TensorRT&Sample&Python[fc_plugin_caffe_mnist]



本文是基于TensorRT 5.0.2基础上,关于其内部的fc_plugin_caffe_mnist例子的分析和介绍。
本例子相较于前面例子的不同在于,其还包含cpp代码,且此时依赖项还挺多。该例子展示如何使用基于cpp写的plugin,用tensorrt python 绑定接口和caffe解析器一起工作的过程。该例子使用cuBLAS和cuDNn实现一个全连接层,然后实现成tensorrt plugin,然后用pybind11生成对应python绑定,这些绑定随后被用来注册为caffe解析器的一部分。

1 引言

假设当前路径为:

TensorRT-5.0.2.6/samples

其对应当前例子文件目录树为:

# tree python

python
├── common.py
├── fc_plugin_caffe_mnist
│   ├── CMakeLists.txt
│   ├── __init__.py
│   ├── plugin
│   │   ├── FullyConnected.h
│   │   └── pyFullyConnected.cpp
│   ├── README.md
│   ├── requirements.txt
│   └── sample.py

其中:

  • plugin包含FullyConnected 层的plugin:

    • FullyConnected.h 基于CUDA,cuDNN,cuBLAS实现该插件;
    • pyFullyConnected.cpp 生成关于FCPlugin和FCPluginFactory插件的python绑定;
  • sample.py 使用提供的FullyConnected 层插件运行MNIST网络;

2 安装依赖

git clone -b v2.2.3 https://github.com/pybind/pybind11.git
  • 安装python包:
Pillow
pycuda
numpy
argparse

3 编译该插件

  • 创建build文件夹,然后进入该文件夹
mkdir build && pushd build
  • cmake生成对应Makefile,此处可以自由设定一些参数。如果其中有些依赖不在默认位置路径上,可以cmake手动指定,关于Cmake的文档,可参考
cmake .. -DCUDA_ROOT=/usr/local/cuda-9.0 \
         -DPYBIND11_DIR=/root/pybind11/ \
         -DPYTHON3_INC_DIR=/usr/local/python3/include/python3.5m/ \
         -DNVINFER_LIB=/TensorRT-5.0.2.6/lib/libnvinfer.so  \
         -D_NVINFER_PLUGIN_LIB=/TensorRT-5.0.2.6/lib/ \
         -D_NVPARSERS_LIB=/TensorRT-5.0.2.6/lib \
         -DTRT_INC_DIR=/TensorRT-5.0.2.6/include/

注意cmake打出的日志中的VARIABLE_NAME-NOTFOUND

  • 进行编译
make -j32
  • 跳出build
popd

4 代码解析

首先,按上面编译过程所述,在build文件夹中会需要调用cmake命令,而该命令会读取上一层,也就是CMakeLists.txt,
其中关于find_library, include_directories, add_subdirectory的可以参考cmake-command文档

cmake_minimum_required(VERSION 3.2 FATAL_ERROR) # 最小cmake版本限定
project(FCPlugin LANGUAGES CXX C) # 项目名称和对应的编程语言

# 设定一个宏set_ifndef,用于操作当变量未找到时的行为:此处将未找到变量var 设定为val
macro(set_ifndef var val)
    if(NOT ${var})
        set(${var} ${val})
    endif()
    message(STATUS "Configurable variable ${var} set to ${${var}}")
endmacro()

# -------- CONFIGURATION --------
# Set module name here. MUST MATCH the module name specified in the .cpp
set_ifndef(PY_MODULE_NAME fcplugin) 
set(CMAKE_CXX_STANDARD 11) # 设定C++11标注
set(PYBIND11_CPP_STANDARD -std=c++11) # pybind11 defaults to c++14.

set_ifndef(PYBIND11_DIR $ENV{HOME}/pybind11/)
set_ifndef(CUDA_VERSION 10.0)
set_ifndef(CUDA_ROOT /usr/local/cuda-${CUDA_VERSION})
set_ifndef(CUDNN_ROOT ${CUDA_ROOT})
set_ifndef(PYTHON_ROOT /usr/include)
set_ifndef(TRT_LIB_DIR /usr/lib/x86_64-linux-gnu)
set_ifndef(TRT_INC_DIR /usr/include/x86_64-linux-gnu)

