Comments (4)
你好,我最近也在研究使用TensorRT来加速YOLO v2模型,想问下,但不知道如何着手。希望能给予我一些指导
from tensorrt_tutorial.
TensorRT 3.0 Have YOLO plugins headers. Plz double check before usage.
Find it in NvInferPlugin.h in /usr/src/TensorRT/include.
It need Jetpack 3.2 support.
When deploy it, you need to write plugin, plz refer to FasterRCNN sample.
Sorry for using English because i'm on TX2.
from tensorrt_tutorial.
抱歉最近没怎么关注git。BN的实现可以用scale层代替,直接将BN层的公式转换成scale层公式就可以了。我之前的实现代码如下,仅供参考。
float *scale_values = new float[l_ptr->out_c];
float *shift_values = new float[l_ptr->out_c];
for (int i = 0; i < l_ptr->out_c; ++i)
{
scale_values[i] = 1 / (sqrt(l_ptr->rolling_variance[i] + .00001f)) * l_ptr->scales[i];
shift_values[i] = -l_ptr->rolling_mean[i] / (sqrt(l_ptr->rolling_variance[i] + .00001f)) * l_ptr->scales[i] + l_ptr->biases[i];
}
Weights scale {DataType::kFLOAT, scale_values, l_ptr->out_c};
Weights shift {DataType::kFLOAT, shift_values, l_ptr->out_c};
Weights power {DataType::kFLOAT, nullptr, 0};
auto scale_1 = network->addScale(*inputs_v[0], ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1 != nullptr);
inputs_v[0] = scale_1->getOutput(0);
from tensorrt_tutorial.
已经解决了,非常感谢!
from tensorrt_tutorial.
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from tensorrt_tutorial.