org.nd4j.linalg.api.ops.impl.layers.convolution.DepthToSpace Maven / Gradle / Ivy
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* 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.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Inverse operation to SpaceToDepth. This operation takes 4D array in, in either NCHW or NHWC format,
* and moves data from channels (C) to spatial dimensions (HW) for given blockSize.
*
* Example:
* blockSize = 4
* dataFormat = "NCHW"
* input shape = [128, 4, 4, 48]
* output shape = [128, 4*4, 4*4, 48/4/4]
*
* @author [email protected], Max Pumperla
*/
public class DepthToSpace extends DynamicCustomOp {
private String dataFormat = "NHWC";
private int blockSize;
public DepthToSpace() {
}
public DepthToSpace(SameDiff sameDiff, SDVariable[] args, int blockSize, String dataFormat) {
super(null, sameDiff, args, false);
this.blockSize = blockSize;
this.dataFormat = dataFormat;
boolean isNHWC = dataFormat.equals("NHWC");
addIArgument(blockSize, isNHWC ? 1 : 0);
}
public DepthToSpace(INDArray in, INDArray out, int blockSize, String dataFormat) {
super(null, in, out, null, null);
this.blockSize = blockSize;
this.dataFormat = dataFormat;
boolean isNHWC = dataFormat.equals("NHWC");
addIArgument(blockSize, isNHWC ? 1 : 0);
}
@Override
public List doDiff(List i_v) {
// Gradient to DepthToSpace is just SpaceToDepth of same block size and data format.
SDVariable gradient = i_v.get(0);
SDVariable ret = sameDiff.cnn().spaceToDepth(gradient, blockSize, dataFormat);
return Arrays.asList(ret);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
boolean isNHWC = dataFormat.equals("NHWC");
addIArgument(blockSize, isNHWC ? 1 : 0);
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map attrs = new LinkedHashMap<>();
val blockSize = PropertyMapping.builder()
.tfAttrName("block_size")
.propertyNames(new String[]{"blockSize"})
.build();
attrs.put("blockSize", blockSize);
val dataFormatMapping = PropertyMapping.builder()
.tfAttrName("data_format")
.propertyNames(new String[]{"dataFormat"})
.build();
attrs.put("dataFormat", dataFormatMapping);
ret.put(tensorflowName(), attrs);
return ret;
}
@Override
public String opName() {
return "depth_to_space";
}
@Override
public String[] tensorflowNames() {
return new String[]{"DepthToSpace"};
}
@Override
public String tensorflowName() {
return "DepthToSpace";
}
@Override
public List calculateOutputDataTypes(List dataTypes){
return Collections.singletonList(dataTypes.get(0));
}
}