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;
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.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.ConvolutionUtils;
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.Arrays;
import java.util.Collection;
import java.util.Map;
/**
* Zero padding layer for convolutional neural networks (2D CNNs). Allows padding to be done separately for
* top/bottom/left/right
*
* @author Alex Black
*/
@Data
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
public class ZeroPaddingLayer extends NoParamLayer {
private int[] padding;
public ZeroPaddingLayer(int padTopBottom, int padLeftRight) {
this(new Builder(padTopBottom, padLeftRight));
}
public ZeroPaddingLayer(int padTop, int padBottom, int padLeft, int padRight) {
this(new Builder(padTop, padBottom, padLeft, padRight));
}
private ZeroPaddingLayer(Builder builder) {
super(builder);
if (builder.padding == null || builder.padding.length != 4) {
throw new IllegalArgumentException(
"Invalid padding values: must have exactly 4 values [top, bottom, left, right]." + " Got: "
+ (builder.padding == null ? null : Arrays.toString(builder.padding)));
}
this.padding = builder.padding;
}
@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.ZeroPaddingLayer ret =
new org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer(conf, networkDataType);
ret.setListeners(trainingListeners);
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) {
int[] hwd = ConvolutionUtils.getHWDFromInputType(inputType);
int outH = hwd[0] + padding[0] + padding[1];
int outW = hwd[1] + padding[2] + padding[3];
return InputType.convolutional(outH, outW, hwd[2]);
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
Preconditions.checkArgument(inputType != null, "Invalid input for ZeroPaddingLayer layer (layer name=\""
+ getLayerName() + "\"): InputType is null");
return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getLayerName());
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
InputType outputType = getOutputType(-1, inputType);
return new LayerMemoryReport.Builder(layerName, ZeroPaddingLayer.class, inputType, outputType)
.standardMemory(0, 0) //No params
//Inference and training is same - just output activations, no working memory in addition to that
.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 {
/**
* Padding value for top, bottom, left, and right. Must be length 4 array
*/
@Setter(AccessLevel.NONE)
private int[] padding = new int[] {0, 0, 0, 0}; //Padding: top, bottom, left, right
/**
* @param padding Padding value for top, bottom, left, and right. Must be length 4 array
*/
public void setPadding(int... padding) {
this.padding = ValidationUtils.validate4NonNegative(padding, "padding");
}
/**
* @param padHeight Padding for both the top and bottom
* @param padWidth Padding for both the left and right
*/
public Builder(int padHeight, int padWidth) {
this(padHeight, padHeight, padWidth, padWidth);
}
/**
* @param padTop Top padding value
* @param padBottom Bottom padding value
* @param padLeft Left padding value
* @param padRight Right padding value
*/
public Builder(int padTop, int padBottom, int padLeft, int padRight) {
this(new int[] {padTop, padBottom, padLeft, padRight});
}
/**
* @param padding Must be a length 1 array with values [paddingAll], a length 2 array with values
* [padTopBottom, padLeftRight], or a length 4 array with
* values [padTop, padBottom, padLeft, padRight]
*/
public Builder(int[] padding) {
this.setPadding(padding);
}
@Override
@SuppressWarnings("unchecked")
public ZeroPaddingLayer build() {
for (int p : padding) {
if (p < 0) {
throw new IllegalStateException(
"Invalid zero padding layer config: padding [top, bottom, left, right]"
+ " must be > 0 for all elements. Got: "
+ Arrays.toString(padding));
}
}
return new ZeroPaddingLayer(this);
}
}
}
