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>
#include <NativeOps.h>
#include <cuda_exception.h>
#include <queue>
namespace nd4j {
namespace ops {
namespace helpers {
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// needToSuppressWithThreshold - predicate for suppression
// boxes - boxes tensor buffer
// boxesShape boxes tensor shape
// previousIndex - index for current pos value
// nextIndex - index for neighbor pos value
// threshold - threashold value to suppress
//
// return value: true, if threshold is overcome, false otherwise
//
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};
// we have rectangle with given max values. Compute vexes of rectangle first
T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]);
T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]);
T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]);
T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]);
T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]);
T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]);
T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]);
T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]);
// compute areas for comparation
T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev);
T areaNext = (maxYNext - minYNext) * (maxXNext - minXNext);
// of course, areas should be positive
if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false;
// compute intersection of rectangles
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);
// final check
return intersectionValue > threshold;
}
template <typename T>
static __device__ T similirityV3(T* boxes, Nd4jLong* boxesShape, int previousIndex, int nextIndex) {
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};
// we have rectangle with given max values. Compute vexes of rectangle first
T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]);
T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]);
T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]);
T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]);
T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]);
T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]);
T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]);
T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]);
// compute areas for comparation
T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev);
T areaNext = (maxYNext - minYNext) * (maxXNext - minXNext);
// of course, areas should be positive
if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false;
// compute intersection of rectangles
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);
// final check
return intersectionValue;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// shouldSelectKernel - compute status for all selected rectangles (boxes)
//
// we compute boolean flag as shared uint32 and return it on final only for the first thread
//
template <typename T, typename I>
static __global__ void shouldSelectKernel(T* boxesBuf, Nd4jLong* boxesShape, I* indexBuf, I* selectedIndicesData, double threshold, int numSelected, int i, bool* shouldSelect) {
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
auto step = gridDim.x * blockDim.x;
__shared__ unsigned int shouldSelectShared;
if (threadIdx.x == 0) {
shouldSelectShared = (unsigned int)shouldSelect[0];
}
__syncthreads();
for (int j = numSelected - 1 - tid; j >= 0; j -= step) {
if (shouldSelectShared) {
if (needToSuppressWithThreshold(boxesBuf, boxesShape, indexBuf[i],
indexBuf[selectedIndicesData[j]], T(threshold)))
atomicCAS(&shouldSelectShared, 1, 0); // exchange only when need to suppress
}
}
__syncthreads();
// final move: collect result
if (threadIdx.x == 0) {
*shouldSelect = shouldSelectShared > 0;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// indices - type depended, indicesLong - type defined (only 64bit integers)
//
template <typename I>
static __global__ void copyIndices(void* indices, void* indicesLong, Nd4jLong len) {
I* indexBuf = reinterpret_cast<I*>(indices);
Nd4jLong* srcBuf = reinterpret_cast<Nd4jLong*>(indicesLong);;
auto tid = threadIdx.x + blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (auto i = tid; i < len; i += step)
indexBuf[i] = (I)srcBuf[i];
}
template <typename T, typename I>
static __global__ void suppressScores(T* scores, I* indices, Nd4jLong length, T scoreThreshold) {
auto start = blockIdx.x * blockDim.x;
auto step = gridDim.x * blockDim.x;
for (auto e = start + threadIdx.x; e < (int)length; e += step) {
if (scores[e] < scoreThreshold) {
scores[e] = scoreThreshold;
indices[e] = -1;
}
else {
indices[e] = I(e);
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// nonMaxSuppressionV2 algorithm - given from TF NonMaxSuppressionV2 implementation
//
template <typename T, typename I>
static void nonMaxSuppressionV2_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) {
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({output}, {boxes, scales});
std::unique_ptr<NDArray> indices(NDArrayFactory::create_<I>('c', {scales->lengthOf()})); // - 1, scales->lengthOf()); //, scales->getContext());
NDArray scores(*scales);
