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org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2D 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.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.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 Conv2D extends DynamicCustomOp {
protected Conv2DConfig config;
@Builder(builderMethodName = "builder")
public Conv2D(SameDiff sameDiff,
SDVariable[] inputFunctions,
INDArray[] inputArrays, INDArray[] outputs,
Conv2DConfig config) {
super(null, inputArrays, outputs);
this.sameDiff = sameDiff;
this.config = config;
addArgs();
sameDiff.putFunctionForId(this.getOwnName(), this); //Normally called in DynamicCustomOp constructor, via setInstanceId - but sameDiff field is null at that point
sameDiff.addArgsFor(inputFunctions, this);
}
protected void addArgs() {
addIArgument(config.getKH(),
config.getKW(),
config.getSH(),
config.getSW(),
config.getPH(),
config.getPW(),
config.getDH(),
config.getDW(),
ArrayUtil.fromBoolean(config.isSameMode()),
ArrayUtil.fromBoolean(config.isNHWC()));
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public Object getValue(Field property) {
if (config == null) {
config = Conv2DConfig.builder().build();
}
return config.getValue(property);
}
@Override
public void setValueFor(Field target, Object value) {
config.setValueFor(target, value);
}
@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("NCHW", 2, 0, fields.get("dataFormat")));
tfMappings.put("kW", new ConditionalFieldValueNDArrayShapeAdapter("NCHW", 3, 1, fields.get("dataFormat")));
tfMappings.put("sH", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 2, 1, fields.get("dataFormat")));
tfMappings.put("sW", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 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("NHWC"));
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[]{"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);
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 "conv2d";
}
@Override
public List doDiff(List f1) {
List inputs = new ArrayList<>(Arrays.asList(args()));
inputs.add(f1.get(0));
Conv2DDerivative conv2DDerivative = Conv2DDerivative.derivativeBuilder()
.sameDiff(sameDiff)
.config(config)
.outputs(outputArguments())
.inputFunctions(inputs.toArray(new SDVariable[inputs.size()]))
.build();
List ret = Arrays.asList(conv2DDerivative.outputVariables());
return ret;
}
@Override
public String onnxName() {
return "Conv";
}
@Override
public String tensorflowName() {
return "Conv2D";
}
@Override
public String[] tensorflowNames() {
return new String[]{"Conv2D"};
}
}
