TensorRT-5.1.5.0-SSD
文档：
https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/tensorrt-513
https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/index.html
安装：
https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html
https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing-tar
export CUDA_HOME=/usr/local/cuda
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/boyun/NVIDIA/TensorRT-5.1.5.0/libsudo ./cuda_9.0.176_384.81_linux.run
sudo ./cuda_9.0.176.1_linux.run
sudo ./cuda_9.0.176.2_linux.run
sudo ./cuda_9.0.176.3_linux.run
sudo ./cuda_9.0.176.4_linux.run
cp cudnn-9.0-linux-x64-v7.5.1.10.solitairetheme8 cudnn-9.0-linux-x64-v7.5.1.10.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda-9.0/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda-9.0/lib64/
cd /usr/local/cuda-9.0/lib64/
sudo rm -rf libcudnn.so.7
sudo rm -rf libcudnn.so
sudo ln -s libcudnn.so.7.5.1 libcudnn.so.7
sudo ln -s libcudnn.so.7 libcudnn.so
sudo gedit ~/.bashrc
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/boyun/NVIDIA/TensorRT-5.1.5.0/lib
cd /home/boyun/NVIDIA/TensorRT-5.1.5.0/python
sudo pip install tensorrt-5.1.5.0-cp27-none-linux_x86_64.whl
一些插件，比如使用TensorFlow的时候，所以我们都安装上：
cd /home/boyun/NVIDIA/TensorRT-5.1.5.0/uff
sudo pip install uff-0.6.3-py2.py3-none-any.whl
cd /home/boyun/NVIDIA/TensorRT-5.1.5.0/graphsurgeon
sudo pip install graphsurgeon-0.4.1-py2.py3-none-any.whl
测试安装：
import tensorrt编译：
cd /home/boyun/NVIDIA/TensorRT-5.1.5.0/samples/
make -20
cd /home/boyun/NVIDIA/TensorRT-5.1.5.0/bin/
跑例子
./sample_googlenet
&&&& RUNNING TensorRT.sample_googlenet # ./sample_googlenet
[I] Building and running a GPU inference engine for GoogleNet
[I] Ran ./sample_googlenet with:
[I] Input(s): data
[I] Output(s): prob
&&&& PASSED TensorRT.sample_googlenet # ./sample_googlenet
测试时间
./trtexec
&&&& RUNNING TensorRT.trtexec # ./trtexec
Mandatory params:
--deploy= Caffe deploy file
OR --uff= UFF file
OR --onnx= ONNX Model file
OR --loadEngine= Load a saved engine
Mandatory params for UFF:
--uffInput=,C,H,W Input blob name and its dimensions for UFF parser (can be specified multiple times)
--output= Output blob name (can be specified multiple times)
Mandatory params for Caffe:
--output= Output blob name (can be specified multiple times)
Optional params:
--model= Caffe model file (default = no model, random weights used)
--batch=N Set batch size (default = 1)
--device=N Set cuda device to N (default = 0)
--iterations=N Run N iterations (default = 10)
--avgRuns=N Set avgRuns to N - perf is measured as an average of avgRuns (default=10)
--percentile=P For each iteration, report the percentile time at P percentage (0<=P<=100, with 0 representing min, and 100 representing max; default = 99.0%)
--workspace=N Set workspace size in megabytes (default = 16)
--safe Only test the functionality available in safety restricted flows.
--fp16 Run in fp16 mode (default = false). Permits 16-bit kernels
--int8 Run in int8 mode (default = false). Currently no support for ONNX model.
--verbose Use verbose logging (default = false)
--saveEngine= Save a serialized engine to file.
--loadEngine= Load a serialized engine from file.
--calib= Read INT8 calibration cache file. Currently no support for ONNX model.
--useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
--allowGPUFallback If --useDLACore flag is present and if a layer can't run on DLA, then run on GPU.
--useSpinWait Actively wait for work completion. This option may decrease multi-process synchronization time at the cost of additional CPU usage. (default = false)
--dumpOutput Dump outputs at end of test.
