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[Model] Yolov5/v5lite/v6/v7/v7end2end: CUDA preprocessing (#370)

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* add yolo cuda preprocessing

* cmake build cuda src

* yolov5 support cuda preprocessing

* yolov5 cuda preprocessing configurable

* yolov5 update get mat data api

* yolov5 check cuda preprocess args

* refactor cuda function name

* yolo cuda preprocess padding value configurable

* yolov5 release cuda memory

* cuda preprocess pybind api update

* move use_cuda_preprocessing option to yolov5 model

* yolov5lite cuda preprocessing

* yolov6 cuda preprocessing

* yolov7 cuda preprocessing

* yolov7_e2e cuda preprocessing

* remove cuda preprocessing in runtime option

* refine log and cmake variable name

* fix model runtime ptr type

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
Wang Xinyu
2022-10-19 16:04:58 +08:00
committed by GitHub
parent 4b3e93223f
commit c8d6c8244e
26 changed files with 752 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
CMakeLists.txt
View File

@@ -92,6 +92,16 @@ if(BUILD_ON_JETSON)
set(ENABLE_ORT_BACKEND ON)
endif()
# Whether to build CUDA source files in fastdeploy
# CUDA source files include CUDA preprocessing, TRT plugins, etc.
if(WITH_GPU AND UNIX)
set(BUILD_CUDA_SRC ON)
enable_language(CUDA)
set(CUDA_PROPAGATE_HOST_FLAGS FALSE)
else()
set(BUILD_CUDA_SRC OFF)
endif()
# config GIT_URL with github mirrors to speed up dependent repos clone
option(GIT_URL "Git URL to clone dependent repos" ${GIT_URL})
if(NOT GIT_URL)
@@ -174,6 +184,7 @@ file(GLOB_RECURSE DEPLOY_TRT_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastde
file(GLOB_RECURSE DEPLOY_OPENVINO_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/backends/openvino/*.cc)
file(GLOB_RECURSE DEPLOY_LITE_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/backends/lite/*.cc)
file(GLOB_RECURSE DEPLOY_VISION_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/vision/*.cc)
file(GLOB_RECURSE DEPLOY_VISION_CUDA_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/vision/*.cu)
file(GLOB_RECURSE DEPLOY_TEXT_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/text/*.cc)
file(GLOB_RECURSE DEPLOY_PYBIND_SRCS ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/pybind/*.cc ${PROJECT_SOURCE_DIR}/${CSRCS_DIR_NAME}/fastdeploy/*_pybind.cc)
list(REMOVE_ITEM ALL_DEPLOY_SRCS ${DEPLOY_ORT_SRCS} ${DEPLOY_PADDLE_SRCS} ${DEPLOY_POROS_SRCS} ${DEPLOY_TRT_SRCS} ${DEPLOY_OPENVINO_SRCS} ${DEPLOY_LITE_SRCS} ${DEPLOY_VISION_SRCS} ${DEPLOY_TEXT_SRCS})
@@ -373,6 +384,10 @@ if(ENABLE_VISION)
endif()
add_subdirectory(${PROJECT_SOURCE_DIR}/third_party/yaml-cpp)
list(APPEND DEPEND_LIBS yaml-cpp)
if(BUILD_CUDA_SRC)
add_definitions(-DENABLE_CUDA_PREPROCESS)
list(APPEND DEPLOY_VISION_SRCS ${DEPLOY_VISION_CUDA_SRCS})
endif()
list(APPEND ALL_DEPLOY_SRCS ${DEPLOY_VISION_SRCS})
include_directories(${PROJECT_SOURCE_DIR}/third_party/yaml-cpp/include)
include(${PROJECT_SOURCE_DIR}/cmake/opencv.cmake)
@@ -428,7 +443,13 @@ elseif(ANDROID)
set_target_properties(${LIBRARY_NAME} PROPERTIES LINK_FLAGS_MINSIZEREL ${COMMON_LINK_FLAGS_REL})
elseif(MSVC)
else()
set_target_properties(${LIBRARY_NAME} PROPERTIES COMPILE_FLAGS "-fvisibility=hidden")
if(BUILD_CUDA_SRC)
set_target_properties(${LIBRARY_NAME} PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
set_target_properties(${LIBRARY_NAME} PROPERTIES INTERFACE_COMPILE_OPTIONS
"$<$<BUILD_INTERFACE:$<COMPILE_LANGUAGE:CXX>>:-fvisibility=hidden>$<$<BUILD_INTERFACE:$<COMPILE_LANGUAGE:CUDA>>:-Xcompiler=-fvisibility=hidden>")
else()
set_target_properties(${LIBRARY_NAME} PROPERTIES COMPILE_FLAGS "-fvisibility=hidden")
endif()
set_target_properties(${LIBRARY_NAME} PROPERTIES LINK_FLAGS "-Wl,--exclude-libs,ALL")
set_target_properties(${LIBRARY_NAME} PROPERTIES LINK_FLAGS_RELEASE -s)
endif()

