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org.nd4j.linalg.api.ops.impl.layers.convolution.Conv3D 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.Getter;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.nd4j.autodiff.functions.DifferentialFunction;
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.api.ops.impl.layers.convolution.config.Conv3DConfig;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.lang.reflect.Field;
import java.util.*;
/**
* Conv3D operation
*/
@Slf4j
@Getter
public class Conv3D extends DynamicCustomOp {
protected Conv3DConfig config;
private static final String INVALID_CONFIGURATION = "Invalid Conv3D configuration : sW = %s pH = %s dW = %s ";
public Conv3D() {
}
@Builder(builderMethodName = "builder")
public Conv3D(SameDiff sameDiff, SDVariable[] inputFunctions, INDArray[] inputs, INDArray[] outputs,
Conv3DConfig conv3DConfig) {
super(null, sameDiff, inputFunctions, false);
setSameDiff(sameDiff);
if (inputs != null)
addInputArgument(inputs);
if (outputs != null)
addOutputArgument(outputs);
this.config = conv3DConfig;
Preconditions.checkState(config.getSW() >= 1 && config.getPH() >= 0 && config.getDW() >= 1,
INVALID_CONFIGURATION,
config.getSW(), config.getPH(), config.getDW());
addArgs();
//for (val arg: iArgs())
// System.out.println(getIArgument(arg));
}
private void addArgs() {
addIArgument(
// TODO: support bias terms
// ArrayUtil.fromBoolean(getConfig().isBiasUsed()),
getConfig().getKD(),
getConfig().getKH(),
getConfig().getKW(),
getConfig().getSD(),
getConfig().getSH(),
getConfig().getSW(),
getConfig().getPD(),
getConfig().getPH(),
getConfig().getPW(),
getConfig().getDD(),
getConfig().getDH(),
getConfig().getDW(),
getConfig().isSameMode() ? 1 : 0,
getConfig().isNCDHW() ? 0 : 1
);
}
@Override
public Object getValue(Field property) {
if (config == null && !iArguments.isEmpty()) {
config = Conv3DConfig.builder()
.kD(iArguments.get(0))
.kH(iArguments.get(1))
.kW(iArguments.get(2))
.sD(iArguments.get(3))
.sH(iArguments.get(4))
.sW(iArguments.get(5))
.pD(iArguments.get(6))
.pH(iArguments.get(7))
.pW(iArguments.get(8))
.dD(iArguments.get(9))
.dH(iArguments.get(10))
.dW(iArguments.get(11))
.isSameMode(iArguments.get(12) == 1)
.dataFormat(iArguments.get(13) == 1 ? Conv3DConfig.NCDHW : Conv3DConfig.NDHWC)
.build();
}
return config.getValue(property);
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new LinkedHashMap<>();
Map tfAdapters = new LinkedHashMap<>();
val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
//TF uses [kD, kH, kW, iC, oC] for weights
tfAdapters.put("kD", new NDArrayShapeAdapter(0));
tfAdapters.put("kH", new NDArrayShapeAdapter(1));
tfAdapters.put("kW", new NDArrayShapeAdapter(2));
tfAdapters.put("sD", new IntArrayIntIndexAdpater(1));
tfAdapters.put("sH", new IntArrayIntIndexAdpater(2));
tfAdapters.put("sW", new IntArrayIntIndexAdpater(3));
tfAdapters.put("pD", new IntArrayIntIndexAdpater(1));
tfAdapters.put("pH", new IntArrayIntIndexAdpater(2));
tfAdapters.put("pW", new IntArrayIntIndexAdpater(3));
tfAdapters.put("isSameMode", new StringNotEqualsAdapter("VALID"));
ret.put(tensorflowName(), tfAdapters);
return ret;
}
@Override
public Map propertiesForFunction() {
if (config == null) {
return Collections.emptyMap();
}
return config.toProperties();
}
@Override
public String opName() {
return "conv3dnew";
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val kernelMapping = PropertyMapping.builder()
.propertyNames(new String[]{"kD", "kW", "kH"})
.tfInputPosition(1)
.onnxAttrName("kernel_shape")
.build();
val strideMapping = PropertyMapping.builder()
.tfAttrName("strides")
.onnxAttrName("strides")
.propertyNames(new String[]{"sD", "sW", "sH"})
.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[]{"pD", "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[]{"aD", "aH", "aW"})
.build();
val biasUsed = PropertyMapping.builder()
.propertyNames(new String[]{"biasUsed"})
.build();
for (val propertyMapping : new PropertyMapping[]{
kernelMapping,
strideMapping,
dilationMapping,
sameMode,
paddingWidthHeight,
dataFormat,
outputPadding, biasUsed}) {
for (val keys : propertyMapping.getPropertyNames())
map.put(keys, propertyMapping);
}
ret.put(tensorflowName(), map);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
@Override
public List doDiff(List f1) {
List ret = new ArrayList<>();
List inputs = new ArrayList<>();
inputs.addAll(Arrays.asList(args()));
inputs.add(f1.get(0));
Conv3DDerivative conv3DDerivative = Conv3DDerivative.derivativeBuilder()
.conv3DConfig(config)
.inputFunctions(args())
.outputs(outputArguments())
.inputFunctions(inputs.toArray(new SDVariable[inputs.size()]))
.sameDiff(sameDiff)
.build();
ret.addAll(Arrays.asList(conv3DDerivative.outputVariables()));
return ret;
}
@Override
public void resolvePropertiesFromSameDiffBeforeExecution() {
if (numIArguments() < 1) {
addArgs();
}
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No ONNX op name found for: " + getClass().getName());
}
@Override
public String tensorflowName() {
return "Conv3D";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}