-h, --help Print usage
&&&& FAILED TensorRT.trtexec # ./trtexec
测试时间程序：请使用绝对路径
./trtexec --deploy=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.prototxt --output=prob --model=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.caffemodel
&&&& RUNNING TensorRT.trtexec # ./trtexec --deploy=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.prototxt --output=prob --model=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.caffemodel
[I] deploy: /home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.prototxt
[I] output: prob
[I] model: /home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.caffemodel
[I] Input "data": 3x224x224
[I] Output "prob": 1000x1x1
[I] Average over 10 runs is 1.42812 ms (host walltime is 1.55343 ms, 99% percentile time is 1.43962).
[I] Average over 10 runs is 1.42616 ms (host walltime is 1.51039 ms, 99% percentile time is 1.43974).
[I] Average over 10 runs is 1.42445 ms (host walltime is 1.60433 ms, 99% percentile time is 1.43638).
[I] Average over 10 runs is 1.42804 ms (host walltime is 1.5044 ms, 99% percentile time is 1.43565).
[I] Average over 10 runs is 1.42922 ms (host walltime is 1.49007 ms, 99% percentile time is 1.43565).
[I] Average over 10 runs is 1.42908 ms (host walltime is 1.59964 ms, 99% percentile time is 1.44486).
[I] Average over 10 runs is 1.43004 ms (host walltime is 1.47835 ms, 99% percentile time is 1.43974).
[I] Average over 10 runs is 1.4258 ms (host walltime is 1.60077 ms, 99% percentile time is 1.43667).
[I] Average over 10 runs is 1.42793 ms (host walltime is 1.48041 ms, 99% percentile time is 1.43872).
[I] Average over 10 runs is 1.4263 ms (host walltime is 1.618 ms, 99% percentile time is 1.43155).
&&&& PASSED TensorRT.trtexec # ./trtexec --deploy=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.prototxt --output=prob --model=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.caffemodel
将推理出的TensorRT的模型保存
mkdir saveEngine
./trtexec --deploy=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.prototxt --output=prob --model=/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/googlenet/googlenet.caffemodel --saveEngine=/home/boyun/NVIDIA/TensorRT-5.1.5.0/saveEngine/googlenet
注释：
sampleGoogleNet.cpp:
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*/
//!
//! sampleGoogleNet.cpp
//! This file contains the implementation of the GoogleNet sample. It creates the network using
//! the GoogleNet caffe model.
//! It can be run with the following command line:
//! Command: ./sample_googlenet [-h or --help] [-d=/path/to/data/dir or --datadir=/path/to/data/dir]
//!
#include "argsParser.h"
#include "buffers.h"
#include "logger.h"
#include "common.h"
#include "NvCaffeParser.h"
#include "NvInfer.h"
#include
#include
#include
#include
#include
//yangninghua
#include "time.h"
const std::string gSampleName = "TensorRT.sample_googlenet";
//!
//! \brief The SampleGoogleNet class implements the GoogleNet sample
//!
//! \details It creates the network using a caffe model
//!
class SampleGoogleNet
{
template
using SampleUniquePtr = std::unique_ptr;
public:
SampleGoogleNet(const samplesCommon::CaffeSampleParams& params)
: mParams(params)
{
}
//!
//! \brief Function builds the network engine
//!
//构建网络引擎
bool build();
//!
//! \brief This function runs the TensorRT inference engine for this sample
//!
//运行TensorRT推理引擎
bool infer();
//!
//! \brief This function can be used to clean up any state created in the sample class
//!
//清空状态
bool teardown();
samplesCommon::CaffeSampleParams mParams;
private:
//用于运行网络的TensorRT引擎
std::shared_ptr mEngine = nullptr; //!< The TensorRT engine used to run the network
//!
//! \brief This function parses a Caffe model for GoogleNet and creates a TensorRT network
//!
//此功能解析GoogleNet的Caffe模型并创建TensorRT网络
void constructNetwork(SampleUniquePtr& builder, SampleUniquePtr& network, SampleUniquePtr& parser);
};
//!
//! \brief This function creates the network, configures the builder and creates the network engine
//!
//! \details This function creates the GoogleNet network by parsing the caffe model and builds
//! the engine that will be used to run GoogleNet (mEngine)
//!