# 寻找依赖
message("\nThe following variables are derived from the values of the previous variables unless provided explicitly:\n")

find_path(_CUDA_INC_DIR cuda_runtime_api.h HINTS ${CUDA_ROOT} PATH_SUFFIXES include)
set_ifndef(CUDA_INC_DIR ${_CUDA_INC_DIR})

find_library(_CUDA_LIB cudart HINTS ${CUDA_ROOT} PATH_SUFFIXES lib lib64)
set_ifndef(CUDA_LIB ${_CUDA_LIB})

find_library(_CUBLAS_LIB cublas HINTS ${CUDA_ROOT} PATH_SUFFIXES lib lib64)
set_ifndef(CUBLAS_LIB ${_CUBLAS_LIB})

find_path(_CUDNN_INC_DIR cudnn.h HINTS ${CUDNN_ROOT} PATH_SUFFIXES include x86_64-linux-gnu)
set_ifndef(CUDNN_INC_DIR ${_CUDNN_INC_DIR})

find_library(_CUDNN_LIB cudnn HINTS ${CUDNN_ROOT} PATH_SUFFIXES lib lib64 x86_64-linux-gnu)
set_ifndef(CUDNN_LIB ${_CUDNN_LIB})

find_library(_TRT_INC_DIR NvInfer.h HINTS ${TRT_INC_DIR} PATH_SUFFIXES include x86_64-linux-gnu)
set_ifndef(TRT_INC_DIR ${_TRT_INC_DIR})

find_library(_NVINFER_LIB nvinfer HINTS ${TRT_LIB_DIR} PATH_SUFFIXES lib lib64 x86_64-linux-gnu)
set_ifndef(NVINFER_LIB ${_NVINFER_LIB})

find_library(_NVPARSERS_LIB nvparsers HINTS ${TRT_LIB_DIR} PATH_SUFFIXES lib lib64 x86_64-linux-gnu)
set_ifndef(NVPARSERS_LIB ${_NVPARSERS_LIB})

find_library(_NVINFER_PLUGIN_LIB nvinfer_plugin HINTS ${TRT_LIB_DIR} PATH_SUFFIXES lib lib64 x86_64-linux-gnu)
set_ifndef(NVINFER_PLUGIN_LIB ${_NVINFER_PLUGIN_LIB})

find_path(_PYTHON2_INC_DIR Python.h HINTS ${PYTHON_ROOT} PATH_SUFFIXES python2.7)
set_ifndef(PYTHON2_INC_DIR ${_PYTHON2_INC_DIR})

find_path(_PYTHON3_INC_DIR Python.h HINTS ${PYTHON_ROOT} PATH_SUFFIXES python3.7 python3.6 python3.5 python3.4)
set_ifndef(PYTHON3_INC_DIR ${_PYTHON3_INC_DIR})

# -------- BUILDING --------

# 增加include文件夹路径
include_directories(${TRT_INC_DIR} ${CUDA_INC_DIR} ${CUDNN_INC_DIR} ${PYBIND11_DIR}/include/)

# CMAKE_BINARY_DIR:表示build的根路径,这里是在build文件夹增加pybind11文件夹
add_subdirectory(${PYBIND11_DIR} ${CMAKE_BINARY_DIR}/pybind11)

# CMAKE_SOURCE_DIR:表示项目的根路径
file(GLOB_RECURSE SOURCE_FILES ${CMAKE_SOURCE_DIR}/plugin/*.cpp)

# Bindings library. The module name MUST MATCH the module name specified in the .cpp
# 是否支持python3
if(PYTHON3_INC_DIR AND NOT (${PYTHON3_INC_DIR} STREQUAL "None"))
    pybind11_add_module(${PY_MODULE_NAME} SHARED THIN_LTO ${SOURCE_FILES})
    target_include_directories(${PY_MODULE_NAME} BEFORE PUBLIC ${PYTHON3_INC_DIR})
    target_link_libraries(${PY_MODULE_NAME} PRIVATE ${CUDNN_LIB} ${CUDA_LIB} ${CUBLAS_LIB} ${NVINFER_LIB} ${NVPARSERS_LIB} ${NVINFER_PLUGIN_LIB})
endif()

