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org.nd4j.linalg.api.ops.impl.shape.StridedSlice 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 lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
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.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
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.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
* Strided Slice function
*
* @author Adam Gibson
*/
@Slf4j
public class StridedSlice extends DynamicCustomOp {
private long[] begin;
private long[] end;
private long[] strides;
private int beginMask;
private int endMask;
private int ellipsisMask;
private int newAxisMask;
private int shrinkAxisMask;
public StridedSlice() {}
public StridedSlice(SameDiff sameDiff, SDVariable in, int[] begin, int[] end, int[] strides){
this(sameDiff, in, begin, end, strides, 0, 0, 0, 0, 0);
}
public StridedSlice(SameDiff sameDiff, SDVariable in, long[] begin, long[] end, long[] strides){
this(sameDiff, in, begin, end, strides, 0, 0, 0, 0, 0);
}
public StridedSlice(SameDiff sameDiff, SDVariable in, @NonNull long[] begin, @NonNull long[] end, @NonNull long[] strides,
int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask){
super(null, sameDiff, new SDVariable[]{in});
this.begin = begin;
this.end = end;
this.strides = strides;
this.beginMask = beginMask;
this.endMask = endMask;
this.ellipsisMask = ellipsisMask;
this.newAxisMask = newAxisMask;
this.shrinkAxisMask = shrinkAxisMask;
//https://github.com/deeplearning4j/libnd4j/blob/master/include/ops/declarable/generic/parity_ops/strided_slice.cpp#L279
addArguments();
}
public StridedSlice(SameDiff sameDiff, SDVariable in, @NonNull int[] begin, @NonNull int[] end, @NonNull int[] strides,
int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask){
super(null, sameDiff, new SDVariable[]{in});
this.begin = ArrayUtil.toLongArray(begin);
this.end = ArrayUtil.toLongArray(end);
this.strides = ArrayUtil.toLongArray(strides);
this.beginMask = beginMask;
this.endMask = endMask;
this.ellipsisMask = ellipsisMask;
this.newAxisMask = newAxisMask;
this.shrinkAxisMask = shrinkAxisMask;
addArguments();
//https://github.com/deeplearning4j/libnd4j/blob/master/include/ops/declarable/generic/parity_ops/strided_slice.cpp#L279
}
private void addArguments(){
addIArgument(beginMask);
addIArgument(ellipsisMask);
addIArgument(endMask);
addIArgument(newAxisMask);
addIArgument(shrinkAxisMask);
addIArgument(begin);
addIArgument(end);
addIArgument(strides);
}
@Override
public String opName() {
return "stridedslice";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx opName found for " + opName());
}
@Override
public String tensorflowName() {
return "StridedSlice";
}
@Override
public void assertValidForExecution() {
if(numInputArguments() != 1 && numInputArguments() != 3 && numInputArguments() != 4) {
throw new ND4JIllegalStateException("Num input arguments must be 1 3 or 4.");
}
if(numIArguments() < 5) {
throw new ND4JIllegalStateException("Number of integer arguments must >= 5");
}
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val inputBegin = nodeDef.getInput(1);
val inputEnd = nodeDef.getInput(2);
val inputStrides = nodeDef.getInput(3);
// bit masks for this slice
val bm = nodeDef.getAttrOrThrow("begin_mask");
val xm = nodeDef.getAttrOrThrow("ellipsis_mask");
val em = nodeDef.getAttrOrThrow("end_mask");
val nm = nodeDef.getAttrOrThrow("new_axis_mask");
val sm = nodeDef.getAttrOrThrow("shrink_axis_mask");
beginMask = (int)bm.getI();
ellipsisMask = (int) xm.getI();
endMask = (int) em.getI();
newAxisMask = (int) nm.getI();
shrinkAxisMask = (int) sm.getI();
addIArgument(beginMask);
addIArgument(ellipsisMask);
addIArgument(endMask);
addIArgument(newAxisMask);
addIArgument(shrinkAxisMask);
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val beginMapping = PropertyMapping.builder()
.tfInputPosition(1)
.propertyNames(new String[]{"begin"})
.build();
val end = PropertyMapping.builder()
.tfInputPosition(2)
.propertyNames(new String[]{"end"})
.build();
val strides = PropertyMapping.builder()
.tfInputPosition(3)
.propertyNames(new String[]{"strides"})
.build();
val beginMask = PropertyMapping.builder()
.tfAttrName("begin_mask")
.propertyNames(new String[]{"beginMask"})
.build();
val ellipsisMask = PropertyMapping.builder()
.tfAttrName("ellipsis_mask")
.propertyNames(new String[]{"ellipsisMask"})
.build();
val endMask = PropertyMapping.builder()
.tfAttrName("end_mask")
.propertyNames(new String[]{"endMask"})
.build();
val newAxisMask = PropertyMapping.builder()
.tfAttrName("new_axis_mask")
.propertyNames(new String[]{"newAxisMask"})
.build();
val shrinkAxisMask = PropertyMapping.builder()
.tfAttrName("shrink_axis_mask")
.propertyNames(new String[]{"shrinkAxisMask"})
.build();
map.put("begin",beginMapping);
map.put("end",end);
map.put("strides",strides);
map.put("beginMask",beginMask);
map.put("ellipsisMask",ellipsisMask);
map.put("endMask",endMask);
map.put("newAxisMask",newAxisMask);
map.put("shrinkAxisMask",shrinkAxisMask);
ret.put(tensorflowName(),map);
return ret;
}
@Override
public List doDiff(List i_v) {
if(args().length == 1) {
//Array inputs for begin/end/strides
return Collections.singletonList(f().stridedSliceBp(arg(), i_v.get(0), begin, end, strides, beginMask, endMask,
ellipsisMask, newAxisMask, shrinkAxisMask));
} else {
//SDVariable inputs for begin/end/strides
return Collections.singletonList(f().stridedSliceBp(arg(), i_v.get(0), arg(1), arg(2), arg(3), beginMask, endMask,
ellipsisMask, newAxisMask, shrinkAxisMask));
}
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && (dataTypes.size() == 1 || dataTypes.size() == 4),
"Expected 1 or 4 input datatypes for %s, got %s", getClass(), dataTypes);
//Output type is same as input type. 1 or 4 depending on whether using iargs or arrays (for TF import etc)
return Collections.singletonList(dataTypes.get(0));
}
}