//! \return Returns true if the engine was created successfully and false otherwise
//!
bool SampleGoogleNet::build()
{
//一个名为的全局TensorRT API方法 createInferBuilder（gLogger） 用于创建类型的对象 IBuilder
//创建IBuilder同iLogger作为输入参数
auto builder = SampleUniquePtr(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
if (!builder)
return false;
//一种叫做的方法 createNetwork 为iBuilder定义用于创建类型的对象 iNetworkDefinition'
//createNetwork()用于创建网络
auto network = SampleUniquePtr(builder->createNetwork());
if (!network)
return false;
//创建一个可用的解析器（Caffe，ONNX或UFF）
//ONNX: auto parser = nvonnxparser::createParser(*network, gLogger);
//Caffe: auto parser = nvcaffeparser1::createCaffeParser();
//UFF: auto parser = nvuffparser::createUffParser();
auto parser = SampleUniquePtr(nvcaffeparser1::createCaffeParser());
if (!parser)
return false;
//加载caffe模型
//解析模型文件
constructNetwork(builder, network, parser);
//一种叫做的方法buildCudaEngine()的IBuilder被调用来创建一个对象ICudaEngine类型
//创建TensorRT引擎
//可以选择将引擎序列化并转储到文件中。
mEngine = std::shared_ptr(builder->buildCudaEngine(*network), samplesCommon::InferDeleter());
if (!mEngine)
return false;
return true;
}
//!
//! \brief This function uses a caffe parser to create the googlenet Network and marks the
//! output layers
//!
//! \param network Pointer to the network that will be populated with the googlenet network
//!
//! \param builder Pointer to the engine builder
//!
void SampleGoogleNet::constructNetwork(SampleUniquePtr& builder, SampleUniquePtr& network, SampleUniquePtr& parser)
{
//对象iParser调用parse方法读取模型文件并填充TensorRT网络
//params.dataDirs.push_back("data/googlenet/");
//params.dataDirs.push_back("data/samples/googlenet/");
//params.prototxtFileName = "googlenet.prototxt";
//params.weightsFileName = "googlenet.caffemodel";
const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor = parser->parse(
locateFile(mParams.prototxtFileName, mParams.dataDirs).c_str(),
locateFile(mParams.weightsFileName, mParams.dataDirs).c_str(),
*network,
nvinfer1::DataType::kFLOAT);
//params.outputTensorNames.push_back("prob");
for (auto& s : mParams.outputTensorNames)
network->markOutput(*blobNameToTensor->find(s.c_str()));
//params.batchSize = 4;
//params.dlaCore = args.useDLACore;
builder->setMaxBatchSize(mParams.batchSize);
builder->setMaxWorkspaceSize(16_MB);
//builder->setFp16Mode(true);
//builder->setInt8Mode(true);
samplesCommon::enableDLA(builder.get(), mParams.dlaCore);
}
//!
//! \brief This function runs the TensorRT inference engine for this sample
//!
//! \details This function is the main execution function of the sample. It allocates the buffer,
//! sets inputs and executes the engine.
//!
bool SampleGoogleNet::infer()
{
// Create RAII buffer manager object
samplesCommon::BufferManager buffers(mEngine, mParams.batchSize);
//IExecutionContext用于执行推理引擎
auto context = SampleUniquePtr(mEngine->createExecutionContext());
if (!context)
return false;
// Fetch host buffers and set host input buffers to all zeros
//获取主机缓冲区并将主机输入缓冲区设置为全零
for (auto& input : mParams.inputTensorNames)
{
const auto bufferSize = buffers.size(input);
if (bufferSize == samplesCommon::BufferManager::kINVALID_SIZE_VALUE)
