Fix YOLO kernels

- Fix YOLO kernels
- Update deprecated functions

nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu

@@ -0,0 +1,80 @@
+/*
+ * Created 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 gpuRegionLayer(const float* input, float* output, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
+                               const uint numBBoxes)
+{
+    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 >= gridSizeX) || (y_id >= gridSizeY) || (z_id >= numBBoxes))
+    {
+        return;
+    }
+
+    const int numGridCells = gridSizeX * gridSizeY;
+    const int bbindex = y_id * gridSizeX + x_id;
+
+    output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
+        = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]);
+
+    output[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
+        = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]);
+
+    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_v2(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
+                            const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream);
+
+cudaError_t cudaYoloLayer_v2(const void* input, void* output, const uint& batchSize, const uint& gridSizeX, const uint& gridSizeY,
+                            const uint& numOutputClasses, const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream)
+{
+    dim3 threads_per_block(16, 16, 4);
+    dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
+                          (gridSizeY / threads_per_block.y) + 1,
+                          (numBBoxes / threads_per_block.z) + 1);
+    for (unsigned int batch = 0; batch < batchSize; ++batch)
+    {
+        gpuRegionLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
+            reinterpret_cast<const float*>(input) + (batch * outputSize),
+            reinterpret_cast<float*>(output) + (batch * outputSize), gridSizeX, gridSizeY, numOutputClasses,
+            numBBoxes);
+    }
+    return cudaGetLastError();
+}