关于yolov4的测速 about yolo-tensorrt HOT 6 CLOSED

@qinxianglinya 时间这里你能描述的再清楚一点吗？cudaMemcpyAsync是异步拷贝数据到显存，因为是异步所以函数会立即执行完不阻塞。后面的cudaStreamSynchronize是用于同步的，等待执行完成。所以你统计出来的26 ms基本就是执行时间。还是不理解的话你可以查下那两个cuda函数的作用

from yolo-tensorrt.

下面是我打印的时间信息，不知道是不是打印的有问题。
clock_t start = clock();
m_Context->enqueue(batchSize, m_DeviceBuffers.data(), m_CudaStream, nullptr);
clock_t end = clock();
std::cout << "infer time:" << (float)(end - start) << "ms" << std::endl;
std::cout << m_OutputTensors.size() << std::endl;
clock_t start1 = clock();
for (auto& tensor : m_OutputTensors)
{
NV_CUDA_CHECK(cudaMemcpyAsync(tensor.hostBuffer, m_DeviceBuffers.at(tensor.bindingIndex),
batchSize * tensor.volume * sizeof(float),
cudaMemcpyDeviceToHost, m_CudaStream));
}
clock_t end1 = clock();
std::cout << "gpu to cpu:" << float(end1 - start1) << "ms" << std:: endl;
clock_t start2 = clock();
cudaStreamSynchronize(m_CudaStream);
clock_t end2 = clock();
std::cout << "cudaStreamSynchronize time:" << float(end2 - start2) << "ms" <<std::endl;

from yolo-tensorrt.

uncomment line536 and line550

Timer timer;
    assert(batchSize <= m_BatchSize && "Image batch size exceeds TRT engines batch size");
    NV_CUDA_CHECK(cudaMemcpyAsync(m_DeviceBuffers.at(m_InputBindingIndex), input,
                                  batchSize * m_InputSize * sizeof(float), cudaMemcpyHostToDevice,
                                  m_CudaStream));
    m_Context->enqueue(batchSize, m_DeviceBuffers.data(), m_CudaStream, nullptr);
    for (auto& tensor : m_OutputTensors)
    {
        NV_CUDA_CHECK(cudaMemcpyAsync(tensor.hostBuffer, m_DeviceBuffers.at(tensor.bindingIndex),
                                      batchSize * tensor.volume * sizeof(float),
                                      cudaMemcpyDeviceToHost, m_CudaStream));
    }
    cudaStreamSynchronize(m_CudaStream);
    timer.out("inference");

from yolo-tensorrt.

取消注释第536行和第550行

Timer timer;
    assert(batchSize <= m_BatchSize && "Image batch size exceeds TRT engines batch size");
    NV_CUDA_CHECK(cudaMemcpyAsync(m_DeviceBuffers.at(m_InputBindingIndex), input,
                                  batchSize * m_InputSize * sizeof(float), cudaMemcpyHostToDevice,
                                  m_CudaStream));
    m_Context->enqueue(batchSize, m_DeviceBuffers.data(), m_CudaStream, nullptr);
    for (auto& tensor : m_OutputTensors)
    {
        NV_CUDA_CHECK(cudaMemcpyAsync(tensor.hostBuffer, m_DeviceBuffers.at(tensor.bindingIndex),
                                      batchSize * tensor.volume * sizeof(float),
                                      cudaMemcpyDeviceToHost, m_CudaStream));
    }
    cudaStreamSynchronize(m_CudaStream);
    timer.out("inference");

ok,谢谢

from yolo-tensorrt.

@qinxianglinya 觉得有用点个start哈

from yolo-tensorrt.

@qinxianglinya 觉得有用点个start哈

嗯嗯

from yolo-tensorrt.