{
gLogError << "input tensor missing: " << input << "\n";
exit(EXIT_FAILURE);
}
memset(buffers.getHostBuffer(input), 0, bufferSize);
}
// Memcpy from host input buffers to device input buffers
//Memcpy从主机输入缓冲区到设备输入缓冲区
buffers.copyInputToDevice();
clock_t start, finish;
double duration;
start = clock();
bool status = context->execute(mParams.batchSize, buffers.getDeviceBindings().data());
if (!status)
return false;
finish = clock();
duration = (double)(finish - start) / CLOCKS_PER_SEC;
cout<<"前向推理时间"<] [--useDLACore=]\n";
std::cout << "--help Display help information\n";
std::cout << "--datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use data/samples/googlenet/ and data/googlenet/" << std::endl;
std::cout << "--useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform." << std::endl;
}
int main(int argc, char** argv)
{
samplesCommon::Args args;
bool argsOK = samplesCommon::parseArgs(args, argc, argv);
if (args.help)
{
printHelpInfo();
return EXIT_SUCCESS;
}
if (!argsOK)
{
gLogError << "Invalid arguments" << std::endl;
printHelpInfo();
return EXIT_FAILURE;
}
auto sampleTest = gLogger.defineTest(gSampleName, argc, const_cast(argv));
//函数reportTestEnd与其对应
//&&&& RUNNING TensorRT.sample_googlenet # ./sample_googlenet
gLogger.reportTestStart(sampleTest);
//参数初始化
samplesCommon::CaffeSampleParams params = initializeSampleParams(args);
SampleGoogleNet sample(params);
gLogInfo << "Building and running a GPU inference engine for GoogleNet" << std::endl;
if (!sample.build())
{
return gLogger.reportFail(sampleTest);
}
if (!sample.infer())
{
return gLogger.reportFail(sampleTest);
}
if (!sample.teardown())
{
return gLogger.reportFail(sampleTest);
}
//[I] Ran ./sample_googlenet with:
gLogInfo << "Ran " << argv[0] << " with: " << std::endl;
std::stringstream ss;
//可能有多个输入，进行遍历
//[I] Input(s): data
ss << "Input(s): ";
for (auto& input : sample.mParams.inputTensorNames)
ss << input << " ";
gLogInfo << ss.str() << std::endl;
ss.str(std::string());
//可能有多个输出，进行遍历
//[I] Output(s): prob
ss << "Output(s): ";
for (auto& output : sample.mParams.outputTensorNames)
ss << output << " ";
gLogInfo << ss.str() << std::endl;
//reportTestEnd函数在reportPass中被调用
//&&&& PASSED TensorRT.sample_googlenet # ./sample_googlenet
return gLogger.reportPass(sampleTest);
}
接着修改SSD的例子：
https://github.com/NVIDIA/TensorRT/tree/release/5.1/samples/opensource/sampleSSD
下载SSD的权重和deploy.prototxt还有labelmap_voc.prototxt
https://drive.google.com/file/d/0BzKzrI_SkD1_WVVTSmQxU0dVRzA/view
里面有VGG_VOC0712_SSD_300x300_iter_120000.caffemodel和deploy.prototxt
https://github.com/intel/caffe/blob/master/data/VOC0712/labelmap_voc.prototxt
可以找到labelmap_voc.prototxt
mv deploy.prototxt /data/ssd/ssd.prototxt
然后修改这个ssd.prototxt
首先：param，weight_filler，bias_filler参数全部不要(只做推理，要这没用)但是不去也行，我懒了抱歉
更改为：
所有的Flatten改为Reshape这样
Update the detection_out layer to add the keep_count output as expected by the TensorRT DetectionOutput Plugin. top: "keep_count"
&&&& RUNNING TensorRT.sample_ssd # ./sample_ssd
[I] Begin parsing model...
[I] FP32 mode running...
[I] End parsing model...
[I] Begin building engine...
[I] End building engine...
