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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.transforms.custom;
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.adapters.DataTypeAdapter;
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.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Fill an array of given "shape" with the provided "value", e.g.
* shape [2, 2] and value 42 returns [[42, 42], [42, 42]].
*
* @author Max Pumperla
*/
public class Fill extends DynamicCustomOp {
private double value;
private DataType outputDataType;
public Fill() {
}
public Fill(SameDiff sameDiff, SDVariable shape, DataType outputDataType, double value) {
super(null,sameDiff, new SDVariable[] {shape}, false);
this.value = value;
val shp = shape.getArr();
this.outputDataType = outputDataType;
addArgs();
}
public Fill(INDArray shape, INDArray result, double value) {
super(null, shape, result, Collections.singletonList(value), null);
this.value = value;
}
protected void addArgs() {
addTArgument(value);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
if(nodeDef.getInputCount() == 2) {
val targetNode = TFGraphMapper.getInstance().getNodeWithNameFromGraph(graph,nodeDef.getInput(1));
val mapper = TFGraphMapper.getInstance();
val secondInputAsScalar = mapper.getNDArrayFromTensor("value",targetNode,graph);
//must be scalar
if(secondInputAsScalar.length() == 1) {
addTArgument(secondInputAsScalar.getDouble(0));
}
else {
throw new ND4JIllegalStateException("Second input to node " + nodeDef + " should be scalar!");
}
org.tensorflow.framework.DataType dt = attributesForNode.get("T").getType();
this.outputDataType = DataTypeAdapter.dtypeConv(dt);
}
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
@Override
public void assertValidForExecution() {
val descriptor = getDescriptor();
if(descriptor.getNumInputs() > 0 && numInputArguments() > 2 || numInputArguments() < 1)
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.getNumInputs());
//< 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() < 1)
throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of inputs is invalid for execution. Specified " + numTArguments() + " but should be " + descriptor.getNumTArgs());
}
@Override
public List calculateOutputShape() {
int numArgs = args().length;
if(numArgs < 1)
return Collections.emptyList();
SDVariable[] args = args();
INDArray shape = args()[0].getArr();
INDArray value = (args.length > 1 ? args()[1].getArr() : null);
if(shape == null)
return Collections.emptyList();
else {
//TODO properly allow customizing datatype
if(shape.isEmpty()){
//Edge case, mainly for TF import
return Collections.singletonList(LongShapeDescriptor.fromShape(new long[0], value == null ? Nd4j.defaultFloatingPointType() : value.dataType())); //TODO is this OK?
} else {
return Arrays.asList(LongShapeDescriptor.fromShape(shape.data().asLong(), value == null ? Nd4j.defaultFloatingPointType() : value.dataType()));
}
}
}
@Override
public String opName() {
return "fill";
}
@Override
public String onnxName() {
return "ConstantFill";
}
@Override
public String tensorflowName() {
return "Fill";
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public List doDiff(List gradients){
return Collections.singletonList(sameDiff.zerosLike(arg()));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//1 or 2 possible: 2 for TF import (fill with specified value
Preconditions.checkState(dataTypes != null && (dataTypes.size() == 1 || dataTypes.size() == 2),
"Expected 1 or 2 input datatypes for %s, got %s", getClass(), dataTypes);
Preconditions.checkNotNull(outputDataType, "Output datatype was null (not set)");
return Collections.singletonList(outputDataType);
}
}