1
cmake/summary.cmake
View File

@@ -51,6 +51,7 @@ function(fastdeploy_summary)
message(STATUS " WITH_GPU : ${WITH_GPU}")
message(STATUS " CUDA_DIRECTORY : ${CUDA_DIRECTORY}")
message(STATUS " TRT_DRECTORY : ${TRT_DIRECTORY}")
message(STATUS " BUILD_CUDA_SRC : ${BUILD_CUDA_SRC}")
endif()
message(STATUS " ENABLE_VISION : ${ENABLE_VISION}")
message(STATUS " ENABLE_TEXT : ${ENABLE_TEXT}")

6
fastdeploy/fastdeploy_model.cc
View File

@@ -57,7 +57,7 @@ bool FastDeployModel::InitRuntime() {
}
if (is_supported) {
runtime_ = std::unique_ptr<Runtime>(new Runtime());
runtime_ = std::shared_ptr<Runtime>(new Runtime());
if (!runtime_->Init(runtime_option)) {
return false;
}
@@ -107,7 +107,7 @@ bool FastDeployModel::CreateCpuBackend() {
continue;
}
runtime_option.backend = valid_cpu_backends[i];
runtime_ = std::unique_ptr<Runtime>(new Runtime());
runtime_ = std::shared_ptr<Runtime>(new Runtime());
if (!runtime_->Init(runtime_option)) {
return false;
}
@@ -130,7 +130,7 @@ bool FastDeployModel::CreateGpuBackend() {
continue;
}
runtime_option.backend = valid_gpu_backends[i];
runtime_ = std::unique_ptr<Runtime>(new Runtime());
runtime_ = std::shared_ptr<Runtime>(new Runtime());
if (!runtime_->Init(runtime_option)) {
return false;
}

2
fastdeploy/fastdeploy_model.h
View File

@@ -99,7 +99,7 @@ class FASTDEPLOY_DECL FastDeployModel {
std::vector<Backend> valid_external_backends;
private:
std::unique_ptr<Runtime> runtime_;
std::shared_ptr<Runtime> runtime_;
bool runtime_initialized_ = false;
// whether to record inference time
bool enable_record_time_of_runtime_ = false;

