From 923b61794f9b68830e9294988539c7effba5e73b Mon Sep 17 00:00:00 2001 From: nhzlx Date: Thu, 11 Oct 2018 16:01:26 +0800 Subject: [PATCH] cherry-pick from develop for paddle_trt doc --- .../inference/image/model_graph_original.png | Bin 0 -> 89934 bytes .../howto/inference/image/model_graph_trt.png | Bin 0 -> 47270 bytes .../user_guides/howto/inference/index.rst | 1 + .../howto/inference/paddle_tensorrt_infer.md | 132 ++++++++++++++++++ 4 files changed, 133 insertions(+) create mode 100644 doc/fluid/user_guides/howto/inference/image/model_graph_original.png create mode 100644 doc/fluid/user_guides/howto/inference/image/model_graph_trt.png create mode 100644 doc/fluid/user_guides/howto/inference/paddle_tensorrt_infer.md diff --git a/doc/fluid/user_guides/howto/inference/image/model_graph_original.png b/doc/fluid/user_guides/howto/inference/image/model_graph_original.png new file mode 100644 index 0000000000000000000000000000000000000000..c1ce03d1cd77f7a8d07ccbca3964642f2faefe00 GIT 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z!xZ7uy$(bD;VZv;IX`qn5;CwlG#|D|-aD_4jE$%+@_GuM)e z+_(3+ReC4QW!4l42yB?AaTT42dh^$jO`-2d*)xG$9#~x^O!P@>%Vv}CqE2ic`(ELsXq4$PaR0g2A zID8;VVA(!3o9rvu$J&zLUv3TV@iUZu`g^rAa7(LCb-HzNBeFnU&xv0aW$$J-L1>Q&Eyv&V4;5=5x$v{{$S@up+Z~ z7Sm9RUEQCX?r*#9qc}I9yG$VHJ+#F}*|9fI#H`?g)mex3z2jhrbnK}Z4AX_zw0QhH z{7>}oO`z218F*}aqU(FER2?5zwL4hRMxO4-V5Di>?=jPI@>|;dG?fR4C$faG=Y<=Z0s{fBVaer7K_7npEhv z4y|vji9%&%AzQ}y1D$kOTpu{kZX8WanD#dnV9rmqTTRv6n<#i53)0fnBQX5OE7vio z4M->lP4z)?cb!QRbG*>i=T+iI6~vGG(mRgylxN>;d-Ny`6nFkp7-*`j1}(J&&?(config); + + // 3. 创建输入 tensor + int height = 224; + int width = 224; + float data[batch_size * 3 * height * width] = {0}; + + PaddleTensor tensor; + tensor.shape = std::vector({batch_size, 3, height, width}); + tensor.data = PaddleBuf(static_cast(data), + sizeof(float) * (batch_size * 3 * height * width)); + tensor.dtype = PaddleDType::FLOAT32; + std::vector paddle_tensor_feeds(1, tensor); + + // 4. 创建输出 tensor + std::vector outputs; + // 5. 预测 + predictor->Run(paddle_tensor_feeds, &outputs, batch_size); + + const size_t num_elements = outputs.front().data.length() / sizeof(float); + auto *data = static_cast(outputs.front().data.data()); + for (size_t i = 0; i < num_elements; i++) { + std::cout << "output: " << data[i] << std::endl; + } +} +} // namespace paddle + +int main() { + // 模型下载地址 http://paddle-inference-dist.cdn.bcebos.com/tensorrt_test/mobilenet.tar.gz + paddle::RunTensorRT(1, “./mobilenet"); + return 0; +} +``` +编译过程可以参照[这里](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api/demo_ci)。 + +## 子图运行原理 + PaddlePaddle采用子图的形式对TensorRT进行集成,当模型加载后,神经网络可以表示为由变量和运算节点组成的计算图。Paddle TensorRT实现的功能是能够对整个图进行扫描,发现图中可以使用TensorRT优化的子图,并使用TensorRT节点替换它们。在模型的推断期间,如果遇到TensorRT节点,Paddle会调用TensoRT库对该节点进行优化,其他的节点调用Paddle的原生实现。TensorRT在推断期间能够进行Op的横向和纵向融合,过滤掉冗余的Op,并对特定平台下的特定的Op选择合适的kenel等进行优化,能够加快模型的预测速度。 + +下图使用一个简单的模型展示了这个过程: + +**原始网络** +

+ +

+ +**转换的网络** +

+ +

+ + + 我们可以在原始模型网络中看到,绿色节点表示可以被TensorRT支持的节点,红色节点表示网络中的变量,黄色表示Paddle只能被Paddle原生实现执行的节点。那些在原始网络中的绿色节点被提取出来汇集成子图,并由一个TensorRT节点代替,成为转换网络中的`block-25` 节点。在网络运行过程中,如果遇到该节点,Paddle将调用TensorRT库来对其执行。 + + + + + + + -- GitLab