org.nd4j.linalg.api.ops.impl.shape.Stack Maven / Gradle / Ivy
The newest version!
/*
* ******************************************************************************
* *
* *
* * 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.shape;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
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.*;
public class Stack extends DynamicCustomOp {
protected int jaxis;
public Stack() {
}
public Stack(INDArray[] inputs, INDArray output, int axis){
super(null, inputs, output == null ? null : new INDArray[]{output}, null, (List)null);
this.jaxis = axis;
addArgs();
}
public Stack(INDArray[] input, int axis) {
addInputArgument(input);
this.jaxis = axis;
addArgs();
}
public Stack(SameDiff sameDiff, SDVariable values, int axis) {
this(sameDiff, new SDVariable[]{values}, axis);
}
public Stack(SameDiff sameDiff, SDVariable[] values, int axis) {
super(null, sameDiff, values, false);
this.jaxis = axis;
addArgs();
}
public void addArgs() {
addIArgument(jaxis);
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx opName found for " + opName());
}
@Override
public String tensorflowName() {
return "stack";
}
@Override
public String toString() {
return opName();
}
@Override
public String[] tensorflowNames() {
return new String[]{"Pack", "Stack"};
}
@Override
public String opName() {
return "stack";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
throw new UnsupportedOperationException("No analog found for onnx for " + opName());
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val axisMapping = PropertyMapping.builder()
.onnxAttrName("axis")
.tfAttrName("axis")
.propertyNames(new String[]{"jaxis"})
.build();
map.put("jaxis", axisMapping);
for (val name : tensorflowNames())
ret.put(name, map);
return ret;
}
@Override
public List doDiff(List f1) {
return Arrays.asList(sameDiff.unstack(f1.get(0), jaxis, args().length));
}
@Override
public void configureFromArguments() {
if(!iArguments.isEmpty()) {
this.jaxis = iArguments.get(0).intValue();
}
}
@Override
public void setPropertiesForFunction(Map properties) {
if(properties.containsKey("dimensions")) {
Long dimension = (Long) properties.get("dimensions");
this.jaxis = dimension.intValue();
}
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
DataType first = dataTypes.get(0);
for( int i = 1; i < dataTypes.size(); i++) {
DataType dt = dataTypes.get(i);
Preconditions.checkState(first == dt, "All inputs must have same datatype - got %s and %s for inputs 0 and %s respectively", first, dt, i);
}
//Output type is same as input types
return Collections.singletonList(first);
}
}