Refactored cpu implementatio and added cuda aproach.
parent
78443ffebf
commit
6cf3a8fa9c
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@ -32,28 +32,25 @@ namespace helpers {
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// height = images->sizeAt(1), width = images->sizeAt(2)
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// colors - colors for each box given
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// set up color for each box as frame
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auto batchSize = images->sizeAt(0);
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auto height = images->sizeAt(1);
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auto width = images->sizeAt(2);
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auto channels = images->sizeAt(3);
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auto imageList = images->allTensorsAlongDimension({1, 2, 3}); // split images by batch
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auto boxList = boxes->allTensorsAlongDimension({1, 2}); // split boxes by batch
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output->assign(images);
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for (auto b = 0; b < imageList->size(); ++b) { // loop by batch
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// auto image = imageList->at(b);
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auto box = boxList->at(b);
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auto internalBoxes = box->allTensorsAlongDimension({1});
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//auto imageList = images->allTensorsAlongDimension({1, 2, 3}); // split images by batch
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// auto boxList = boxes->allTensorsAlongDimension({1, 2}); // split boxes by batch
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auto colorSet = colors->allTensorsAlongDimension({1});
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output->assign(images); // fill up all output with input images, then fill up boxes
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for (auto b = 0; b < batchSize; ++b) { // loop by batch
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// auto image = imageList->at(b);
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for (auto c = 0; c < colorSet->size(); ++c) {
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// box with shape
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auto internalBox = internalBoxes->at(c);
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auto internalBox = (*boxes)(b, {0})(c, {0});//internalBoxes->at(c);
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auto color = colorSet->at(c);
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auto rowStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((height - 1) * internalBox->e<float>(0)));
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auto rowEnd = nd4j::math::nd4j_min(Nd4jLong (height - 1), Nd4jLong ((height - 1) * internalBox->e<float>(2)));
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auto colStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((width - 1) * internalBox->e<float>(1)));
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auto colEnd = nd4j::math::nd4j_min(Nd4jLong(width - 1), Nd4jLong ((width - 1) * internalBox->e<float>(3)));
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auto rowStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((height - 1) * internalBox.e<float>(0)));
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auto rowEnd = nd4j::math::nd4j_min(Nd4jLong (height - 1), Nd4jLong ((height - 1) * internalBox.e<float>(2)));
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auto colStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((width - 1) * internalBox.e<float>(1)));
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auto colEnd = nd4j::math::nd4j_min(Nd4jLong(width - 1), Nd4jLong ((width - 1) * internalBox.e<float>(3)));
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for (auto y = rowStart; y <= rowEnd; y++) {
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for (auto e = 0; e < color->lengthOf(); ++e) {
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output->p(b, y, colStart, e, color->e(e));
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@ -67,11 +64,11 @@ namespace helpers {
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}
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}
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}
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delete colorSet;
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delete internalBoxes;
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// delete internalBoxes;
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}
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delete imageList;
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delete boxList;
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delete colorSet;
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// delete imageList;
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// delete boxList;
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}
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}
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@ -24,9 +24,61 @@ namespace nd4j {
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namespace ops {
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namespace helpers {
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void drawBoundingBoxesFunctor(nd4j::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output) {
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template <typename T>
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static __global__ void drawBoundignBoxesKernel(T const* images, Nd4jLong* imagesShape, T const* boxes,
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Nd4jLong* boxesShape, T const* colors, Nd4jLong* colorsShape, T* output, Nd4jLong* outputShape) {
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for (auto b = 0; b < batchSize; ++b) { // loop by batch
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for (auto c = 0; c < colorSet->size(); ++c) {
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// box with shape
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auto internalBox = (*boxes)(b, {0})(c, {0});//internalBoxes->at(c);
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auto color = colorSet->at(c);
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auto rowStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((height - 1) * internalBox.e<float>(0)));
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auto rowEnd = nd4j::math::nd4j_min(Nd4jLong (height - 1), Nd4jLong ((height - 1) * internalBox.e<float>(2)));
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auto colStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((width - 1) * internalBox.e<float>(1)));
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auto colEnd = nd4j::math::nd4j_min(Nd4jLong(width - 1), Nd4jLong ((width - 1) * internalBox.e<float>(3)));
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for (auto y = rowStart; y <= rowEnd; y++) {
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for (auto e = 0; e < color->lengthOf(); ++e) {
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output->p(b, y, colStart, e, color->e(e));
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output->p(b, y, colEnd, e, color->e(e));
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}
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}
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for (auto x = colStart + 1; x < colEnd; x++) {
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for (auto e = 0; e < color->lengthOf(); ++e) {
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output->p(b, rowStart, x, e, color->e(e));
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output->p(b, rowEnd, x, e, color->e(e));
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}
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}
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}
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}
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}
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template <typename T>
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void drawBoundingBoxesH(nd4j::LaunchContext* context, NDArray const* images, NDArray const* boxes, NDArray const* colors, NDArray* output) {
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drawBoundingBoxesKernel<<<128, 256, 1024, *stream>>>(imagesBuf, imagesShape, boxesBuf, boxesShape, colorsBuf, colorsShape,
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outputBuf, outputShape, );
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}
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void drawBoundingBoxesFunctor(nd4j::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output) {
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// images - batch of 3D images with BW (last dim = 1), RGB (last dim = 3) or RGBA (last dim = 4) channel set
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// boxes - batch of 2D bounds with last dim (y_start, x_start, y_end, x_end) to compute i and j as
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// floor((height - 1 ) * y_start) => rowStart, floor((height - 1) * y_end) => rowEnd
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// floor((width - 1 ) * x_start) => colStart, floor((width - 1) * x_end) => colEnd
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// height = images->sizeAt(1), width = images->sizeAt(2)
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// colors - colors for each box given
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// set up color for each box as frame
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auto height = images->sizeAt(1);
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auto width = images->sizeAt(2);
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auto channels = images->sizeAt(3);
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auto imageList = images->allTensorsAlongDimension({1, 2, 3}); // split images by batch
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auto boxList = boxes->allTensorsAlongDimension({1, 2}); // split boxes by batch
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auto colorSet = colors->allTensorsAlongDimension({1});
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output->assign(images);
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delete colorSet;
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delete imageList;
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delete boxList;
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}
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}
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}
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@ -2072,8 +2072,8 @@ TEST_F(DeclarableOpsTests10, Image_DrawBoundingBoxes_1) {
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto result = results->at(0);
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result->printBuffer("Bounded boxes");
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expected.printBuffer("Bounded expec");
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// result->printBuffer("Bounded boxes");
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// expected.printBuffer("Bounded expec");
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ASSERT_TRUE(expected.isSameShapeStrict(result));
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ASSERT_TRUE(expected.equalsTo(result));
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