haotian/duan8
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add: Scaled yolov4 (#524)

Browse Source 
* add: mish, yololayer, layers

* update: CMake

* fix: compile

* add: yolov4-csp net def

* update: cuda kernel for scaled_yolov4

* increase nms thresh

* update: README

* add: gen_wts

* update: CMake

* fix memory leak

* Update README.md

* Update README.md

* Update README.md

* Update README.md
This commit is contained in:
makaveli 2021-05-06 08:35:20 +05:30 committed by GitHub
parent 4ffc56a99b
commit f4c384d1b1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 2106 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

37
scaled-yolov4/CMakeLists.txt Normal file
View File

@ -0,0 +1,37 @@
cmake_minimum_required(VERSION 2.6)
project(yolov4)
add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
find_package(CUDA REQUIRED)
include_directories(${PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/usr/include/x86_64-linux-gnu/)
link_directories(/usr/lib/x86_64-linux-gnu/)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")
cuda_add_library(myplugins SHARED ${PROJECT_SOURCE_DIR}/yololayer.cu ${PROJECT_SOURCE_DIR}/mish.cu)
target_link_libraries(myplugins nvinfer cudart)
find_package(OpenCV)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(yolov4csp ${PROJECT_SOURCE_DIR}/yolov4_csp.cpp)
target_link_libraries(yolov4csp nvinfer)
target_link_libraries(yolov4csp cudart)
target_link_libraries(yolov4csp myplugins)
target_link_libraries(yolov4csp ${OpenCV_LIBS})
add_definitions(-O2 -pthread)

56
scaled-yolov4/README.md Normal file
View File

@ -0,0 +1,56 @@
# scaled-yolov4
The Pytorch implementation is from [WongKinYiu/ScaledYOLOv4 yolov4-csp branch](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp). It can load yolov4-csp.cfg and yolov4-csp.weights(from AlexeyAB/darknet).
Note: There is a slight difference in yolov4-csp.cfg for darknet and pytorch. Use the one given in the above repo.
## Config
- Input shape `INPUT_H`, `INPUT_W` defined in yololayer.h
- Number of classes `CLASS_NUM` defined in yololayer.h
- FP16/FP32 can be selected by the macro `USE_FP16` in yolov4_csp.cpp
- GPU id can be selected by the macro `DEVICE` in yolov4_csp.cpp
- NMS thresh `NMS_THRESH` in yolov4_csp.cpp
- bbox confidence threshold `BBOX_CONF_THRESH` in yolov4_csp.cpp
- `BATCH_SIZE` in yolov4_csp.cpp
## How to run
1. generate yolov4_csp.wts from pytorch implementation with yolov4-csp.cfg and yolov4-csp.weights.
```
git clone https://github.com/wang-xinyu/tensorrtx.git
git clone -b yolov4-csp https://github.com/WongKinYiu/ScaledYOLOv4.git
// download yolov4-csp.weights from https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp#yolov4-csp
cp {tensorrtx}/scaled-yolov4/gen_wts.py {ScaledYOLOv4/}
cd {ScaledYOLOv4/}
python gen_wts.py yolov4-csp.weights
// a file 'yolov4_csp.wts' will be generated.
```
2. put yolov4_csp.wts into {tensorrtx}/scaled-yolov4, build and run
```
mv yolov4_csp.wts {tensorrtx}/scaled-yolov4/
cd {tensorrtx}/scaled-yolov4
mkdir build
cd build
cmake ..
make
sudo ./yolov4csp -s // serialize model to plan file i.e. 'yolov4csp.engine'
sudo ./yolov4csp -d ../../yolov3-spp/samples // deserialize plan file and run inference, the images in samples will be processed.
```
3. check the images generated, as follows. _zidane.jpg and _bus.jpg
<p align="center">
<img src= https://user-images.githubusercontent.com/39617050/117172509-824cf980-ade9-11eb-8e4c-27dbe658e355.jpg>
</p>
<p align="center">
<img src= https://user-images.githubusercontent.com/39617050/117172880-dbb52880-ade9-11eb-839a-0814fd46198e.jpg>
</p>
## More Information
See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)

196
scaled-yolov4/common.hpp Normal file
View File

@ -0,0 +1,196 @@
#include <fstream>
#include <map>
#include <sstream>
#include <vector>
#include <opencv2/opencv.hpp>
#include "NvInfer.h"
#include "yololayer.h"
#include "mish.h"
using namespace nvinfer1;
cv::Mat preprocess_img(cv::Mat& img) {
int w, h, x, y;
float r_w = Yolo::INPUT_W / (img.cols*1.0);
float r_h = Yolo::INPUT_H / (img.rows*1.0);
if (r_h > r_w) {
w = Yolo::INPUT_W;
h = r_w * img.rows;
x = 0;
y = (Yolo::INPUT_H - h) / 2;
} else {
w = r_h* img.cols;
h = Yolo::INPUT_H;
x = (Yolo::INPUT_W - w) / 2;
y = 0;
}
cv::Mat re(h, w, CV_8UC3);
cv::resize(img, re, re.size());
cv::Mat out(Yolo::INPUT_H, Yolo::INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
return out;
}
cv::Rect get_rect(cv::Mat& img, float bbox[4]) {
int l, r, t, b;
float r_w = Yolo::INPUT_W / (img.cols * 1.0);
float r_h = Yolo::INPUT_H / (img.rows * 1.0);
if (r_h > r_w) {
l = bbox[0] - bbox[2]/2.f;
r = bbox[0] + bbox[2]/2.f;
t = bbox[1] - bbox[3]/2.f - (Yolo::INPUT_H - r_w * img.rows) / 2;
b = bbox[1] + bbox[3]/2.f - (Yolo::INPUT_H - r_w * img.rows) / 2;
l = l / r_w;
r = r / r_w;
t = t / r_w;
b = b / r_w;
} else {
l = bbox[0] - bbox[2]/2.f - (Yolo::INPUT_W - r_h * img.cols) / 2;
r = bbox[0] + bbox[2]/2.f - (Yolo::INPUT_W - r_h * img.cols) / 2;
t = bbox[1] - bbox[3]/2.f;
b = bbox[1] + bbox[3]/2.f;
l = l / r_h;
r = r / r_h;
t = t / r_h;
b = b / r_h;
}
return cv::Rect(l, t, r-l, b-t);
}
float iou(float lbox[4], float rbox[4]) {
float interBox[] = {
std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left
std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right
std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top
std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom
};
if(interBox[2] > interBox[3] || interBox[0] > interBox[1])
return 0.0f;
float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]);
return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS);
}
bool cmp(const Yolo::Detection& a, const Yolo::Detection& b) {
return a.det_confidence > b.det_confidence;
}
void nms(std::vector<Yolo::Detection>& res, float *output, float conf_thresh, float nms_thresh = 0.5) {
int det_size = sizeof(Yolo::Detection) / sizeof(float);
std::map<float, std::vector<Yolo::Detection>> m;
for (int i = 0; i < output[0] && i < Yolo::MAX_OUTPUT_BBOX_COUNT; i++) {
if (output[1 + det_size * i + 4] <= conf_thresh) continue;
Yolo::Detection det;
memcpy(&det, &output[1 + det_size * i], det_size * sizeof(float));
if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector<Yolo::Detection>());
m[det.class_id].push_back(det);
}
for (auto it = m.begin(); it != m.end(); it++) {
//std::cout << it->second[0].class_id << " --- " << std::endl;
auto& dets = it->second;
std::sort(dets.begin(), dets.end(), cmp);
for (size_t m = 0; m < dets.size(); ++m) {
auto& item = dets[m];
res.push_back(item);
for (size_t n = m + 1; n < dets.size(); ++n) {
if (iou(item.bbox, dets[n].bbox) > nms_thresh) {
dets.erase(dets.begin()+n);
--n;
}
}
}
}
}
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file) {
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{DataType::kFLOAT, nullptr, 0};
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float *gamma = (float*)weightMap[lname + ".weight"].values;
float *beta = (float*)weightMap[lname + ".bias"].values;
float *mean = (float*)weightMap[lname + ".running_mean"].values;
float *var = (float*)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_var"].count;
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{DataType::kFLOAT, scval, len};
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{DataType::kFLOAT, shval, len};
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{DataType::kFLOAT, pval, len};
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
ILayer* convBnMish(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int p, int linx) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(DimsHW{p, p});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "module_list." + std::to_string(linx) + ".BatchNorm2d", 1e-4);
auto creator = getPluginRegistry()->getPluginCreator("Mish_TRT", "1");
const PluginFieldCollection* pluginData = creator->getFieldNames();
IPluginV2 *pluginObj = creator->createPlugin(("mish" + std::to_string(linx)).c_str(), pluginData);
ITensor* inputTensors[] = {bn1->getOutput(0)};
auto mish = network->addPluginV2(&inputTensors[0], 1, *pluginObj);
return mish;
}

