diff --git a/README.md b/README.md index 59ffba8..03dbc0f 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ So why don't we just skip all parsers? We just use TensorRT network definition A I wrote this project to get familiar with tensorrt API, and also to share and learn from the community. -All the models are implemented in pytorch first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference. Some pytorch implementations can be found in my repo [Pytorchx](https://github.com/wang-xinyu/pytorchx), the remaining are from polular open-source pytorch implementations. +All the models are implemented in pytorch or mxnet first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference. Some pytorch implementations can be found in my repo [Pytorchx](https://github.com/wang-xinyu/pytorchx), the remaining are from polular open-source implementations. ## News @@ -50,6 +50,7 @@ Following models are implemented. |[yolov4](./yolov4)| CSPDarknet53, weights from [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet#pre-trained-models), pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) | |[retinaface](./retinaface)| resnet-50, weights from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) | |[arcface](./arcface)| LResNet50E-IR, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface) | +|[retinafaceAntiCov](./retinafaceAntiCov)| mobilenet0.25, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface), retinaface anti-COVID-19, detect face and mask attribute | ## Tricky Operations diff --git a/retinafaceAntiCov/CMakeLists.txt b/retinafaceAntiCov/CMakeLists.txt new file mode 100644 index 0000000..570d9a3 --- /dev/null +++ b/retinafaceAntiCov/CMakeLists.txt @@ -0,0 +1,41 @@ +cmake_minimum_required(VERSION 2.6) + +project(retinafaceAntiCov) + +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) + +set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11;-g;-G;-gencode;arch=compute_30;code=sm_30) + +include_directories(${PROJECT_SOURCE_DIR}/include) +if (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") + message("embed_platform on") + include_directories(/usr/local/cuda/targets/aarch64-linux/include) + link_directories(/usr/local/cuda/targets/aarch64-linux/lib) +else() + message("embed_platform off") + include_directories(/usr/local/cuda/include) + link_directories(/usr/local/cuda/lib64) +endif() + + +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}/decode.cu) + +find_package(OpenCV) +include_directories(OpenCV_INCLUDE_DIRS) + +add_executable(retinafaceAntiCov ${PROJECT_SOURCE_DIR}/retinafaceAntiCov.cpp) +target_link_libraries(retinafaceAntiCov nvinfer) +target_link_libraries(retinafaceAntiCov cudart) +target_link_libraries(retinafaceAntiCov myplugins) +target_link_libraries(retinafaceAntiCov ${OpenCV_LIBS}) + +add_definitions(-O2 -pthread) + diff --git a/retinafaceAntiCov/README.md b/retinafaceAntiCov/README.md new file mode 100644 index 0000000..4b5d3e8 --- /dev/null +++ b/retinafaceAntiCov/README.md @@ -0,0 +1,45 @@ +# RetinaFaceAntiCov + + The mxnet implementation is [deepinsight/insightface/RetinaFaceAntiCov](https://github.com/deepinsight/insightface/tree/master/RetinaFaceAntiCov). + +## Run + +``` +1. generate retinafaceAntiCov.wts from mxnet implementation. + +git clone https://github.com/deepinsight/insightface.git +cd insightface/RetinaFaceAntiCov +// download its weights 'cov2.zip', put it into insightface/RetinaFaceAntiCov, and unzip it +// put tensorrtx/retinafaceAntiCov/gen_wts.py into insightface/RetinaFaceAntiCov +python gen_wts.py +// a file 'retinafaceAntiCov.wts' will be generated. + +2. put retinafaceAntiCov.wts into tensorrtx/retinafaceAntiCov, build and run + +git clone https://github.com/wang-xinyu/tensorrtx.git +cd tensorrtx/retinafaceAntiCov +// put retinafaceAntiCov.wts here +mkdir build +cd build +cmake .. +make +sudo ./retinafaceAntiCov -s // build and serialize model to file i.e. 'retinafaceAntiCov.engine' +wget http://www.kaixian.tv/gd/d/file/201611/07/23efff3a26e2385620e719378c654fb1.jpg -O test.jpg +sudo ./retinafaceAntiCov -d // deserialize model file and run inference. + +3. check the images generated, as follows. out.jpg +``` + +

