90 lines
3.7 KiB
C++
90 lines
3.7 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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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// @author @cpuheater
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_confusion_matrix)
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#include <ops/declarable/CustomOperations.h>
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#include <helpers/ShapeUtils.h>
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#include <NDArray.h>
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#include <array/NDArrayList.h>
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#include <array>
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#include <ops/declarable/helpers/confusion.h>
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namespace nd4j {
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namespace ops {
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DECLARE_TYPES(confusion_matrix) {
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getOpDescriptor()
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->setAllowedInputTypes({ALL_INTS, ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS, ALL_INTS});
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}
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CUSTOM_OP_IMPL(confusion_matrix, 2, 1, false, 0, -2) {
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auto labels = INPUT_VARIABLE(0);
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auto predictions = INPUT_VARIABLE(1);
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NDArray *weights = nullptr;
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if(block.width() > 2){
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weights = INPUT_VARIABLE(2);
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REQUIRE_TRUE(weights->isSameShape(predictions),0, "CONFUSION_MATRIX: Weights and predictions should have equal shape");
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}
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auto output = OUTPUT_VARIABLE(0);
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output->assign(0.);
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int minPrediction = predictions->reduceNumber(reduce::Min).e<int>(0);
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int minLabel = labels->reduceNumber(reduce::Min).e<int>(0);
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REQUIRE_TRUE(minLabel >=0, 0, "CONFUSION_MATRIX: Labels contains negative values !");
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REQUIRE_TRUE(minPrediction >=0, 0, "CONFUSION_MATRIX: Predictions contains negative values !");
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REQUIRE_TRUE(labels->isVector(), 0, "CONFUSION_MATRIX: Labels input should be a Vector, but got %iD instead", labels->rankOf());
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REQUIRE_TRUE(predictions->isVector(), 0, "CONFUSION_MATRIX: Predictions input should be Vector, but got %iD instead", predictions->rankOf());
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REQUIRE_TRUE(labels->isSameShape(predictions),0, "CONFUSION_MATRIX: Labels and predictions should have equal shape");
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helpers::confusionFunctor(block.launchContext(), labels, predictions, weights, output);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(confusion_matrix) {
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auto labels = INPUT_VARIABLE(0);
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auto predictions = INPUT_VARIABLE(1);
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auto dtype = block.dataType();
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dtype = nd4j::DataType::INT64; // dtype - should be a param with int argument
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if (block.numI() > 1)
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dtype = (nd4j::DataType)INT_ARG(1);
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int numClasses = 0;
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if (block.getIArguments()->size() > 0) {
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numClasses = INT_ARG(0);
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}
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else {
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int maxPrediction = predictions->reduceNumber(reduce::Max).e<int>(0);
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int maxLabel = labels->reduceNumber(reduce::Max).e<int>(0);
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numClasses = (maxPrediction >= maxLabel) ? maxPrediction+1 : maxLabel+1;
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}
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std::array<Nd4jLong, 2> shape = {{numClasses,numClasses}};
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auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(dtype, 'c', 2, shape.data());
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return SHAPELIST(newShape);
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}
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}
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}
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#endif |