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org.nd4j.linalg.api.ops.impl.layers.convolution.Conv1D 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.extern.slf4j.Slf4j;
import lombok.val;
import onnx.OnnxProto3;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.ConditionalFieldValueNDArrayShapeAdapter;
import org.nd4j.imports.descriptors.properties.adapters.SizeThresholdIntArrayIntIndexAdpater;
import org.nd4j.imports.descriptors.properties.adapters.StringEqualsAdapter;
import org.nd4j.imports.graphmapper.onnx.OnnxGraphMapper;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig;
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.*;
/**
* Conv2D operation
*/
@Slf4j
@Getter
@NoArgsConstructor
public class Conv1D extends DynamicCustomOp {
protected Conv1DConfig config;
@Builder(builderMethodName = "builder")
public Conv1D(SameDiff sameDiff,
SDVariable[] inputFunctions,
INDArray[] inputArrays, INDArray[] outputs,
Conv1DConfig config) {
super(null, inputArrays, outputs);
this.sameDiff = sameDiff;
this.config = config;
addArgs();
sameDiff.putFunctionForId(this.getOwnName(), this);
sameDiff.addArgsFor(inputFunctions, this);
}
protected void addArgs() {
if (config == null)
config = Conv1DConfig.builder().build();
addIArgument(config.getK(),
config.getS(),
config.getP(),
ArrayUtil.fromBoolean(config.isSameMode()),
ArrayUtil.fromBoolean(config.isNWC()));
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public Object getValue(Field property) {
if (config == null && !iArguments.isEmpty()) {
config = Conv1DConfig.builder()
.s(iArguments.get(0))
.p(iArguments.get(1))
.isSameMode(iArguments.get(2) == 1)
.dataFormat(iArguments.get(3) == 1 ? Conv1DConfig.NCW : Conv1DConfig.NWC)
.build();
}
return config.getValue(property);
}
@Override
public Map propertiesForFunction() {
return config.toProperties();
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
OnnxGraphMapper.getInstance().initFunctionFromProperties(node.getOpType(), this, attributesForNode, node, graph);
addArgs();
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new HashMap<>();
Map tfMappings = new LinkedHashMap<>();
val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
tfMappings.put("kH", new ConditionalFieldValueNDArrayShapeAdapter("NHW", 2, 0, fields.get("dataFormat")));
tfMappings.put("kW", new ConditionalFieldValueNDArrayShapeAdapter("NHW", 3, 1, fields.get("dataFormat")));
tfMappings.put("sH", new ConditionalFieldValueIntIndexArrayAdapter("NHW", 2, 1, fields.get("dataFormat")));
tfMappings.put("sW", new ConditionalFieldValueIntIndexArrayAdapter("NHW", 3, 2, fields.get("dataFormat")));
tfMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
tfMappings.put("isNHWC", new StringEqualsAdapter("NHWC"));
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"));
onnxMappings.put("isNHWC", new StringEqualsAdapter("NHC"));
ret.put(tensorflowName(), tfMappings);
ret.put(onnxName(), onnxMappings);
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[]{"s"})
.build();
val kernelMapping = PropertyMapping.builder()
.propertyNames(new String[]{"k"})
.tfInputPosition(1)
.shapePosition(0)
.onnxAttrName("kernel_shape")
.build();
val paddingMapping = PropertyMapping.builder()
.onnxAttrName("padding")
.propertyNames(new String[]{"p"})
.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();
map.put("s", strideMapping);
map.put("k", kernelMapping);
map.put("p", paddingMapping);
map.put("isSameMode", sameMode);
map.put("dataFormat", dataFormat);
map.put("isNHWC", nhwc);
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 "conv1d";
}
@Override
public String onnxName() {
return "Conv";
}
@Override
public String tensorflowName() {
return "Conv1D";
}
@Override
public String[] tensorflowNames() {
return new String[]{"Conv1D"};
}
}