Nd4jPointer extras[2] = {nullptr, stream};
auto indexBuf = indices->dataBuffer()->specialAsT<I>();///reinterpret_cast<I*>(indices->specialBuffer());
auto scoreBuf = scores.dataBuffer()->specialAsT<T>();
suppressScores<T,I><<<128, 128, 128, *stream>>>(scoreBuf, indexBuf, scores.lengthOf(), T(scoreThreshold));
indices->tickWriteDevice();
sortByValue(extras, indices->buffer(), indices->shapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(), scores.buffer(), scores.shapeInfo(), scores.specialBuffer(), scores.specialShapeInfo(), true);
indices->tickWriteDevice();
NDArray selectedIndices = NDArrayFactory::create<I>('c', {output->lengthOf()});
int numSelected = 0;
int numBoxes = boxes->sizeAt(0);
auto boxesBuf = reinterpret_cast<T*>(boxes->specialBuffer());
auto selectedIndicesData = reinterpret_cast<I*>(selectedIndices.specialBuffer());
auto outputBuf = reinterpret_cast<I*>(output->specialBuffer());
bool* shouldSelectD;
auto err = cudaMalloc(&shouldSelectD, sizeof(bool));
if (err) {
throw cuda_exception::build("helpers::nonMaxSuppressionV2: Cannot allocate memory for bool flag", err);
}
for (I i = 0; i < boxes->sizeAt(0); ++i) {
bool shouldSelect = numSelected < output->lengthOf();
if (shouldSelect) {
err = cudaMemcpy(shouldSelectD, &shouldSelect, sizeof(bool), cudaMemcpyHostToDevice);
if (err) {
throw cuda_exception::build("helpers::nonMaxSuppressionV2: Cannot set up bool flag to device", err);
}
shouldSelectKernel<T,I><<<128, 256, 1024, *stream>>>(boxesBuf, boxes->specialShapeInfo(), indexBuf, selectedIndicesData, threshold, numSelected, i, shouldSelectD);
err = cudaMemcpy(&shouldSelect, shouldSelectD, sizeof(bool), cudaMemcpyDeviceToHost);
if (err) {
throw cuda_exception::build("helpers::nonMaxSuppressionV2: Cannot set up bool flag to host", err);
}
}
if (shouldSelect) {
cudaMemcpy(reinterpret_cast<I*>(output->specialBuffer()) + numSelected, indexBuf + i, sizeof(I), cudaMemcpyDeviceToDevice);
cudaMemcpy(selectedIndicesData + numSelected, &i, sizeof(I), cudaMemcpyHostToDevice);
numSelected++;
}
}
err = cudaFree(shouldSelectD);
if (err) {
throw cuda_exception::build("helpers::nonMaxSuppressionV2: Cannot deallocate memory for bool flag", err);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T, typename I>
static __device__ bool checkOverlapBoxes(T* boxes, Nd4jLong* shape, T* scores, I* indices, I* selectedIndices, I* startIndices, I selectedSize, I nextCandidateIndex, T overlapThreshold, T scoreThreshold, bool simple) {
bool shouldHardSuppress = false;
T& nextCandidateScore = scores[nextCandidateIndex];
I selectedIndex = indices[nextCandidateIndex];
I finish = startIndices[nextCandidateIndex];
for (int j = selectedSize; j > finish; --j) {
T boxVal;
if (simple) {
Nd4jLong xPos[] = {selectedIndex, selectedIndices[j - 1]};
auto xShift = shape::getOffset(shape, xPos, 0);
boxVal = boxes[xShift];
}
else {
boxVal = similirityV3(boxes, shape, selectedIndex, selectedIndices[j - 1]);
}
if (boxVal > static_cast<T>(overlapThreshold))
nextCandidateScore = static_cast<T>(0.f);
// First decide whether to perform hard suppression
if (boxVal >= overlapThreshold) {
shouldHardSuppress = true;
break;
}
// If nextCandidate survives hard suppression, apply soft suppression
if (nextCandidateScore <= static_cast<T>(scoreThreshold)) break;
}
return shouldHardSuppress;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T, typename I>
static __global__ void
suppressNonMaxOverlapKernel(T* boxes, Nd4jLong* boxesShape, T* scoresData, I* indices, I* startIndices, Nd4jLong length, I maxOutputLen,
T overlapThreshold, T scoreThreshold, I* output, Nd4jLong* outputShape, I* outputLength, bool simple) {
__shared__ I selectedSize;
__shared__ I* tempOutput;
if (threadIdx.x == 0) {
selectedSize = outputLength?*outputLength:maxOutputLen;
extern __shared__ unsigned char shmem[];
tempOutput = (I*)shmem;
}
__syncthreads();
auto start = blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (I nextCandidateIndex = start + threadIdx.x; selectedSize < maxOutputLen && nextCandidateIndex < (I)length; ) {
auto originalScore = scoresData[nextCandidateIndex];//nextCandidate._score;
I nextCandidateBoxIndex = indices[nextCandidateIndex];
auto selectedSizeMark = selectedSize;
// skip for cases when index is less than 0 (under score threshold)
if (nextCandidateBoxIndex < 0) {
nextCandidateIndex += step;
continue;
}
// check for overlaps
bool shouldHardSuppress = checkOverlapBoxes(boxes, boxesShape, scoresData, indices, tempOutput, startIndices, selectedSize,
nextCandidateIndex, overlapThreshold, scoreThreshold, simple);//false;
T nextCandidateScore = scoresData[nextCandidateIndex];
startIndices[nextCandidateIndex] = selectedSize;
if (!shouldHardSuppress) {
if (nextCandidateScore == originalScore) {
// Suppression has not occurred, so select nextCandidate
if (output)
output[selectedSize] = nextCandidateBoxIndex;
tempOutput[selectedSize] = nextCandidateBoxIndex;
math::atomics::nd4j_atomicAdd(&selectedSize, (I)1);
}
if (nextCandidateScore > scoreThreshold) {
// Soft suppression has occurred and current score is still greater than
// scoreThreshold; add nextCandidate back onto priority queue.