23
scaled-yolov4/gen_wts.py Normal file
View File

@ -0,0 +1,23 @@
import struct
import sys
from models.models import *
from utils import *
model = Darknet('models/yolov4-csp.cfg', (512, 512))
weights = sys.argv[1]
device = torch_utils.select_device('0')
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
load_darknet_weights(model, weights)
with open('yolov4_csp.wts', 'w') as f:
f.write('{}\n'.format(len(model.state_dict().keys())))
for k, v in model.state_dict().items():
vr = v.reshape(-1).cpu().numpy()
f.write('{} {} '.format(k, len(vr)))
for vv in vr:
f.write(' ')
f.write(struct.pack('>f',float(vv)).hex())
f.write('\n')

507
scaled-yolov4/logging.h Normal file
View File

@ -0,0 +1,507 @@
/*
* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef TENSORRT_LOGGING_H
#define TENSORRT_LOGGING_H
#include "NvInferRuntimeCommon.h"
#include <cassert>
#include <ctime>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <sstream>
#include <string>
using Severity = nvinfer1::ILogger::Severity;
class LogStreamConsumerBuffer : public std::stringbuf
{
public:
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
: mOutput(stream)
, mPrefix(prefix)
, mShouldLog(shouldLog)
{
}
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
: mOutput(other.mOutput)
, mPrefix(other.mPrefix)
, mShouldLog(other.mShouldLog)
{
}
~LogStreamConsumerBuffer()
{
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
// if the pointer to the beginning is not equal to the pointer to the current position,
// call putOutput() to log the output to the stream
if (pbase() != pptr())
{
putOutput();
}
}
// synchronizes the stream buffer and returns 0 on success
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
// resetting the buffer and flushing the stream
virtual int sync()
{
putOutput();
return 0;
}
void putOutput()
{
if (mShouldLog)
{
// prepend timestamp
std::time_t timestamp = std::time(nullptr);
tm* tm_local = std::localtime(&timestamp);
std::cout << "[";
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
// std::stringbuf::str() gets the string contents of the buffer
// insert the buffer contents pre-appended by the appropriate prefix into the stream
mOutput << mPrefix << str();
// set the buffer to empty
str("");
// flush the stream
mOutput.flush();
}
}
void setShouldLog(bool shouldLog)
{
mShouldLog = shouldLog;
}
private:
std::ostream& mOutput;
std::string mPrefix;
bool mShouldLog;
};
//!
//! \class LogStreamConsumerBase
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
//!
class LogStreamConsumerBase
{
public:
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
: mBuffer(stream, prefix, shouldLog)
{
}
protected:
LogStreamConsumerBuffer mBuffer;
};
//!
//! \class LogStreamConsumer
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
//! Please do not change the order of the parent classes.
//!
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
{
public:
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
//! Reportable severity determines if the messages are severe enough to be logged.
LogStreamConsumer(Severity reportableSeverity, Severity severity)
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
, std::ostream(&mBuffer) // links the stream buffer with the stream
, mShouldLog(severity <= reportableSeverity)
, mSeverity(severity)
{
}
LogStreamConsumer(LogStreamConsumer&& other)
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
, std::ostream(&mBuffer) // links the stream buffer with the stream
, mShouldLog(other.mShouldLog)
, mSeverity(other.mSeverity)
{
}
void setReportableSeverity(Severity reportableSeverity)
{
mShouldLog = mSeverity <= reportableSeverity;
mBuffer.setShouldLog(mShouldLog);
}
private:
static std::ostream& severityOstream(Severity severity)
{
return severity >= Severity::kINFO ? std::cout : std::cerr;
}
static std::string severityPrefix(Severity severity)
{
switch (severity)
{
case Severity::kINTERNAL_ERROR: return "[F] ";
case Severity::kERROR: return "[E] ";
case Severity::kWARNING: return "[W] ";
case Severity::kINFO: return "[I] ";
case Severity::kVERBOSE: return "[V] ";
default: assert(0); return "";
}
}
bool mShouldLog;
Severity mSeverity;
};
//! \class Logger
//!
//! \brief Class which manages logging of TensorRT tools and samples
//!
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
//! and supports logging two types of messages:
//!
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
//! - Test pass/fail messages
//!
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
//!
//! In the future, this class could be extended to support dumping test results to a file in some standard format
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
//!
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
//! library and messages coming from the sample.
//!
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
//! object.
class Logger : public nvinfer1::ILogger
{
public:
Logger(Severity severity = Severity::kWARNING)
: mReportableSeverity(severity)
{
}
//!
//! \enum TestResult
//! \brief Represents the state of a given test
//!
enum class TestResult
{
kRUNNING, //!< The test is running
kPASSED, //!< The test passed
kFAILED, //!< The test failed
kWAIVED //!< The test was waived
};
//!
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
//! \return The nvinfer1::ILogger associated with this Logger
//!
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
//! we can eliminate the inheritance of Logger from ILogger
//!
nvinfer1::ILogger& getTRTLogger()
{
return *this;
}
//!
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
//!
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
//! inheritance from nvinfer1::ILogger
//!
void log(Severity severity, const char* msg) override
{
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
}
//!
//! \brief Method for controlling the verbosity of logging output
//!
//! \param severity The logger will only emit messages that have severity of this level or higher.
//!
void setReportableSeverity(Severity severity)
{
mReportableSeverity = severity;
}
//!
//! \brief Opaque handle that holds logging information for a particular test
//!
//! This object is an opaque handle to information used by the Logger to print test results.
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
//! with Logger::reportTest{Start,End}().
//!
class TestAtom
{
public:
TestAtom(TestAtom&&) = default;
private:
friend class Logger;
TestAtom(bool started, const std::string& name, const std::string& cmdline)
: mStarted(started)
, mName(name)
, mCmdline(cmdline)
{
}
bool mStarted;
std::string mName;
std::string mCmdline;
};
//!
//! \brief Define a test for logging
//!
//! \param[in] name The name of the test. This should be a string starting with
//! "TensorRT" and containing dot-separated strings containing
//! the characters [A-Za-z0-9_].
//! For example, "TensorRT.sample_googlenet"
//! \param[in] cmdline The command line used to reproduce the test
//
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
//!
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
{
return TestAtom(false, name, cmdline);
}
//!
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
//! as input
//!
//! \param[in] name The name of the test
//! \param[in] argc The number of command-line arguments
//! \param[in] argv The array of command-line arguments (given as C strings)
//!
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
{
auto cmdline = genCmdlineString(argc, argv);
return defineTest(name, cmdline);
}
//!
//! \brief Report that a test has started.
//!
//! \pre reportTestStart() has not been called yet for the given testAtom
//!
//! \param[in] testAtom The handle to the test that has started
//!
static void reportTestStart(TestAtom& testAtom)
{
reportTestResult(testAtom, TestResult::kRUNNING);
assert(!testAtom.mStarted);
testAtom.mStarted = true;
}
//!
//! \brief Report that a test has ended.
//!
//! \pre reportTestStart() has been called for the given testAtom
//!
//! \param[in] testAtom The handle to the test that has ended
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
//! TestResult::kFAILED, TestResult::kWAIVED
//!
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
{
assert(result != TestResult::kRUNNING);
assert(testAtom.mStarted);
reportTestResult(testAtom, result);
}
static int reportPass(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kPASSED);
return EXIT_SUCCESS;
}
static int reportFail(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kFAILED);
return EXIT_FAILURE;
}
static int reportWaive(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kWAIVED);
return EXIT_SUCCESS;
}
static int reportTest(const TestAtom& testAtom, bool pass)
{
return pass ? reportPass(testAtom) : reportFail(testAtom);
}
Severity getReportableSeverity() const
{
return mReportableSeverity;
}
private:
//!
//! \brief returns an appropriate string for prefixing a log message with the given severity
//!
static const char* severityPrefix(Severity severity)
{
switch (severity)
{
case Severity::kINTERNAL_ERROR: return "[F] ";
case Severity::kERROR: return "[E] ";
case Severity::kWARNING: return "[W] ";
case Severity::kINFO: return "[I] ";
case Severity::kVERBOSE: return "[V] ";
default: assert(0); return "";
}
}
//!
//! \brief returns an appropriate string for prefixing a test result message with the given result
//!
static const char* testResultString(TestResult result)
{
switch (result)
{
case TestResult::kRUNNING: return "RUNNING";
case TestResult::kPASSED: return "PASSED";
case TestResult::kFAILED: return "FAILED";
case TestResult::kWAIVED: return "WAIVED";
default: assert(0); return "";
}
}
//!
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
//!
static std::ostream& severityOstream(Severity severity)
{
return severity >= Severity::kINFO ? std::cout : std::cerr;
}
//!
//! \brief method that implements logging test results
//!
static void reportTestResult(const TestAtom& testAtom, TestResult result)
{
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
<< testAtom.mCmdline << std::endl;
}
//!
//! \brief generate a command line string from the given (argc, argv) values
//!
static std::string genCmdlineString(int argc, char const* const* argv)
{
std::stringstream ss;
for (int i = 0; i < argc; i++)
{
if (i > 0)
{
ss << " ";
}
ss << argv[i];
}
return ss.str();
}
Severity mReportableSeverity;
};
namespace
{
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
//!
//! Example usage:
//!
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
//!
//! Example usage:
//!
//! LOG_INFO(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_INFO(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
//!
//! Example usage:
//!
//! LOG_WARN(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_WARN(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
//!
//! Example usage:
//!
//! LOG_ERROR(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
//! ("fatal" severity)
//!
//! Example usage:
//!
//! LOG_FATAL(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
}
} // anonymous namespace
#endif // TENSORRT_LOGGING_H

