90 lines
3.5 KiB
Java
90 lines
3.5 KiB
Java
/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.nd4j.autodiff.validation;
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import lombok.Data;
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import lombok.NonNull;
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import lombok.experimental.Accessors;
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import org.nd4j.autodiff.validation.functions.EqualityFn;
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import org.nd4j.autodiff.validation.functions.RelErrorFn;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ops.DynamicCustomOp;
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import org.nd4j.linalg.api.shape.LongShapeDescriptor;
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import org.nd4j.common.function.Function;
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import java.util.HashMap;
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import java.util.LinkedHashMap;
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import java.util.Map;
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@Data
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@Accessors(fluent = true)
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public class OpTestCase {
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private final DynamicCustomOp op;
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private Map<Integer, Function<INDArray, String>> testFns = new LinkedHashMap<>();
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private Map<Integer, LongShapeDescriptor> expShapes = new HashMap<>();
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public OpTestCase(@NonNull DynamicCustomOp op) {
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this.op = op;
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}
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/**
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* Validate the op output using INDArray.equals(INDArray)
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*
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* @param outputNum Number of the output
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* @param expected Expected INDArray
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*/
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public OpTestCase expectedOutput(int outputNum, INDArray expected) {
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testFns.put(outputNum, new EqualityFn(expected));
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expShapes.put(outputNum, expected.shapeDescriptor());
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return this;
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}
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/**
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* Validate the output for a single variable using element-wise relative error:
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* relError = abs(x-y)/(abs(x)+abs(y)), with x=y=0 case defined to be 0.0.
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* Also has a minimum absolute error condition, which must be satisfied for the relative error failure to be considered
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* legitimate
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*
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* @param outputNum output number
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* @param expected Expected INDArray
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* @param maxRelError Maximum allowable relative error
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* @param minAbsError Minimum absolute error for a failure to be considered legitimate
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*/
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public OpTestCase expectedOutputRelError(int outputNum, @NonNull INDArray expected, double maxRelError, double minAbsError) {
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testFns.put(outputNum, new RelErrorFn(expected, maxRelError, minAbsError));
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expShapes.put(outputNum, expected.shapeDescriptor());
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return this;
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}
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/**
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* @param outputNum Output number to check
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* @param expShape Expected shape for the output
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* @param validationFn Function to use to validate the correctness of the specific Op. Should return null
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* if validation passes, or an error message if the op validation fails
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*/
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public OpTestCase expectedOutput(int outputNum, @NonNull LongShapeDescriptor expShape, @NonNull Function<INDArray, String> validationFn) {
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testFns.put(outputNum, validationFn);
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expShapes.put(outputNum, expShape);
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return this;
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}
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}
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