org.nd4j.linalg.api.ops.impl.shape.ShapeN Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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 onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
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.nd4j.linalg.api.ops.Op;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Returns the shape of N input array as N output arrays
*
* @author Alex Black
*/
public class ShapeN extends DynamicCustomOp {
protected DataType dataType;
public ShapeN() {}
public ShapeN(SameDiff sameDiff, SDVariable[] inputs, boolean inPlace) {
super(null, sameDiff, inputs, inPlace);
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx name found for shape " + opName());
}
@Override
public String opName() {
return "shapes_of";
}
@Override
public String tensorflowName() {
return "ShapeN";
}
@Override
public List doDiff(List i_v) {
List out = new ArrayList<>();
for(SDVariable in : args()){
out.add(f().zerosLike(in));
}
return out;
}
@Override
public int getNumOutputs(){
return args().length;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
dataType = TFGraphMapper.convertType(nodeDef.getAttrOrThrow("out_type").getType());
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Output type is always long (i.e., shape of array) - for each input
//TODO TF allows customizing int or long
int n = getNumOutputs();
List outputTypes = new ArrayList<>(n);
for(int i=0; i