96
fastdeploy/vision/detection/contrib/yolov5.cc
View File

@@ -16,6 +16,9 @@
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
#ifdef ENABLE_CUDA_PREPROCESS
#include "fastdeploy/vision/utils/cuda_utils.h"
#endif // ENABLE_CUDA_PREPROCESS
namespace fastdeploy {
namespace vision {
@@ -104,9 +107,20 @@ bool YOLOv5::Initialize() {
// if (!is_dynamic_input_) {
// is_mini_pad_ = false;
// }
return true;
}
YOLOv5::~YOLOv5() {
#ifdef ENABLE_CUDA_PREPROCESS
if (use_cuda_preprocessing_) {
CUDA_CHECK(cudaFreeHost(input_img_cuda_buffer_host_));
CUDA_CHECK(cudaFree(input_img_cuda_buffer_device_));
CUDA_CHECK(cudaFree(input_tensor_cuda_buffer_device_));
}
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv5::Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info,
const std::vector<int>& size,
@@ -156,6 +170,69 @@ bool YOLOv5::Preprocess(Mat* mat, FDTensor* output,
return true;
}
void YOLOv5::UseCudaPreprocessing(int max_image_size) {
#ifdef ENABLE_CUDA_PREPROCESS
use_cuda_preprocessing_ = true;
is_scale_up_ = true;
if (input_img_cuda_buffer_host_ == nullptr) {
// prepare input data cache in GPU pinned memory
CUDA_CHECK(cudaMallocHost((void**)&input_img_cuda_buffer_host_, max_image_size * 3));
// prepare input data cache in GPU device memory
CUDA_CHECK(cudaMalloc((void**)&input_img_cuda_buffer_device_, max_image_size * 3));
CUDA_CHECK(cudaMalloc((void**)&input_tensor_cuda_buffer_device_, 3 * size_[0] * size_[1] * sizeof(float)));
}
#else
FDWARNING << "The FastDeploy didn't compile with BUILD_CUDA_SRC=ON."
<< std::endl;
use_cuda_preprocessing_ = false;
#endif
}
bool YOLOv5::CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info,
const std::vector<int>& size,
const std::vector<float> padding_value,
bool is_mini_pad, bool is_no_pad, bool is_scale_up,
int stride, float max_wh, bool multi_label) {
#ifdef ENABLE_CUDA_PREPROCESS
if (is_mini_pad != false || is_no_pad != false || is_scale_up != true) {
FDERROR << "Preprocessing with CUDA is only available when the arguments satisfy (is_mini_pad=false, is_no_pad=false, is_scale_up=true)." << std::endl;
return false;
}
// Record the shape of image and the shape of preprocessed image
(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
int src_img_buf_size = mat->Height() * mat->Width() * mat->Channels();
memcpy(input_img_cuda_buffer_host_, mat->Data(), src_img_buf_size);
CUDA_CHECK(cudaMemcpyAsync(input_img_cuda_buffer_device_,
input_img_cuda_buffer_host_,
src_img_buf_size, cudaMemcpyHostToDevice, stream));
utils::CudaYoloPreprocess(input_img_cuda_buffer_device_, mat->Width(),
mat->Height(), input_tensor_cuda_buffer_device_,
size[0], size[1], padding_value, stream);
cudaStreamSynchronize(stream);
cudaStreamDestroy(stream);
// Record output shape of preprocessed image
(*im_info)["output_shape"] = {static_cast<float>(size[0]), static_cast<float>(size[1])};
output->SetExternalData({mat->Channels(), size[0], size[1]}, FDDataType::FP32,
input_tensor_cuda_buffer_device_);
output->device = Device::GPU;
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
return true;
#else
FDERROR << "CUDA src code was not enabled." << std::endl;
return false;
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv5::Postprocess(
std::vector<FDTensor>& infer_results, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -262,11 +339,20 @@ bool YOLOv5::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
std::map<std::string, std::array<float, 2>> im_info;
if (!Preprocess(&mat, &input_tensors[0], &im_info, size_, padding_value_,
is_mini_pad_, is_no_pad_, is_scale_up_, stride_, max_wh_,
multi_label_)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
if (use_cuda_preprocessing_) {
if (!CudaPreprocess(&mat, &input_tensors[0], &im_info, size_, padding_value_,
is_mini_pad_, is_no_pad_, is_scale_up_, stride_, max_wh_,
multi_label_)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
} else {
if (!Preprocess(&mat, &input_tensors[0], &im_info, size_, padding_value_,
is_mini_pad_, is_no_pad_, is_scale_up_, stride_, max_wh_,
multi_label_)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
}
input_tensors[0].name = InputInfoOfRuntime(0).name;

