74 lines
3.0 KiB
Plaintext
74 lines
3.0 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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__global__ void gpuYoloLayer_x(const float* input, 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,
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const uint numOutputClasses, const uint64_t outputSize, 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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if (x_id >= outputSize)
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return;
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const float objectness = input[x_id * (5 + numOutputClasses) + 4];
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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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float x = (input[x_id * (5 + numOutputClasses) + 0] + anchors[x_id * 2]) * mask[x_id];
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float y = (input[x_id * (5 + numOutputClasses) + 1] + anchors[x_id * 2 + 1]) * mask[x_id];
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float w = __expf(input[x_id * (5 + numOutputClasses) + 2]) * mask[x_id];
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float h = __expf(input[x_id * (5 + numOutputClasses) + 3]) * mask[x_id];
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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 = input[x_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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}
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cudaError_t cudaYoloLayer_x(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
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void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
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const uint& netHeight, const uint& numOutputClasses, const void* anchors, const void* mask, cudaStream_t stream);
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cudaError_t cudaYoloLayer_x(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
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void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
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const uint& netHeight, const uint& numOutputClasses, const void* anchors, const void* mask, cudaStream_t stream)
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{
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int threads_per_block = 16;
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int number_of_blocks = (outputSize / threads_per_block) + 1;
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for (unsigned int batch = 0; batch < batchSize; ++batch) {
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gpuYoloLayer_x<<<number_of_blocks, threads_per_block, 0, stream>>>(
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reinterpret_cast<const float*>(input) + (batch * (5 + numOutputClasses) * outputSize),
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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, numOutputClasses, outputSize, reinterpret_cast<const float*>(anchors),
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reinterpret_cast<const int*>(mask));
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}
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return cudaGetLastError();
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}
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