org.nd4j.linalg.api.ops.impl.shape.Squeeze Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.shape;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.shade.guava.primitives.Ints;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
*
*/
public class Squeeze extends DynamicCustomOp {
private int[] squeezeDims;
public Squeeze() {
}
public Squeeze(SameDiff sameDiff, SDVariable arg, int squeezeDims) {
this(sameDiff, arg, new int[] {squeezeDims});
}
public Squeeze(SameDiff sameDiff, SDVariable arg, int[] squeezeDims) {
super(null, sameDiff, new SDVariable[]{arg});
this.squeezeDims = squeezeDims;
addIArgument(squeezeDims);
}
public Squeeze(INDArray x, int axis) {
addInputArgument(x);
addIArgument(axis);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
nodeDef.getAttrMap().get("squeeze_dims");
List dimList = attributesForNode.get("squeeze_dims").getList().getIList();
squeezeDims = new int[dimList.size()];
for( int i = 0; i properties) {
//squeezeDims are mapped in arguments
}
@Override
public List doDiff(List i_v) {
if (squeezeDims == null) {
//TODO Strictly speaking this *is* possible by inspecting the input array
throw new IllegalStateException("Cannot do Squeeze backprop with no dimensions");
}
SDVariable ret = i_v.get(0);
for (int d : squeezeDims) {
ret = sameDiff.expandDims(ret, d);
}
return Arrays.asList(ret);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(!dataTypes.isEmpty(), "Expected list with at least 1 datatype for %s, got %s", getClass(), dataTypes);
//Output type is same as input type
return Arrays.asList(dataTypes.get(0));
}
}