22
fastdeploy/vision/detection/contrib/yolov5.h
View File

@@ -13,6 +13,7 @@
// limitations under the License.
#pragma once
#include "fastdeploy/fastdeploy_model.h"
#include "fastdeploy/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/result.h"
@@ -27,6 +28,8 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
const RuntimeOption& custom_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX);
~YOLOv5();
std::string ModelName() const { return "yolov5"; }
virtual bool Predict(cv::Mat* im, DetectionResult* result,
@@ -42,6 +45,17 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
bool is_scale_up = false, int stride = 32,
float max_wh = 7680.0, bool multi_label = true);
void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
bool CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info,
const std::vector<int>& size = {640, 640},
const std::vector<float> padding_value = {114.0, 114.0,
114.0},
bool is_mini_pad = false, bool is_no_pad = false,
bool is_scale_up = false, int stride = 32,
float max_wh = 7680.0, bool multi_label = true);
static bool Postprocess(
std::vector<FDTensor>& infer_results, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -85,6 +99,14 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
// value will
// auto check by fastdeploy after the internal Runtime already initialized.
bool is_dynamic_input_;
// CUDA host buffer for input image
uint8_t* input_img_cuda_buffer_host_ = nullptr;
// CUDA device buffer for input image
uint8_t* input_img_cuda_buffer_device_ = nullptr;
// CUDA device buffer for TRT input tensor
float* input_tensor_cuda_buffer_device_ = nullptr;
// Whether to use CUDA preprocessing
bool use_cuda_preprocessing_ = false;
};
} // namespace detection

4
fastdeploy/vision/detection/contrib/yolov5_pybind.cc
View File

@@ -27,6 +27,10 @@ void BindYOLOv5(pybind11::module& m) {
self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
return res;
})
.def("use_cuda_preprocessing",
[](vision::detection::YOLOv5& self, int max_image_size) {
self.UseCudaPreprocessing(max_image_size);
})
.def_static("preprocess",
[](pybind11::array& data, const std::vector<int>& size,
const std::vector<float> padding_value, bool is_mini_pad,

85
fastdeploy/vision/detection/contrib/yolov5lite.cc
View File

@@ -15,6 +15,9 @@
#include "fastdeploy/vision/detection/contrib/yolov5lite.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
#ifdef ENABLE_CUDA_PREPROCESS
#include "fastdeploy/vision/utils/cuda_utils.h"
#endif // ENABLE_CUDA_PREPROCESS
namespace fastdeploy {
namespace vision {
@@ -136,6 +139,16 @@ bool YOLOv5Lite::Initialize() {
return true;
}
YOLOv5Lite::~YOLOv5Lite() {
#ifdef ENABLE_CUDA_PREPROCESS
if (use_cuda_preprocessing_) {
CUDA_CHECK(cudaFreeHost(input_img_cuda_buffer_host_));
CUDA_CHECK(cudaFree(input_img_cuda_buffer_device_));
CUDA_CHECK(cudaFree(input_tensor_cuda_buffer_device_));
}
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv5Lite::Preprocess(
Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
@@ -176,6 +189,65 @@ bool YOLOv5Lite::Preprocess(
return true;
}
void YOLOv5Lite::UseCudaPreprocessing(int max_image_size) {
#ifdef ENABLE_CUDA_PREPROCESS
use_cuda_preprocessing_ = true;
is_scale_up = true;
if (input_img_cuda_buffer_host_ == nullptr) {
// prepare input data cache in GPU pinned memory
CUDA_CHECK(cudaMallocHost((void**)&input_img_cuda_buffer_host_, max_image_size * 3));
// prepare input data cache in GPU device memory
CUDA_CHECK(cudaMalloc((void**)&input_img_cuda_buffer_device_, max_image_size * 3));
CUDA_CHECK(cudaMalloc((void**)&input_tensor_cuda_buffer_device_, 3 * size[0] * size[1] * sizeof(float)));
}
#else
FDWARNING << "The FastDeploy didn't compile with BUILD_CUDA_SRC=ON."
<< std::endl;
use_cuda_preprocessing_ = false;
#endif
}
bool YOLOv5Lite::CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
#ifdef ENABLE_CUDA_PREPROCESS
if (is_mini_pad != false || is_no_pad != false || is_scale_up != true) {
FDERROR << "Preprocessing with CUDA is only available when the arguments satisfy (is_mini_pad=false, is_no_pad=false, is_scale_up=true)." << std::endl;
return false;
}
// Record the shape of image and the shape of preprocessed image
(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
int src_img_buf_size = mat->Height() * mat->Width() * mat->Channels();
memcpy(input_img_cuda_buffer_host_, mat->Data(), src_img_buf_size);
CUDA_CHECK(cudaMemcpyAsync(input_img_cuda_buffer_device_,
input_img_cuda_buffer_host_,
src_img_buf_size, cudaMemcpyHostToDevice, stream));
utils::CudaYoloPreprocess(input_img_cuda_buffer_device_, mat->Width(),
mat->Height(), input_tensor_cuda_buffer_device_,
size[0], size[1], padding_value, stream);
cudaStreamSynchronize(stream);
cudaStreamDestroy(stream);
// Record output shape of preprocessed image
(*im_info)["output_shape"] = {static_cast<float>(size[0]), static_cast<float>(size[1])};
output->SetExternalData({mat->Channels(), size[0], size[1]}, FDDataType::FP32,
input_tensor_cuda_buffer_device_);
output->device = Device::GPU;
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
return true;
#else
FDERROR << "CUDA src code was not enabled." << std::endl;
return false;
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv5Lite::PostprocessWithDecode(
FDTensor& infer_result, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -348,9 +420,16 @@ bool YOLOv5Lite::Predict(cv::Mat* im, DetectionResult* result,
im_info["output_shape"] = {static_cast<float>(mat.Height()),
static_cast<float>(mat.Width())};
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
if (use_cuda_preprocessing_) {
if (!CudaPreprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
} else {
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
}
input_tensors[0].name = InputInfoOfRuntime(0).name;

