* initial commit Signed-off-by: raver119 <raver119@gmail.com> * - gruCell_bp further Signed-off-by: Yurii <yurii@skymind.io> * - further work on gruCell_bp Signed-off-by: Yurii <yurii@skymind.io> * Inverse matrix cublas implementation. Partial working revision. * Separation of segment ops helpers. Max separation. * Separated segment_min ops. * Separation of segment_mean/sum/prod/sqrtN ops heleprs. * Fixed diagonal processing with LUP decomposition. * Modified inversion approach using current state of LU decomposition. * Implementation of matrix_inverse op with cuda kernels. Working revision. * Implemented sequence_mask cuda helper. Eliminated waste printf with matrix_inverse implementation. Added proper tests. * - further work on gruCell_bp (ff/cuda) Signed-off-by: Yurii <yurii@skymind.io> * comment one test for gruCell_bp Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda static_rnn Signed-off-by: Yurii <yurii@skymind.io> * Refactored random_shuffle op to use new random generator. * Refactored random_shuffle op helper. * Fixed debug tests with random ops tests. * Implement random_shuffle op cuda kernel helper and tests. * - provide cuda scatter_update Signed-off-by: Yurii <yurii@skymind.io> * Implementation of random_shuffle for linear case with cuda kernels and tests. * Implemented random_shuffle with cuda kernels. Final revision. * - finally gruCell_bp is completed Signed-off-by: Yurii <yurii@skymind.io> * Dropout op cuda helper implementation. * Implemented dropout_bp cuda helper. * Implemented alpha_dropout_bp with cuda kernel helpers. * Refactored helper. * Implementation of suppresion helper with cuda kernels. * - provide cpu code fot hsvToRgb, rgbToHsv, adjustHue Signed-off-by: Yurii <yurii@skymind.io> * Using sort by value method. * Implementation of image.non_max_suppression op cuda-based helper. * - correcting and testing adjust_hue, adjust_saturation cpu/cuda code Signed-off-by: Yurii <yurii@skymind.io> * Added cuda device prefixes to declarations. * Implementation of hashcode op with cuda helper. Initital revision. * rnn cu impl removed Signed-off-by: raver119 <raver119@gmail.com>
91 lines
4.4 KiB
C++
91 lines
4.4 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 sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/image_suppression.h>
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//#include <blas/NDArray.h>
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#include <algorithm>
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#include <numeric>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void nonMaxSuppressionV2_(NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
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std::vector<Nd4jLong> indices(scales->lengthOf());
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std::iota(indices.begin(), indices.end(), 0);
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std::sort(indices.begin(), indices.end(), [scales](int i, int j) {return scales->e<T>(i) > scales->e<T>(j);});
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// std::vector<int> selected(output->lengthOf());
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std::vector<int> selectedIndices(output->lengthOf(), 0);
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auto needToSuppressWithThreshold = [] (NDArray& boxes, int previousIndex, int nextIndex, T threshold) -> bool {
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T minYPrev = nd4j::math::nd4j_min(boxes.e<T>(previousIndex, 0), boxes.e<T>(previousIndex, 2));
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T minXPrev = nd4j::math::nd4j_min(boxes.e<T>(previousIndex, 1), boxes.e<T>(previousIndex, 3));
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T maxYPrev = nd4j::math::nd4j_max(boxes.e<T>(previousIndex, 0), boxes.e<T>(previousIndex, 2));
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T maxXPrev = nd4j::math::nd4j_max(boxes.e<T>(previousIndex, 1), boxes.e<T>(previousIndex, 3));
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T minYNext = nd4j::math::nd4j_min(boxes.e<T>(nextIndex, 0), boxes.e<T>(nextIndex, 2));
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T minXNext = nd4j::math::nd4j_min(boxes.e<T>(nextIndex, 1), boxes.e<T>(nextIndex, 3));
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T maxYNext = nd4j::math::nd4j_max(boxes.e<T>(nextIndex, 0), boxes.e<T>(nextIndex, 2));
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T maxXNext = nd4j::math::nd4j_max(boxes.e<T>(nextIndex, 1), boxes.e<T>(nextIndex, 3));
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T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev);
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T areaNext = (maxYNext - minYNext) * (maxXNext - minXNext);
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if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false;
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T minIntersectionY = nd4j::math::nd4j_max(minYPrev, minYNext);
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T minIntersectionX = nd4j::math::nd4j_max(minXPrev, minXNext);
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T maxIntersectionY = nd4j::math::nd4j_min(maxYPrev, maxYNext);
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T maxIntersectionX = nd4j::math::nd4j_min(maxXPrev, maxXNext);
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T intersectionArea =
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nd4j::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) *
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nd4j::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f));
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T intersectionValue = intersectionArea / (areaPrev + areaNext - intersectionArea);
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return intersectionValue > threshold;
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};
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// int numSelected = 0;
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int numBoxes = boxes->sizeAt(0);
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int numSelected = 0;
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for (int i = 0; i < numBoxes; ++i) {
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bool shouldSelect = numSelected < output->lengthOf();
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PRAGMA_OMP_PARALLEL_FOR //_ARGS(firstprivate(numSelected))
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for (int j = numSelected - 1; j >= 0; --j) {
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if (shouldSelect)
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if (needToSuppressWithThreshold(*boxes, indices[i], indices[selectedIndices[j]], T(threshold))) {
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shouldSelect = false;
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}
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}
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if (shouldSelect) {
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output->p(numSelected, indices[i]);
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selectedIndices[numSelected++] = i;
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}
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}
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}
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void nonMaxSuppressionV2(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
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BUILD_SINGLE_SELECTOR(boxes->dataType(), nonMaxSuppressionV2_, (boxes, scales, maxSize, threshold, output), NUMERIC_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void nonMaxSuppressionV2_, (NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output), NUMERIC_TYPES);
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}
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}
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} |