196
scaled-yolov4/mish.cu Normal file
View File

@ -0,0 +1,196 @@
#include <cmath>
#include <stdio.h>
#include <cassert>
#include <iostream>
#include "mish.h"
namespace nvinfer1
{
MishPlugin::MishPlugin()
{
}
MishPlugin::~MishPlugin()
{
}
// create the plugin at runtime from a byte stream
MishPlugin::MishPlugin(const void* data, size_t length)
{
assert(length == sizeof(input_size_));
input_size_ = *reinterpret_cast<const int*>(data);
}
void MishPlugin::serialize(void* buffer) const
{
*reinterpret_cast<int*>(buffer) = input_size_;
}
size_t MishPlugin::getSerializationSize() const
{
return sizeof(input_size_);
}
int MishPlugin::initialize()
{
return 0;
}
Dims MishPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{
assert(nbInputDims == 1);
assert(index == 0);
input_size_ = inputs[0].d[0] * inputs[0].d[1] * inputs[0].d[2];
// Output dimensions
return Dims3(inputs[0].d[0], inputs[0].d[1], inputs[0].d[2]);
}
// Set plugin namespace
void MishPlugin::setPluginNamespace(const char* pluginNamespace)
{
mPluginNamespace = pluginNamespace;
}
const char* MishPlugin::getPluginNamespace() const
{
return mPluginNamespace;
}
// Return the DataType of the plugin output at the requested index
DataType MishPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
{
return DataType::kFLOAT;
}
// Return true if output tensor is broadcast across a batch.
bool MishPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
{
return false;
}
// Return true if plugin can use input that is broadcast across batch without replication.
bool MishPlugin::canBroadcastInputAcrossBatch(int inputIndex) const
{
return false;
}
void MishPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput)
{
}
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
void MishPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
{
}
// Detach the plugin object from its execution context.
void MishPlugin::detachFromContext() {}
const char* MishPlugin::getPluginType() const
{
return "Mish_TRT";
}
const char* MishPlugin::getPluginVersion() const
{
return "1";
}
void MishPlugin::destroy()
{
delete this;
}
// Clone the plugin
IPluginV2IOExt* MishPlugin::clone() const
{
MishPlugin *p = new MishPlugin();
p->input_size_ = input_size_;
p->setPluginNamespace(mPluginNamespace);
return p;
}
__device__ float tanh_activate_kernel(float x){return (2/(1 + expf(-2*x)) - 1);}
__device__ float softplus_kernel(float x, float threshold = 20) {
if (x > threshold) return x; // too large
else if (x < -threshold) return expf(x); // too small
return logf(expf(x) + 1);
}
__global__ void mish_kernel(const float *input, float *output, int num_elem) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= num_elem) return;
//float t = exp(input[idx]);
//if (input[idx] > 20.0) {
// t *= t;
// output[idx] = (t - 1.0) / (t + 1.0);
//} else {
// float tt = t * t;
// output[idx] = (tt + 2.0 * t) / (tt + 2.0 * t + 2.0);
//}
//output[idx] *= input[idx];
output[idx] = input[idx] * tanh_activate_kernel(softplus_kernel(input[idx]));
}
void MishPlugin::forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize) {
int block_size = thread_count_;
int grid_size = (input_size_ * batchSize + block_size - 1) / block_size;
mish_kernel<<<grid_size, block_size>>>(inputs[0], output, input_size_ * batchSize);
}
int MishPlugin::enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream)
{
//assert(batchSize == 1);
//GPU
//CUDA_CHECK(cudaStreamSynchronize(stream));
forwardGpu((const float *const *)inputs, (float*)outputs[0], stream, batchSize);
return 0;
}
PluginFieldCollection MishPluginCreator::mFC{};
std::vector<PluginField> MishPluginCreator::mPluginAttributes;
MishPluginCreator::MishPluginCreator()
{
mPluginAttributes.clear();
mFC.nbFields = mPluginAttributes.size();
mFC.fields = mPluginAttributes.data();
}
const char* MishPluginCreator::getPluginName() const
{
return "Mish_TRT";
}
const char* MishPluginCreator::getPluginVersion() const
{
return "1";
}
const PluginFieldCollection* MishPluginCreator::getFieldNames()
{
return &mFC;
}
IPluginV2IOExt* MishPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
{
MishPlugin* obj = new MishPlugin();
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
IPluginV2IOExt* MishPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
{
// This object will be deleted when the network is destroyed, which will
// call MishPlugin::destroy()
MishPlugin* obj = new MishPlugin(serialData, serialLength);
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
}

108
scaled-yolov4/mish.h Normal file
View File

@ -0,0 +1,108 @@
#ifndef TRTX_MISH_PLUGIN_H
#define TRTX_MISH_PLUGIN_H
#include <string>
#include <vector>
#include "NvInfer.h"
namespace nvinfer1
{
class MishPlugin: public IPluginV2IOExt
{
public:
explicit MishPlugin();
MishPlugin(const void* data, size_t length);
~MishPlugin();
int getNbOutputs() const override
{
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
int initialize() override;
virtual void terminate() override {};
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;
virtual size_t getSerializationSize() const override;
virtual void serialize(void* buffer) const override;
bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const override {
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
}
const char* getPluginType() const override;
const char* getPluginVersion() const override;
void destroy() override;
IPluginV2IOExt* clone() const override;
void setPluginNamespace(const char* pluginNamespace) override;
const char* getPluginNamespace() const override;
DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override;
bool canBroadcastInputAcrossBatch(int inputIndex) const override;
void attachToContext(
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) override;
void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) override;
void detachFromContext() override;
int input_size_;
private:
void forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize = 1);
int thread_count_ = 256;
const char* mPluginNamespace;
};
class MishPluginCreator : public IPluginCreator
{
public:
MishPluginCreator();
~MishPluginCreator() override = default;
const char* getPluginName() const override;
const char* getPluginVersion() const override;
const PluginFieldCollection* getFieldNames() override;
IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) override;
IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
void setPluginNamespace(const char* libNamespace) override
{
mNamespace = libNamespace;
}
const char* getPluginNamespace() const override
{
return mNamespace.c_str();
}
private:
std::string mNamespace;
static PluginFieldCollection mFC;
static std::vector<PluginField> mPluginAttributes;
};
REGISTER_TENSORRT_PLUGIN(MishPluginCreator);
};
#endif // TRTX_MISH_PLUGIN_H