+ +

+ +## Config + +- Input shape `INPUT_H`, `INPUT_W` defined in `decode.h` +- FP16/FP32 can be selected by the macro `USE_FP16` in `retinafaceAntiCov.cpp` +- GPU id can be selected by the macro `DEVICE` in `retinafaceAntiCov.cpp` + +## More Information + +See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx) diff --git a/retinafaceAntiCov/decode.cu b/retinafaceAntiCov/decode.cu new file mode 100644 index 0000000..92dd0fb --- /dev/null +++ b/retinafaceAntiCov/decode.cu @@ -0,0 +1,227 @@ +#include "decode.h" +#include "stdio.h" + +namespace nvinfer1 +{ + DecodePlugin::DecodePlugin() + { + } + + DecodePlugin::~DecodePlugin() + { + } + + // create the plugin at runtime from a byte stream + DecodePlugin::DecodePlugin(const void* data, size_t length) + { + } + + void DecodePlugin::serialize(void* buffer) const + { + } + + size_t DecodePlugin::getSerializationSize() const + { + return 0; + } + + int DecodePlugin::initialize() + { + return 0; + } + + Dims DecodePlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims) + { + //output the result to channel + int totalCount = 0; + totalCount += decodeplugin::INPUT_H / 8 * decodeplugin::INPUT_W / 8 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); + totalCount += decodeplugin::INPUT_H / 16 * decodeplugin::INPUT_W / 16 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); + totalCount += decodeplugin::INPUT_H / 32 * decodeplugin::INPUT_W / 32 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); + + return Dims3(totalCount + 1, 1, 1); + } + + // Set plugin namespace + void DecodePlugin::setPluginNamespace(const char* pluginNamespace) + { + mPluginNamespace = pluginNamespace; + } + + const char* DecodePlugin::getPluginNamespace() const + { + return mPluginNamespace; + } + + // Return the DataType of the plugin output at the requested index + DataType DecodePlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const + { + return DataType::kFLOAT; + } + + // Return true if output tensor is broadcast across a batch. + bool DecodePlugin::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 DecodePlugin::canBroadcastInputAcrossBatch(int inputIndex) const + { + return false; + } + + void DecodePlugin::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 DecodePlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) + { + } + + // Detach the plugin object from its execution context. + void DecodePlugin::detachFromContext() {} + + const char* DecodePlugin::getPluginType() const + { + return "Decode_TRT"; + } + + const char* DecodePlugin::getPluginVersion() const + { + return "1"; + } + + void DecodePlugin::destroy() + { + delete this; + } + + // Clone the plugin + IPluginV2IOExt* DecodePlugin::clone() const + { + DecodePlugin *p = new DecodePlugin(); + p->setPluginNamespace(mPluginNamespace); + return p; + } + + __device__ float Logist(float data){ return 1./(1. + expf(-data)); }; + + __global__ void CalDetection(const float *input, float *output, int num_elem, int step, int anchor) { + + int idx = threadIdx.x + blockDim.x * blockIdx.x; + if (idx >= num_elem) return; + + int h = decodeplugin::INPUT_H / step; + int w = decodeplugin::INPUT_W / step; + int y = idx / w; + int x = idx % w; + const float *cls_reg = &input[2 * num_elem]; + const float *bbox_reg = &input[4 * num_elem]; + const float *lmk_reg = &input[12 * num_elem]; + const float *mask_reg = &input[36 * num_elem]; + + for (int k = 0; k < 2; ++k) { + float conf = cls_reg[idx + k * num_elem]; + if (conf < 0.5) continue; + + float *res_count = output; + int count = (int)atomicAdd(res_count, 1); + char* data = (char *)res_count + sizeof(float) + count * sizeof(decodeplugin::Detection); + decodeplugin::Detection* det = (decodeplugin::Detection*)(data); + + float prior[4]; + prior[0] = 