18
fastdeploy/vision/detection/contrib/yolov5lite.h
View File

@@ -27,12 +27,16 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
const RuntimeOption& custom_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX);
~YOLOv5Lite();
virtual std::string ModelName() const { return "YOLOv5-Lite"; }
virtual bool Predict(cv::Mat* im, DetectionResult* result,
float conf_threshold = 0.45,
float nms_iou_threshold = 0.25);
void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
// tuple of (width, height)
std::vector<int> size;
// padding value, size should be same with Channels
@@ -79,7 +83,9 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
bool Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool Postprocess(FDTensor& infer_result, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
float conf_threshold, float nms_iou_threshold);
@@ -97,7 +103,7 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
const std::vector<float>& color, bool _auto,
bool scale_fill = false, bool scale_up = true,
int stride = 32);
// generate anchors for decodeing when ONNX file without decode module.
void GenerateAnchors(const std::vector<int>& size,
const std::vector<int>& downsample_strides,
@@ -109,6 +115,14 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
// value will
// auto check by fastdeploy after the internal Runtime already initialized.
bool is_dynamic_input_;
// CUDA host buffer for input image
uint8_t* input_img_cuda_buffer_host_ = nullptr;
// CUDA device buffer for input image
uint8_t* input_img_cuda_buffer_device_ = nullptr;
// CUDA device buffer for TRT input tensor
float* input_tensor_cuda_buffer_device_ = nullptr;
// Whether to use CUDA preprocessing
bool use_cuda_preprocessing_ = false;
};
} // namespace detection
} // namespace vision

4
fastdeploy/vision/detection/contrib/yolov5lite_pybind.cc
View File

@@ -28,6 +28,10 @@ void BindYOLOv5Lite(pybind11::module& m) {
self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
return res;
})
.def("use_cuda_preprocessing",
[](vision::detection::YOLOv5Lite& self, int max_image_size) {
self.UseCudaPreprocessing(max_image_size);
})
.def_readwrite("size", &vision::detection::YOLOv5Lite::size)
.def_readwrite("padding_value",
&vision::detection::YOLOv5Lite::padding_value)