[I] *** deserializing
[I] Image name:../../../data/ssd/bus.ppm, Label: car, confidence: 96.0588 xmin: 4.14485 ymin: 117.443 xmax: 244.102 ymax: 241.829
&&&& PASSED TensorRT.sample_ssd # ./sample_ssd生成的图保存在：
附上修改后的ssd.prototxt
name: "VGG_VOC0712_SSD_300x300_deploy"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 300
dim: 300
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
dilation: 1
}
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
dilation: 1
}
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
dilation: 1
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "fc6"
type: "Convolution"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 6
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
dilation: 6
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "fc7"
type: "Convolution"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 1024
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "conv6_1"
type: "Convolution"
bottom: "fc7"
top: "conv6_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_1_relu"
type: "ReLU"
bottom: "conv6_1"
top: "conv6_1"
}
layer {
name: "conv6_2"
type: "Convolution"
bottom: "conv6_1"
top: "conv6_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_relu"
type: "ReLU"
bottom: "conv6_2"
top: "conv6_2"
}
layer {
name: "conv7_1"
type: "Convolution"
bottom: "conv6_2"
top: "conv7_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_1_relu"
type: "ReLU"
bottom: "conv7_1"
top: "conv7_1"
}
layer {
name: "conv7_2"
type: "Convolution"
bottom: "conv7_1"
top: "conv7_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_relu"
type: "ReLU"
bottom: "conv7_2"
top: "conv7_2"
}
layer {
name: "conv8_1"
type: "Convolution"
bottom: "conv7_2"
top: "conv8_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_1_relu"
type: "ReLU"
bottom: "conv8_1"
top: "conv8_1"
}
layer {
name: "conv8_2"
type: "Convolution"
bottom: "conv8_1"
top: "conv8_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_relu"
type: "ReLU"
bottom: "conv8_2"
top: "conv8_2"
}
layer {
name: "conv9_1"
type: "Convolution"
bottom: "conv8_2"
top: "conv9_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_1_relu"
type: "ReLU"
bottom: "conv9_1"
top: "conv9_1"
}
layer {
name: "conv9_2"
type: "Convolution"
bottom: "conv9_1"
top: "conv9_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_2_relu"
type: "ReLU"
bottom: "conv9_2"
top: "conv9_2"
}
layer {
name: "conv4_3_norm"
type: "Normalize"
bottom: "conv4_3"
top: "conv4_3_norm"
norm_param {
across_spatial: false
scale_filler {
type: "constant"
value: 20
}
channel_shared: false
}
}
layer {
name: "conv4_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv4_3_norm"
top: "conv4_3_norm_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_loc"
top: "conv4_3_norm_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv4_3_norm_mbox_loc_flat"
# type: "Flatten"
# bottom: "conv4_3_norm_mbox_loc_perm"
# top: "conv4_3_norm_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv4_3_norm_mbox_loc_flat"
type: "Reshape"
bottom: "conv4_3_norm_mbox_loc_perm"
top: "conv4_3_norm_mbox_loc_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv4_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv4_3_norm"
top: "conv4_3_norm_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_conf"
top: "conv4_3_norm_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv4_3_norm_mbox_conf_flat"
# type: "Flatten"
# bottom: "conv4_3_norm_mbox_conf_perm"
# top: "conv4_3_norm_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv4_3_norm_mbox_conf_flat"
type: "Reshape"
bottom: "conv4_3_norm_mbox_conf_perm"
top: "conv4_3_norm_mbox_conf_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv4_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv4_3_norm"
bottom: "data"
top: "conv4_3_norm_mbox_priorbox"
prior_box_param {
min_size: 30.0
max_size: 60.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 8
offset: 0.5
}
}
layer {
name: "fc7_mbox_loc"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc7_mbox_loc_perm"
type: "Permute"
bottom: "fc7_mbox_loc"
top: "fc7_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "fc7_mbox_loc_flat"
# type: "Flatten"
# bottom: "fc7_mbox_loc_perm"
# top: "fc7_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "fc7_mbox_loc_flat"
type: "Reshape"
