217 lines
11 KiB
Plaintext
217 lines
11 KiB
Plaintext
/*******************************************************************************
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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 <NDArrayFactory.h>
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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 __device__ bool needToSuppressWithThreshold(T* boxes, Nd4jLong* boxesShape, int previousIndex, int nextIndex, T threshold) {
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Nd4jLong previous0[] = {previousIndex, 0};
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Nd4jLong previous1[] = {previousIndex, 1};
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Nd4jLong previous2[] = {previousIndex, 2};
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Nd4jLong previous3[] = {previousIndex, 3};
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Nd4jLong next0[] = {nextIndex, 0};
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Nd4jLong next1[] = {nextIndex, 1};
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Nd4jLong next2[] = {nextIndex, 2};
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Nd4jLong next3[] = {nextIndex, 3};
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T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous0, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous2, 2)]);
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T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous1, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous3, 2)]);
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T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous0, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous2, 2)]);
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T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous1, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), previous3, 2)]);
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T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next0, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next2, 2)]);
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T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next1, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next3, 2)]);
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T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next0, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next2, 2)]);
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T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next1, 2)], boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), next3, 2)]);
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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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template <typename T, typename I>
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static __global__ void nonMaxSuppressionKernel(T* boxes, Nd4jLong* boxesShape, I* indices, int* selectedIndices, Nd4jLong numBoxes, I* output, Nd4jLong* outputShape, T threshold) {
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__shared__ Nd4jLong outputLen;
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if (threadIdx.x == 0) {
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outputLen = shape::length(outputShape);
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}
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__syncthreads();
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auto numSelected = blockIdx.x;
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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// for (int numSelected = blockIdx.x; numSelected < outputLen; numSelected += gridDim.x) {
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for (int i = start; i < numBoxes; i += step) {
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bool shouldSelect = true;
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for (int j = numSelected - 1; shouldSelect && j >= 0; --j) {
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if (needToSuppressWithThreshold<T>(boxes, boxesShape, indices[i], indices[selectedIndices[j]], 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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auto zPos = shape::getIndexOffset(numSelected, outputShape, outputLen);
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output[zPos] = 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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template <typename T, typename I>
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static __global__ void sortIndices(I* indices, Nd4jLong* indexShape, T* scores, Nd4jLong* scoreShape) {
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__shared__ Nd4jLong len;
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// __shared__ Nd4jLong* sortedPart;
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// __shared__ Nd4jLong part;
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// __shared__ Nd4jLong partSize;
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if (threadIdx.x == 0) {
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// blocksPerArr = (gridDim.x + numOfArrs - 1) / numOfArrs; // ceil
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// part = blockIdx.x / blocksPerArr;
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len = shape::length(indexShape);
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// __shared__ Nd4jLong* shmem = shared[];
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// sortedPart = shmem;
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}
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for (int m = 0; m < len; m++) {
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if (m % 2 == 0) {
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for (int tid = threadIdx.x; tid < len; tid += blockDim.x) {
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auto top = 2 * tid + 1;
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if (top < len) {
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auto t0 = shape::getIndexOffset(top - 1, indexShape, len);
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auto t1 = shape::getIndexOffset(top, indexShape, len);
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auto z0 = shape::getIndexOffset(top - 1, scoreShape, len);
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auto z1 = shape::getIndexOffset(top, scoreShape, len);
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if (scores[t0] < scores[t1]) {
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// swap indices first
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Nd4jLong di0 = indices[t0];
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indices[t0] = indices[t1];
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indices[t1] = di0;
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//swap scores next
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// T dz0 = scores[z0];
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// scores[z0] = scores[z1];
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// scores[z1] = dz0;
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}
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}
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}
