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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.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.api.ops.impl.shape.bp.ConcatBp;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
@Slf4j
public class Concat extends DynamicCustomOp {
private int concatDimension = -1;
public Concat(){
}
public Concat(SameDiff sameDiff, int concatDimension, SDVariable... inputs){
super(null, sameDiff, inputs);
addIArgument(concatDimension);
this.concatDimension = concatDimension;
}
@Override
public String opName() {
return "concat";
}
@Override
public void assertValidForExecution() {
val descriptor = getDescriptor();
if(descriptor == null)
throw new NoOpNameFoundException("No descriptor found for op name " + opName());
if(descriptor.getNumInputs() > 0 && numInputArguments() < 2)
throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of inputs is invalid for execution. Specified " + numInputArguments() + " but should be " + descriptor.getNumInputs());
if(descriptor.getNumOutputs() > 0 && numOutputArguments() != descriptor.getNumOutputs())
throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of outputs is invalid for execution. Specified " + numOutputArguments() + " but should be " + descriptor.getNumOutputs());
//< 0 means dynamic size
if(descriptor.getNumIArgs() >= 0 && numIArguments() != descriptor.getNumIArgs())
throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of integer arguments is invalid for execution. Specified " + numIArguments() + " but should be " + descriptor.getNumIArgs());
if(descriptor.getNumTArgs() >= 0 && numTArguments() != descriptor.getNumTArgs())
throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of inputs is invalid for execution. Specified " + numTArguments() + " but should be " + descriptor.getNumTArgs());
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map concatMap = new HashMap<>();
val concatDimProps = PropertyMapping.builder()
.tfInputPosition(0)
.onnxAttrName("axis")
.build();
concatMap.put("concatDimension",concatDimProps);
Map concatV2Map = new HashMap<>();
val concat2DimProps = PropertyMapping.builder()
//lalst position
.tfInputPosition(-1)
.onnxAttrName("axis")
.build();
concatV2Map.put("concatDimension",concat2DimProps);
//note that onnx is already covered here
ret.put(tensorflowNames()[0],concatMap);
ret.put(tensorflowNames()[1],concatV2Map);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
int concatDimension = -1;
String input = null;
val inputCount = nodeDef.getInputCount();
for(int i = 0; i < inputCount; i++) {
if(nodeDef.getInput(i).contains("/concat_dim")) {
input = nodeDef.getInput(i);
break;
}
}
//older versions may specify a concat_dim, usually it's the last argument
if(input == null) {
input = nodeDef.getInput(nodeDef.getInputCount() - 1);
}
val variable = initWith.getVariable(input);
// concat dimension is only possible
if (variable != null && variable.getArr() == null) {
sameDiff.addPropertyToResolve(this, input);
} else if (variable != null) {
val arr = variable.getArr();
if (arr.length() == 1) {
concatDimension = arr.getInt(0);
}
this.concatDimension = concatDimension;
addIArgument(this.concatDimension);
log.trace("Concat dimension: {}", concatDimension);
}
//don't pass both iArg and last axis down to libnd4j
if(inputArguments().length == nodeDef.getInputCount()) {
val inputArgs = inputArguments();
removeInputArgument(inputArgs[inputArguments().length - 1]);
}
sameDiff.removeArgFromFunction(input,this);
}
@Override
public Map propertiesForFunction() {
Map ret = new LinkedHashMap<>();
ret.put("concatDimension",concatDimension);
return ret;
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
@Override
public String onnxName() {
return "Concat";
}
@Override
public String tensorflowName() {
return "Concat";
}
@Override
public String[] tensorflowNames() {
return new String[] {"Concat","ConcatV2"};
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public List doDiff(List i_v) {
SDVariable[] args = args();
SDVariable[] bpArgs = Arrays.copyOf(args, args.length + 1);
bpArgs[bpArgs.length-1] = i_v.get(0);
return Arrays.asList(new ConcatBp(sameDiff, concatDimension, bpArgs).outputVariables());
}
@Override
public List calculateOutputDataTypes(List dataTypes){
DataType first = dataTypes.get(0);
for( int i=1; i