# 是否支持python2
if(PYTHON2_INC_DIR AND NOT (${PYTHON2_INC_DIR} STREQUAL "None"))
    # Suffix the cmake target name with a 2 to differentiate from the Python 3 bindings target.
    pybind11_add_module(${PY_MODULE_NAME}2 SHARED THIN_LTO ${SOURCE_FILES})
    target_include_directories(${PY_MODULE_NAME}2 BEFORE PUBLIC ${PYTHON2_INC_DIR})
    target_link_libraries(${PY_MODULE_NAME}2 PRIVATE ${CUDNN_LIB} ${CUDA_LIB} ${CUBLAS_LIB} ${NVINFER_LIB} ${NVPARSERS_LIB} ${NVINFER_PLUGIN_LIB})
    # Rename to remove the .cpython-35... extension.
    set_target_properties(${PY_MODULE_NAME}2 PROPERTIES OUTPUT_NAME ${PY_MODULE_NAME} SUFFIX ".so")
    # Python 2 requires an empty __init__ file to be able to import.
    file(WRITE ${CMAKE_BINARY_DIR}/__init__.py "")
endif()

运行结果如图:

现在来看FullyConnected.h,因为长期不写cpp,所以对cpp代码都生疏了

#ifndef _FULLY_CONNECTED_H_
#define _FULLY_CONNECTED_H_

#include <cassert>
#include <cstring>
#include <cuda_runtime_api.h>
#include <cudnn.h>
#include <cublas_v2.h>
#include <stdexcept>

#include "NvInfer.h" //在路径 /TensorRT-5.0.2.6/include/
#include "NvCaffeParser.h" //在路径 /TensorRT-5.0.2.6/include/

#define CHECK(status) { if (status != 0) throw std::runtime_error(__FILE__ +  __LINE__ + std::string{"CUDA Error: "} + std::to_string(status)); }

// 将数据从host移动到device
nvinfer1::Weights copyToDevice(const void* hostData, int count)
{
    void* deviceData;
    CHECK(cudaMalloc(&deviceData, count * sizeof(float)));
    CHECK(cudaMemcpy(deviceData, hostData, count * sizeof(float), cudaMemcpyHostToDevice));
    return nvinfer1::Weights{nvinfer1::DataType::kFLOAT, deviceData, count};
}

//将数据从device移动到host
int copyFromDevice(char* hostBuffer, nvinfer1::Weights deviceWeights)
{
    *reinterpret_cast<int*>(hostBuffer) = deviceWeights.count;
    CHECK(cudaMemcpy(hostBuffer + sizeof(int), deviceWeights.values, deviceWeights.count * sizeof(float), cudaMemcpyDeviceToHost));
    return sizeof(int) + deviceWeights.count * sizeof(float);
}
//-----------------------------

/*建立FCPlugin类*/
class FCPlugin: public nvinfer1::IPluginExt
{
public:
    // In this simple case we're going to infer the number of output channels from the bias weights.
    // The knowledge that the kernel weights are weights[0] and the bias weights are weights[1] was
    // divined from the caffe innards
    FCPlugin(const nvinfer1::Weights* weights, int nbWeights)
    {
        assert(nbWeights == 2);
        mKernelWeights = copyToDevice(weights[0].values, weights[0].count);
        mBiasWeights = copyToDevice(weights[1].values, weights[1].count);
    }

    // 构造函数,用于从一个字节流中创建plugin
    FCPlugin(const void* data, size_t length)
    {
        const char* d = reinterpret_cast<const char*>(data), *a = d;
        mKernelWeights = copyToDevice(d + sizeof(int), reinterpret_cast<const int&>(d));
        d += sizeof(int) + mKernelWeights.count * sizeof(float);
        mBiasWeights = copyToDevice(d + sizeof(int), reinterpret_cast<const int&>(d));
        d += sizeof(int) + mBiasWeights.count * sizeof(float);
        assert(d == a + length);
    }

    virtual int getNbOutputs() const override { return 1; }

    virtual nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) override
    {
        assert(index == 0 && nbInputDims == 1 && inputs[0].nbDims == 3);
        return nvinfer1::DimsCHW{static_cast<int>(mBiasWeights.count), 1, 1};
    }

    virtual int initialize() override
    {
        CHECK(cudnnCreate(&mCudnn));
        CHECK(cublasCreate(&mCublas));
        // Create cudnn tensor descriptors for bias addition.
        CHECK(cudnnCreateTensorDescriptor(&mSrcDescriptor));
        CHECK(cudnnCreateTensorDescriptor(&mDstDescriptor));
        return 0;
    }

    virtual void terminate() override
    {
        CHECK(cudnnDestroyTensorDescriptor(mSrcDescriptor));
        CHECK(cudnnDestroyTensorDescriptor(mDstDescriptor));
        CHECK(cublasDestroy(mCublas));
        CHECK(cudnnDestroy(mCudnn));
    }

