org.nd4j.linalg.api.ops.impl.transforms.custom.FakeQuantWithMinMaxVars Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms.custom;
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.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
public class FakeQuantWithMinMaxVars extends DynamicCustomOp {
protected boolean narrowRange;
protected int numBits;
public FakeQuantWithMinMaxVars(SameDiff sd, SDVariable input, SDVariable min, SDVariable max, boolean narrowRange, int numBits){
super(sd, new SDVariable[]{input, min, max});
Preconditions.checkState(numBits >= 2 && numBits <= 16, "NumBits arg must be in range 2 to 16 inclusive, got %s", numBits);
this.narrowRange = narrowRange;
this.numBits = numBits;
addArgs();
}
public FakeQuantWithMinMaxVars(INDArray x, INDArray min, INDArray max, int num_bits, boolean narrow) {
Preconditions.checkArgument(min.isVector() && max.isVector() &&
min.length() == max.length(),
"FakeQuantWithMinMaxVars: min and max should be 1D tensors with the same length");
addInputArgument(x,min,max);
addIArgument(num_bits);
addBArgument(narrow);
}
public FakeQuantWithMinMaxVars(){ }
protected void addArgs(){
iArguments.clear();
bArguments.clear();
addIArgument(numBits);
addBArgument(narrowRange);
}
@Override
public String opName(){
return "fake_quant_with_min_max_vars";
}
@Override
public String tensorflowName(){
return "FakeQuantWithMinMaxVars";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
if(attributesForNode.containsKey("narrow_range")){
this.narrowRange = attributesForNode.get("narrow_range").getB();
}
this.numBits = (int)attributesForNode.get("num_bits").getI();
addArgs();
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected exactly 3 inputs, got %s", inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
@Override
public List doDiff(List gradients){
return Arrays.asList(sameDiff.zerosLike(arg(0)), sameDiff.zerosLike(arg(1)), sameDiff.zerosLike(arg(2)));
}
}