90 lines
3.7 KiB
Plaintext
90 lines
3.7 KiB
Plaintext
/*
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* Created by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*/
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <string.h>
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inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
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__global__ void gpuYoloLayer_r(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
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const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
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{
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uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
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uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
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uint z_id = blockIdx.z * blockDim.z + threadIdx.z;
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if ((x_id >= gridSizeX) || (y_id >= gridSizeY) || (z_id >= numBBoxes))
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{
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return;
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}
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const int numGridCells = gridSizeX * gridSizeY;
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const int bbindex = y_id * gridSizeX + x_id;
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const float alpha = scaleXY;
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const float beta = -0.5 * (scaleXY - 1);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta + x_id;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta + y_id;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
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= __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2) * anchors[mask[z_id] * 2];
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
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= __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2) * anchors[mask[z_id] * 2 + 1];
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const float objectness
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
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float maxProb = 0.0f;
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int maxIndex = -1;
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for (uint i = 0; i < numOutputClasses; ++i)
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{
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float prob
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
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if (prob > maxProb)
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{
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maxProb = prob;
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maxIndex = i;
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}
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}
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
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= objectness * maxProb;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 5)]
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= maxIndex;
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}
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cudaError_t cudaYoloLayer_r(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
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const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
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const float scaleXY, const void* anchors, const void* mask);
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cudaError_t cudaYoloLayer_r(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
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const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream,
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const float scaleXY, const void* anchors, const void* mask)
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{
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dim3 threads_per_block(16, 16, 4);
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dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
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(gridSizeY / threads_per_block.y) + 1,
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(numBBoxes / threads_per_block.z) + 1);
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for (unsigned int batch = 0; batch < batchSize; ++batch)
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{
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gpuYoloLayer_r<<<number_of_blocks, threads_per_block, 0, stream>>>(
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reinterpret_cast<const float*>(input) + (batch * outputSize),
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reinterpret_cast<float*>(output) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
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numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
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}
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return cudaGetLastError();
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}
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