cavis/libnd4j/include/ops/declarable/helpers/cpu/image_suppression.cpp

/*******************************************************************************
 * 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
 ******************************************************************************/

//
//  @author sgazeos@gmail.com
//

#include <ops/declarable/helpers/image_suppression.h>
//#include <blas/NDArray.h>
#include <algorithm>
#include <numeric>
#include <queue>

namespace nd4j {
namespace ops {
namespace helpers {

    template <typename T>
    static void nonMaxSuppressionV2_(NDArray* boxes, NDArray* scales, int maxSize, double overlapThreshold,
            double scoreThreshold, NDArray* output) {
        std::vector<int> indices(scales->lengthOf());
        std::iota(indices.begin(), indices.end(), 0);
        for (auto e = 0; e < scales->lengthOf(); e++) {
            if (scales->e<double>(e) < scoreThreshold) indices[e] = -1;
        }
        std::sort(indices.begin(), indices.end(), [scales](int i, int j) {return scales->e<T>(i) > scales->e<T>(j);});

//        std::vector<int> selected(output->lengthOf());
        std::vector<int> selectedIndices(output->lengthOf(), 0);
        auto needToSuppressWithThreshold = [] (NDArray& boxes, int previousIndex, int nextIndex, T threshold) -> bool {
            if (previousIndex < 0 || nextIndex < 0) return true;
            T minYPrev = nd4j::math::nd4j_min(boxes.e<T>(previousIndex, 0), boxes.e<T>(previousIndex, 2));
            T minXPrev = nd4j::math::nd4j_min(boxes.e<T>(previousIndex, 1), boxes.e<T>(previousIndex, 3));
            T maxYPrev = nd4j::math::nd4j_max(boxes.e<T>(previousIndex, 0), boxes.e<T>(previousIndex, 2));
            T maxXPrev = nd4j::math::nd4j_max(boxes.e<T>(previousIndex, 1), boxes.e<T>(previousIndex, 3));
            T minYNext = nd4j::math::nd4j_min(boxes.e<T>(nextIndex, 0), boxes.e<T>(nextIndex, 2));
            T minXNext = nd4j::math::nd4j_min(boxes.e<T>(nextIndex, 1), boxes.e<T>(nextIndex, 3));
            T maxYNext = nd4j::math::nd4j_max(boxes.e<T>(nextIndex, 0), boxes.e<T>(nextIndex, 2));
            T maxXNext = nd4j::math::nd4j_max(boxes.e<T>(nextIndex, 1), boxes.e<T>(nextIndex, 3));
            T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev);
            T areaNext = (maxYNext - minYNext) * (maxXNext - minXNext);

            if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false;

            T minIntersectionY = nd4j::math::nd4j_max(minYPrev, minYNext);
            T minIntersectionX = nd4j::math::nd4j_max(minXPrev, minXNext);
            T maxIntersectionY = nd4j::math::nd4j_min(maxYPrev, maxYNext);
            T maxIntersectionX = nd4j::math::nd4j_min(maxXPrev, maxXNext);
            T intersectionArea =
                    nd4j::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) *
                            nd4j::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f));
            T intersectionValue = intersectionArea / (areaPrev + areaNext - intersectionArea);
            return intersectionValue > threshold;

        };
//        int numSelected = 0;
        int numBoxes = boxes->sizeAt(0);
        int numSelected = 0;

        for (int i = 0; i < numBoxes; ++i) {
            bool shouldSelect = numSelected < output->lengthOf();

            // FIXME: add parallelism here
            for (int j = numSelected - 1; j >= 0; --j) {
                if (shouldSelect)
                if (needToSuppressWithThreshold(*boxes, indices[i], indices[selectedIndices[j]], T(overlapThreshold))) {
                    shouldSelect = false;
                }
            }
            if (shouldSelect) {
                output->p(numSelected, indices[i]);
                selectedIndices[numSelected++] = i;
            }
        }
    }
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
    template <typename T, typename I>
    static Nd4jLong
    nonMaxSuppressionGeneric_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int outputSize,
            double overlapThreshold, double scoreThreshold, NDArray* output) {

//        const int outputSize = maxSize->e<int>(0);
        auto numBoxes = boxes->sizeAt(0);
        //std::vector<T> scoresData(numBoxes);
        T* scoresData = scores->dataBuffer()->primaryAsT<T>();
        //std::copy_n(scores->getDataBuffer()->primaryAsT<T>(), numBoxes, scoresData.begin());