7.5 + (float)(x * step); + prior[1] = 7.5 + (float)(y * step); + prior[2] = anchor * 2 / (k + 1); + prior[3] = prior[2]; + + //Location + det->bbox[0] = prior[0] + bbox_reg[idx + k * num_elem * 4] * prior[2]; + det->bbox[1] = prior[1] + bbox_reg[idx + k * num_elem * 4 + num_elem] * prior[3]; + det->bbox[2] = prior[2] * expf(bbox_reg[idx + k * num_elem * 4 + num_elem * 2]); + det->bbox[3] = prior[3] * expf(bbox_reg[idx + k * num_elem * 4 + num_elem * 3]); + det->bbox[0] -= (det->bbox[2] - 1) / 2; + det->bbox[1] -= (det->bbox[3] - 1) / 2; + det->bbox[2] += det->bbox[0]; + det->bbox[3] += det->bbox[1]; + det->class_confidence = conf; + for (int i = 0; i < 10; i += 2) { + det->landmark[i] = prior[0] + lmk_reg[idx + k * num_elem * 10 + num_elem * i] * 0.2 * prior[2]; + det->landmark[i+1] = prior[1] + lmk_reg[idx + k * num_elem * 10 + num_elem * (i + 1)] * 0.2 * prior[3]; + } + det->mask_confidence = mask_reg[idx + k * num_elem];; + } + } + + void DecodePlugin::forwardGpu(const float *const * inputs, float * output, cudaStream_t stream, int batchSize) + { + int num_elem = 0; + int base_step = 8; + int base_anchor = 16; + int thread_count; + cudaMemset(output, 0, sizeof(float)); + for (unsigned int i = 0; i < 3; ++i) + { + num_elem = decodeplugin::INPUT_H / base_step * decodeplugin::INPUT_W / base_step; + thread_count = (num_elem < thread_count_) ? num_elem : thread_count_; + CalDetection<<< (num_elem + thread_count - 1) / thread_count, thread_count>>> + (inputs[i], output, num_elem, base_step, base_anchor); + base_step *= 2; + base_anchor *= 4; + } + } + + int DecodePlugin::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 DecodePluginCreator::mFC{}; + std::vector DecodePluginCreator::mPluginAttributes; + + DecodePluginCreator::DecodePluginCreator() + { + mPluginAttributes.clear(); + + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); + } + + const char* DecodePluginCreator::getPluginName() const + { + return "Decode_TRT"; + } + + const char* DecodePluginCreator::getPluginVersion() const + { + return "1"; + } + + const PluginFieldCollection* DecodePluginCreator::getFieldNames() + { + return &mFC; + } + + IPluginV2IOExt* DecodePluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) + { + DecodePlugin* obj = new DecodePlugin(); + obj->setPluginNamespace(mNamespace.c_str()); + return obj; + } + + IPluginV2IOExt* DecodePluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength) + { + // This object will be deleted when the network is destroyed, which will + // call PReluPlugin::destroy() + DecodePlugin* obj = new DecodePlugin(serialData, serialLength); + obj->setPluginNamespace(mNamespace.c_str()); + return obj; + } + +} diff --git a/retinafaceAntiCov/decode.h b/retinafaceAntiCov/decode.h new file mode 100644 index 0000000..454d026 --- /dev/null +++ b/retinafaceAntiCov/decode.h @@ -0,0 +1,119 @@ +#ifndef _DECODE_CU_H +#define _DECODE_CU_H + +#include +#include +#include "NvInfer.h" + +namespace decodeplugin +{ + struct alignas(float) Detection{ + float bbox[4]; //x1 y1 x2 y2 + float class_confidence; + float landmark[10]; + float mask_confidence; + }; + static const int INPUT_H = 640; + static const int INPUT_W = 640; +} + +namespace nvinfer1 +{ + class DecodePlugin: public IPluginV2IOExt + { + public: + DecodePlugin(); + DecodePlugin(const void* data, size_t length); + + ~DecodePlugin(); + + 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 DecodePluginCreator : public IPluginCreator + { + public: + DecodePluginCreator(); + + ~DecodePluginCreator() 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 mPluginAttributes; + }; +}; + +#endif diff --git a/retinafaceAntiCov/logging.h b/retinafaceAntiCov/logging.h new file mode 100644 index 0000000..602b69f --- /dev/null +++ b/retinafaceAntiCov/logging.h @@ -0,0 +1,503 @@ +/* + * Copyright (c) 2019, 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 +#include +#include +#include +#include +#include +#include + +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) + { + } + + ~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(×tamp); + 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 diff --git a/retinafaceAntiCov/retinafaceAntiCov.cpp b/retinafaceAntiCov/retinafaceAntiCov.cpp new file mode 100644 index 0000000..509b262 --- /dev/null +++ b/retinafaceAntiCov/retinafaceAntiCov.cpp @@ -0,0 +1,570 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include "logging.h" +#include "decode.h" + +#define CHECK(status) \ + do\ + {\ + auto ret = (status);\ + if (ret != 0)\ + {\ + std::cerr << "Cuda failure: " << ret << std::endl;\ + abort();\ + }\ + } while (0) + +//#define USE_FP16 // comment out this if want to use FP32 +#define DEVICE 0 // GPU id +#define BATCH_SIZE 1 // currently, only support BATCH=1 + +using namespace nvinfer1; + +// stuff we know about the network and the input/output blobs +static const int INPUT_H = 640; +static const int INPUT_W = 640; +static const int DETECTION_SIZE = sizeof(decodeplugin::Detection) / sizeof(float); +static const int OUTPUT_SIZE = (INPUT_H / 8 * INPUT_W / 8 + INPUT_H / 16 * INPUT_W / 16 + INPUT_H / 32 * INPUT_W / 32) * 2 * DETECTION_SIZE + 1; +const char* INPUT_BLOB_NAME = "data"; +const char* OUTPUT_BLOB_NAME = "prob"; +static Logger gLogger; +REGISTER_TENSORRT_PLUGIN(DecodePluginCreator); + +cv::Mat preprocess_img(cv::Mat& img) { + int w, h, x, y; + float r_w = INPUT_W / (img.cols*1.0); + float r_h = INPUT_H / (img.rows*1.0); + if (r_h > r_w) { + w = INPUT_W; + h = r_w * img.rows; + x = 0; + y = (INPUT_H - h) / 2; + } else { + w = r_h* img.cols; + h = INPUT_H; + x = (INPUT_W - w) / 2; + y = 0; + } + cv::Mat re(h, w, CV_8UC3); + cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC); + cv::Mat out(INPUT_H, 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_adapt_landmark(cv::Mat& img, float bbox[4], float lmk[10]) { + int l, r, t, b; + float r_w = INPUT_W / (img.cols * 1.0); + float r_h = INPUT_H / (img.rows * 1.0); + if (r_h > r_w) { + l = bbox[0] / r_w; + r = bbox[2] / r_w; + t = (bbox[1] - (INPUT_H - r_w * img.rows) / 2) / r_w; + b = (bbox[3] - (INPUT_H - r_w * img.rows) / 2) / r_w; + for (int i = 0; i < 10; i += 2) { + lmk[i] /= r_w; + lmk[i + 1] = (lmk[i + 1] - (INPUT_H - r_w * img.rows) / 2) / r_w; + } + } else { + l = (bbox[0] - (INPUT_W - r_h * img.cols) / 2) / r_h; + r = (bbox[2] - (INPUT_W - r_h * img.cols) / 2) / r_h; + t = bbox[1] / r_h; + b = bbox[3] / r_h; + for (int i = 0; i < 10; i += 2) { + lmk[i] = (lmk[i] - (INPUT_W - r_h * img.cols) / 2) / r_h; + lmk[i + 1] /= 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], rbox[0]), //left + std::min(lbox[2], rbox[2]), //right + std::max(lbox[1], rbox[1]), //top + std::min(lbox[3], rbox[3]), //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[0]) * (lbox[3] - lbox[1]) + (rbox[2] - rbox[0]) * (rbox[3] - rbox[1]) -interBoxS + 0.000001f); +} + +bool cmp(decodeplugin::Detection& a, decodeplugin::Detection& b) { + return a.class_confidence > b.class_confidence; +} + +void nms(std::vector& res, float *output, float nms_thresh = 0.4) { + std::vector dets; + for (int i = 0; i < output[0]; i++) { + if (output[DETECTION_SIZE * i + 1 + 4] <= 0.1) continue; + decodeplugin::Detection det; + memcpy(&det, &output[DETECTION_SIZE * i + 1], sizeof(decodeplugin::Detection)); + dets.push_back(det); + } + std::sort(dets.begin(), dets.end(), cmp); + if (dets.size() > 5000) dets.erase(dets.begin() + 5000, dets.end()); + for (size_t m = 0; m < dets.size(); ++m) { + auto& item = dets[m]; + res.push_back(item); + //std::cout << item.class_confidence << " bbox " << item.bbox[0] << ", " << item.bbox[1] << ", " << item.bbox[2] << ", " << item.bbox[3] << std::endl; + 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] +std::map loadWeights(const std::string file) { + std::cout << "Loading weights: " << file << std::endl; + std::map 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(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& weightMap, ITensor& input, std::string