/*
 * Copyright (c) 2018-2019 NVIDIA Corporation.  All rights reserved.
 *
 * NVIDIA Corporation and its licensors retain all intellectual property
 * and proprietary rights in and to this software, related documentation
 * and any modifications thereto.  Any use, reproduction, disclosure or
 * distribution of this software and related documentation without an express
 * license agreement from NVIDIA Corporation is strictly prohibited.
 *
 * Edited by Marcos Luciano
 * https://www.github.com/marcoslucianops
 *
 */

#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
#include <stdio.h>
#include <string.h>

inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }

__global__ void gpuYoloLayer(const float* input, float* output, const uint gridSize, const uint numOutputClasses,
                               const uint numBBoxes, const uint new_coords, const float scale_x_y, char type)
{
    uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
    uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
    uint z_id = blockIdx.z * blockDim.z + threadIdx.z;

    if ((x_id >= gridSize) || (y_id >= gridSize) || (z_id >= numBBoxes))
    {
        return;
    }

    const int numGridCells = gridSize * gridSize;
    const int bbindex = y_id * gridSize + x_id;

    float alpha = scale_x_y;
    float beta = -0.5 * (scale_x_y - 1);

    if (type == 'y') {
        if (new_coords == 1) {
            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
                = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta;

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
                = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta;

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
                = pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2);

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
                = pow(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2);

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
                = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];

            for (uint i = 0; i < numOutputClasses; ++i)
            {
                output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
                    = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
            }
        }
        else {
            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
                = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta;

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
                = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta;

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
                = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
                = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);

            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
                = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);

            for (uint i = 0; i < numOutputClasses; ++i)
            {
                output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]
                    = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
            }
        }
    }
    else if (type == 'r') {
        output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
            = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta;

        output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
            = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta;

        output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]
            = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]);

        output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]
            = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]);

        output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]
            = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);

        float temp = 1.0;
        int i;
        float sum = 0;
        float largest = -INFINITY;
        for(i = 0; i < numOutputClasses; ++i){
            int val = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
            largest = (val>largest) ? val : largest;
        }
        for(i = 0; i < numOutputClasses; ++i){
            float e = exp(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] / temp - largest / temp);
            sum += e;
            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] = e;
        }
        for(i = 0; i < numOutputClasses; ++i){
            output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))] /= sum;
        }
    }
}

cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint& gridSize,
                            const uint& numOutputClasses, const uint& numBBoxes,
                            uint64_t outputSize, cudaStream_t stream, const uint new_coords, const float scale_x_y, char type);

cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint& gridSize,
                            const uint& numOutputClasses, const uint& numBBoxes,
                            uint64_t outputSize, cudaStream_t stream, const uint new_coords, const float scale_x_y, char type)
{
    dim3 threads_per_block(16, 16, 4);
    dim3 number_of_blocks((gridSize / threads_per_block.x) + 1,
                          (gridSize / threads_per_block.y) + 1,
                          (numBBoxes / threads_per_block.z) + 1);
    for (unsigned int batch = 0; batch < batchSize; ++batch)
    {
        gpuYoloLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
            reinterpret_cast<const float*>(input) + (batch * outputSize),
            reinterpret_cast<float*>(output) + (batch * outputSize), gridSize, numOutputClasses,
            numBBoxes, new_coords, scale_x_y, type);
    }
    return cudaGetLastError();
}