39
scaled-yolov4/utils.h Normal file
View File

@ -0,0 +1,39 @@
#ifndef __TRT_UTILS_H_
#define __TRT_UTILS_H_
#include <iostream>
#include <vector>
#include <algorithm>
#include <cudnn.h>
#ifndef CUDA_CHECK
#define CUDA_CHECK(callstr) \
{ \
cudaError_t error_code = callstr; \
if (error_code != cudaSuccess) { \
std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \
assert(0); \
} \
}
#endif
namespace Tn
{
template<typename T>
void write(char*& buffer, const T& val)
{
*reinterpret_cast<T*>(buffer) = val;
buffer += sizeof(T);
}
template<typename T>
void read(const char*& buffer, T& val)
{
val = *reinterpret_cast<const T*>(buffer);
buffer += sizeof(T);
}
}
#endif

274
scaled-yolov4/yololayer.cu Normal file
View File

@ -0,0 +1,274 @@
#include <assert.h>
#include "yololayer.h"
#include "utils.h"
using namespace Yolo;
namespace nvinfer1
{
YoloLayerPlugin::YoloLayerPlugin()
{
mClassCount = CLASS_NUM;
mYoloKernel.clear();
mYoloKernel.push_back(yolo1);
mYoloKernel.push_back(yolo2);
mYoloKernel.push_back(yolo3);
mKernelCount = mYoloKernel.size();
CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
size_t AnchorLen = sizeof(float)* CHECK_COUNT*2;
for(int ii = 0; ii < mKernelCount; ii ++)
{
CUDA_CHECK(cudaMalloc(&mAnchor[ii],AnchorLen));
const auto& yolo = mYoloKernel[ii];
CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
}
}
YoloLayerPlugin::~YoloLayerPlugin()
{
}
// create the plugin at runtime from a byte stream
YoloLayerPlugin::YoloLayerPlugin(const void* data, size_t length)
{
using namespace Tn;
const char *d = reinterpret_cast<const char *>(data), *a = d;
read(d, mClassCount);
read(d, mThreadCount);
read(d, mKernelCount);
mYoloKernel.resize(mKernelCount);
auto kernelSize = mKernelCount*sizeof(YoloKernel);
memcpy(mYoloKernel.data(),d,kernelSize);
d += kernelSize;
CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
size_t AnchorLen = sizeof(float)* CHECK_COUNT*2;
for(int ii = 0; ii < mKernelCount; ii ++)
{
CUDA_CHECK(cudaMalloc(&mAnchor[ii],AnchorLen));
const auto& yolo = mYoloKernel[ii];
CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
}
assert(d == a + length);
}
void YoloLayerPlugin::serialize(void* buffer) const
{
using namespace Tn;
char* d = static_cast<char*>(buffer), *a = d;
write(d, mClassCount);
write(d, mThreadCount);
write(d, mKernelCount);
auto kernelSize = mKernelCount*sizeof(YoloKernel);
memcpy(d,mYoloKernel.data(),kernelSize);
d += kernelSize;
assert(d == a + getSerializationSize());
}
size_t YoloLayerPlugin::getSerializationSize() const
{
return sizeof(mClassCount) + sizeof(mThreadCount) + sizeof(mKernelCount) + sizeof(Yolo::YoloKernel) * mYoloKernel.size();
}
int YoloLayerPlugin::initialize()
{
return 0;
}
Dims YoloLayerPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{
//output the result to channel
int totalsize = MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float);
return Dims3(totalsize + 1, 1, 1);
}
// Set plugin namespace
void YoloLayerPlugin::setPluginNamespace(const char* pluginNamespace)
{
mPluginNamespace = pluginNamespace;
}
const char* YoloLayerPlugin::getPluginNamespace() const
{
return mPluginNamespace;
}
// Return the DataType of the plugin output at the requested index
DataType YoloLayerPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
{
return DataType::kFLOAT;
}
// Return true if output tensor is broadcast across a batch.
bool YoloLayerPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
{
return false;
}
// Return true if plugin can use input that is broadcast across batch without replication.
bool YoloLayerPlugin::canBroadcastInputAcrossBatch(int inputIndex) const
{
return false;
}
void YoloLayerPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput)
{
}
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
void YoloLayerPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
{
}
// Detach the plugin object from its execution context.
void YoloLayerPlugin::detachFromContext() {}
const char* YoloLayerPlugin::getPluginType() const
{
return "YoloLayer_TRT";
}
const char* YoloLayerPlugin::getPluginVersion() const
{
return "1";
}
void YoloLayerPlugin::destroy()
{
delete this;
}
// Clone the plugin
IPluginV2IOExt* YoloLayerPlugin::clone() const
{
YoloLayerPlugin *p = new YoloLayerPlugin();
p->setPluginNamespace(mPluginNamespace);
return p;
}
__device__ float Logist(float data){ return 1./(1. + exp(-data)); };
__global__ void CalDetection(const float *input, float *output, int noElements,
int yoloWidth, int yoloHeight, const float anchors[CHECK_COUNT*2],int classes,int outputElem) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= noElements) return;
int total_grid = yoloWidth * yoloHeight;
int bnIdx = idx / total_grid;
idx = idx - total_grid*bnIdx;
int info_len_i = 5 + classes;
const float* curInput = input + bnIdx * (info_len_i * total_grid * CHECK_COUNT);
for (int k = 0; k < 3; ++k) {
int class_id = 0;
float max_cls_prob = 0.0;
for (int i = 5; i < info_len_i; ++i) {
float p = Logist(curInput[idx + k * info_len_i * total_grid + i * total_grid]);
if (p > max_cls_prob) {
max_cls_prob = p;
class_id = i - 5;
}
}
float box_prob = Logist(curInput[idx + k * info_len_i * total_grid + 4 * total_grid]);
if (max_cls_prob < IGNORE_THRESH || box_prob < IGNORE_THRESH) continue;
float *res_count = output + bnIdx*outputElem;
int count = (int)atomicAdd(res_count, 1);
if (count >= MAX_OUTPUT_BBOX_COUNT) return;
char* data = (char * )res_count + sizeof(float) + count*sizeof(Detection);
Detection* det = (Detection*)(data);
int row = idx / yoloWidth;
int col = idx % yoloWidth;
//Location
det->bbox[0] = (col + (2 * (Logist(curInput[idx + k * info_len_i * total_grid + 0 * total_grid]))) - 0.5) * INPUT_W / yoloWidth;
det->bbox[1] = (row + (2 * (Logist(curInput[idx + k * info_len_i * total_grid + 1 * total_grid]))) - 0.5) * INPUT_H / yoloHeight;
det->bbox[2] = (powf(2 * (Logist(curInput[idx + k * info_len_i * total_grid + 2 * total_grid])), 2)) * anchors[2*k];
det->bbox[3] = (powf(2 * (Logist(curInput[idx + k * info_len_i * total_grid + 3 * total_grid])), 2)) * anchors[2*k + 1];
det->det_confidence = box_prob;
det->class_id = class_id;
det->class_confidence = max_cls_prob;
}
}
void YoloLayerPlugin::forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize) {
int outputElem = 1 + MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float);
for(int idx = 0 ; idx < batchSize; ++idx) {
CUDA_CHECK(cudaMemset(output + idx*outputElem, 0, sizeof(float)));
}
int numElem = 0;
for (unsigned int i = 0;i< mYoloKernel.size();++i)
{
const auto& yolo = mYoloKernel[i];
numElem = yolo.width*yolo.height*batchSize;
if (numElem < mThreadCount)
mThreadCount = numElem;
CalDetection<<< (yolo.width*yolo.height*batchSize + mThreadCount - 1) / mThreadCount, mThreadCount>>>
(inputs[i],output, numElem, yolo.width, yolo.height, (float *)mAnchor[i], mClassCount ,outputElem);
}
}
int YoloLayerPlugin::enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream)
{
//assert(batchSize == 1);
//GPU
//CUDA_CHECK(cudaStreamSynchronize(stream));
forwardGpu((const float *const *)inputs, (float*)outputs[0], stream, batchSize);
return 0;
}
PluginFieldCollection YoloPluginCreator::mFC{};
std::vector<PluginField> YoloPluginCreator::mPluginAttributes;
YoloPluginCreator::YoloPluginCreator()
{
mPluginAttributes.clear();
mFC.nbFields = mPluginAttributes.size();
mFC.fields = mPluginAttributes.data();
}
const char* YoloPluginCreator::getPluginName() const
{
return "YoloLayer_TRT";
}
const char* YoloPluginCreator::getPluginVersion() const
{
return "1";
}
const PluginFieldCollection* YoloPluginCreator::getFieldNames()
{
return &mFC;
}
IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
{
YoloLayerPlugin* obj = new YoloLayerPlugin();
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
IPluginV2IOExt* YoloPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
{
// This object will be deleted when the network is destroyed, which will
// call MishPlugin::destroy()
YoloLayerPlugin* obj = new YoloLayerPlugin(serialData, serialLength);
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
}