lname, float eps) { + float *gamma = (float*)weightMap[lname + "_gamma"].values; + float *beta = (float*)weightMap[lname + "_beta"].values; + float *mean = (float*)weightMap[lname + "_moving_mean"].values; + float *var = (float*)weightMap[lname + "_moving_var"].values; + int len = weightMap[lname + "_moving_var"].count; + + float *scval = reinterpret_cast(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(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(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* convBnRelu(INetworkDefinition *network, std::map& weightMap, ITensor& input, int num_filters, int k, int s, int p, int g, std::string lname) { + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + IConvolutionLayer* conv = network->addConvolutionNd(input, num_filters, DimsHW{k, k}, weightMap[lname + "_conv2d_weight"], emptywts); + assert(conv); + conv->setStrideNd(DimsHW{s, s}); + conv->setPaddingNd(DimsHW{p, p}); + conv->setNbGroups(g); + auto bn = addBatchNorm2d(network, weightMap, *conv->getOutput(0), lname + "_batchnorm", 1e-3); + IActivationLayer* relu = network->addActivation(*bn->getOutput(0), ActivationType::kRELU); + assert(relu); + return relu; +} + +ILayer* convBiasBnRelu(INetworkDefinition *network, std::map& weightMap, ITensor& input, int num_filters, int k, int s, int p, std::string lname) { + IConvolutionLayer* conv = network->addConvolutionNd(input, num_filters, DimsHW{k, k}, weightMap[lname + "_weight"], weightMap[lname + "_bias"]); + assert(conv); + conv->setStrideNd(DimsHW{s, s}); + conv->setPaddingNd(DimsHW{p, p}); + auto bn = addBatchNorm2d(network, weightMap, *conv->getOutput(0), lname + "_bn", 2e-5); + IActivationLayer* relu = network->addActivation(*bn->getOutput(0), ActivationType::kRELU); + assert(relu); + return relu; +} + +ILayer* head(INetworkDefinition *network, std::map& weightMap, ITensor& input, std::string lname) { + auto conv1 = network->addConvolutionNd(input, 32, DimsHW{3, 3}, weightMap[lname + "_conv1_weight"], weightMap[lname + "_conv1_bias"]); + assert(conv1); + conv1->setPaddingNd(DimsHW{1, 1}); + auto conv1bn = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "_conv1_bn", 2e-5); + + auto ctxconv1 = convBiasBnRelu(network, weightMap, input, 16, 3, 1, 1, lname + "_context_conv1"); + + auto ctxconv2 = network->addConvolutionNd(*ctxconv1->getOutput(0), 16, DimsHW{3, 3}, weightMap[lname + "_context_conv2_weight"], weightMap[lname + "_context_conv2_bias"]); + assert(ctxconv2); + ctxconv2->setPaddingNd(DimsHW{1, 1}); + auto ctxconv2bn = addBatchNorm2d(network, weightMap, *ctxconv2->getOutput(0), lname + "_context_conv2_bn", 2e-5); + + auto ctxconv3_1 = convBiasBnRelu(network, weightMap, *ctxconv1->getOutput(0), 16, 3, 1, 1, lname + "_context_conv3_1"); + auto ctxconv3_2 = network->addConvolutionNd(*ctxconv3_1->getOutput(0), 16, DimsHW{3, 3}, weightMap[lname + "_context_conv3_2_weight"], weightMap[lname + "_context_conv3_2_bias"]); + assert(ctxconv3_2); + ctxconv3_2->setPaddingNd(DimsHW{1, 1}); + auto ctxconv3_2bn = addBatchNorm2d(network, weightMap, *ctxconv3_2->getOutput(0), lname + "_context_conv3_2_bn", 2e-5); + + ITensor* inputTensors[] = {conv1bn->getOutput(0), ctxconv2bn->getOutput(0), ctxconv3_2bn->getOutput(0)}; + auto cat = network->addConcatenation(inputTensors, 3); + assert(cat); + + IActivationLayer* relu = network->addActivation(*cat->getOutput(0), ActivationType::kRELU); + assert(relu); + return relu; +} + +ILayer* reshapeSoftmax(INetworkDefinition *network, ITensor& input, int c) { + auto re1 = network->addShuffle(input); + assert(re1); + re1->setReshapeDimensions(Dims3(c / 2, -1, 0)); + + auto sm = network->addSoftMax(*re1->getOutput(0)); + assert(sm); + + auto re2 = network->addShuffle(*sm->getOutput(0)); + assert(re2); + re2->setReshapeDimensions(Dims3(c, -1, 0)); + + return re2; +} + +// 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 weightMap = loadWeights("../retinafaceAntiCov.wts"); + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + + auto conv1 = convBnRelu(network, weightMap, *data, 16, 3, 2, 1, 1, "conv_1"); + auto conv2 = convBnRelu(network, weightMap, *conv1->getOutput(0), 32, 1, 1, 0, 1, "conv_2"); + auto conv3dw = convBnRelu(network, weightMap, *conv2->getOutput(0), 32, 3, 2, 1, 32, "conv_3_dw"); + auto conv3 = convBnRelu(network, weightMap, *conv3dw->getOutput(0), 32, 1, 1, 0, 1, "conv_3"); + auto conv4dw = convBnRelu(network, weightMap, *conv3->getOutput(0), 32, 3, 1, 1, 32, "conv_4_dw"); + auto conv4 = convBnRelu(network, weightMap, *conv4dw->getOutput(0), 32, 1, 1, 0, 1, "conv_4"); + auto conv5dw = convBnRelu(network, weightMap, *conv4->getOutput(0), 32, 3, 2, 1, 32, "conv_5_dw"); + auto conv5 = convBnRelu(network, weightMap, *conv5dw->getOutput(0), 64, 1, 1, 0, 1, "conv_5"); + auto conv6dw = convBnRelu(network, weightMap, *conv5->getOutput(0), 64, 3, 1, 1, 64, "conv_6_dw"); + auto conv6 = convBnRelu(network, weightMap, *conv6dw->getOutput(0), 64, 1, 1, 0, 1, "conv_6"); + // conv6 to c1 + auto conv7dw = convBnRelu(network, weightMap, *conv6->getOutput(0), 64, 3, 2, 1, 64, "conv_7_dw"); + auto conv7 = convBnRelu(network, weightMap, *conv7dw->getOutput(0), 128, 1, 1, 0, 1, "conv_7"); + auto conv8dw = convBnRelu(network, weightMap, *conv7->getOutput(0), 128, 3, 1, 1, 128, "conv_8_dw"); + auto conv8 = convBnRelu(network, weightMap, *conv8dw->getOutput(0), 128, 1, 1, 0, 1, "conv_8"); + auto conv9dw = convBnRelu(network, weightMap, *conv8->getOutput(0), 128, 3, 1, 1, 128, "conv_9_dw"); + auto conv9 = convBnRelu(network, weightMap, *conv9dw->getOutput(0), 128, 1, 1, 0, 1, "conv_9"); + auto conv10dw = convBnRelu(network, weightMap, *conv9->getOutput(0), 128, 3, 1, 1, 128, "conv_10_dw"); + auto conv10 = convBnRelu(network, weightMap, *conv10dw->getOutput(0), 128, 1, 1, 0, 1, "conv_10"); + auto conv11dw = convBnRelu(network, weightMap, *conv10->getOutput(0), 128, 3, 1, 1, 128, "conv_11_dw"); + auto conv11 = convBnRelu(network, weightMap, *conv11dw->getOutput(0), 128, 1, 1, 0, 1, "conv_11"); + auto conv12dw = convBnRelu(network, weightMap, *conv11->getOutput(0), 128, 3, 1, 1, 128, "conv_12_dw"); + auto conv12 = convBnRelu(network, weightMap, *conv12dw->getOutput(0), 128, 1, 1, 0, 1, "conv_12"); + // conv12 to c2 + auto conv13dw = convBnRelu(network, weightMap, *conv12->getOutput(0), 128, 3, 2, 1, 128, "conv_13_dw"); + auto conv13 = convBnRelu(network, weightMap, *conv13dw->getOutput(0), 256, 1, 1, 0, 1, "conv_13"); + auto conv14dw = convBnRelu(network, weightMap, *conv13->getOutput(0), 256, 3, 1, 1, 256, "conv_14_dw"); + auto conv14 = convBnRelu(network, weightMap, *conv14dw->getOutput(0), 256, 1, 1, 0, 1, "conv_14"); + auto conv_final = convBnRelu(network, weightMap, *conv14->getOutput(0), 256, 1, 1, 0, 1, "conv_final"); + // convfinal to c3 + + auto rf_c3_lateral = convBiasBnRelu(network, weightMap, *conv_final->getOutput(0), 64, 1, 1, 0, "rf_c3_lateral"); + auto rf_head_s32 = head(network, weightMap, *rf_c3_lateral->getOutput(0), "rf_head_stride32"); + ILayer *cls_score_s32 = network->addConvolutionNd(*rf_head_s32->getOutput(0), 4, DimsHW{1, 1}, weightMap["face_rpn_cls_score_stride32_weight"], weightMap["face_rpn_cls_score_stride32_bias"]); + cls_score_s32 = reshapeSoftmax(network, *cls_score_s32->getOutput(0), 4); + auto bbox_s32 = network->addConvolutionNd(*rf_head_s32->getOutput(0), 8, DimsHW{1, 1}, weightMap["face_rpn_bbox_pred_stride32_weight"], weightMap["face_rpn_bbox_pred_stride32_bias"]); + auto landmark_s32 = network->addConvolutionNd(*rf_head_s32->getOutput(0), 20, DimsHW{1, 1}, weightMap["face_rpn_landmark_pred_stride32_weight"], weightMap["face_rpn_landmark_pred_stride32_bias"]); + auto rf_head2_s32 = head(network, weightMap, *rf_c3_lateral->getOutput(0), "rf_head2_stride32"); + ILayer *type_score_s32 = network->addConvolutionNd(*rf_head2_s32->getOutput(0), 6, DimsHW{1, 