        // Data structure for a selection candidate in NMS.
        struct Candidate {
            int _boxIndex;
            T _score;
            int _suppressBeginIndex;
        };

        auto cmp = [](const Candidate& bsI, const Candidate& bsJ) -> bool{
            return ((bsI._score == bsJ._score) && (bsI._boxIndex > bsJ._boxIndex)) ||
                    (bsI._score < bsJ._score);
        };
        std::priority_queue<Candidate, std::deque<Candidate>, decltype(cmp)> candidatePriorityQueue(cmp);
        for (auto i = 0; i < scores->lengthOf(); ++i) {
            if (scoresData[i] > scoreThreshold) {
                candidatePriorityQueue.emplace(Candidate({i, scoresData[i], 0}));
            }
        }

        std::vector<I> selected;
        T similarity, originalScore;
        Candidate nextCandidate;

        while (selected.size() < outputSize && !candidatePriorityQueue.empty()) {
            nextCandidate = candidatePriorityQueue.top();
            originalScore = nextCandidate._score;
            candidatePriorityQueue.pop();

            // Overlapping boxes are likely to have similar scores, therefore we
            // iterate through the previously selected boxes backwards in order to
            // see if `nextCandidate` should be suppressed. We also enforce a property
            // that a candidate can be suppressed by another candidate no more than
            // once via `suppress_begin_index` which tracks which previously selected
            // boxes have already been compared against next_candidate prior to a given
            // iteration.  These previous selected boxes are then skipped over in the
            // following loop.
            bool shouldHardSuppress = false;
            for (int j = static_cast<int>(selected.size()) - 1; j >= nextCandidate._suppressBeginIndex; --j) {
                similarity = boxes->t<T>(nextCandidate._boxIndex, selected[j]);
                nextCandidate._score *= T(similarity <= overlapThreshold?1.0:0.); //suppressWeightFunc(similarity);

                // First decide whether to perform hard suppression
                if (similarity >= static_cast<T>(overlapThreshold)) {
                    shouldHardSuppress = true;
                    break;
                }

                // If next_candidate survives hard suppression, apply soft suppression
                if (nextCandidate._score <= scoreThreshold) break;
            }
            // If `nextCandidate._score` has not dropped below `scoreThreshold`
            // by this point, then we know that we went through all of the previous
            // selections and can safely update `suppress_begin_index` to
            // `selected.size()`. If on the other hand `next_candidate.score`
            // *has* dropped below the score threshold, then since `suppressWeight`
            // always returns values in [0, 1], further suppression by items that were
            // not covered in the above for loop would not have caused the algorithm
            // to select this item. We thus do the same update to
            // `suppressBeginIndex`, but really, this element will not be added back
            // into the priority queue in the following.
            nextCandidate._suppressBeginIndex = selected.size();

            if (!shouldHardSuppress) {
                if (nextCandidate._score == originalScore) {
                    // Suppression has not occurred, so select next_candidate
                    selected.push_back(nextCandidate._boxIndex);
//                    selected_scores.push_back(nextCandidate._score);
                }
                if (nextCandidate._score > scoreThreshold) {
                    // Soft suppression has occurred and current score is still greater than
                    // score_threshold; add next_candidate back onto priority queue.
                    candidatePriorityQueue.push(nextCandidate);
                }
            }
        }

        if (output) {
            DataBuffer buf(selected.data(), selected.size() * sizeof(I), DataTypeUtils::fromT<I>());
            output->dataBuffer()->copyBufferFrom(buf, buf.getLenInBytes());
        }

        return (Nd4jLong)selected.size();
    }

    Nd4jLong
    nonMaxSuppressionGeneric(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize,
                              double overlapThreshold, double scoreThreshold, NDArray* output) {
        BUILD_DOUBLE_SELECTOR(boxes->dataType(), output == nullptr?DataType::INT32:output->dataType(), return nonMaxSuppressionGeneric_, (context, boxes, scores, maxSize, overlapThreshold, scoreThreshold, output), FLOAT_TYPES, INTEGER_TYPES);
        return 0;
    }

    BUILD_DOUBLE_TEMPLATE(template Nd4jLong nonMaxSuppressionGeneric_, (nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize,
            double overlapThreshold, double scoreThreshold, NDArray* output), FLOAT_TYPES, INTEGER_TYPES);

    void
    nonMaxSuppression(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize,
            double overlapThreshold, double scoreThreshold, NDArray* output) {
        BUILD_SINGLE_SELECTOR(boxes->dataType(), nonMaxSuppressionV2_, (boxes, scales, maxSize,
                overlapThreshold, scoreThreshold, output), NUMERIC_TYPES);
    }
    BUILD_SINGLE_TEMPLATE(template void nonMaxSuppressionV2_, (NDArray* boxes, NDArray* scales, int maxSize,
            double overlapThreshold, double scoreThreshold, NDArray* output), NUMERIC_TYPES);

}
}
}