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/*
* ******************************************************************************
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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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.layers.convolution;
import lombok.Builder;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
@Slf4j
public class SConv2D extends Conv2D {
@Builder(builderMethodName = "sameDiffSBuilder")
public SConv2D(SameDiff sameDiff, SDVariable[] inputFunctions, Conv2DConfig conv2DConfig) {
super(sameDiff, inputFunctions, conv2DConfig);
}
public SConv2D(@NonNull SameDiff sameDiff, @NonNull SDVariable layerInput, @NonNull SDVariable depthWeights,
SDVariable pointWeights, SDVariable bias, @NonNull Conv2DConfig conv2DConfig) {
this(sameDiff, wrapFilterNull(layerInput, depthWeights, pointWeights, bias), conv2DConfig);
}
public SConv2D(INDArray[] inputs, INDArray[] outputs, Conv2DConfig config){
super(inputs, outputs, config);
}
public SConv2D(@NonNull INDArray layerInput, @NonNull INDArray depthWeights, INDArray pointWeights, @NonNull Conv2DConfig Conv2DConfig){
this(wrapFilterNull(layerInput, depthWeights, pointWeights), null, Conv2DConfig);
}
public SConv2D(INDArray layerInput, INDArray depthWeights, INDArray pointWeights, INDArray bias, Conv2DConfig config) {
this(wrapFilterNull(layerInput, depthWeights, pointWeights, bias), null, config);
}
public SConv2D() {}
@Override
public String opName() {
return "sconv2d";
}
@Override
public List doDiff(List f1) {
//Args at libnd4j level: in, gradAtOut, wD, wP, bias
//Args for SConv2d libnd4j: input, wD, wP, bias
List inputs = new ArrayList<>();
inputs.add(arg(0));
inputs.add(f1.get(0));
SDVariable[] args = args();
for( int i=1; i ret = Arrays.asList(conv2DDerivative.outputVariables());
return ret;
}
@Override
public long[] iArgs() {
if (iArguments.size() == 0)
addArgs();
return super.iArgs();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public String[] tensorflowNames() {
throw new NoOpNameFoundException("No op name found for " + opName());
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for op " + opName());
}
@Override
public String tensorflowName() {
return "separable_conv2d";
}
@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));
}
}