1}, weightMap["face_rpn_type_score_stride32_weight"], weightMap["face_rpn_type_score_stride32_bias"]); + type_score_s32 = reshapeSoftmax(network, *type_score_s32->getOutput(0), 6); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 64 * 2 * 2)); + for (int i = 0; i < 64 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts{DataType::kFLOAT, deval, 64 * 2 * 2}; + IDeconvolutionLayer* c3_deconv = network->addDeconvolutionNd(*rf_c3_lateral->getOutput(0), 64, DimsHW{2, 2}, deconvwts, emptywts); + assert(c3_deconv); + c3_deconv->setStrideNd(DimsHW{2, 2}); + c3_deconv->setNbGroups(64); + weightMap["c3_deconv"] = deconvwts; + auto rf_c2_lateral = convBiasBnRelu(network, weightMap, *conv12->getOutput(0), 64, 1, 1, 0, "rf_c2_lateral"); + auto plus0 = network->addElementWise(*c3_deconv->getOutput(0), *rf_c2_lateral->getOutput(0), ElementWiseOperation::kSUM); + auto rf_c2_aggr = convBiasBnRelu(network, weightMap, *plus0->getOutput(0), 64, 3, 1, 1, "rf_c2_aggr"); + auto rf_head_s16 = head(network, weightMap, *rf_c2_aggr->getOutput(0), "rf_head_stride16"); + ILayer *cls_score_s16 = network->addConvolutionNd(*rf_head_s16->getOutput(0), 4, DimsHW{1, 1}, weightMap["face_rpn_cls_score_stride16_weight"], weightMap["face_rpn_cls_score_stride16_bias"]); + cls_score_s16 = reshapeSoftmax(network, *cls_score_s16->getOutput(0), 4); + auto bbox_s16 = network->addConvolutionNd(*rf_head_s16->getOutput(0), 8, DimsHW{1, 1}, weightMap["face_rpn_bbox_pred_stride16_weight"], weightMap["face_rpn_bbox_pred_stride16_bias"]); + auto landmark_s16 = network->addConvolutionNd(*rf_head_s16->getOutput(0), 20, DimsHW{1, 1}, weightMap["face_rpn_landmark_pred_stride16_weight"], weightMap["face_rpn_landmark_pred_stride16_bias"]); + auto rf_head2_s16 = head(network, weightMap, *rf_c2_aggr->getOutput(0), "rf_head2_stride16"); + ILayer *type_score_s16 = network->addConvolutionNd(*rf_head2_s16->getOutput(0), 6, DimsHW{1, 1}, weightMap["face_rpn_type_score_stride16_weight"], weightMap["face_rpn_type_score_stride16_bias"]); + type_score_s16 = reshapeSoftmax(network, *type_score_s16->getOutput(0), 6); + + IDeconvolutionLayer* c2_deconv = network->addDeconvolutionNd(*rf_c2_aggr->getOutput(0), 64, DimsHW{2, 2}, deconvwts, emptywts); + assert(c2_deconv); + c2_deconv->setStrideNd(DimsHW{2, 2}); + c2_deconv->setNbGroups(64); + auto rf_c1_red = convBiasBnRelu(network, weightMap, *conv6->getOutput(0), 64, 1, 1, 0, "rf_c1_red_conv"); + auto plus1 = network->addElementWise(*c2_deconv->getOutput(0), *rf_c1_red->getOutput(0), ElementWiseOperation::kSUM); + auto rf_c1_aggr = convBiasBnRelu(network, weightMap, *plus1->getOutput(0), 64, 3, 1, 1, "rf_c1_aggr"); + auto rf_head_s8 = head(network, weightMap, *rf_c1_aggr->getOutput(0), "rf_head_stride8"); + ILayer *cls_score_s8 = network->addConvolutionNd(*rf_head_s8->getOutput(0), 4, DimsHW{1, 1}, weightMap["face_rpn_cls_score_stride8_weight"], weightMap["face_rpn_cls_score_stride8_bias"]); + cls_score_s8 = reshapeSoftmax(network, *cls_score_s8->getOutput(0), 4); + auto bbox_s8 = network->addConvolutionNd(*rf_head_s8->getOutput(0), 8, DimsHW{1, 1}, weightMap["face_rpn_bbox_pred_stride8_weight"], weightMap["face_rpn_bbox_pred_stride8_bias"]); + auto landmark_s8 = network->addConvolutionNd(*rf_head_s8->getOutput(0), 20, DimsHW{1, 1}, weightMap["face_rpn_landmark_pred_stride8_weight"], weightMap["face_rpn_landmark_pred_stride8_bias"]); + auto rf_head2_s8 = head(network, weightMap, *rf_c1_aggr->getOutput(0), "rf_head2_stride8"); + ILayer *type_score_s8 = network->addConvolutionNd(*rf_head2_s8->getOutput(0), 6, DimsHW{1, 1}, weightMap["face_rpn_type_score_stride8_weight"], weightMap["face_rpn_type_score_stride8_bias"]); + type_score_s8 = reshapeSoftmax(network, *type_score_s8->getOutput(0), 6); + + ITensor* inputTensors_s32[] = {cls_score_s32->getOutput(0), bbox_s32->getOutput(0), landmark_s32->getOutput(0), type_score_s32->getOutput(0)}; + auto cat_s32 = network->addConcatenation(inputTensors_s32, 