Attach the decode kernel to the same CUDA stream
fix to load engine file in DeepStream 6.1.1
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@ -176,14 +176,14 @@ namespace nvinfer1
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totalCount += decodeplugin::INPUT_H / 16 * decodeplugin::INPUT_W / 16 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
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totalCount += decodeplugin::INPUT_H / 16 * decodeplugin::INPUT_W / 16 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
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totalCount += decodeplugin::INPUT_H / 32 * decodeplugin::INPUT_W / 32 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
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totalCount += decodeplugin::INPUT_H / 32 * decodeplugin::INPUT_W / 32 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
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for(int idx = 0 ; idx < batchSize; ++idx) {
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for(int idx = 0 ; idx < batchSize; ++idx) {
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cudaMemset(output + idx * totalCount, 0, sizeof(float));
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cudaMemsetAsync(output + idx * totalCount, 0, sizeof(float), stream);
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}
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}
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for (unsigned int i = 0; i < 3; ++i)
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for (unsigned int i = 0; i < 3; ++i)
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{
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{
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num_elem = batchSize * decodeplugin::INPUT_H / base_step * decodeplugin::INPUT_W / base_step;
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num_elem = batchSize * decodeplugin::INPUT_H / base_step * decodeplugin::INPUT_W / base_step;
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thread_count = (num_elem < thread_count_) ? num_elem : thread_count_;
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thread_count = (num_elem < thread_count_) ? num_elem : thread_count_;
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CalDetection<<< (num_elem + thread_count - 1) / thread_count, thread_count>>>
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CalDetection<<< (num_elem + thread_count - 1) / thread_count, thread_count, 0, stream>>>
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(inputs[i], output, num_elem, base_step, base_anchor, totalCount);
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(inputs[i], output, num_elem, base_step, base_anchor, totalCount);
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base_step *= 2;
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base_step *= 2;
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base_anchor *= 4;
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base_anchor *= 4;
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