154
scaled-yolov4/yololayer.h Normal file
View File

@ -0,0 +1,154 @@
#ifndef _YOLO_LAYER_H
#define _YOLO_LAYER_H
#include <iostream>
#include <vector>
#include "NvInfer.h"
namespace Yolo
{
static constexpr int CHECK_COUNT = 3;
static constexpr float IGNORE_THRESH = 0.1f;
static constexpr int MAX_OUTPUT_BBOX_COUNT = 1000;
static constexpr int CLASS_NUM = 80;
static constexpr int INPUT_H = 512;
static constexpr int INPUT_W = 512;
struct YoloKernel
{
int width;
int height;
float anchors[CHECK_COUNT*2];
};
static constexpr YoloKernel yolo1 = {
INPUT_W / 8,
INPUT_H / 8,
{12,16, 19,36, 40,28}
};
static constexpr YoloKernel yolo2 = {
INPUT_W / 16,
INPUT_H / 16,
{36,75, 76,55, 72,146}
};
static constexpr YoloKernel yolo3 = {
INPUT_W / 32,
INPUT_H / 32,
{142,110, 192,243, 459,401}
};
static constexpr int LOCATIONS = 4;
struct alignas(float) Detection{
//x y w h
float bbox[LOCATIONS];
float det_confidence;
float class_id;
float class_confidence;
};
}
namespace nvinfer1
{
class YoloLayerPlugin: public IPluginV2IOExt
{
public:
explicit YoloLayerPlugin();
YoloLayerPlugin(const void* data, size_t length);
~YoloLayerPlugin();
int getNbOutputs() const override
{
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
int initialize() override;
virtual void terminate() override {};
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;
virtual size_t getSerializationSize() const override;
virtual void serialize(void* buffer) const override;
bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const override {
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
}
const char* getPluginType() const override;
const char* getPluginVersion() const override;
void destroy() override;
IPluginV2IOExt* clone() const override;
void setPluginNamespace(const char* pluginNamespace) override;
const char* getPluginNamespace() const override;
DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override;
bool canBroadcastInputAcrossBatch(int inputIndex) const override;
void attachToContext(
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) override;
void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) override;
void detachFromContext() override;
private:
void forwardGpu(const float *const * inputs,float * output, cudaStream_t stream, int batchSize = 1);
int mClassCount;
int mKernelCount;
std::vector<Yolo::YoloKernel> mYoloKernel;
int mThreadCount = 256;
void** mAnchor;
const char* mPluginNamespace;
};
class YoloPluginCreator : public IPluginCreator
{
public:
YoloPluginCreator();
~YoloPluginCreator() override = default;
const char* getPluginName() const override;
const char* getPluginVersion() const override;
const PluginFieldCollection* getFieldNames() override;
IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) override;
IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
void setPluginNamespace(const char* libNamespace) override
{
mNamespace = libNamespace;
}
const char* getPluginNamespace() const override
{
return mNamespace.c_str();
}
private:
std::string mNamespace;
static PluginFieldCollection mFC;
static std::vector<PluginField> mPluginAttributes;
};
REGISTER_TENSORRT_PLUGIN(YoloPluginCreator);
};
#endif

