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Add Deconv3DTF (#25) 

Signed-off-by: AlexDBlack <blacka101@gmail.com>
master
Alex Black 2019-11-04 12:42:11 +11:00 committed by GitHub
parent 0cdb5750e0
commit 4763547c9e
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 209 additions and 60 deletions
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1
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java
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@ -109,6 +109,7 @@ public class ImportClassMapping {
org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv2DDerivative.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv2DTF.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv3D.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv3DTF.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv3DDerivative.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.DepthToSpace.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.DepthwiseConv2D.class,

60
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv3D.java
View File

@ -147,66 +147,6 @@ public class DeConv3D extends DynamicCustomOp {
return config.getValue(property);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
val aStrides = nodeDef.getAttrOrThrow("strides");
val tfStrides = aStrides.getList().getIList();
int sD, sH, sW, kD, kH, kW;
val aPadding = nodeDef.getAttrOrDefault("padding", null);
val paddingMode = aPadding.getS().toStringUtf8();
val args = args();
INDArray arr = sameDiff.getVariable(args[1].name()).getArr();
if (arr == null) {
arr = TFGraphMapper.getNDArrayFromTensor(nodeDef);
val varForOp = initWith.getVariable(args[1].name());
if (arr != null)
initWith.associateArrayWithVariable(arr, varForOp);
}
String dataFormat = "nhwc";
if (nodeDef.containsAttr("data_format")) {
val attr = nodeDef.getAttrOrThrow("data_format");
dataFormat = attr.getS().toStringUtf8().toLowerCase();
}
if (dataFormat.equalsIgnoreCase(DeConv3DConfig.NCDHW)) {
sD = tfStrides.get(2).intValue();
sH = tfStrides.get(3).intValue();
sW = tfStrides.get(4).intValue();
kD = (int) arr.size(2);
kH = (int) arr.size(3);
kW = (int) arr.size(4);
} else {
sD = tfStrides.get(1).intValue();
sH = tfStrides.get(2).intValue();
sW = tfStrides.get(3).intValue();
kD = (int) arr.size(0);
kH = (int) arr.size(1);
kW = (int) arr.size(2);
}
boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
DeConv3DConfig conv2DConfig = DeConv3DConfig.builder()
.kD(kD)
.kH(kH)
.kW(kW)
.sD(sD)
.sH(sW)
.sW(sH)
.isSameMode(isSameMode)
.dataFormat(dataFormat.equalsIgnoreCase(DeConv3DConfig.NCDHW) ? DeConv3DConfig.NCDHW : DeConv3DConfig.NDHWC)
.build();
this.config = conv2DConfig;
addArgs();
}
@Override
public String opName() {

208
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv3DTF.java Normal file
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@ -0,0 +1,208 @@
/*******************************************************************************
* 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.Getter;
import lombok.NoArgsConstructor;
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.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.DeConv3DConfig;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.lang.reflect.Field;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* DeConv3D operation, TF-wrapper
*/
@Slf4j
@Getter
@NoArgsConstructor
public class DeConv3DTF extends DynamicCustomOp {
protected DeConv3DConfig config;
public DeConv3DTF(@NonNull SameDiff sameDiff, @NonNull SDVariable shape, @NonNull SDVariable weights, @NonNull SDVariable input, @NonNull DeConv3DConfig config) {
super(sameDiff, new SDVariable[]{shape, weights, input});
this.config = config;
addArgs();
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public Map<String, Object> propertiesForFunction() {
if(config == null && !iArguments.isEmpty()){
config = DeConv3DConfig.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 ? DeConv3DConfig.NDHWC : DeConv3DConfig.NCDHW)
.build();
}
return config.toProperties();
}
private void addArgs() {
addIArgument(config.getKD());
addIArgument(config.getKH());
addIArgument(config.getKW());
addIArgument(config.getSD());
addIArgument(config.getSH());
addIArgument(config.getSW());
addIArgument(config.getPD());
addIArgument(config.getPH());
addIArgument(config.getPW());
addIArgument(config.getDD());
addIArgument(config.getDH());
addIArgument(config.getDW());
addIArgument(ArrayUtil.fromBoolean(config.isSameMode()));
addIArgument(config.getDataFormat().equalsIgnoreCase(DeConv3DConfig.NCDHW) ? 0 : 1);
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public Object getValue(Field property) {
if (config == null) {
config = DeConv3DConfig.builder().build();
}
return config.getValue(property);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
val aStrides = nodeDef.getAttrOrThrow("strides");
val aDilations = nodeDef.getAttrOrDefault("dilations", null);
val tfStrides = aStrides.getList().getIList();
val tfDilation = aDilations == null ? null : aDilations.getList().getIList();
int sD, sH, sW, dD, dH, dW;
val aPadding = nodeDef.getAttrOrDefault("padding", null);
String paddingMode = aPadding.getS().toStringUtf8();
String dataFormat = DeConv3DConfig.NDHWC;
if (nodeDef.containsAttr("data_format")) {
val attr = nodeDef.getAttrOrThrow("data_format");
dataFormat = attr.getS().toStringUtf8().toLowerCase();
}
if (dataFormat.equalsIgnoreCase(DeConv3DConfig.NCDHW)) {
sD = tfStrides.get(2).intValue();
sH = tfStrides.get(3).intValue();
sW = tfStrides.get(4).intValue();
dD = tfDilation == null ? 1 : tfDilation.get(2).intValue();
dH = tfDilation == null ? 1 : tfDilation.get(3).intValue();
dW = tfDilation == null ? 1 : tfDilation.get(4).intValue();
} else {
sD = tfStrides.get(1).intValue();
sH = tfStrides.get(2).intValue();
sW = tfStrides.get(3).intValue();
dD = tfDilation == null ? 1 : tfDilation.get(1).intValue();
dH = tfDilation == null ? 1 : tfDilation.get(2).intValue();
dW = tfDilation == null ? 1 : tfDilation.get(3).intValue();
}
boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
DeConv3DConfig conv3DConfig = DeConv3DConfig.builder()
.kD(-1)
.kH(-1)
.kW(-1)
.sD(sD)
.sH(sW)
.sW(sH)
.dD(dD)
.dH(dH)
.dW(dW)
.isSameMode(isSameMode)
.dataFormat(dataFormat.equalsIgnoreCase(DeConv3DConfig.NCDHW) ? DeConv3DConfig.NCDHW : DeConv3DConfig.NDHWC)
.build();
this.config = conv3DConfig;
addArgs();
}
@Override
public String opName() {
return "deconv3d_tf";
}
@Override
public String[] tensorflowNames() {
return new String[]{"Conv3DBackpropInput", "Conv3DBackpropInputV2"};
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {
throw new UnsupportedOperationException("Backprop not yet implemented for " + getClass());
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){ //inShape, weights, input
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(2));
}
}