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org.nd4j.linalg.api.ops.custom.FusedBatchNorm 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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
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.Arrays;
import java.util.List;
import java.util.Map;
public class FusedBatchNorm extends DynamicCustomOp {
private DataType outputDataType;
public FusedBatchNorm() {}
public FusedBatchNorm(@NonNull INDArray x, @NonNull INDArray scale, @NonNull INDArray offset,
int dataFormat, int isTraining,
INDArray yOut, INDArray batchMeanOut, INDArray batchMeanVar) {
addInputArgument(x, scale, offset);
addIArgument(dataFormat, isTraining);
if (yOut != null && batchMeanOut != null && batchMeanVar != null) {
addOutputArgument(yOut, batchMeanOut, batchMeanVar);
}
this.outputDataType = x.dataType();
}
public FusedBatchNorm(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable scale, @NonNull SDVariable offset,
@NonNull SDVariable dataFormat, @NonNull SDVariable isTraining) {
super("", sameDiff, new SDVariable[]{x, scale, offset, dataFormat, isTraining});
this.outputDataType = x.dataType();
}
public FusedBatchNorm(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable scale, @NonNull SDVariable offset,
int dataFormat, int isTraining) {
super("", sameDiff, new SDVariable[]{x, scale, offset});
addIArgument(dataFormat, isTraining);
this.outputDataType = x.dataType();
}
@Override
public String opName() {
return "fused_batch_norm";
}
@Override
public String[] tensorflowNames() {
return new String[]{"FusedBatchNormV2","FusedBatchNormV3"};
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
boolean isNchw = attributesForNode.containsKey("data_format") && attributesForNode.get("data_format").getS().toStringUtf8().equalsIgnoreCase("NCHW");
boolean training = !attributesForNode.containsKey("is_training") ? true : attributesForNode.get("is_training").getB();
addIArgument(isNchw ? 1 : 0);
addIArgument(training ? 1 : 0);
if(attributesForNode.containsKey("T")){
outputDataType = TFGraphMapper.convertType(attributesForNode.get("T").getType());
}
}
@Override
public List calculateOutputDataTypes(List inputDataTypes) {
int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
if(!dArguments.isEmpty()) {
return Arrays.asList(dArguments.get(0),dArguments.get(0),dArguments.get(0));
}
return Arrays.asList(outputDataType == null ? DataType.FLOAT : outputDataType,
outputDataType == null ? DataType.FLOAT : outputDataType,
outputDataType == null ? DataType.FLOAT : outputDataType);
}
}