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/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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.custom;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
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.Collections;
import java.util.List;
import java.util.Map;
public class FakeQuantWithMinMaxVarsPerChannel extends DynamicCustomOp {
protected boolean narrowRange;
protected int numBits;
public FakeQuantWithMinMaxVarsPerChannel() {}
public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, int num_bits, boolean narrow) {
Preconditions.checkArgument(min.isVector() && max.isVector() &&
min.length() == max.length(),
"FakeQuantWithMinMaxVarsPerChannel: min and max should be 1D tensors with the same length");
addInputArgument(x,min,max);
addIArgument(num_bits);
addBArgument(narrow);
}
public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, int num_bits) {
this(x, min, max, num_bits, false);
}
public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, boolean narrow) {
this(x, min, max, 8, narrow);
}
public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max) {
this(x, min, max, 8, false);
}
public FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff, SDVariable x, SDVariable min, SDVariable max,
int num_bits, boolean narrow) {
super("", sameDiff, new SDVariable[]{x, min, max});
addIArgument(num_bits);
addBArgument(narrow);
}
@Override
public String opName() {
return "fake_quant_with_min_max_vars_per_channel";
}
@Override
public String tensorflowName() {
return "FakeQuantWithMinMaxVarsPerChannel";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
if(attributesForNode.containsKey("narrow_range")){
this.narrowRange = attributesForNode.get("narrow_range").getB();
}
if(attributesForNode.containsKey("num_bits")) {
this.numBits = (int) attributesForNode.get("num_bits").getI();
}
addIArgument(numBits);
addBArgument(narrowRange);
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected exactly 3 inputs, got %s", inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}