    // This plugin requires no workspace memory during build time.
    virtual size_t getWorkspaceSize(int maxBatchSize) const override { return 0; }

    virtual int enqueue(int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) override
    {
        int nbOutputChannels = mBiasWeights.count;
        int nbInputChannels = mKernelWeights.count / nbOutputChannels;
        constexpr float kONE = 1.0f, kZERO = 0.0f;
        // Do matrix multiplication.
        cublasSetStream(mCublas, stream);
        cudnnSetStream(mCudnn, stream);
        CHECK(cublasSgemm(mCublas, CUBLAS_OP_T, CUBLAS_OP_N, nbOutputChannels, batchSize, nbInputChannels, &kONE,
                reinterpret_cast<const float*>(mKernelWeights.values), nbInputChannels,
                reinterpret_cast<const float*>(inputs[0]), nbInputChannels, &kZERO,
                reinterpret_cast<float*>(outputs[0]), nbOutputChannels));
        // Add bias.
        CHECK(cudnnSetTensor4dDescriptor(mSrcDescriptor, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, nbOutputChannels, 1, 1));
        CHECK(cudnnSetTensor4dDescriptor(mDstDescriptor, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batchSize, nbOutputChannels, 1, 1));
        CHECK(cudnnAddTensor(mCudnn, &kONE, mSrcDescriptor, mBiasWeights.values, &kONE, mDstDescriptor, outputs[0]));
        return 0;
    }

    // For this sample, we'll only support float32 with NCHW.
    virtual bool supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const override
    {
        return (type == nvinfer1::DataType::kFLOAT && format == nvinfer1::PluginFormat::kNCHW);
    }

    void configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs, const nvinfer1::Dims* outputDims, int nbOutputs, nvinfer1::DataType type, nvinfer1::PluginFormat format, int maxBatchSize)
    {
        assert(nbInputs == 1 && inputDims[0].d[1] == 1 && inputDims[0].d[2] == 1);
        assert(nbOutputs == 1 && outputDims[0].d[1] == 1 && outputDims[0].d[2] == 1);
        assert(mKernelWeights.count == inputDims[0].d[0] * inputDims[0].d[1] * inputDims[0].d[2] * mBiasWeights.count);
    }

    virtual size_t getSerializationSize() override
    {
        return sizeof(int) * 2 + mKernelWeights.count * sizeof(float) + mBiasWeights.count * sizeof(float);
    }

    virtual void serialize(void* buffer) override
    {
        char* d = reinterpret_cast<char*>(buffer), *a = d;
        d += copyFromDevice(d, mKernelWeights);
        d += copyFromDevice(d, mBiasWeights);
        assert(d == a + getSerializationSize());
    }

    // 析构函数,释放buffer.
    virtual ~FCPlugin()
    {
        cudaFree(const_cast<void*>(mKernelWeights.values));
        mKernelWeights.values = nullptr;
        cudaFree(const_cast<void*>(mBiasWeights.values));
        mBiasWeights.values = nullptr;
    }

private:
    cudnnHandle_t mCudnn;
    cublasHandle_t mCublas;
    nvinfer1::Weights mKernelWeights{nvinfer1::DataType::kFLOAT, nullptr}, mBiasWeights{nvinfer1::DataType::kFLOAT, nullptr};
    cudnnTensorDescriptor_t mSrcDescriptor, mDstDescriptor;
};


/*建立FCPluginFactory类*/
class FCPluginFactory : public nvcaffeparser1::IPluginFactoryExt, public nvinfer1::IPluginFactory
{
public:
    bool isPlugin(const char* name) override { return isPluginExt(name); }

    bool isPluginExt(const char* name) override { return !strcmp(name, "ip2"); }

    // Create a plugin using provided weights.
    virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) override
    {
        assert(isPluginExt(layerName) && nbWeights == 2);
        assert(mPlugin == nullptr);
        // This plugin will need to be manually destroyed after parsing the network, by calling destroyPlugin.
        mPlugin = new FCPlugin{weights, nbWeights};
        return mPlugin;
    }