85
fastdeploy/vision/detection/contrib/yolov6.cc
View File

@@ -15,6 +15,9 @@
#include "fastdeploy/vision/detection/contrib/yolov6.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
#ifdef ENABLE_CUDA_PREPROCESS
#include "fastdeploy/vision/utils/cuda_utils.h"
#endif // ENABLE_CUDA_PREPROCESS
namespace fastdeploy {
@@ -108,6 +111,16 @@ bool YOLOv6::Initialize() {
return true;
}
YOLOv6::~YOLOv6() {
#ifdef ENABLE_CUDA_PREPROCESS
if (use_cuda_preprocessing_) {
CUDA_CHECK(cudaFreeHost(input_img_cuda_buffer_host_));
CUDA_CHECK(cudaFree(input_img_cuda_buffer_device_));
CUDA_CHECK(cudaFree(input_tensor_cuda_buffer_device_));
}
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv6::Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
// process after image load
@@ -147,6 +160,65 @@ bool YOLOv6::Preprocess(Mat* mat, FDTensor* output,
return true;
}
void YOLOv6::UseCudaPreprocessing(int max_image_size) {
#ifdef ENABLE_CUDA_PREPROCESS
use_cuda_preprocessing_ = true;
is_scale_up = true;
if (input_img_cuda_buffer_host_ == nullptr) {
// prepare input data cache in GPU pinned memory
CUDA_CHECK(cudaMallocHost((void**)&input_img_cuda_buffer_host_, max_image_size * 3));
// prepare input data cache in GPU device memory
CUDA_CHECK(cudaMalloc((void**)&input_img_cuda_buffer_device_, max_image_size * 3));
CUDA_CHECK(cudaMalloc((void**)&input_tensor_cuda_buffer_device_, 3 * size[0] * size[1] * sizeof(float)));
}
#else
FDWARNING << "The FastDeploy didn't compile with BUILD_CUDA_SRC=ON."
<< std::endl;
use_cuda_preprocessing_ = false;
#endif
}
bool YOLOv6::CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
#ifdef ENABLE_CUDA_PREPROCESS
if (is_mini_pad != false || is_no_pad != false || is_scale_up != true) {
FDERROR << "Preprocessing with CUDA is only available when the arguments satisfy (is_mini_pad=false, is_no_pad=false, is_scale_up=true)." << std::endl;
return false;
}
// Record the shape of image and the shape of preprocessed image
(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
int src_img_buf_size = mat->Height() * mat->Width() * mat->Channels();
memcpy(input_img_cuda_buffer_host_, mat->Data(), src_img_buf_size);
CUDA_CHECK(cudaMemcpyAsync(input_img_cuda_buffer_device_,
input_img_cuda_buffer_host_,
src_img_buf_size, cudaMemcpyHostToDevice, stream));
utils::CudaYoloPreprocess(input_img_cuda_buffer_device_, mat->Width(),
mat->Height(), input_tensor_cuda_buffer_device_,
size[0], size[1], padding_value, stream);
cudaStreamSynchronize(stream);
cudaStreamDestroy(stream);
// Record output shape of preprocessed image
(*im_info)["output_shape"] = {static_cast<float>(size[0]), static_cast<float>(size[1])};
output->SetExternalData({mat->Channels(), size[0], size[1]}, FDDataType::FP32,
input_tensor_cuda_buffer_device_);
output->device = Device::GPU;
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
return true;
#else
FDERROR << "CUDA src code was not enabled." << std::endl;
return false;
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv6::Postprocess(
FDTensor& infer_result, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -225,9 +297,16 @@ bool YOLOv6::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
im_info["output_shape"] = {static_cast<float>(mat.Height()),
static_cast<float>(mat.Width())};
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
if (use_cuda_preprocessing_) {
if (!CudaPreprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
} else {
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
}
input_tensors[0].name = InputInfoOfRuntime(0).name;