continue; // in some cases, this index not 0
}
}
nextCandidateIndex += step;
}
if (threadIdx.x == 0) {
if (outputLength)
*outputLength = selectedSize;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T, typename I>
static Nd4jLong
nonMaxSuppressionGeneric_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int outputSize,
double overlapThreshold, double scoreThreshold, NDArray* output, bool simple) {
auto stream = context->getCudaStream();
if (output)
NDArray::prepareSpecialUse({output}, {boxes, scores});
else {
if (!boxes->isActualOnDeviceSide())
boxes->syncToDevice();
if (!scores->isActualOnDeviceSide())
scores->syncToDevice();
}
NDArray indices = NDArrayFactory::create<I>('c', {scores->lengthOf()}); // - 1, scales->lengthOf()); //, scales->getContext());
NDArray startPositions = NDArrayFactory::create<I>('c', {scores->lengthOf()});
NDArray selectedScores(*scores);
Nd4jPointer extras[2] = {nullptr, stream};
auto indexBuf = indices.dataBuffer()->specialAsT<I>();///reinterpret_cast<I*>(indices->specialBuffer());
suppressScores<<<128, 128, 128, *stream>>>(selectedScores.dataBuffer()->specialAsT<T>(), indexBuf, selectedScores.lengthOf(), T(scoreThreshold));
sortByValue(extras, indices.buffer(), indices.shapeInfo(), indices.specialBuffer(), indices.specialShapeInfo(), selectedScores.buffer(), selectedScores.shapeInfo(), selectedScores.specialBuffer(), selectedScores.specialShapeInfo(), true);
indices.tickWriteDevice();
selectedScores.tickWriteDevice();
auto scoresData = selectedScores.dataBuffer()->specialAsT<T>();//, numBoxes, scoresData.begin());
auto startIndices = startPositions.dataBuffer()->specialAsT<I>();
I selectedSize = 0;
Nd4jLong res = 0;
if (output) { // this part used when output shape already calculated to fill up values on output
DataBuffer selectedSizeBuf(&selectedSize, sizeof(I), DataTypeUtils::fromT<I>());
suppressNonMaxOverlapKernel<<<1, 1, 1024, *stream >>> (boxes->dataBuffer()->specialAsT<T>(),
boxes->specialShapeInfo(), scoresData, indexBuf, startIndices, scores->lengthOf(), (I) outputSize,
T(overlapThreshold), T(scoreThreshold), output->dataBuffer()->specialAsT<I>(), output->specialShapeInfo(),
selectedSizeBuf.specialAsT<I>(), simple);
}
else { // this case used on calculation of output shape. Output and output shape shoulde be nullptr.
DataBuffer selectedSizeBuf(&selectedSize, sizeof(I), DataTypeUtils::fromT<I>());
suppressNonMaxOverlapKernel<<<1, 1, 1024, *stream >>> (boxes->dataBuffer()->specialAsT<T>(),
boxes->specialShapeInfo(), scoresData, indexBuf, startIndices, scores->lengthOf(), (I)outputSize,
T(overlapThreshold), T(scoreThreshold), (I*)nullptr, (Nd4jLong*) nullptr, selectedSizeBuf.specialAsT<I>(), simple);
selectedSizeBuf.syncToPrimary(context, true);
res = *selectedSizeBuf.primaryAsT<I>();
}
if (output)
NDArray::registerSpecialUse({output}, {boxes, scores});
return res;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
void nonMaxSuppression(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) {
BUILD_DOUBLE_SELECTOR(boxes->dataType(), output->dataType(), nonMaxSuppressionV2_,
(context, boxes, scales, maxSize, threshold, scoreThreshold, output),
FLOAT_TYPES, INDEXING_TYPES);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
Nd4jLong nonMaxSuppressionGeneric(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) {
BUILD_DOUBLE_SELECTOR(boxes->dataType(), output ? output->dataType():DataType::INT32, return nonMaxSuppressionGeneric_,
(context, boxes, scales, maxSize, threshold, scoreThreshold, output, true),
FLOAT_TYPES, INDEXING_TYPES);
return boxes->sizeAt(0);
}
Nd4jLong
nonMaxSuppressionV3(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize,
double overlapThreshold, double scoreThreshold, NDArray* output) {
BUILD_DOUBLE_SELECTOR(boxes->dataType(), output ? output->dataType():DataType::INT32, return nonMaxSuppressionGeneric_,
(context, boxes, scores, maxSize, overlapThreshold, scoreThreshold, output, false),
FLOAT_TYPES, INDEXING_TYPES);
return boxes->sizeAt(0);
}
}
}
}