Add YOLOv8 support
This commit is contained in:
Marcos Luciano
@@ -7,119 +7,100 @@
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inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
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__device__ void softmaxGPU(
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const float* input, const int bbindex, const int numGridCells, uint z_id, const uint numOutputClasses, float temp,
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float* output)
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__device__ void softmaxGPU(const float* input, const int bbindex, const int numGridCells, uint z_id,
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const uint numOutputClasses, float temp, float* output)
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{
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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 = __expf(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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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 = __expf(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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__global__ void gpuRegionLayer(
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const float* input, float* softmax, int* num_detections, float* detection_boxes, float* detection_scores,
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int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX,
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const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float* anchors)
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__global__ void gpuRegionLayer(const float* input, float* softmax, int* num_detections, float* detection_boxes,
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float* detection_scores, int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight,
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const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float* anchors)
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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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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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if (x_id >= gridSizeX || y_id >= gridSizeY || z_id >= numBBoxes)
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return;
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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 int numGridCells = gridSizeX * gridSizeY;
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const int bbindex = y_id * gridSizeX + x_id;
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const float objectness
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= sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
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const float objectness = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
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if (objectness < scoreThreshold)
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return;
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if (objectness < scoreThreshold)
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return;
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int count = (int)atomicAdd(num_detections, 1);
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int count = (int)atomicAdd(num_detections, 1);
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float x
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= (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)])
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+ x_id) * netWidth / gridSizeX;
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float x = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) + x_id) * netWidth / gridSizeX;
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float y
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= (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)])
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+ y_id) * netHeight / gridSizeY;
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float y = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) + y_id) * netHeight / gridSizeY;
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float w
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= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)])
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* anchors[z_id * 2] * netWidth / gridSizeX;
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float w = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * anchors[z_id * 2] * netWidth /
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gridSizeX;
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float h
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= __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)])
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* anchors[z_id * 2 + 1] * netHeight / gridSizeY;
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float h = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * anchors[z_id * 2 + 1] * netHeight /
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gridSizeY;
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softmaxGPU(input, bbindex, numGridCells, z_id, numOutputClasses, 1.0, softmax);
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softmaxGPU(input, bbindex, numGridCells, z_id, numOutputClasses, 1.0, softmax);
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float maxProb = 0.0f;
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int maxIndex = -1;
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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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= softmax[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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for (uint i = 0; i < numOutputClasses; ++i) {
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float prob = softmax[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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detection_boxes[count * 4 + 0] = x - 0.5 * w;
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detection_boxes[count * 4 + 1] = y - 0.5 * h;
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detection_boxes[count * 4 + 2] = x + 0.5 * w;
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detection_boxes[count * 4 + 3] = y + 0.5 * h;
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detection_scores[count] = objectness * maxProb;
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detection_classes[count] = maxIndex;
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detection_boxes[count * 4 + 0] = x - 0.5 * w;
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detection_boxes[count * 4 + 1] = y - 0.5 * h;
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detection_boxes[count * 4 + 2] = x + 0.5 * w;
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detection_boxes[count * 4 + 3] = y + 0.5 * h;
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detection_scores[count] = objectness * maxProb;
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detection_classes[count] = maxIndex;
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}
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cudaError_t cudaRegionLayer(
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const void* input, void* softmax, void* num_detections, void* detection_boxes, void* detection_scores,
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void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
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const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
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const uint& numBBoxes, const void* anchors, cudaStream_t stream);
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cudaError_t cudaRegionLayer(const void* input, void* softmax, void* num_detections, void* detection_boxes,
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void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize,
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const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY,
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const uint& numOutputClasses, const uint& numBBoxes, const void* anchors, cudaStream_t stream);
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cudaError_t cudaRegionLayer(
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const void* input, void* softmax, void* num_detections, void* detection_boxes, void* detection_scores,
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void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
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const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
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const uint& numBBoxes, const void* anchors, cudaStream_t stream)
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cudaError_t cudaRegionLayer(const void* input, void* softmax, void* num_detections, void* detection_boxes,
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void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize,
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const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY,
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const uint& numOutputClasses, const uint& numBBoxes, const void* anchors, 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,
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(gridSizeY / threads_per_block.y) + 1,
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(numBBoxes / threads_per_block.z) + 1);
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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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{
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gpuRegionLayer<<<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*>(softmax) + (batch * inputSize),
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reinterpret_cast<int*>(num_detections) + (batch),
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reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
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reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
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reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
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scoreThreshold, netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes,
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reinterpret_cast<const float*>(anchors));
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}
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return cudaGetLastError();
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for (unsigned int batch = 0; batch < batchSize; ++batch) {
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gpuRegionLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
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reinterpret_cast<const float*>(input) + (batch * inputSize), reinterpret_cast<float*>(softmax) + (batch * inputSize),
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reinterpret_cast<int*>(num_detections) + (batch),
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reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
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reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
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reinterpret_cast<int*>(detection_classes) + (batch * outputSize), scoreThreshold, netWidth, netHeight, gridSizeX,
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gridSizeY, numOutputClasses, numBBoxes, reinterpret_cast<const float*>(anchors));
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}
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return cudaGetLastError();
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}
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