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org.nd4j.linalg.api.ops.impl.layers.convolution.FullConv3D 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.layers.convolution;
import lombok.Builder;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.descriptors.properties.AttributeAdapter;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.descriptors.properties.adapters.IntArrayIntIndexAdpater;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.FullConv3DConfig;
import org.nd4j.linalg.util.ArrayUtil;
import java.lang.reflect.Field;
import java.util.*;
/**
* FullConv3D operation
*/
@Slf4j
public class FullConv3D extends DynamicCustomOp {
protected FullConv3DConfig config;
@Builder(builderMethodName = "builder")
public FullConv3D(SameDiff sameDiff, SDVariable[] inputFunctions, INDArray[] inputs, INDArray[] outputs, FullConv3DConfig config) {
super(null,sameDiff, inputFunctions, false);
this.config = config;
if(inputs != null) {
addInputArgument(inputs);
}
if(outputs != null) {
addOutputArgument(outputs);
}
addArgs();
}
public FullConv3D() {}
@Override
public Map propertiesForFunction() {
return config.toProperties();
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new LinkedHashMap<>();
Map tfAdapters = new LinkedHashMap<>();
tfAdapters.put("dT", new IntArrayIntIndexAdpater(1));
tfAdapters.put("dW", new IntArrayIntIndexAdpater(2));
tfAdapters.put("dH",new IntArrayIntIndexAdpater(3));
tfAdapters.put("pT", new IntArrayIntIndexAdpater(1));
tfAdapters.put("pW", new IntArrayIntIndexAdpater(2));
tfAdapters.put("pH",new IntArrayIntIndexAdpater(3));
ret.put(tensorflowName(),tfAdapters);
return ret;
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val strideMapping = PropertyMapping.builder()
.tfAttrName("strides")
.onnxAttrName("strides")
.propertyNames(new String[]{"dT","dW","dH"})
.build();
val dilationMapping = PropertyMapping.builder()
.onnxAttrName("dilations")
.propertyNames(new String[]{"dD","dH","dW"})
.tfAttrName("rates")
.build();
val sameMode = PropertyMapping.builder()
.onnxAttrName("auto_pad")
.propertyNames(new String[]{"isSameMode"})
.tfAttrName("padding")
.build();
val paddingWidthHeight = PropertyMapping.builder()
.onnxAttrName("padding")
.propertyNames(new String[]{"pT","pW","pH"})
.build();
val dataFormat = PropertyMapping.builder()
.onnxAttrName("data_format")
.tfAttrName("data_format")
.propertyNames(new String[]{"dataFormat"})
.build();
val outputPadding = PropertyMapping.builder()
.propertyNames(new String[]{"aT","aH","aW"})
.build();
val biasUsed = PropertyMapping.builder()
.propertyNames(new String[]{"biasUsed"})
.build();
for(val propertyMapping : new PropertyMapping[] {
strideMapping,
dilationMapping,
sameMode,
paddingWidthHeight,
dataFormat,
outputPadding,biasUsed}) {
for(val keys : propertyMapping.getPropertyNames())
map.put(keys,propertyMapping);
}
ret.put(onnxName(),map);
ret.put(tensorflowName(),map);
return ret;
}
private void addArgs() {
addIArgument(new long[]{
config.getDT(),
config.getDW(),
config.getDH(),
config.getPT(),
config.getPW(),
config.getPH(),
config.getDilationT(),
config.getDilationW(),
config.getDilationH(),
config.getAT(),
config.getAW(),
config.getAH(),
ArrayUtil.fromBoolean(config.isBiasUsed())});
}
@Override
public String opName() {
return "fullconv3d";
}
@Override
public List doDiff(List f1) {
List inputs = new ArrayList<>();
inputs.addAll(Arrays.asList(args()));
inputs.addAll(f1);
List ret = new ArrayList<>();
FullConv3DDerivative fullConv3DDerivative = FullConv3DDerivative.derivativeBuilder()
.conv3DConfig(config)
.sameDiff(sameDiff)
.inputFunctions(inputs.toArray(new SDVariable[inputs.size()]))
.build();
ret.addAll(Arrays.asList(fullConv3DDerivative.outputVariables()));
return ret;
}
}