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marcoslucianops
142
native/nvdsinfer_custom_impl_Yolo/yoloForward.cu
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142
native/nvdsinfer_custom_impl_Yolo/yoloForward.cu
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/*
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* Copyright (c) 2018-2019 NVIDIA Corporation. All rights reserved.
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*
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* NVIDIA Corporation and its licensors retain all intellectual property
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* and proprietary rights in and to this software, related documentation
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* and any modifications thereto. Any use, reproduction, disclosure or
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* distribution of this software and related documentation without an express
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* license agreement from NVIDIA Corporation is strictly prohibited.
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*
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* Edited by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*
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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(const float* input, float* output, const uint gridSize, const uint numOutputClasses,
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const uint numBBoxes, const uint new_coords, const float scale_x_y, char type)
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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 >= gridSize) || (y_id >= gridSize) || (z_id >= numBBoxes))
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{
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return;
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}
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const int numGridCells = gridSize * gridSize;
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const int bbindex = y_id * gridSize + x_id;
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float alpha = scale_x_y;
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float beta = -0.5 * (scale_x_y - 1);
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if (type == 'y') {
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if (new_coords == 1) {
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
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= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
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= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
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= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
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= pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
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= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
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for (uint i = 0; i < numOutputClasses; ++i)
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{
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
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= input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
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}
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}
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else {
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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;
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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;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
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= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
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= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
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for (uint i = 0; i < numOutputClasses; ++i)
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{
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
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}
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}
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}
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else if (type == 'r') {
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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;
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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;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
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= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
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= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
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float temp = 1.0;
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int i;
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float sum = 0;
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float largest = -INFINITY;
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for(i = 0; i < numOutputClasses; ++i){
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int val = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
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largest = (val>largest) ? val : largest;
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}
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for(i = 0; i < numOutputClasses; ++i){
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float e = exp(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] / temp - largest / temp);
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sum += e;
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] = e;
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}
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for(i = 0; i < numOutputClasses; ++i){
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output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] /= sum;
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}
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}
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}
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cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint& gridSize,
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const uint& numOutputClasses, const uint& numBBoxes,
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uint64_t outputSize, cudaStream_t stream, const uint new_coords, const float scale_x_y, char type);
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cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint& gridSize,
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const uint& numOutputClasses, const uint& numBBoxes,
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uint64_t outputSize, cudaStream_t stream, const uint new_coords, const float scale_x_y, char type)
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{
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dim3 threads_per_block(16, 16, 4);
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dim3 number_of_blocks((gridSize / threads_per_block.x) + 1,
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(gridSize / 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<<<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), gridSize, numOutputClasses,
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numBBoxes, new_coords, scale_x_y, type);
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}
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return cudaGetLastError();
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}
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