bottom: "fc7_mbox_loc_perm"
top: "fc7_mbox_loc_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "fc7_mbox_conf"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc7_mbox_conf_perm"
type: "Permute"
bottom: "fc7_mbox_conf"
top: "fc7_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "fc7_mbox_conf_flat"
# type: "Flatten"
# bottom: "fc7_mbox_conf_perm"
# top: "fc7_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "fc7_mbox_conf_flat"
type: "Reshape"
bottom: "fc7_mbox_conf_perm"
top: "fc7_mbox_conf_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "fc7_mbox_priorbox"
type: "PriorBox"
bottom: "fc7"
bottom: "data"
top: "fc7_mbox_priorbox"
prior_box_param {
min_size: 60.0
max_size: 111.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 16
offset: 0.5
}
}
layer {
name: "conv6_2_mbox_loc"
type: "Convolution"
bottom: "conv6_2"
top: "conv6_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_mbox_loc_perm"
type: "Permute"
bottom: "conv6_2_mbox_loc"
top: "conv6_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv6_2_mbox_loc_flat"
# type: "Flatten"
# bottom: "conv6_2_mbox_loc_perm"
# top: "conv6_2_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv6_2_mbox_loc_flat"
type: "Reshape"
bottom: "conv6_2_mbox_loc_perm"
top: "conv6_2_mbox_loc_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv6_2_mbox_conf"
type: "Convolution"
bottom: "conv6_2"
top: "conv6_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_mbox_conf_perm"
type: "Permute"
bottom: "conv6_2_mbox_conf"
top: "conv6_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv6_2_mbox_conf_flat"
# type: "Flatten"
# bottom: "conv6_2_mbox_conf_perm"
# top: "conv6_2_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv6_2_mbox_conf_flat"
type: "Reshape"
bottom: "conv6_2_mbox_conf_perm"
top: "conv6_2_mbox_conf_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv6_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv6_2"
bottom: "data"
top: "conv6_2_mbox_priorbox"
prior_box_param {
min_size: 111.0
max_size: 162.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 32
offset: 0.5
}
}
layer {
name: "conv7_2_mbox_loc"
type: "Convolution"
bottom: "conv7_2"
top: "conv7_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_mbox_loc_perm"
type: "Permute"
bottom: "conv7_2_mbox_loc"
top: "conv7_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv7_2_mbox_loc_flat"
# type: "Flatten"
# bottom: "conv7_2_mbox_loc_perm"
# top: "conv7_2_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv7_2_mbox_loc_flat"
type: "Reshape"
bottom: "conv7_2_mbox_loc_perm"
top: "conv7_2_mbox_loc_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv7_2_mbox_conf"
type: "Convolution"
bottom: "conv7_2"
top: "conv7_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_mbox_conf_perm"
type: "Permute"
bottom: "conv7_2_mbox_conf"
top: "conv7_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv7_2_mbox_conf_flat"
# type: "Flatten"
# bottom: "conv7_2_mbox_conf_perm"
# top: "conv7_2_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv7_2_mbox_conf_flat"
type: "Reshape"
bottom: "conv7_2_mbox_conf_perm"
top: "conv7_2_mbox_conf_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv7_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv7_2"
bottom: "data"
top: "conv7_2_mbox_priorbox"
prior_box_param {
min_size: 162.0
max_size: 213.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 64
offset: 0.5
}
}
layer {
name: "conv8_2_mbox_loc"
type: "Convolution"
bottom: "conv8_2"
top: "conv8_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_mbox_loc_perm"
type: "Permute"
bottom: "conv8_2_mbox_loc"
top: "conv8_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv8_2_mbox_loc_flat"
# type: "Flatten"
# bottom: "conv8_2_mbox_loc_perm"
# top: "conv8_2_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv8_2_mbox_loc_flat"
type: "Reshape"
bottom: "conv8_2_mbox_loc_perm"
top: "conv8_2_mbox_loc_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv8_2_mbox_conf"
type: "Convolution"
bottom: "conv8_2"
top: "conv8_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_mbox_conf_perm"
type: "Permute"
bottom: "conv8_2_mbox_conf"
top: "conv8_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv8_2_mbox_conf_flat"
