deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasDeconvolution3D.java
/*
* ******************************************************************************
* *
* *
* * 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.modelimport.keras.layers.convolutional;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasActivationUtils;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Deconvolution3D;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasInitilizationUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.weights.IWeightInit;
import java.util.Map;
import static org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasConvolutionUtils.*;
@Slf4j
@Data
@EqualsAndHashCode(callSuper = false)
public class KerasDeconvolution3D extends KerasConvolution {
/**
* Pass-through constructor from KerasLayer
*
* @param kerasVersion major keras version
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasDeconvolution3D(Integer kerasVersion) throws UnsupportedKerasConfigurationException {
super(kerasVersion);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasDeconvolution3D(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
this(layerConfig, true);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @param enforceTrainingConfig whether to enforce training-related configuration options
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasDeconvolution3D(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
hasBias = KerasLayerUtils.getHasBiasFromConfig(layerConfig, conf);
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 3, conf, false);
IWeightInit init = KerasInitilizationUtils.getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
Deconvolution3D.Builder builder = new Deconvolution3D.Builder().name(this.layerName)
.nOut(KerasLayerUtils.getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(KerasActivationUtils.getIActivationFromConfig(layerConfig, conf))
.weightInit(init)
.dataFormat(KerasConvolutionUtils.getCNN3DDataFormatFromConfig(layerConfig,conf))
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion))
.hasBias(hasBias)
.stride(getStrideFromConfig(layerConfig, 3, conf));
int[] padding = getPaddingFromBorderModeConfig(layerConfig, 3, conf, kerasMajorVersion);
if (hasBias)
builder.biasInit(0.0);
if (padding != null)
builder.padding(padding);
if (dilationRate != null)
builder.dilation(dilationRate);
if (biasConstraint != null)
builder.constrainBias(biasConstraint);
if (weightConstraint != null)
builder.constrainWeights(weightConstraint);
this.layer = builder.build();
Deconvolution3D deconvolution3D = (Deconvolution3D) layer;
deconvolution3D.setDefaultValueOverriden(true);
}
/**
* Get DL4J ConvolutionLayer.
*
* @return ConvolutionLayer
*/
public Deconvolution3D getDeconvolution3DLayer() {
return (Deconvolution3D) this.layer;
}
/**
* Get layer output type.
*
* @param inputType Array of InputTypes
* @return output type as InputType
* @throws InvalidKerasConfigurationException Invalid Keras config
*/
@Override
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
if (inputType.length > 1)
throw new InvalidKerasConfigurationException(
"Keras Convolution layer accepts only one input (received " + inputType.length + ")");
return this.getDeconvolution3DLayer().getOutputType(-1, inputType[0]);
}
}