TensorRT&Sample&Python[fc_plugin_caffe_mnist]
本文是基於TensorRT 5.0.2基礎上，關於其內部的fc_plugin_caffe_mnist例子的分析和介紹。
本例子相較於前面例子的不一樣在於，其還包含cpp代碼，且此時依賴項還挺多。該例子展現如何使用基於cpp寫的plugin，用tensorrt python 綁定接口和caffe解析器一塊兒工做的過程。該例子使用cuBLAS和cuDNn實現一個全鏈接層，而後實現成tensorrt plugin，而後用pybind11生成對應python綁定，這些綁定隨後被用來註冊爲caffe解析器的一部分。html
1 引言
假設當前路徑爲：node
TensorRT-5.0.2.6/samples
其對應當前例子文件目錄樹爲：python
# 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：linux
plugin包含FullyConnected 層的plugin：c++
- FullyConnected.h 基於CUDA，cuDNN，cuBLAS實現該插件；
- pyFullyConnected.cpp 生成關於FCPlugin和FCPluginFactory插件的python綁定；
sample.py 使用提供的FullyConnected 層插件運行MNIST網絡；git
2 安裝依賴
git clone -b v2.2.3 https://github.com/pybind/pybind11.git
- 安裝python包:
Pillow pycuda numpy argparse
3 編譯該插件
- 建立build文件夾，而後進入該文件夾
mkdir build && pushd build
- cmake生成對應Makefile，此處能夠自由設定一些參數。若是其中有些依賴不在默認位置路徑上，能夠cmake手動指定，關於Cmake的文檔，可參考
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打出的日誌中的VARIABLE_NAME-NOTFOUNDgithub
- 進行編譯
make -j32
- 跳出build
popd
4 代碼解析
首先，按上面編譯過程所述，在build文件夾中會須要調用cmake命令，而該命令會讀取上一層，也就是CMakeLists.txt,
其中關於find_library, include_directories, add_subdirectory的能夠參考cmake-command文檔編程
cmake_minimum_required(VERSION 3.2 FATAL_ERROR) # 最小cmake版本限定
project(FCPlugin LANGUAGES CXX C) # 項目名稱和對應的編程語言
# 設定一個宏set_ifndef，用於操做當變量未找到時的行爲：此處將未找到變量var 設定爲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) # 設定C++11標註
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)
# 尋找依賴
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 ${_PYTHON3_INC_DIR})
# -------- BUILDING --------
# 增長include文件夾路徑
include_directories(${TRT_INC_DIR} ${CUDA_INC_DIR} ${CUDNN_INC_DIR} ${PYBIND11_DIR}/include/)
# CMAKE_BINARY_DIR：表示build的根路徑，這裏是在build文件夾增長pybind11文件夾
add_subdirectory(${PYBIND11_DIR} ${CMAKE_BINARY_DIR}/pybind11)
# CMAKE_SOURCE_DIR:表示項目的根路徑
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()
運行結果如圖：
api
如今來看FullyConnected.h,由於長期不寫cpp，因此對cpp代碼都生疏了數組
#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" //在路徑 /TensorRT-5.0.2.6/include/
#include "NvCaffeParser.h" //在路徑 /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)); }
// 將數據從host移動到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};
}
//將數據從device移動到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);
}
//-----------------------------
/*創建FCPlugin類*/
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);
}
// 構造函數，用於從一個字節流中建立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());
}
// 析構函數，釋放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;
};
/*創建FCPluginFactory類*/
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
如今來看pyFullyConnected.cpp該源碼中用到了pybind11，關於其文檔
#include "FullyConnected.h"
#include "NvInfer.h"
#include "NvCaffeParser.h"
#include <pybind11/pybind11.h>
PYBIND11_MODULE(fcplugin, m)
{
namespace py = pybind11;
// 以python方式導入tensorrt模塊.
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的代碼就先不解釋了。。。
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
# 這裏將common中的GiB和find_sample_data，do_inference等函數移動到該py文件中，保證自包含。
def GiB(val):
'''以GB爲單位，計算所須要的存儲值，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=[]):
'''該函數就是一個參數解析函數。
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
'''
# 爲了簡潔，這裏直接將路徑硬編碼到代碼中。
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
# 用一個解析器從binary_proto中檢索mean data.
def retrieve_mean(mean_proto):
with trt.CaffeParser() as parser:
return parser.parse_binary_proto(mean_proto)
# 建立解析器的plugin factory. 設定成全局是由於能夠在engine銷燬以後再銷燬.
fc_factory = fcplugin.FCPluginFactory()
'''main第二步：構建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)
# 設定解析器的plugin factory。這裏將其綁定到引用是爲了後續可以手動銷燬
# parser.plugin_factory_ext 是一個 write-only屬性
''' plugin_factory_ext是CaffeParser特有的接口，爲了接入用戶定義的組件
https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/parsers/Caffe/pyCaffe.html?highlight=plugin_factory_ext
'''
parser.plugin_factory_ext = fc_factory
# 解析該模型，並構建engine
model_tensors = parser.parse(deploy=deploy_file, model=model_file, network=network, dtype=ModelData.DTYPE)
# 標記網絡的輸出
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)
# 將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：選擇測試樣本 '''
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圖像爲1維數組，而後歸一化，並copy到pagelocked內存中。
img = np.array(Image.open(test_case_path)).ravel()
np.copyto(pagelocked_buffer, img - mean)
return case_num
'''main中第五步：執行inference '''
# 該函數能夠適應多個輸入/輸出；輸入和輸出格式爲HostDeviceMem對象組成的列表
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
# 將數據移動到GPU
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# 執行inference.
context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
# 將結果從 GPU寫回到host端
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# 同步stream
stream.synchronize()
# 返回host端的輸出結果
return [out.host for out in outputs]
def main():
''' 1 - 讀取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 - 基於build_engine構建engine'''
with build_engine(deploy_file, model_file) as engine:
''' 3 - 構建engine, 分配buffers， 建立一個流 '''
inputs, outputs, bindings, stream = allocate_buffers(engine)
mean = retrieve_mean(mean_proto)
with engine.create_execution_context() as context:
''' 4 - 讀取測試樣本，並歸一化'''
case_num = load_normalized_test_case(data_path, inputs[0].host, mean)
''' 5 -執行inference，do_inference函數會返回一個list類型，此處只有一個元素 '''
[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銷燬以後，這裏手動銷燬plugin'''
fc_factory.destroy_plugin()
if __name__ == "__main__":
main()
運行結果如圖：
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
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