4); + assert(cat_s32); + + ITensor* inputTensors_s16[] = {cls_score_s16->getOutput(0), bbox_s16->getOutput(0), landmark_s16->getOutput(0), type_score_s16->getOutput(0)}; + auto cat_s16 = network->addConcatenation(inputTensors_s16, 4); + assert(cat_s16); + + ITensor* inputTensors_s8[] = {cls_score_s8->getOutput(0), bbox_s8->getOutput(0), landmark_s8->getOutput(0), type_score_s8->getOutput(0)}; + auto cat_s8 = network->addConcatenation(inputTensors_s8, 4); + assert(cat_s8); + + auto creator = getPluginRegistry()->getPluginCreator("Decode_TRT", "1"); + PluginFieldCollection pfc; + IPluginV2 *pluginObj = creator->createPlugin("decode", &pfc); + ITensor* inputTensors[] = {cat_s8->getOutput(0), cat_s16->getOutput(0), cat_s32->getOutput(0)}; + auto decodelayer = network->addPluginV2(inputTensors, 3, *pluginObj); + assert(decodelayer); + + decodelayer->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*decodelayer->getOutput(0)); + + // Build engine + builder->setMaxBatchSize(maxBatchSize); + config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB +#ifdef USE_FP16 + config->setFlag(BuilderFlag::kFP16); +#endif + std::cout << "Building 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); + 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 engine + (*modelStream) = engine->serialize(); + + // Close everything down + engine->destroy(); + builder->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 + CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float))); + CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float))); + + // Create stream + cudaStream_t stream; + CHECK(cudaStreamCreate(&stream)); + + // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host + CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); + context.enqueue(batchSize, buffers, stream, nullptr); + CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); + cudaStreamSynchronize(stream); + + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); +} + +int read_files_in_dir(const char *p_dir_name, std::vector &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_dir_name); + //cur_file_name += "/"; + //cur_file_name += p_file->d_name; + 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("retinafaceAntiCov.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 0; + } else if (argc == 2 && std::string(argv[1]) == "-d") { + std::ifstream file("retinafaceAntiCov.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 << "./retinafaceAntiCov -s // serialize model to plan file" << std::endl; + std::cerr << "./retinafaceAntiCov -d // deserialize plan file and run inference" << 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; + + cv::Mat img = cv::imread("test.jpg"); + cv::Mat pr_img = preprocess_img(img); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = ((float)pr_img.at(i)[2] - 127.5) * 0.0078125; + data[i + INPUT_H * INPUT_W] = ((float)pr_img.at(i)[1] - 127.5) * 0.0078125; + data[i + 2 * INPUT_H * INPUT_W] = ((float)pr_img.at(i)[0] - 127.5) * 0.0078125; + } + + // 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(end - start).count() << "ms" << std::endl; + + std::vector res; + nms(res, prob); + + for (size_t j = 0; j < res.size(); j++) { + //if (res[j].class_confidence < 0.1) continue; + cv::Rect r = get_rect_adapt_landmark(img, res[j].bbox, res[j].landmark); + cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); + cv::putText(img, "face: " + std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y + 20), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1); + for (int k = 0; k < 10; k += 2) { + cv::circle(img, cv::Point(res[j].landmark[k], res[j].landmark[k + 1]), 1, cv::Scalar(255 * (k > 2), 255 * (k > 0 && k < 8), 255 * (k < 6)), 4); + } + cv::putText(img, "mask: " + std::to_string((int)(res[j].mask_confidence * 100)) + "%", cv::Point(r.x, r.y + 40), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0x00, 0x00, 0xFF), 1); + } + cv::imwrite("out.jpg", img); + + // 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; +}