84 lines
3.6 KiB
Plaintext
84 lines
3.6 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 <stdint.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(const float* input, float* output, const uint netWidth, const uint netHeight,
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const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes,
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const uint64_t lastInputSize, 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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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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float xc = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta + x_id)
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* netWidth / gridSizeX;
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float yc = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta + y_id)
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* netHeight / gridSizeY;
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float w = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * anchors[mask[z_id] * 2];
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float h = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * anchors[mask[z_id] * 2 + 1];
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const float objectness = 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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float prob = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
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if (prob > maxProb) {
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maxProb = prob;
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maxIndex = i;
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}
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}
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int count = numGridCells * z_id + bbindex + lastInputSize;
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output[count * 6 + 0] = xc - w * 0.5;
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output[count * 6 + 1] = yc - h * 0.5;
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output[count * 6 + 2] = xc + w * 0.5;
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output[count * 6 + 3] = yc + h * 0.5;
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output[count * 6 + 4] = maxProb * objectness;
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output[count * 6 + 5] = (float) maxIndex;
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}
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cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint64_t& inputSize,
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const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth, const uint& netHeight,
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const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
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const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
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cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint64_t& inputSize,
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const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth, const uint& netHeight,
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const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
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const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
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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, (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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gpuYoloLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
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reinterpret_cast<const float*> (input) + (batch * inputSize),
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reinterpret_cast<float*> (output) + (batch * 6 * outputSize),
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netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize, scaleXY,
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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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