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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.nd4j.linalg.api.ops.impl.layers.convolution;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv3DConfig;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
/**
* DeConv3DDerivative operation
*/
@Slf4j
public class DeConv3DDerivative extends DynamicCustomOp {
protected DeConv3DConfig config;
public DeConv3DDerivative() {}
public DeConv3DDerivative(SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, SDVariable grad, DeConv3DConfig config) {
super(sameDiff, toArr(input, weights, bias, grad));
this.config = config;
addArgs();
}
private static SDVariable[] toArr(SDVariable input, SDVariable weights, SDVariable bias, SDVariable grad){
if(bias != null){
return new SDVariable[]{input, weights, bias, grad};
} else {
return new SDVariable[]{input, weights, grad};
}
}
@Override
public String opName() {
return "deconv3d_bp";
}
@Override
public Map propertiesForFunction() {
if(config == null && !iArguments.isEmpty()){
config = DeConv3DConfig.builder()
.kD(iArguments.get(0))
.kH(iArguments.get(1))
.kW(iArguments.get(2))
.sD(iArguments.get(3))
.sH(iArguments.get(4))
.sW(iArguments.get(5))
.pD(iArguments.get(6))
.pH(iArguments.get(7))
.pW(iArguments.get(8))
.dD(iArguments.get(9))
.dH(iArguments.get(10))
.dW(iArguments.get(11))
.isSameMode(iArguments.get(12) == 1)
.dataFormat(iArguments.get(13) == 1 ? DeConv3DConfig.NDHWC : DeConv3DConfig.NCDHW)
.build();
}
return config.toProperties();
}
private void addArgs() {
addIArgument(config.getKD());
addIArgument(config.getKH());
addIArgument(config.getKW());
addIArgument(config.getSD());
addIArgument(config.getSH());
addIArgument(config.getSW());
addIArgument(config.getPD());
addIArgument(config.getPH());
addIArgument(config.getPW());
addIArgument(config.getDD());
addIArgument(config.getDH());
addIArgument(config.getDW());
addIArgument(ArrayUtil.fromBoolean(config.isSameMode()));
addIArgument(config.getDataFormat().equalsIgnoreCase(DeConv3DConfig.NCDHW) ? 0 : 1);
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No op name found for backwards.");
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No op name found for backwards");
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException("Gradient of DeConv3DDerivative not supported.");
}
@Override
public int getNumOutputs(){
//Inputs: in, weights, optional bias, gradOut 3 req, 1 optional
//Outputs: gradAtInput, gradW, optional gradB 2 req, 1 optional
SDVariable[] args = args();
return args.length - 1;
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
int n = args().length; //Original inputs + gradient at
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
List out = new ArrayList<>(n-1);
for( int i=0; i