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} else {
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for (int tid = threadIdx.x; tid < len; tid += blockDim.x) {
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auto top = 2 * tid + 2;
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if (top < len) {
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auto t0 = shape::getIndexOffset(top - 1, indexShape, len);
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auto t1 = shape::getIndexOffset(top, indexShape, len);
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auto z0 = shape::getIndexOffset(top - 1, scoreShape, len);
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auto z1 = shape::getIndexOffset(top, scoreShape, len);
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if (scores[t0] < scores[t1]) {
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// swap indices first
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Nd4jLong di0 = indices[t0];
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indices[t0] = indices[t1];
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indices[t1] = di0;
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//swap scores next
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// T dz0 = scores[z0];
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// scores[z0] = scores[z1];
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// scores[z1] = dz0;
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}
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}
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}
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}
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__syncthreads();
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}
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}
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template <typename T, typename I>
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static void nonMaxSuppressionV2_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
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auto stream = context->getCudaStream();
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NDArray::prepareSpecialUse({output}, {boxes, scales});
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NDArray* indices = NDArrayFactory::create_<I>('c', {scales->lengthOf()}); // - 1, scales->lengthOf()); //, scales->getContext());
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indices->linspace(0);
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NDArray scores(*scales);
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indices->syncToHost(); //linspace(0);
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I* indexBuf = reinterpret_cast<I*>(indices->specialBuffer());
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T* scoreBuf = reinterpret_cast<T*>(scores.specialBuffer());
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sortIndices<T, I><<<1, 32, 128, *stream>>>(indexBuf, indices->specialShapeInfo(), scoreBuf, scores.specialShapeInfo());
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// TO DO: sort indices using scales as value row
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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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indices->tickWriteDevice();
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indices->syncToHost();
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indices->printIndexedBuffer("AFTERSORT OUTPUT");
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NDArray selected = NDArrayFactory::create<int>({output->lengthOf()});
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NDArray selectedIndices = NDArrayFactory::create<int>({output->lengthOf()});
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int numSelected = 0;
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int numBoxes = boxes->sizeAt(0);
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T* boxesBuf = reinterpret_cast<T*>(boxes->specialBuffer());
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// Nd4jLong* indicesData = reinterpret_cast<Nd4jLong*>(indices->specialBuffer());
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// int* selectedData = reinterpret_cast<int*>(selected.specialBuffer());
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int* selectedIndicesData = reinterpret_cast<int*>(selectedIndices.specialBuffer());
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I* outputBuf = reinterpret_cast<I*>(output->specialBuffer());
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nonMaxSuppressionKernel<T, I><<<output->lengthOf(), 512, 1024, *stream>>>(boxesBuf, boxes->specialShapeInfo(), indexBuf, selectedIndicesData, numBoxes, outputBuf, output->specialShapeInfo(), T(threshold));
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NDArray::registerSpecialUse({output}, {boxes, scales});
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// for (int i = 0; i < boxes->sizeAt(0); ++i) {
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// if (selected.size() >= output->lengthOf()) break;
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// bool shouldSelect = true;
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// // Overlapping boxes are likely to have similar scores,
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// // therefore we iterate through the selected boxes backwards.
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// for (int j = numSelected - 1; j >= 0; --j) {
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// if (needToSuppressWithThreshold(*boxes, indices[i], indices[selectedIndices[j]], T(threshold)) {
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// shouldSelect = false;
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// break;
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// }
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// }
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// if (shouldSelect) {
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// selected.push_back(indices[i]);
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// selectedIndices[numSelected++] = i;
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// }
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// }
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// for (size_t e = 0; e < selected.size(); ++e)
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// output->p<int>(e, selected[e]);
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//
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delete indices;
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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_DOUBLE_SELECTOR(boxes->dataType(), output->dataType(), nonMaxSuppressionV2_, (context, boxes, scales, maxSize, threshold, output), FLOAT_TYPES, INTEGER_TYPES);
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}
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BUILD_DOUBLE_TEMPLATE(template void nonMaxSuppressionV2_, (nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output), FLOAT_TYPES, INTEGER_TYPES);
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}
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}
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} |