516
scaled-yolov4/yolov4_csp.cpp Normal file
View File

@ -0,0 +1,516 @@
#include <iostream>
#include <chrono>
#include <dirent.h>
#include "logging.h"
#include "utils.h"
#include "cuda_runtime_api.h"
#include "common.hpp"
#define USE_FP16 // comment out this if want to use FP32
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.4
#define BBOX_CONF_THRESH 0.5
#define BATCH_SIZE 1
// stuff we know about the network and the input/output blobs
static const int INPUT_H = Yolo::INPUT_H;
static const int INPUT_W = Yolo::INPUT_W;
static const int DETECTION_SIZE = sizeof(Yolo::Detection) / sizeof(float);
static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * DETECTION_SIZE + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
static Logger gLogger;
// Creat the engine using only the API and not any parser.
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
INetworkDefinition* network = builder -> createNetworkV2(0U);
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
ITensor* data = network -> addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
assert(data);
std::map<std::string, Weights> weightMap = loadWeights("../yolov4_csp.wts");
Weights emptywts{DataType::kFLOAT, nullptr, 0};
// define yolov4 csp layers
auto l0 = convBnMish(network, weightMap, *data, 32, 3, 1, 1, 0);
auto l1 = convBnMish(network, weightMap, *l0 -> getOutput(0), 64, 3, 2, 1, 1);
auto l2 = convBnMish(network, weightMap, *l1 -> getOutput(0), 32, 1, 1, 0, 2);
auto l3 = convBnMish(network, weightMap, *l2 -> getOutput(0), 64, 3, 1, 1, 3);
auto ew4 = network -> addElementWise(*l3 -> getOutput(0), *l1 -> getOutput(0), ElementWiseOperation::kSUM);
auto l5 = convBnMish(network, weightMap, *ew4 -> getOutput(0), 128, 3, 2, 1, 5);
auto l6 = convBnMish(network, weightMap, *l5 -> getOutput(0), 64, 1, 1, 0, 6);
auto l7 = l5;
auto l8 = convBnMish(network, weightMap, *l7 -> getOutput(0), 64, 1, 1, 0, 8);
auto l9 = convBnMish(network, weightMap, *l8 -> getOutput(0), 64, 1, 1, 0, 9);
auto l10 = convBnMish(network, weightMap, *l9 -> getOutput(0), 64, 3, 1, 1, 10);
auto ew11 = network -> addElementWise(*l10 -> getOutput(0), *l8 -> getOutput(0), ElementWiseOperation::kSUM);
auto l12 = convBnMish(network, weightMap, *ew11 -> getOutput(0), 64, 1, 1, 0, 12);
auto l13 = convBnMish(network, weightMap, *l12 -> getOutput(0), 64, 3, 1, 1, 13);
auto ew14 = network -> addElementWise(*l13 -> getOutput(0), *ew11 -> getOutput(0), ElementWiseOperation::kSUM);
auto l15 = convBnMish(network, weightMap, *ew14 -> getOutput(0), 64, 1, 1, 0, 15);
ITensor* inputTensors16[] = {l15 -> getOutput(0), l6 -> getOutput(0)};
auto cat16 = network -> addConcatenation(inputTensors16, 2);
auto l17 = convBnMish(network, weightMap, *cat16 -> getOutput(0), 128, 1, 1, 0, 17);
auto l18 = convBnMish(network, weightMap, *l17 -> getOutput(0), 256, 3, 2, 1, 18);
auto l19 = convBnMish(network, weightMap, *l18 -> getOutput(0), 128, 1, 1, 0, 19);
auto l20 = l18;
auto l21 = convBnMish(network, weightMap, *l20 -> getOutput(0), 128, 1, 1, 0, 21);
auto l22 = convBnMish(network, weightMap, *l21 -> getOutput(0), 128, 1, 1, 0, 22);
auto l23 = convBnMish(network, weightMap, *l22 -> getOutput(0), 128, 3, 1, 1, 23);
auto ew24 = network -> addElementWise(*l23 -> getOutput(0), *l21 -> getOutput(0), ElementWiseOperation::kSUM);
auto l25 = convBnMish(network, weightMap, *ew24 -> getOutput(0), 128, 1, 1, 0, 25);
auto l26 = convBnMish(network, weightMap, *l25 -> getOutput(0), 128, 3, 1, 1, 26);
auto ew27 = network -> addElementWise(*l26 -> getOutput(0), *ew24 -> getOutput(0), ElementWiseOperation::kSUM);
auto l28 = convBnMish(network, weightMap, *ew27 -> getOutput(0), 128, 1, 1, 0, 28);
auto l29 = convBnMish(network, weightMap, *l28 -> getOutput(0), 128, 3, 1, 1, 29);
auto ew30 = network -> addElementWise(*l29 -> getOutput(0), *ew27 -> getOutput(0), ElementWiseOperation::kSUM);
auto l31 = convBnMish(network, weightMap, *ew30 -> getOutput(0), 128, 1, 1, 0, 31);
auto l32 = convBnMish(network, weightMap, *l31 -> getOutput(0), 128, 3, 1, 1, 32);
auto ew33 = network -> addElementWise(*l32 -> getOutput(0), *ew30 -> getOutput(0), ElementWiseOperation::kSUM);
auto l34 = convBnMish(network, weightMap, *ew33 -> getOutput(0), 128, 1, 1, 0, 34);
auto l35 = convBnMish(network, weightMap, *l34 -> getOutput(0), 128, 3, 1, 1, 35);
auto ew36 = network -> addElementWise(*l35 -> getOutput(0), *ew33 -> getOutput(0), ElementWiseOperation::kSUM);
auto l37 = convBnMish(network, weightMap, *ew36 -> getOutput(0), 128, 1, 1, 0, 37);
auto l38 = convBnMish(network, weightMap, *l37 -> getOutput(0), 128, 3, 1, 1, 38);
auto ew39 = network -> addElementWise(*l38 -> getOutput(0), *ew36 -> getOutput(0), ElementWiseOperation::kSUM);
auto l40 = convBnMish(network, weightMap, *ew39 -> getOutput(0), 128, 1, 1, 0, 40);
auto l41 = convBnMish(network, weightMap, *l40 -> getOutput(0), 128, 3, 1, 1, 41);
auto ew42 = network -> addElementWise(*l41 -> getOutput(0), *ew39 -> getOutput(0), ElementWiseOperation::kSUM);
auto l43 = convBnMish(network, weightMap, *ew42 -> getOutput(0), 128, 1, 1, 0, 43);
auto l44 = convBnMish(network, weightMap, *l43 -> getOutput(0), 128, 3, 1, 1, 44);
auto ew45 = network -> addElementWise(*l44 -> getOutput(0), *ew42 -> getOutput(0), ElementWiseOperation::kSUM);
auto l46 = convBnMish(network, weightMap, *ew45 -> getOutput(0), 128, 1, 1, 0, 46);
ITensor* inputTensors47[] = {l46 -> getOutput(0), l19 -> getOutput(0)};
auto cat47 = network -> addConcatenation(inputTensors47, 2);
auto l48 = convBnMish(network, weightMap, *cat47 -> getOutput(0), 256, 1, 1, 0, 48);
auto l49 = convBnMish(network, weightMap, *l48 -> getOutput(0), 512, 3, 2, 1, 49);
auto l50 = convBnMish(network, weightMap, *l49 -> getOutput(0), 256, 1, 1, 0, 50);
auto l51 = l49;
auto l52 = convBnMish(network, weightMap, *l51 -> getOutput(0), 256, 1, 1, 0, 52);
auto l53 = convBnMish(network, weightMap, *l52 -> getOutput(0), 256, 1, 1, 0, 53);
auto l54 = convBnMish(network, weightMap, *l53 -> getOutput(0), 256, 3, 1, 1, 54);
auto ew55 = network -> addElementWise(*l54 -> getOutput(0), *l52 -> getOutput(0), ElementWiseOperation::kSUM);
auto l56 = convBnMish(network, weightMap, *ew55 -> getOutput(0), 256, 1, 1, 0, 56);
auto l57 = convBnMish(network, weightMap, *l56 -> getOutput(0), 256, 3, 1, 1, 57);
auto ew58 = network -> addElementWise(*l57 -> getOutput(0), *ew55 -> getOutput(0), ElementWiseOperation::kSUM);
auto l59 = convBnMish(network, weightMap, *ew58 -> getOutput(0), 256, 1, 1, 0, 59);
auto l60 = convBnMish(network, weightMap, *l59 -> getOutput(0), 256, 3, 1, 1, 60);
auto ew61 = network -> addElementWise(*l60 -> getOutput(0), *ew58 -> getOutput(0), ElementWiseOperation::kSUM);
auto l62 = convBnMish(network, weightMap, *ew61 -> getOutput(0), 256, 1, 1, 0, 62);
auto l63 = convBnMish(network, weightMap, *l62 -> getOutput(0), 256, 3, 1, 1, 63);
auto ew64 = network -> addElementWise(*l63 -> getOutput(0), *ew61 -> getOutput(0), ElementWiseOperation::kSUM);
auto l65 = convBnMish(network, weightMap, *ew64 -> getOutput(0), 256, 1, 1, 0, 65);
auto l66 = convBnMish(network, weightMap, *l65 -> getOutput(0), 256, 3, 1, 1, 66);
auto ew67 = network -> addElementWise(*l66 -> getOutput(0), *ew64 -> getOutput(0), ElementWiseOperation::kSUM);
auto l68 = convBnMish(network, weightMap, *ew67 -> getOutput(0), 256, 1, 1, 0, 68);
auto l69 = convBnMish(network, weightMap, *l68 -> getOutput(0), 256, 3, 1, 1, 69);
auto ew70 = network -> addElementWise(*l69 -> getOutput(0), *ew67 -> getOutput(0), ElementWiseOperation::kSUM);
auto l71 = convBnMish(network, weightMap, *ew70 -> getOutput(0), 256, 1, 1, 0, 71);
auto l72 = convBnMish(network, weightMap, *l71 -> getOutput(0), 256, 3, 1, 1, 72);
