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org.nd4j.linalg.api.ops.impl.transforms.custom.Dilation2D 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.transforms.custom;
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.converters.DifferentialFunctionClassHolder;
import org.nd4j.imports.descriptors.properties.AttributeAdapter;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.descriptors.properties.adapters.*;
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.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Dilation2D op wrapper
*
* @author [email protected]
*/
public class Dilation2D extends DynamicCustomOp {
protected boolean isSameMode;
// rates
protected int r0, r1, r2, r3;
// strides
protected int s0, s1, s2, s3;
public Dilation2D() {
}
public Dilation2D(SameDiff sameDiff, SDVariable[] inputAndWeights, int[] strides,
int[] rates, boolean isSameMode, boolean inPlace ) {
super(null, sameDiff, inputAndWeights, inPlace);
if (rates.length < 4)
throw new IllegalArgumentException("Dilation rate length must be 4.");
if (strides.length < 4)
throw new IllegalArgumentException("Strides length must be 4.");
r0 = rates[0];
r1 = rates[1];
r2 = rates[2];
r3 = rates[3];
s0 = strides[0];
s1 = strides[1];
s2 = strides[2];
s3 = strides[3];
this.isSameMode = isSameMode;
addArgs();
}
public Dilation2D(INDArray[] inputArrays, INDArray[] outputs) {
super(null, inputArrays, outputs);
}
protected void addArgs() {
addIArgument(isSameMode ? 1 : 0,
r0, r1, r2, r3,
s0, s1, s2, s3);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode,nodeDef, graph);
addArgs();
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val sameMode = PropertyMapping.builder()
.tfAttrName("padding")
.propertyNames(new String[]{"isSameMode"})
.build();
val ratesMapping = PropertyMapping.builder()
.tfAttrName("rates")
.propertyNames(new String[]{"r0", "r1", "r2", "r3"})
.build();
val stridesMapping = PropertyMapping.builder()
.tfAttrName("strides")
.propertyNames(new String[]{"s0", "s1", "s2", "s3"})
.build();
map.put("isSameMode", sameMode);
map.put("r0", ratesMapping);
map.put("r1", ratesMapping);
map.put("r2", ratesMapping);
map.put("r3", ratesMapping);
map.put("s0", stridesMapping);
map.put("s1", stridesMapping);
map.put("s2", stridesMapping);
map.put("s3", stridesMapping);
try {
ret.put(onnxName(), map);
}catch(NoOpNameFoundException e) {
//ignore, we dont care about onnx for this set of ops
}
try {
ret.put(tensorflowName(),map);
}catch(NoOpNameFoundException e) {
throw new RuntimeException(e);
}
return ret;
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new HashMap<>();
Map tfMappings = new LinkedHashMap<>();
val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
tfMappings.put("r0", new IntArrayIntIndexAdpater(0));
tfMappings.put("r1", new IntArrayIntIndexAdpater(1));
tfMappings.put("r2", new IntArrayIntIndexAdpater(2));
tfMappings.put("r3", new IntArrayIntIndexAdpater(3));
tfMappings.put("s0", new IntArrayIntIndexAdpater(0));
tfMappings.put("s1", new IntArrayIntIndexAdpater(1));
tfMappings.put("s2", new IntArrayIntIndexAdpater(2));
tfMappings.put("s3", new IntArrayIntIndexAdpater(3));
tfMappings.put("isSameMode",new StringEqualsAdapter("SAME"));
// Onnx doesn't have this op i think?
Map onnxMappings = new HashMap<>();
onnxMappings.put("isSameMode",new StringEqualsAdapter("SAME"));
ret.put(tensorflowName(), tfMappings);
ret.put(onnxName(), onnxMappings);
return ret;
}
@Override
public String opName() {
return "dilation2d";
}
@Override
public String onnxName() {
return "Dilation_2D";
}
@Override
public String tensorflowName() {
return "Dilation2D";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){ //Input and weights, optional rates/strides
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() >= 2 && inputDataTypes.size() <= 4,
"Expected 2 to 4 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}