    // Create a plugin from serialized data.
    virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const void* serialData, size_t serialLength) override
    {
        assert(isPlugin(layerName));
        // This will be automatically destroyed when the engine is destroyed.
        return new FCPlugin{serialData, serialLength};
    }

    // User application destroys plugin when it is safe to do so.
    // Should be done after consumers of plugin (like ICudaEngine) are destroyed.
    void destroyPlugin() { delete mPlugin; }

    FCPlugin* mPlugin{ nullptr };
};

#endif //_FULLY_CONNECTED_H

现在来看pyFullyConnected.cpp该源码中用到了pybind11,关于其文档

#include "FullyConnected.h"
#include "NvInfer.h"
#include "NvCaffeParser.h"
#include <pybind11/pybind11.h>

PYBIND11_MODULE(fcplugin, m)
{
    namespace py = pybind11;

    // 以python方式导入tensorrt模块.
    py::module::import("tensorrt");

    // Note that we only need to bind the constructors manually. Since all other methods override IPlugin functionality, they will be automatically available in the python bindings.
    // The `std::unique_ptr<FCPlugin, py::nodelete>` specifies that Python is not responsible for destroying the object. This is required because the destructor is private.
    py::class_<FCPlugin, nvinfer1::IPluginExt, std::unique_ptr<FCPlugin, py::nodelete>>(m, "FCPlugin")
        // Bind the normal constructor as well as the one which deserializes the plugin
        .def(py::init<const nvinfer1::Weights*, int>())
        .def(py::init<const void*, size_t>())
    ;

    // Since the createPlugin function overrides IPluginFactory functionality, we do not need to explicitly bind it here.
    // We specify py::multiple_inheritance because we have not explicitly specified nvinfer1::IPluginFactory as a base class.
    py::class_<FCPluginFactory, nvcaffeparser1::IPluginFactoryExt>(m, "FCPluginFactory", py::multiple_inheritance())
        // Bind the default constructor.
        .def(py::init<>())
        // The destroy_plugin function does not override the base class, so we must bind it explicitly.
        .def("destroy_plugin", &FCPluginFactory::destroyPlugin)
    ;
}

cpp的代码就先不解释了。。。
接着分析sample.py

# This sample uses a Caffe model along with a custom plugin to create a TensorRT engine.
from random import randint
from PIL import Image
import numpy as np

import pycuda.driver as cuda
import pycuda.autoinit

import tensorrt as trt

try:
    from build import fcplugin
except ImportError as err:
    raise ImportError("""ERROR: Failed to import module ({})
Please build the FullyConnected sample plugin.
For more information, see the included README.md
Note that Python 2 requires the presence of `__init__.py` in the build folder""".format(err))

import sys, os
sys.path.insert(1, os.path.join(sys.path[0], ".."))
# import common
# 这里将common中的GiB和find_sample_data,do_inference等函数移动到该py文件中,保证自包含。
def GiB(val):
    '''以GB为单位,计算所需要的存储值,向左位移10bit表示KB,20bit表示MB '''
    return val * 1 << 30

def find_sample_data(description="Runs a TensorRT Python sample", subfolder="", find_files=[]):
    '''该函数就是一个参数解析函数。
    Parses sample arguments.
    Args:
        description (str): Description of the sample.
        subfolder (str): The subfolder containing data relevant to this sample
        find_files (str): A list of filenames to find. Each filename will be replaced with an absolute path.
    Returns:
        str: Path of data directory.
    Raises:
        FileNotFoundError
    '''
    # 为了简洁,这里直接将路径硬编码到代码中。
    data_root = kDEFAULT_DATA_ROOT = os.path.abspath("/TensorRT-5.0.2.6/python/data/")

    subfolder_path = os.path.join(data_root, subfolder)
    if not os.path.exists(subfolder_path):
        print("WARNING: " + subfolder_path + " does not exist. Using " + data_root + " instead.")
    data_path = subfolder_path if os.path.exists(subfolder_path) else data_root

    if not (os.path.exists(data_path)):
        raise FileNotFoundError(data_path + " does not exist.")

    for index, f in enumerate(find_files):
        find_files[index] = os.path.abspath(os.path.join(data_path, f))
        if not os.path.exists(find_files[index]):
            raise FileNotFoundError(find_files[index] + " does not exist. ")

    if find_files:
        return data_path, find_files
    else:
        return data_path
#-----------------

