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/*******************************************************************************
* 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 lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.OnnxProto3;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.onnx.OnnxGraphMapper;
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.exception.ND4JIllegalStateException;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Reshape function
*
* @author Adam Gibson
*/
@Slf4j
public class Reshape extends DynamicCustomOp {
private long[] shape;
private String arrName;
public Reshape(SameDiff sameDiff, SDVariable i_v, long[] shape) {
super(null, sameDiff, new SDVariable[]{i_v});
this.shape = shape;
addIArgument(shape);
}
public Reshape(SameDiff sameDiff, SDVariable i_v, SDVariable shape) {
super(null, sameDiff, new SDVariable[]{i_v, shape});
}
public Reshape(INDArray in, INDArray shape, INDArray out){
super(null, new INDArray[]{in, shape}, new INDArray[]{out}, null, (List)null);
}
public Reshape() {
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
if (!nodeDef.containsAttr("TShape") && nodeDef.getInputCount() == 1) {
this.shape = new long[]{};
return;
} else if (nodeDef.getInputCount() > 1) {
val shapeNode = nodeDef.getInput(1);
NodeDef shapeNodeInGraph = null;
for (int i = 0; i < graph.getNodeCount(); i++) {
if (graph.getNode(i).getName().equals(shapeNode)) {
shapeNodeInGraph = graph.getNode(i);
}
}
val arr = TFGraphMapper.getInstance().getNDArrayFromTensor("value", shapeNodeInGraph, graph);
if (arr != null && arr.isEmpty()) {
// special case: empty array
this.shape = new long[0];
} else if (arr != null) {
this.shape = arr.data().asLong();
//all TF is c
if (!ArrayUtil.containsAnyNegative(this.shape))
addIArgument(this.shape);
else {
arrName = nodeDef.getName();
}
}
} else {
val shape = nodeDef.getAttrOrThrow("Tshape");
if (!shape.hasShape()) {
val shapeRet = new long[2];
shapeRet[0] = 1;
shapeRet[1] = shape.getValueCase().getNumber();
this.shape = shapeRet;
} else {
val shapeVals = shape.getShape().getDimList();
if (shapeVals.size() > 1) {
this.shape = new long[shapeVals.size()];
for (int i = 0; i < shapeVals.size(); i++) {
this.shape[i] = (int) shapeVals.get(i).getSize();
}
} else {
this.shape = new long[2];
this.shape[0] = 1;
this.shape[1] = (int) shapeVals.get(0).getSize();
}
}
//all TF is c
if (this.shape != null) {
addIArgument(this.shape);
}
}
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
val shape = new OnnxGraphMapper().getShape(node);
this.shape = shape;
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val shapeMapping = PropertyMapping.builder()
.onnxAttrName("shape")
.tfInputPosition(-1)
.propertyNames(new String[]{"shape"})
.build();
map.put("shape", shapeMapping);
ret.put(tensorflowName(), map);
ret.put(onnxName(), map);
return ret;
}
@Override
public String opName() {
return "reshape";
}
@Override
public String onnxName() {
return "Reshape";
}
@Override
public String tensorflowName() {
return "Reshape";
}
@Override
public List doDiff(List i_v) {
SDVariable origShape = f().shape(arg());
SDVariable ret = f().reshape(i_v.get(0), origShape);
return Collections.singletonList(ret);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Output type is always same as input type
return Collections.singletonList(dataTypes.get(0));
}
}