cavis/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu

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/*******************************************************************************
* 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 <NDArrayFactory.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static __device__ bool needToSuppressWithThreshold(T* boxes, Nd4jLong* boxesShape, int previousIndex, int nextIndex, T threshold) {
Nd4jLong previous0[] = {previousIndex, 0};
Nd4jLong previous1[] = {previousIndex, 1};
Nd4jLong previous2[] = {previousIndex, 2};
Nd4jLong previous3[] = {previousIndex, 3};
Nd4jLong next0[] = {nextIndex, 0};
Nd4jLong next1[] = {nextIndex, 1};
Nd4jLong next2[] = {nextIndex, 2};
Nd4jLong next3[] = {nextIndex, 3};
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)]);
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)]);
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)]);
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)]);
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)]);
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)]);
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)]);
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)]);
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;
};
template <typename T, typename I>
static __global__ void nonMaxSuppressionKernel(T* boxes, Nd4jLong* boxesShape, I* indices, int* selectedIndices, Nd4jLong numBoxes, I* output, Nd4jLong* outputShape, T threshold) {
__shared__ Nd4jLong outputLen;
if (threadIdx.x == 0) {
outputLen = shape::length(outputShape);
}
__syncthreads();
auto numSelected = blockIdx.x;
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
// for (int numSelected = blockIdx.x; numSelected < outputLen; numSelected += gridDim.x) {
for (int i = start; i < numBoxes; i += step) {
bool shouldSelect = true;
for (int j = numSelected - 1; shouldSelect && j >= 0; --j) {
if (needToSuppressWithThreshold<T>(boxes, boxesShape, indices[i], indices[selectedIndices[j]], threshold)) {
shouldSelect = false;
}
}
if (shouldSelect) {
auto zPos = shape::getIndexOffset(numSelected, outputShape, outputLen);
output[zPos] = indices[i];
selectedIndices[numSelected] = i;
}
}
}
template <typename T, typename I>
static __global__ void sortIndices(I* indices, Nd4jLong* indexShape, T* scores, Nd4jLong* scoreShape) {
__shared__ Nd4jLong len;
// __shared__ Nd4jLong* sortedPart;
// __shared__ Nd4jLong part;
// __shared__ Nd4jLong partSize;
if (threadIdx.x == 0) {
// blocksPerArr = (gridDim.x + numOfArrs - 1) / numOfArrs; // ceil
// part = blockIdx.x / blocksPerArr;
len = shape::length(indexShape);
// __shared__ Nd4jLong* shmem = shared[];
// sortedPart = shmem;
}
for (int m = 0; m < len; m++) {
if (m % 2 == 0) {
for (int tid = threadIdx.x; tid < len; tid += blockDim.x) {
auto top = 2 * tid + 1;
if (top < len) {
auto t0 = shape::getIndexOffset(top - 1, indexShape, len);
auto t1 = shape::getIndexOffset(top, indexShape, len);
auto z0 = shape::getIndexOffset(top - 1, scoreShape, len);
auto z1 = shape::getIndexOffset(top, scoreShape, len);
if (scores[t0] < scores[t1]) {
// swap indices first
Nd4jLong di0 = indices[t0];
indices[t0] = indices[t1];
indices[t1] = di0;
//swap scores next
// T dz0 = scores[z0];
// scores[z0] = scores[z1];
// scores[z1] = dz0;
}
}
}
} else {
for (int tid = threadIdx.x; tid < len; tid += blockDim.x) {
auto top = 2 * tid + 2;
if (top < len) {
auto t0 = shape::getIndexOffset(top - 1, indexShape, len);
auto t1 = shape::getIndexOffset(top, indexShape, len);
auto z0 = shape::getIndexOffset(top - 1, scoreShape, len);
auto z1 = shape::getIndexOffset(top, scoreShape, len);
if (scores[t0] < scores[t1]) {
// swap indices first
Nd4jLong di0 = indices[t0];
indices[t0] = indices[t1];
indices[t1] = di0;
//swap scores next
// T dz0 = scores[z0];
// scores[z0] = scores[z1];
// scores[z1] = dz0;
}
}
}
}
__syncthreads();
}
}
template <typename T, typename I>
static void nonMaxSuppressionV2_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({output}, {boxes, scales});
NDArray* indices = NDArrayFactory::create_<I>('c', {scales->lengthOf()}); // - 1, scales->lengthOf()); //, scales->getContext());
indices->linspace(0);
NDArray scores(*scales);
indices->syncToHost(); //linspace(0);
I* indexBuf = reinterpret_cast<I*>(indices->specialBuffer());
T* scoreBuf = reinterpret_cast<T*>(scores.specialBuffer());
sortIndices<T, I><<<1, 32, 128, *stream>>>(indexBuf, indices->specialShapeInfo(), scoreBuf, scores.specialShapeInfo());
// TO DO: sort indices using scales as value row
//std::sort(indices.begin(), indices.end(), [scales](int i, int j) {return scales->e<T>(i) > scales->e<T>(j);});
indices->tickWriteDevice();
indices->syncToHost();
indices->printIndexedBuffer("AFTERSORT OUTPUT");
NDArray selected = NDArrayFactory::create<int>({output->lengthOf()});
NDArray selectedIndices = NDArrayFactory::create<int>({output->lengthOf()});
int numSelected = 0;
int numBoxes = boxes->sizeAt(0);
T* boxesBuf = reinterpret_cast<T*>(boxes->specialBuffer());
// Nd4jLong* indicesData = reinterpret_cast<Nd4jLong*>(indices->specialBuffer());
// int* selectedData = reinterpret_cast<int*>(selected.specialBuffer());
int* selectedIndicesData = reinterpret_cast<int*>(selectedIndices.specialBuffer());
I* outputBuf = reinterpret_cast<I*>(output->specialBuffer());
nonMaxSuppressionKernel<T, I><<<output->lengthOf(), 512, 1024, *stream>>>(boxesBuf, boxes->specialShapeInfo(), indexBuf, selectedIndicesData, numBoxes, outputBuf, output->specialShapeInfo(), T(threshold));
NDArray::registerSpecialUse({output}, {boxes, scales});
// for (int i = 0; i < boxes->sizeAt(0); ++i) {
// if (selected.size() >= output->lengthOf()) break;
// bool shouldSelect = true;
// // Overlapping boxes are likely to have similar scores,
// // therefore we iterate through the selected boxes backwards.
// for (int j = numSelected - 1; j >= 0; --j) {
// if (needToSuppressWithThreshold(*boxes, indices[i], indices[selectedIndices[j]], T(threshold)) {
// shouldSelect = false;
// break;
// }
// }
// if (shouldSelect) {
// selected.push_back(indices[i]);
// selectedIndices[numSelected++] = i;
// }
// }
// for (size_t e = 0; e < selected.size(); ++e)
// output->p<int>(e, selected[e]);
//
delete indices;
}
void nonMaxSuppressionV2(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
BUILD_DOUBLE_SELECTOR(boxes->dataType(), output->dataType(), nonMaxSuppressionV2_, (context, boxes, scales, maxSize, threshold, output), FLOAT_TYPES, INTEGER_TYPES);
}
BUILD_DOUBLE_TEMPLATE(template void nonMaxSuppressionV2_, (nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output), FLOAT_TYPES, INTEGER_TYPES);
}
}
}