auto ew73 = network -> addElementWise(*l72 -> getOutput(0), *ew70 -> getOutput(0), ElementWiseOperation::kSUM);
auto l74 = convBnMish(network, weightMap, *ew73 -> getOutput(0), 256, 1, 1, 0, 74);
auto l75 = convBnMish(network, weightMap, *l74 -> getOutput(0), 256, 3, 1, 1, 75);
auto ew76 = network -> addElementWise(*l75 -> getOutput(0), *ew73 -> getOutput(0), ElementWiseOperation::kSUM);
auto l77 = convBnMish(network, weightMap, *ew76 -> getOutput(0), 256, 1, 1, 0, 77);
ITensor* inputTensors78[] = {l77 -> getOutput(0), l50 -> getOutput(0)};
auto cat78 = network -> addConcatenation(inputTensors78, 2);
auto l79 = convBnMish(network, weightMap, *cat78 -> getOutput(0), 512, 1, 1, 0, 79);
auto l80 = convBnMish(network, weightMap, *l79 -> getOutput(0), 1024, 3, 2, 1, 80);
auto l81 = convBnMish(network, weightMap, *l80 -> getOutput(0), 512, 1, 1, 0, 81);
auto l82 = l80;
auto l83 = convBnMish(network, weightMap, *l82 -> getOutput(0), 512, 1, 1, 0, 83);
auto l84 = convBnMish(network, weightMap, *l83 -> getOutput(0), 512, 1, 1, 0, 84);
auto l85 = convBnMish(network, weightMap, *l84 -> getOutput(0), 512, 3, 1, 1, 85);
auto ew86 = network -> addElementWise(*l85 -> getOutput(0), *l83 -> getOutput(0), ElementWiseOperation::kSUM);
auto l87 = convBnMish(network, weightMap, *ew86 -> getOutput(0), 512, 1, 1, 0, 87);
auto l88 = convBnMish(network, weightMap, *l87 -> getOutput(0), 512, 3, 1, 1, 88);
auto ew89 = network -> addElementWise(*l88 -> getOutput(0), *ew86 -> getOutput(0), ElementWiseOperation::kSUM);
auto l90 = convBnMish(network, weightMap, *ew89 -> getOutput(0), 512, 1, 1, 0, 90);
auto l91 = convBnMish(network, weightMap, *l90 -> getOutput(0), 512, 3, 1, 1, 91);
auto ew92 = network -> addElementWise(*l91 -> getOutput(0), *ew89 -> getOutput(0), ElementWiseOperation::kSUM);
auto l93 = convBnMish(network, weightMap, *ew92 -> getOutput(0), 512, 1, 1, 0, 93);
auto l94 = convBnMish(network, weightMap, *l93 -> getOutput(0), 512, 3, 1, 1, 94);
auto ew95 = network -> addElementWise(*l94 -> getOutput(0), *ew92 -> getOutput(0), ElementWiseOperation::kSUM);
auto l96 = convBnMish(network, weightMap, *ew95 -> getOutput(0), 512, 1, 1, 0, 96);
ITensor* inputTensors97[] = {l96 -> getOutput(0), l81 -> getOutput(0)};
auto cat97 = network -> addConcatenation(inputTensors97, 2);
auto l98 = convBnMish(network, weightMap, *cat97 -> getOutput(0), 1024, 1, 1, 0, 98);
// ----
auto l99 = convBnMish(network, weightMap, *l98 -> getOutput(0), 512, 1, 1, 0, 99);
auto l100 = l98;
auto l101 = convBnMish(network, weightMap, *l100 -> getOutput(0), 512, 1, 1, 0, 101);
auto l102 = convBnMish(network, weightMap, *l101 -> getOutput(0), 512, 3, 1, 1, 102);
auto l103 = convBnMish(network, weightMap, *l102 -> getOutput(0), 512, 1, 1, 0, 103);
auto pool104 = network -> addPoolingNd(*l103 -> getOutput(0), PoolingType::kMAX, DimsHW{5, 5});
pool104 -> setPaddingNd(DimsHW{2, 2});
pool104 -> setStrideNd(DimsHW{1, 1});
auto l105 = l103;
auto pool106 = network -> addPoolingNd(*l105 -> getOutput(0), PoolingType::kMAX, DimsHW{9, 9});
pool106 -> setPaddingNd(DimsHW{4, 4});
pool106 -> setStrideNd(DimsHW{1, 1});
auto l107 = l103;
auto pool108 = network -> addPoolingNd(*l107 -> getOutput(0), PoolingType::kMAX, DimsHW{13, 13});
pool108 -> setPaddingNd(DimsHW{6, 6});
pool108 -> setStrideNd(DimsHW{1, 1});
ITensor* inputTensors109[] = {pool108 -> getOutput(0), pool106 -> getOutput(0), pool104 -> getOutput(0), l103 -> getOutput(0)};
auto cat109 = network -> addConcatenation(inputTensors109, 4);
// ---- end spp
auto l110 = convBnMish(network, weightMap, *cat109 -> getOutput(0), 512, 1, 1, 0, 110);
auto l111 = convBnMish(network, weightMap, *l110 -> getOutput(0), 512, 3, 1, 1, 111);
ITensor* inputTensors112[] = { l111 -> getOutput(0), l99 -> getOutput(0) };
auto cat112 = network -> addConcatenation(inputTensors112, 2);
auto l113 = convBnMish(network, weightMap, *cat112 -> getOutput(0), 512, 1, 1, 0, 113);
auto l114 = convBnMish(network, weightMap, *l113 -> getOutput(0), 256, 1, 1, 0, 114);
float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 256 * 2 * 2));
for (int i = 0; i < 256 * 2 * 2; i++) {
deval[i] = 1.0;
}
Weights upsamplewts115{DataType::kFLOAT, deval, 256 * 2 * 2};
IDeconvolutionLayer* upsample115 = network -> addDeconvolutionNd(*l114 -> getOutput(0), 256, DimsHW{2, 2}, upsamplewts115, emptywts);
assert(upsample115);
upsample115 -> setStrideNd(DimsHW{2, 2});
upsample115 -> setNbGroups(256);
weightMap["upsample115"] = upsamplewts115;
auto l116 = l79;
auto l117 = convBnMish(network, weightMap, *l116 -> getOutput(0), 256, 1, 1, 0, 117);
ITensor* inputTensors118[] = {l117 -> getOutput(0), upsample115 -> getOutput(0)};
auto cat118 = network -> addConcatenation(inputTensors118, 2);
auto l119 = convBnMish(network, weightMap, *cat118 -> getOutput(0), 256, 1, 1, 0, 119);
auto l120 = convBnMish(network, weightMap, *l119 -> getOutput(0), 256, 1, 1, 0, 120);
auto l121 = l119;
auto l122 = convBnMish(network, weightMap, *l121 -> getOutput(0), 256, 1, 1, 0, 122);
auto l123 = convBnMish(network, weightMap, *l122 -> getOutput(0), 256, 3, 1, 1, 123);
auto l124 = convBnMish(network, weightMap, *l123 -> getOutput(0), 256, 1, 1, 0, 124);
auto l125 = convBnMish(network, weightMap, *l124 -> getOutput(0), 256, 3, 1, 1, 125);
ITensor* inputTensors126[] = {l125 -> getOutput(0), l120 -> getOutput(0)};
auto cat126 = network -> addConcatenation(inputTensors126, 2);
auto l127 = convBnMish(network, weightMap, *cat126 -> getOutput(0), 256, 1, 1, 0, 127);
auto l128 = convBnMish(network, weightMap, *l127 -> getOutput(0), 128, 1, 1, 0, 128);
Weights upsamplewts129{DataType::kFLOAT, deval, 128 * 2 * 2};
IDeconvolutionLayer* upsample129 = network -> addDeconvolutionNd(*l128 -> getOutput(0), 128, DimsHW{2, 2}, upsamplewts129, emptywts);
assert(upsample129);
upsample129 -> setStrideNd(DimsHW{2, 2});
upsample129 -> setNbGroups(128);
auto l130 = l48;
auto l131 = convBnMish(network, weightMap, *l130 -> getOutput(0), 128, 1, 1, 0, 131);
ITensor* inputTensors132[] = {l131 -> getOutput(0), upsample129 -> getOutput(0)};
auto cat132 = network -> addConcatenation(inputTensors132, 2);
auto l133 = convBnMish(network, weightMap, *cat132 -> getOutput(0), 128, 1, 1, 0, 133);
auto l134 = convBnMish(network, weightMap, *l133 -> getOutput(0), 128, 1, 1, 0, 134);
auto l135 = l133;
auto l136 = convBnMish(network, weightMap, *l135 -> getOutput(0), 128, 1, 1, 0, 136);
auto l137 = convBnMish(network, weightMap, *l136 -> getOutput(0), 128, 3, 1, 1, 137);
auto l138 = convBnMish(network, weightMap, *l137 -> getOutput(0), 128, 1, 1, 0, 138);
auto l139 = convBnMish(network, weightMap, *l138 -> getOutput(0), 128, 3, 1, 1, 139);
ITensor* inputTensors140[] = {l139 -> getOutput(0), l134 -> getOutput(0)};
auto cat140 = network -> addConcatenation(inputTensors140, 2);
auto l141 = convBnMish(network, weightMap, *cat140 -> getOutput(0), 128, 1, 1, 0, 141);
// ---
auto l142 = convBnMish(network, weightMap, *l141 -> getOutput(0), 256, 3, 1, 1, 142);
IConvolutionLayer* conv143 = network -> addConvolutionNd(*l142 -> getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.143.Conv2d.weight"], weightMap["module_list.143.Conv2d.bias"]);
assert(conv143);
// 144 is yolo layer
auto l145 = l141;
auto l146 = convBnMish(network, weightMap, *l145 -> getOutput(0), 256, 3, 2, 1, 146);
ITensor* inputTensors147[] = {l146 -> getOutput(0), l127 -> getOutput(0)};
auto cat147 = network -> addConcatenation(inputTensors147, 2);
auto l148 = convBnMish(network, weightMap, *cat147 -> getOutput(0), 256, 1, 1, 0, 148);
auto l149 = convBnMish(network, weightMap, *l148 -> getOutput(0), 256, 1, 1, 0, 149);
auto l150 = l148;
auto l151 = convBnMish(network, weightMap, *l150 -> getOutput(0), 256, 1, 1, 0, 151);