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

class ModelData(object):
    INPUT_NAME = "input"
    INPUT_SHAPE = (1, 28, 28)
    OUTPUT_NAME = "prob"
    OUTPUT_SHAPE = (10, )
    DTYPE = trt.float32


# 用一个解析器从binary_proto中检索mean data.
def retrieve_mean(mean_proto):
    with trt.CaffeParser() as parser:
        return parser.parse_binary_proto(mean_proto)

# 创建解析器的plugin factory. 设定成全局是因为可以在engine销毁之后再销毁.
fc_factory = fcplugin.FCPluginFactory()


'''main第二步:构建engine '''
def build_engine(deploy_file, model_file):

    with trt.Builder(TRT_LOGGER) as builder, \
         builder.create_network() as network, \
         trt.CaffeParser() as parser:

        builder.max_workspace_size = GiB(1)

        # 设定解析器的plugin factory。这里将其绑定到引用是为了后续能够手动销毁
        # parser.plugin_factory_ext 是一个 write-only属性
        ''' plugin_factory_ext是CaffeParser特有的接口,为了接入用户定义的组件
       https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/parsers/Caffe/pyCaffe.html?highlight=plugin_factory_ext
        '''
        parser.plugin_factory_ext = fc_factory

        # 解析该模型,并构建engine
        model_tensors = parser.parse(deploy=deploy_file, model=model_file, network=network, dtype=ModelData.DTYPE)

        # 标记网络的输出
        network.mark_output(model_tensors.find(ModelData.OUTPUT_NAME))

        return builder.build_cuda_engine(network)


'''main中第三步:分配buffer '''
def allocate_buffers(engine):

    inputs = []
    outputs = []
    bindings = []
    stream = cuda.Stream()

    for binding in engine:

        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
        dtype = trt.nptype(engine.get_binding_dtype(binding))

        # 分配host和device端的buffer
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)

        # 将device端的buffer追加到device的bindings.
        bindings.append(int(device_mem))

        # Append to the appropriate list.
        if engine.binding_is_input(binding):
            inputs.append(HostDeviceMem(host_mem, device_mem))
        else:
            outputs.append(HostDeviceMem(host_mem, device_mem))

    return inputs, outputs, bindings, stream


'''main中第四步:选择测试样本 '''
def load_normalized_test_case(data_path, pagelocked_buffer, mean, case_num=randint(0, 9)):

    test_case_path = os.path.join(data_path, str(case_num) + ".pgm")

    # Flatten图像为1维数组,然后归一化,并copy到pagelocked内存中。
    img = np.array(Image.open(test_case_path)).ravel()
    np.copyto(pagelocked_buffer, img - mean)

    return case_num


'''main中第五步:执行inference '''
# 该函数可以适应多个输入/输出;输入和输出格式为HostDeviceMem对象组成的列表
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):

    # 将数据移动到GPU
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]

    # 执行inference.
    context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)

    # 将结果从 GPU写回到host端
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]

    # 同步stream
    stream.synchronize()

    # 返回host端的输出结果
    return [out.host for out in outputs]


def main():

    ''' 1 - 读取caffe生成的模型文件'''
    data_path, [deploy_file, model_file, mean_proto] = find_sample_data(
          description="Runs an MNIST network using a Caffe model file", 
          subfolder="mnist", 
         find_files=["mnist.prototxt",
                 "mnist.caffemodel", 
                 "mnist_mean.binaryproto"])

    ''' 2 - 基于build_engine构建engine'''
    with build_engine(deploy_file, model_file) as engine:

        ''' 3 - 构建engine, 分配buffers, 创建一个流 '''
        inputs, outputs, bindings, stream = allocate_buffers(engine)
        mean = retrieve_mean(mean_proto)

        with engine.create_execution_context() as context:

            ''' 4 - 读取测试样本,并归一化'''
            case_num = load_normalized_test_case(data_path, inputs[0].host, mean)

            ''' 5 -执行inference,do_inference函数会返回一个list类型,此处只有一个元素 '''
            [output] = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
            pred = np.argmax(output)

            print("Test Case: " + str(case_num))
            print("Prediction: " + str(pred))

    ''' 6 - 在engine销毁之后,这里手动销毁plugin'''
    fc_factory.destroy_plugin()


if __name__ == "__main__":
    main()

运行结果如图:

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