org.nd4j.linalg.api.ops.impl.broadcast.BiasAdd Maven / Gradle / Ivy
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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
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.broadcast;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Bias addition gradient operation.
*/
@NoArgsConstructor
public class BiasAdd extends DynamicCustomOp {
protected boolean nchw = true;
public BiasAdd(SameDiff sameDiff, SDVariable input, SDVariable bias, boolean nchw) {
super(null, sameDiff, new SDVariable[] {input, bias}, false);
bArguments.clear();
bArguments.add(nchw);
this.nchw = nchw;
}
public BiasAdd(@NonNull INDArray input, @NonNull INDArray bias, boolean nchw){
this(input, bias, null, nchw);
}
public BiasAdd(@NonNull INDArray input, @NonNull INDArray bias, INDArray output, boolean nchw){
super(new INDArray[]{input, bias}, wrapOrNull(output));
bArguments.clear();
bArguments.add(nchw);
this.nchw = nchw;
}
@Override
public String opName() {
return "biasadd";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
if(attributesForNode.containsKey("data_format")){
nchw = "NCHW".equalsIgnoreCase(attributesForNode.get("data_format").getS().toStringUtf8());
} else {
nchw = false; //TF default is NHWC
}
bArguments.clear();
bArguments.add(nchw);
}
@Override
public String onnxName() {
return "BiasAdd";
}
@Override
public String[] tensorflowNames() {
return new String[]{"BiasAdd","BiasAddV1"};
}
@Override
public List doDiff(List gradient){
return new BiasAddGrad(sameDiff, arg(0), arg(1), gradient.get(0), nchw).outputs();
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected 2 input data types for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}