Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* ******************************************************************************
* *
* *
* * 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
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.layers.convolution;
import lombok.Getter;
import lombok.NoArgsConstructor;
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.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Upsampling operation
*/
@Slf4j
@Getter
@NoArgsConstructor
public class Upsampling2d extends DynamicCustomOp {
protected boolean nchw;
protected int scaleH;
protected int scaleW;
public Upsampling2d(SameDiff sameDiff, SDVariable input, boolean nchw, int scaleH, int scaleW) {
super(null,sameDiff, new SDVariable[]{input});
this.nchw = nchw;
this.scaleH = scaleH;
this.scaleW = scaleW;
addIArgument(scaleH);
addIArgument(scaleW);
addIArgument(nchw ? 1 : 0);
}
public Upsampling2d(SameDiff sameDiff, SDVariable input, int scaleH, int scaleW, boolean nchw) {
this(sameDiff, input, nchw, scaleH, scaleW);
}
public Upsampling2d(SameDiff sameDiff, SDVariable input, int scale) {
super(null,sameDiff, new SDVariable[]{input});
addIArgument(scale);
}
public Upsampling2d(INDArray input, int scale) {
this(input, scale, scale, true);
}
public Upsampling2d(INDArray input, int scaleH, int scaleW, boolean nchw) {
super(new INDArray[]{input}, null);
this.nchw = nchw;
this.scaleH = scaleH;
this.scaleW = scaleW;
addIArgument(scaleH);
addIArgument(scaleW);
addIArgument(nchw ? 1 : 0);
}
@Override
public String opName() {
return "upsampling2d";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public void configureFromArguments() {
if(iArguments.size() >= 3) {
this.scaleH = iArguments.get(0).intValue();
this.scaleW = iArguments.get(1).intValue();
this.nchw = iArguments.get(2) > 0;
}
}
@Override
public void setPropertiesForFunction(Map properties) {
Long factorH = getLongValueFromProperty("factorH",properties);
if(factorH != null) {
this.scaleH = factorH.intValue();
}
Long factorW = getLongValueFromProperty("factorW",properties);
if(factorW != null) {
this.scaleW = factorW.intValue();
}
Long isNCHW = getLongValueFromProperty("isNCHW",properties);
if(isNCHW != null) {
this.nchw = isNCHW > 0;
}
}
@Override
public List doDiff(List f1) {
return new Upsampling2dDerivative(sameDiff, arg(), f1.get(0), nchw, scaleH, scaleW).outputs();
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected 1 input data type for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}