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org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv2D 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.NoArgsConstructor;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
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.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv3DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv2DConfig;
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.*;
/**
* DeConv2D operation
*/
@Slf4j
@Getter
@NoArgsConstructor
public class DeConv2D extends DynamicCustomOp {
protected DeConv2DConfig config;
@Builder(builderMethodName = "sameDiffBuilder")
public DeConv2D(SameDiff sameDiff,
SDVariable[] inputs,
DeConv2DConfig config) {
super(sameDiff, inputs);
this.config = config;
addArgs();
}
public DeConv2D(INDArray[] inputs, INDArray[] outputs, DeConv2DConfig config){
super(inputs, outputs);
this.config = config;
addArgs();
}
public DeConv2D(@NonNull INDArray input, @NonNull INDArray weights, INDArray bias, INDArray output, @NonNull DeConv2DConfig config){
this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config);
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public Map propertiesForFunction() {
if(config == null && !iArguments.isEmpty()){
config = DeConv2DConfig.builder()
.kH(iArguments.get(0))
.kW(iArguments.get(1))
.sH(iArguments.get(2))
.sW(iArguments.get(3))
.pH(iArguments.get(4))
.pW(iArguments.get(5))
.dH(iArguments.get(6))
.dW(iArguments.get(7))
.isSameMode(iArguments.get(8) == 1)
.dataFormat(iArguments.get(9) == 1 ? DeConv2DConfig.NHWC : Conv2DConfig.NCHW)
.build();
}
return config.toProperties();
}
private void addArgs() {
addIArgument(config.getKH());
addIArgument(config.getKW());
addIArgument(config.getSH());
addIArgument(config.getSW());
addIArgument(config.getPH());
addIArgument(config.getPW());
addIArgument(config.getDH());
addIArgument(config.getDW());
addIArgument(ArrayUtil.fromBoolean(config.isSameMode()));
addIArgument(config.getDataFormat().equalsIgnoreCase(DeConv2DConfig.NCHW) ? 0 : 1);
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public Object getValue(Field property) {
if (config == null) {
config = DeConv2DConfig.builder().build();
}
return config.getValue(property);
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val strideMapping = PropertyMapping.builder()
.tfAttrName("strides")
.onnxAttrName("strides")
.build();
val kernelMapping = PropertyMapping.builder()
.propertyNames(new String[]{"kH", "kW"})
.tfInputPosition(1)
.onnxAttrName("kernel_shape")
.build();
val dilationMapping = PropertyMapping.builder()
.onnxAttrName("dilations")
.propertyNames(new String[]{"dW", "dH"})
.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[]{"pH", "pW"})
.build();
map.put("sW", strideMapping);
map.put("sH", strideMapping);
map.put("kH", kernelMapping);
map.put("kW", kernelMapping);
map.put("dW", dilationMapping);
map.put("dH", dilationMapping);
map.put("isSameMode", sameMode);
map.put("pH", paddingWidthHeight);
map.put("pW", paddingWidthHeight);
ret.put(onnxName(), map);
ret.put(tensorflowName(), map);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val aStrides = nodeDef.getAttrOrThrow("strides");
val tfStrides = aStrides.getList().getIList();
int sH = 1;
int sW = 1;
int kH = 1;
int kW = 1;
val aPadding = nodeDef.getAttrOrDefault("padding", null);
val paddingMode = aPadding.getS().toStringUtf8();
val args = args();
INDArray arr = sameDiff.getVariable(args[1].getVarName()).getArr();
if (arr == null) {
arr = TFGraphMapper.getInstance().getNDArrayFromTensor(nodeDef.getInput(0), nodeDef, graph);
// TODO: arguable. it might be easier to permute weights once
//arr = (arr.permute(3, 2, 0, 1).dup('c'));
val varForOp = initWith.getVariable(args[1].getVarName());
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();
}
// FIXME: int cast
if (dataFormat.equalsIgnoreCase(DeConv2DConfig.NCHW)) {
sH = tfStrides.get(2).intValue();
sW = tfStrides.get(3).intValue();
kH = (int) arr.size(2);
kW = (int) arr.size(3);
} else {
sH = tfStrides.get(1).intValue();
sW = tfStrides.get(2).intValue();
kH = (int) arr.size(0);
kW = (int) arr.size(1);
}
boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
DeConv2DConfig conv2DConfig = DeConv2DConfig.builder()
.kH(kH)
.kW(kW)
.sH(sW)
.sW(sH)
.isSameMode(isSameMode)
.dataFormat(dataFormat.equalsIgnoreCase(DeConv2DConfig.NHWC) ? DeConv2DConfig.NHWC : DeConv2DConfig.NCHW)
.build();
this.config = conv2DConfig;
addArgs();
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
val autoPad = !attributesForNode.containsKey("auto_pad") ? "VALID" : attributesForNode.get("auto_pad").getS().toStringUtf8();
val dilations = attributesForNode.get("dilations");
val dilationY = dilations == null ? 1 : dilations.getIntsList().get(0).intValue();
val dilationX = dilations == null ? 1 : dilations.getIntsList().get(1).intValue();
val group = attributesForNode.get("group");
val kernelShape = attributesForNode.get("kernel_shape");
int kH = kernelShape.getIntsList().get(0).intValue();
int kW = kernelShape.getIntsList().size() < 2 ? kH : kernelShape.getIntsList().get(1).intValue();
val vertexId = args()[0];
INDArray arr = vertexId.getArr();
arr = (arr.permute(3, 2, 0, 1).dup('c'));
initWith.associateArrayWithVariable(arr, vertexId);
String dataFormat = "nhwc";
val strides = attributesForNode.get("strides");
val sH = strides.getIntsList().get(0);
val sW = strides.getIntsList().size() < 2 ? sH : strides.getIntsList().get(1);
boolean isSameMode = autoPad
.equalsIgnoreCase("SAME");
DeConv2DConfig conv2DConfig = DeConv2DConfig.builder()
.kH(kH)
.kW(kW)
.sH(sH.intValue())
.sW(sW.intValue())
.isSameMode(isSameMode)
.dataFormat(dataFormat.equalsIgnoreCase("nhwc") ? DeConv2DConfig.NHWC : DeConv2DConfig.NCHW)
.build();
this.config = conv2DConfig;
addArgs();
addOutputArgument(arr);
}
@Override
public String opName() {
return "deconv2d";
}
@Override
public String onnxName() {
return "ConvTranspose";
}
@Override
public String tensorflowName() {
return "Conv2DTranspose";
}
@Override
public List doDiff(List f1) {
List ret = new ArrayList<>();
List inputs = new ArrayList<>();
inputs.addAll(Arrays.asList(args()));
inputs.addAll(f1);
DeConv2DDerivative deConv2DDerivative = DeConv2DDerivative.derivativeBuilder()
.sameDiff(sameDiff)
.config(config)
.inputs(inputs.toArray(new SDVariable[inputs.size()]))
.build();
ret.addAll(Arrays.asList(deConv2DDerivative.outputVariables()));
return ret;
}
@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));
}
}