# type: "Flatten"
# bottom: "conv8_2_mbox_conf_perm"
# top: "conv8_2_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv8_2_mbox_conf_flat"
type: "Reshape"
bottom: "conv8_2_mbox_conf_perm"
top: "conv8_2_mbox_conf_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv8_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv8_2"
bottom: "data"
top: "conv8_2_mbox_priorbox"
prior_box_param {
min_size: 213.0
max_size: 264.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 100
offset: 0.5
}
}
layer {
name: "conv9_2_mbox_loc"
type: "Convolution"
bottom: "conv9_2"
top: "conv9_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_2_mbox_loc_perm"
type: "Permute"
bottom: "conv9_2_mbox_loc"
top: "conv9_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv9_2_mbox_loc_flat"
# type: "Flatten"
# bottom: "conv9_2_mbox_loc_perm"
# top: "conv9_2_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv9_2_mbox_loc_flat"
type: "Reshape"
bottom: "conv9_2_mbox_loc_perm"
top: "conv9_2_mbox_loc_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv9_2_mbox_conf"
type: "Convolution"
bottom: "conv9_2"
top: "conv9_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_2_mbox_conf_perm"
type: "Permute"
bottom: "conv9_2_mbox_conf"
top: "conv9_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
########################################
#layer {
# name: "conv9_2_mbox_conf_flat"
# type: "Flatten"
# bottom: "conv9_2_mbox_conf_perm"
# top: "conv9_2_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "conv9_2_mbox_conf_flat"
type: "Reshape"
bottom: "conv9_2_mbox_conf_perm"
top: "conv9_2_mbox_conf_flat"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "conv9_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv9_2"
bottom: "data"
top: "conv9_2_mbox_priorbox"
prior_box_param {
min_size: 264.0
max_size: 315.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 300
offset: 0.5
}
}
layer {
name: "mbox_loc"
type: "Concat"
bottom: "conv4_3_norm_mbox_loc_flat"
bottom: "fc7_mbox_loc_flat"
bottom: "conv6_2_mbox_loc_flat"
bottom: "conv7_2_mbox_loc_flat"
bottom: "conv8_2_mbox_loc_flat"
bottom: "conv9_2_mbox_loc_flat"
top: "mbox_loc"
concat_param {
axis: 1
}
}
layer {
name: "mbox_conf"
type: "Concat"
bottom: "conv4_3_norm_mbox_conf_flat"
bottom: "fc7_mbox_conf_flat"
bottom: "conv6_2_mbox_conf_flat"
bottom: "conv7_2_mbox_conf_flat"
bottom: "conv8_2_mbox_conf_flat"
bottom: "conv9_2_mbox_conf_flat"
top: "mbox_conf"
concat_param {
axis: 1
}
}
layer {
name: "mbox_priorbox"
type: "Concat"
bottom: "conv4_3_norm_mbox_priorbox"
bottom: "fc7_mbox_priorbox"
bottom: "conv6_2_mbox_priorbox"
bottom: "conv7_2_mbox_priorbox"
bottom: "conv8_2_mbox_priorbox"
bottom: "conv9_2_mbox_priorbox"
top: "mbox_priorbox"
concat_param {
axis: 2
}
}
layer {
name: "mbox_conf_reshape"
type: "Reshape"
bottom: "mbox_conf"
top: "mbox_conf_reshape"
reshape_param {
shape {
dim: 0
dim: -1
dim: 21
}
}
}
layer {
name: "mbox_conf_softmax"
type: "Softmax"
bottom: "mbox_conf_reshape"
top: "mbox_conf_softmax"
softmax_param {
axis: 2
}
}
########################################
#layer {
# name: "mbox_conf_flatten"
# type: "Flatten"
# bottom: "mbox_conf_softmax"
# top: "mbox_conf_flatten"
# flatten_param {
# axis: 1
# }
#}
layer {
name: "mbox_conf_flatten"
type: "Reshape"
bottom: "mbox_conf_softmax"
top: "mbox_conf_flatten"
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
########################################
layer {
name: "detection_out"
type: "DetectionOutput"
bottom: "mbox_loc"
bottom: "mbox_conf_flatten"
bottom: "mbox_priorbox"
top: "detection_out"
top: "keep_count"
include {
phase: TEST
}
detection_output_param {
num_classes: 21
share_location: true
background_label_id: 0
nms_param {
nms_threshold: 0.45
top_k: 400
}
save_output_param {
label_map_file: "/home/boyun/NVIDIA/TensorRT-5.1.5.0/data/ssd/labelmap_voc.prototxt"
}
code_type: CENTER_SIZE
keep_top_k: 200
confidence_threshold: 0.01
}
}
测试时间：
#include "time.h"
clock_t start, finish;
double duration;
start = clock();
context.enqueue(batchSize, buffers, stream, nullptr);
finish = clock();
duration = (double)(finish - start) / CLOCKS_PER_SEC;
cout<<"前向推理时间"<
&&&& RUNNING TensorRT.sample_ssd # ./sample_ssd
[I] Begin parsing model...
[I] FP32 mode running...
[I] End parsing model...
[I] Begin building engine...
[I] End building engine...
