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org.nd4j.linalg.api.ops.impl.layers.convolution.DepthwiseConv2D 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.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.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.ConditionalFieldValueIntIndexArrayAdapter;
import org.nd4j.imports.descriptors.properties.adapters.NDArrayShapeAdapter;
import org.nd4j.imports.descriptors.properties.adapters.SizeThresholdIntArrayIntIndexAdpater;
import org.nd4j.imports.descriptors.properties.adapters.StringEqualsAdapter;
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.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.*;
/**
* Depthwise Conv2D operation
*/
@Slf4j
@Getter
public class DepthwiseConv2D extends DynamicCustomOp {
protected Conv2DConfig config;
@Builder(builderMethodName = "sameDiffBuilder")
public DepthwiseConv2D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig config) {
super(sameDiff, inputFunctions);
this.config = config;
addArgs();
}
public DepthwiseConv2D(INDArray[] inputs, INDArray[] outputs, Conv2DConfig config){
super(inputs, outputs);
this.config = config;
addArgs();
}
public DepthwiseConv2D(@NonNull INDArray input, @NonNull INDArray weights, INDArray bias, INDArray output, @NonNull Conv2DConfig config){
this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config);
}
public DepthwiseConv2D() {
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
protected void addArgs() {
addIArgument(config.getKH(),
config.getKW(),
config.getSH(),
config.getSW(),
config.getPH(),
config.getPW(),
config.getDH(),
config.getDW(),
ArrayUtil.fromBoolean(config.isSameMode()),
config.getDataFormat().equalsIgnoreCase(Conv2DConfig.NCHW) ? 0 : 1);
}
@Override
public Object getValue(Field property) {
if (config == null) {
config = Conv2DConfig.builder().build();
}
try {
val t = config.getValue(property);
return t;
} catch (Exception e) {
throw new RuntimeException(e);
}
}
@Override
public Map propertiesForFunction() {
if(config == null && !iArguments.isEmpty()){
config = Conv2DConfig.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 ? Conv2DConfig.NHWC : Conv2DConfig.NCHW)
.build();
}
return config.toProperties();
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
/*
// we must permute weights once during import
val weightsName = nodeDef.getInput(1);
val variable = initWith.getVariable(weightsName);
val tmp = initWith.getArrForVarName(weightsName);
val array = tmp.permute(3, 2, 0, 1).dup('c');
initWith.associateArrayWithVariable(array, variable);
*/
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new HashMap<>();
Map tfMappings = new LinkedHashMap<>();
val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
//TF uses [kH, kW, inC, outC] always for weights
tfMappings.put("kH", new NDArrayShapeAdapter(0));
tfMappings.put("kW", new NDArrayShapeAdapter(1));
tfMappings.put("sH", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 2, 1, fields.get("dataFormat")));
tfMappings.put("sW", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 3, 2, fields.get("dataFormat")));
tfMappings.put("dH", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 2, 1, fields.get("dataFormat")));
tfMappings.put("dW", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 3, 2, fields.get("dataFormat")));
tfMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
Map onnxMappings = new HashMap<>();
onnxMappings.put("kH", new SizeThresholdIntArrayIntIndexAdpater(0, 2, 0));
onnxMappings.put("kW", new SizeThresholdIntArrayIntIndexAdpater(1, 2, 0));
onnxMappings.put("dH", new SizeThresholdIntArrayIntIndexAdpater(0, 2, 0));
onnxMappings.put("dW", new SizeThresholdIntArrayIntIndexAdpater(1, 2, 0));
onnxMappings.put("sH", new SizeThresholdIntArrayIntIndexAdpater(0, 2, 0));
onnxMappings.put("sW", new SizeThresholdIntArrayIntIndexAdpater(1, 2, 0));
onnxMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
try {
ret.put(tensorflowName(), tfMappings);
} catch (NoOpNameFoundException e) {
//
}
try {
ret.put(onnxName(), onnxMappings);
} catch (NoOpNameFoundException e) {
//
}
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[]{"sW", "sH"})
.build();
val kernelMappingH = PropertyMapping.builder()
.propertyNames(new String[]{"kH"})
.tfInputPosition(1)
.shapePosition(0)
.onnxAttrName("kernel_shape")
.build();
val kernelMappingW = PropertyMapping.builder()
.propertyNames(new String[]{"kW"})
.tfInputPosition(1)
.shapePosition(1)
.onnxAttrName("kernel_shape")
.build();
val dilationMapping = PropertyMapping.builder()
.onnxAttrName("dilations")
.propertyNames(new String[]{"dW", "dH"})
.tfAttrName("rates")
.build();
val dataFormat = PropertyMapping.builder()
.onnxAttrName("data_format")
.tfAttrName("data_format")
.propertyNames(new String[]{"dataFormat"})
.build();
val nhwc = PropertyMapping.builder()
.onnxAttrName("data_format")
.tfAttrName("data_format")
.propertyNames(new String[]{"isNHWC"})
.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", kernelMappingH);
map.put("kW", kernelMappingW);
map.put("dW", dilationMapping);
map.put("dH", dilationMapping);
map.put("isSameMode", sameMode);
map.put("pH", paddingWidthHeight);
map.put("pW", paddingWidthHeight);
map.put("dataFormat", dataFormat);
try {
ret.put(onnxName(), map);
} catch (NoOpNameFoundException e) {
//ignore
}
try {
ret.put(tensorflowName(), map);
} catch (NoOpNameFoundException e) {
//ignore
}
return ret;
}
@Override
public String opName() {
return "depthwise_conv2d";
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException("Not implemented yet");
}
@Override
public String onnxName() {
return "depth_conv";
}
@Override
public String tensorflowName() {
return "DepthwiseConv2dNative";
}
@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));
}
}
