org.nd4j.linalg.api.ops.random.impl.Range 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.random.impl;
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.graphmapper.tf.TFGraphMapper;
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.shape.LongShapeDescriptor;
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;
/**
* Range Op implementation, generates from..to distribution within Z
*
* @author [email protected]
*/
public class Range extends DynamicCustomOp {
public static final DataType DEFAULT_DTYPE = DataType.FLOAT;
private Double from;
private Double to;
private Double delta;
private DataType dataType;
public Range() {
// no-op
}
public Range(SameDiff sd, double from, double to, double step, DataType dataType){
super(null, sd, new SDVariable[0]);
addTArgument(from, to, step);
this.from = from;
this.to = to;
this.delta = step;
this.dataType = dataType;
}
@Override
public int opNum() {
return 4;
}
@Override
public String opName() {
return "range";
}
@Override
public String onnxName() {
return "Range";
}
@Override
public String tensorflowName() {
return "Range";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
if(attributesForNode.containsKey("Tidx")){
dataType = TFGraphMapper.convertType(attributesForNode.get("Tidx").getType());
}
}
@Override
public List calculateOutputShape() {
val iArgs = iArgs();
val tArgs = tArgs();
val inputArgs = inputArguments();
int cnt = 0;
if(args().length > 1) {
if (inputArgs.length > 0)
return Nd4j.getExecutioner().calculateOutputShape(this);
} else if (iArgs.length > 0) {
return Nd4j.getExecutioner().calculateOutputShape(this);
} else if (tArgs.length > 0) {
return Nd4j.getExecutioner().calculateOutputShape(this);
}
return Collections.emptyList();
}
@Override
public List doDiff(List f1) {
return Collections.emptyList();
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes == null || inputDataTypes.isEmpty() || inputDataTypes.size() == 3,
"Expected no input datatypes (no args) or 3 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(dataType == null ? DEFAULT_DTYPE : dataType);
}
}