[I] *** deserializing
前向推理时间0.332ms
[I] Image name:../../../data/ssd/bus.ppm, Label: car, confidence: 96.0588 xmin: 4.14485 ymin: 117.443 xmax: 244.102 ymax: 241.829
&&&& PASSED TensorRT.sample_ssd # ./sample_ssd如果改变一个位置：
&&&& RUNNING TensorRT.sample_ssd # ./sample_ssd
[I] Begin parsing model...
[I] FP32 mode running...
[I] End parsing model...
[I] Begin building engine...
[I] End building engine...
[I] *** deserializing
前向推理时间11.626ms
[I] Image name:../../../data/ssd/bus.ppm, Label: car, confidence: 96.0587 xmin: 4.14484 ymin: 117.443 xmax: 244.102 ymax: 241.829
&&&& PASSED TensorRT.sample_ssd # ./sample_ssd我的显卡是GTX TiTAN X 12G不是Nvidia TiTan X 12G
所以1080ti可能比这个更快
caffe-cuda9.0-cudnn7.5
./build/tools/caffe time -model=/home/boyun/code/caffe-ssd/ssd/deploy.prototxt --weights=/home/boyun/code/caffe-ssd/ssd/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel --iterations=200 -gpu 0I0714 13:52:09.150904 14202 caffe.cpp:412] Average Forward pass: 30.3141 ms.
I0714 13:52:09.150909 14202 caffe.cpp:414] Average Backward pass: 26.9677 ms.
I0714 13:52:09.150918 14202 caffe.cpp:416] Average Forward-Backward: 57.478 ms.
I0714 13:52:09.150923 14202 caffe.cpp:418] Total Time: 11495.6 ms.
I0714 13:52:09.150928 14202 caffe.cpp:419] *** Benchmark ends ***下载：
https://developer.nvidia.com/nvidia-tensorrt-5x-download
一些示例：
Caffe版yolov3+tensorRT：
https://www.jianshu.com/p/e78c5c321248?tdsourcetag=s_pcqq_aiomsg
https://github.com/C-H-D/tensorRT-Caffe
在Caffe中调用TensorRT提供的MNIST model
https://blog.csdn.net/fengbingchun/article/details/78606228
mobileNet-ssd使用tensorRT部署
https://blog.csdn.net/qq_17278169/article/details/82971983
TensorRT基于caffe模型加速MobileNet SSD
https://blog.csdn.net/qq_22764813/article/details/84544409
贤者之路， Caffe转TensorRT
https://blog.csdn.net/chanzhennan/article/details/87085754
caffe-ssd网络模型 tensorRT加速
http://www.pianshen.com/article/4373405601/
TensorRT 的 C++ API 使用详解
https://blog.csdn.net/u010552731/article/details/89501819
Use TensorRT API to implement Caffe-SSD， SSD（channel pruning）， Mobilenet-SSD
https://github.com/chenzhi1992/TensorRT-SSD
TensorRT-Mobilenet-SSD can run 50fps on jetson tx2
https://github.com/Ghustwb/MobileNet-SSD-TensorRT
TensorRT 3.0 RC run SSD error in DetectionOutput layer
https://devtalk.nvidia.com/default/topic/1025153/gpu-accelerated-libraries/tensorrt-3-0-rc-run-ssd-error-in-detectionoutput-layer/post/5214393/#5214393
faster RCNN tensorRT代码
https://github.com/NVIDIA/TensorRT/tree/release/5.1/samples/opensource/sampleFasterRCNN
SSD tensorRT 代码
https://github.com/NVIDIA/TensorRT/tree/release/5.1/samples/opensource/sampleSSDgithub:TensorRT-SSD
https://github.com/onnx/onnx-tensorrt
https://github.com/ardianumam/Tensorflow-TensorRT
视频：
https://www.youtube.com/watch?v=AIGOSz2tFP8&list=PLkRkKTC6HZMwdtzv3PYJanRtR6ilSCZ4f
https://github.com/onnx/models
https://devblogs.nvidia.com/speed-up-inference-tensorrt/
How to Speed Up Deep Learning Inference Using TensorRT
https://developer.nvidia.com/tensorrt
文档：
https://developer.download.nvidia.cn/compute/machine-learning/tensorrt/models/sampleMLP-notebook.html
https://docs.nvidia.com/deeplearning/sdk/pdf/TensorRT-Developer-Guide.pdf
https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html
https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#charRNN_sample