auto l152 = convBnMish(network, weightMap, *l151 -> getOutput(0), 256, 3, 1, 1, 152);
auto l153 = convBnMish(network, weightMap, *l152 -> getOutput(0), 256, 1, 1, 0, 153);
auto l154 = convBnMish(network, weightMap, *l153 -> getOutput(0), 256, 3, 1, 1, 154);
ITensor* inputTensors155[] = {l154 -> getOutput(0), l149 -> getOutput(0)};
auto cat155 = network -> addConcatenation(inputTensors155, 2);
auto l156 = convBnMish(network, weightMap, *cat155 -> getOutput(0), 256, 1, 1, 0, 156);
auto l157 = convBnMish(network, weightMap, *l156 -> getOutput(0), 512, 3, 1, 1, 157);
IConvolutionLayer* conv158 = network -> addConvolutionNd(*l157 -> getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.158.Conv2d.weight"], weightMap["module_list.158.Conv2d.bias"]);
assert(conv158);
// 159 is yolo layer
auto l160 = l156;
auto l161 = convBnMish(network, weightMap, *l160 -> getOutput(0), 512, 3, 2, 1, 161);
ITensor* inputTensors162[] = {l161 -> getOutput(0), l113 -> getOutput(0)};
auto cat162 = network -> addConcatenation(inputTensors162, 2);
auto l163 = convBnMish(network, weightMap, *cat162 -> getOutput(0), 512, 1, 1, 0, 163);
auto l164 = convBnMish(network, weightMap, *l163 -> getOutput(0), 512, 1, 1, 0, 164);
auto l165 = l163;
auto l166 = convBnMish(network, weightMap, *l165 -> getOutput(0), 512, 1, 1, 0, 166);
auto l167 = convBnMish(network, weightMap, *l166 -> getOutput(0), 512, 3, 1, 1, 167);
auto l168 = convBnMish(network, weightMap, *l167 -> getOutput(0), 512, 1, 1, 0, 168);
auto l169 = convBnMish(network, weightMap, *l168 -> getOutput(0), 512, 3, 1, 1, 169);
ITensor* inputTensors170[] = {l169 -> getOutput(0), l164 -> getOutput(0)};
auto cat170 = network -> addConcatenation(inputTensors170, 2);
auto l171 = convBnMish(network, weightMap, *cat170 -> getOutput(0), 512, 1, 1, 0, 171);
auto l172 = convBnMish(network, weightMap, *l171 -> getOutput(0), 1024, 3, 1, 1, 172);
IConvolutionLayer* conv173 = network -> addConvolutionNd(*l172 -> getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.173.Conv2d.weight"], weightMap["module_list.173.Conv2d.bias"]);
assert(conv173);
// 174 is yolo layer
// add yolo plugin
auto creator = getPluginRegistry() -> getPluginCreator("YoloLayer_TRT", "1");
const PluginFieldCollection* pluginData = creator -> getFieldNames();
IPluginV2* pluginObj = creator -> createPlugin("yololayer", pluginData);
ITensor* inputTensorsYolo[] = {conv143 -> getOutput(0), conv158 -> getOutput(0), conv173 -> getOutput(0)};
auto yolo = network -> addPluginV2(inputTensorsYolo, 3, *pluginObj);
yolo -> getOutput(0) -> setName(OUTPUT_BLOB_NAME);
network -> markOutput(*yolo -> getOutput(0));
// Build engine
builder -> setMaxBatchSize(maxBatchSize);
config -> setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
#ifdef USE_FP16
config -> setFlag(BuilderFlag::kFP16);
#endif
std::cout << "Building tensorrt engine, please wait for a while..." << std::endl;
ICudaEngine* engine = builder -> buildEngineWithConfig(*network, *config);
std::cout << "Build engine successfully!" << std::endl;
// Don't need the network any more
network -> destroy();
// Release host memory
for (auto& mem : weightMap)
{
free((void*) (mem.second.values));
}
return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
// create builder
IBuilder* builder = createInferBuilder(gLogger);
// create builder config
IBuilderConfig* config = builder -> createBuilderConfig();
// Create model to populate the network, then set the outputs and create an engine
ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
assert(engine != nullptr);
// serialize the trt engine
(*modelStream) = engine -> serialize();
// Close everything down
engine -> destroy();
builder -> destroy();
config -> destroy();
}
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void* buffers[2];
// In order to bind the buffers, we need to know the names of the input and output tensors.
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
// Create GPU buffers on device
CUDA_CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
CUDA_CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CUDA_CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(batchSize, buffers, stream, nullptr);
CUDA_CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CUDA_CHECK(cudaFree(buffers[inputIndex]));
CUDA_CHECK(cudaFree(buffers[outputIndex]));
}
int read_files_in_dir(const char* p_dir_name, std::vector<std::string> &file_names) {
DIR *p_dir = opendir(p_dir_name);
if (p_dir == nullptr) {
return -1;
}
struct dirent* p_file = nullptr;
while ((p_file = readdir(p_dir)) != nullptr) {
if (strcmp(p_file -> d_name, ".") != 0 &&
strcmp(p_file -> d_name, "..") != 0) {
std::string cur_file_name(p_file->d_name);
file_names.push_back(cur_file_name);
}
}
closedir(p_dir);
return 0;
}
int main(int argc, char** argv){
cudaSetDevice(DEVICE);
// create a model using the API directly and serialize it to a stream
char *trtModelStream{nullptr};
size_t size{0};
if (argc == 2 && std::string(argv[1]) == "-s") {
IHostMemory* modelStream{nullptr};
APIToModel(BATCH_SIZE, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("yolov4csp.engine", std::ios::binary);
if (!p) {
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 0;
} else if (argc == 3 && std::string(argv[1]) == "-d") {
std::ifstream file("yolov4csp.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
} else {
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./yolov4 -s // serialize model to plan file" << std::endl;
std::cerr << "./yolov4 -d ../samples // deserialize plan file and run inference" << std::endl;
return -1;
}
std::vector<std::string> file_names;
if (read_files_in_dir(argv[2], file_names) < 0) {
std::cout << "read_files_in_dir failed." << std::endl;
return -1;
}
// prepare input data ---------------------------
static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
// data[i] = 1.0;
static float prob[BATCH_SIZE * OUTPUT_SIZE];
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
int fcount = 0;
for (int f = 0; f < (int)file_names.size(); f++) {
fcount++;
if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
for (int b = 0; b < fcount; b++) {
cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
if (img.empty()) continue;
cv::Mat pr_img = preprocess_img(img);
for (int i = 0; i < INPUT_H * INPUT_W; i++) {
data[b * 3 * INPUT_H * INPUT_W + i] = pr_img.at<cv::Vec3b>(i)[2] / 255.0;
data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[1] / 255.0;
data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[0] / 255.0;
}
}
// Run inference
auto start = std::chrono::system_clock::now();
doInference(*context, data, prob, BATCH_SIZE);
auto end = std::chrono::system_clock::now();
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
std::vector<std::vector<Yolo::Detection>> batch_res(fcount);
for (int b = 0; b < fcount; b++) {
auto& res = batch_res[b];
nms(res, &prob[b * OUTPUT_SIZE], BBOX_CONF_THRESH, NMS_THRESH);
}
for (int b = 0; b < fcount; b++) {
auto& res = batch_res[b];
//std::cout << res.size() << std::endl;
cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
for (size_t j = 0; j < res.size(); j++) {
float *p = (float*)&res[j];
for (size_t k = 0; k < 7; k++) {
std::cout << p[k] << ", ";
}
std::cout << std::endl;
cv::Rect r = get_rect(img, res[j].bbox);
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
}
cv::imwrite("_" + file_names[f - fcount + 1 + b], img);
}
fcount = 0;
}
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
//Print histogram of the output distribution
//std::cout << "\nOutput:\n\n";
//for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
//{
// std::cout << prob[i] << ", ";
// if (i % 10 == 0) std::cout << i / 10 << std::endl;
//}
//std::cout << std::endl;
return 0;
}