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org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPoolWithArgmax Maven / Gradle / Ivy
/*
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* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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
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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 org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.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.PaddingMode;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.common.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
@Slf4j
@Getter
public class MaxPoolWithArgmax extends DynamicCustomOp {
protected Pooling2DConfig config;
protected DataType outputType;
public MaxPoolWithArgmax() {
}
@Builder(builderMethodName = "sameDiffBuilder")
@SuppressWarnings("Used in lombok")
public MaxPoolWithArgmax(SameDiff sameDiff, SDVariable input, Pooling2DConfig config) {
super(null, sameDiff, new SDVariable[]{input}, false);
config.setType(Pooling2D.Pooling2DType.MAX);
this.config = config;
addArgs();
}
public MaxPoolWithArgmax(@NonNull INDArray input, @NonNull Pooling2DConfig config){
this(input, null, null, config);
}
public MaxPoolWithArgmax(@NonNull INDArray input, INDArray output,INDArray outArgMax, @NonNull Pooling2DConfig config){
super(null, new INDArray[]{input}, wrapFilterNull(output, outArgMax));
config.setType(Pooling2D.Pooling2DType.MAX);
this.config = config;
addArgs();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public Map propertiesForFunction() {
if(config == null && !iArguments.isEmpty()) {
//Perhaps loaded from FlatBuffers - hence we have IArgs but not Config object
config = Pooling2DConfig.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))
.paddingMode(PaddingMode.fromNumber(iArguments.get(8).intValue()))
.extra(iArguments.get(9))
.isNHWC(iArguments.get(10) == 1)
.type(Pooling2D.Pooling2DType.MAX)
.build();
}
return config.toProperties();
}
private void addArgs() {
addIArgument(config.getKH(),
config.getKW(),
config.getSH(),
config.getSW(),
config.getPH(),
config.getPW(),
config.getDH(),
config.getDW(),
config.getPaddingMode().index,
(int) config.getExtra(),
ArrayUtil.fromBoolean(config.isNHWC())
);
}
public String getPoolingPrefix() {
return "max";
}
@Override
public List doDiff(List f1) {
List ret = new ArrayList<>();
List inputs = new ArrayList<>();
inputs.addAll(Arrays.asList(args()));
inputs.add(f1.get(0));
Pooling2DDerivative pooling2DDerivative = Pooling2DDerivative.derivativeBuilder()
.inputs(inputs.toArray(new SDVariable[inputs.size()]))
.sameDiff(sameDiff)
.config(config)
.build();
ret.addAll(Arrays.asList(pooling2DDerivative.outputVariables()));
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val aStrides = nodeDef.getAttrOrThrow("strides");
val tfStrides = aStrides.getList().getIList();
val aKernels = nodeDef.getAttrOrThrow("ksize");
val tfKernels = aKernels.getList().getIList();
int sH = 0;
int sW = 0;
int pH = 0;
int pW = 0;
int kH = 0;
int kW = 0;
val aPadding = nodeDef.getAttrOrThrow("padding");
val padding = aPadding.getList().getIList();
val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");
boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
String data_format = "nhwc";
if (nodeDef.containsAttr("data_format")) {
val attr = nodeDef.getAttrOrThrow("data_format");
data_format = attr.getS().toStringUtf8().toLowerCase();
}
if (data_format.equalsIgnoreCase("nhwc")) {
sH = tfStrides.get(1).intValue();
sW = tfStrides.get(2).intValue();
kH = tfKernels.get(1).intValue();
kW = tfKernels.get(2).intValue();
pH = padding.size() > 0 ? padding.get(1).intValue() : 0;
pW = padding.size() > 0 ? padding.get(2).intValue() : 0;
} else {
sH = tfStrides.get(2).intValue();
sW = tfStrides.get(3).intValue();
kH = tfKernels.get(2).intValue();
kW = tfKernels.get(3).intValue();
pH = padding.size() > 0 ? padding.get(2).intValue() : 0;
pW = padding.size() > 0 ? padding.get(3).intValue() : 0;
}
Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
.sH(sH)
.sW(sW)
.type(Pooling2D.Pooling2DType.MAX)
.paddingMode(isSameMode ? PaddingMode.SAME : PaddingMode.VALID)
.kH(kH)
.kW(kW)
.pH(pH)
.pW(pW)
.isNHWC(data_format.equalsIgnoreCase("nhwc"))
.extra(1.0) // averaging only for non-padded values
.build();
this.config = pooling2DConfig;
addArgs();
if(attributesForNode.containsKey("argmax")) {
outputType = TFGraphMapper.convertType(attributesForNode.get("argmax").getType());
} else {
outputType = DataType.LONG;
}
}
@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 paddingMapping = PropertyMapping.builder()
.onnxAttrName("padding")
.tfAttrName("padding")
.propertyNames(new String[]{"pH", "pW"})
.build();
val kernelMapping = PropertyMapping.builder()
.propertyNames(new String[]{"kH", "kW"})
.tfInputPosition(1)
.onnxAttrName("ksize")
.build();
val dilationMapping = PropertyMapping.builder()
.onnxAttrName("dilations")
.propertyNames(new String[]{"dW", "dH"})
.tfAttrName("rates")
.build();
//data_format
val dataFormatMapping = PropertyMapping.builder()
.propertyNames(new String[]{"isNHWC"})
.tfAttrName("data_format")
.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("pH", paddingMapping);
map.put("pW", paddingMapping);
map.put("isNHWC", dataFormatMapping);
ret.put(onnxName(), map);
ret.put(tensorflowName(), map);
return ret;
}
@Override
public String opName() {
return "max_pool_with_argmax";
}
@Override
public String onnxName() {
return "MaxPoolWithArgmax";
}
@Override
public String tensorflowName() {
return "MaxPoolWithArgmax";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected 1 input data type for %s, got %s", getClass(), inputDataTypes);
List result = new ArrayList<>();
result.add(inputDataTypes.get(0));
if(dArguments.isEmpty())
result.add(outputType == null ? DataType.INT : outputType);
else
result.add(dArguments.get(0));
return result;
}
}