15
fastdeploy/vision/detection/contrib/yolov6.h
View File

@@ -30,12 +30,16 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
const RuntimeOption& custom_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX);
~YOLOv6();
std::string ModelName() const { return "YOLOv6"; }
virtual bool Predict(cv::Mat* im, DetectionResult* result,
float conf_threshold = 0.25,
float nms_iou_threshold = 0.5);
void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
// tuple of (width, height)
std::vector<int> size;
// padding value, size should be same with Channels
@@ -60,6 +64,9 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
bool Preprocess(Mat* mat, FDTensor* outputs,
std::map<std::string, std::array<float, 2>>* im_info);
bool CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool Postprocess(FDTensor& infer_result, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
float conf_threshold, float nms_iou_threshold);
@@ -78,6 +85,14 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
// value will
// auto check by fastdeploy after the internal Runtime already initialized.
bool is_dynamic_input_;
// CUDA host buffer for input image
uint8_t* input_img_cuda_buffer_host_ = nullptr;
// CUDA device buffer for input image
uint8_t* input_img_cuda_buffer_device_ = nullptr;
// CUDA device buffer for TRT input tensor
float* input_tensor_cuda_buffer_device_ = nullptr;
// Whether to use CUDA preprocessing
bool use_cuda_preprocessing_ = false;
};
} // namespace detection

4
fastdeploy/vision/detection/contrib/yolov6_pybind.cc
View File

@@ -27,6 +27,10 @@ void BindYOLOv6(pybind11::module& m) {
self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
return res;
})
.def("use_cuda_preprocessing",
[](vision::detection::YOLOv6& self, int max_image_size) {
self.UseCudaPreprocessing(max_image_size);
})
.def_readwrite("size", &vision::detection::YOLOv6::size)
.def_readwrite("padding_value", &vision::detection::YOLOv6::padding_value)
.def_readwrite("is_mini_pad", &vision::detection::YOLOv6::is_mini_pad)

85
fastdeploy/vision/detection/contrib/yolov7.cc
View File

@@ -15,6 +15,9 @@
#include "fastdeploy/vision/detection/contrib/yolov7.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
#ifdef ENABLE_CUDA_PREPROCESS
#include "fastdeploy/vision/utils/cuda_utils.h"
#endif // ENABLE_CUDA_PREPROCESS
namespace fastdeploy {
namespace vision {
@@ -106,6 +109,16 @@ bool YOLOv7::Initialize() {
return true;
}
YOLOv7::~YOLOv7() {
#ifdef ENABLE_CUDA_PREPROCESS
if (use_cuda_preprocessing_) {
CUDA_CHECK(cudaFreeHost(input_img_cuda_buffer_host_));
CUDA_CHECK(cudaFree(input_img_cuda_buffer_device_));
CUDA_CHECK(cudaFree(input_tensor_cuda_buffer_device_));
}
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv7::Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
// process after image load
@@ -145,6 +158,65 @@ bool YOLOv7::Preprocess(Mat* mat, FDTensor* output,
return true;
}
void YOLOv7::UseCudaPreprocessing(int max_image_size) {
#ifdef ENABLE_CUDA_PREPROCESS
use_cuda_preprocessing_ = true;
is_scale_up = true;
if (input_img_cuda_buffer_host_ == nullptr) {
// prepare input data cache in GPU pinned memory
CUDA_CHECK(cudaMallocHost((void**)&input_img_cuda_buffer_host_, max_image_size * 3));
// prepare input data cache in GPU device memory
CUDA_CHECK(cudaMalloc((void**)&input_img_cuda_buffer_device_, max_image_size * 3));
CUDA_CHECK(cudaMalloc((void**)&input_tensor_cuda_buffer_device_, 3 * size[0] * size[1] * sizeof(float)));
}
#else
FDWARNING << "The FastDeploy didn't compile with BUILD_CUDA_SRC=ON."
<< std::endl;
use_cuda_preprocessing_ = false;
#endif
}
bool YOLOv7::CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
#ifdef ENABLE_CUDA_PREPROCESS
if (is_mini_pad != false || is_no_pad != false || is_scale_up != true) {
FDERROR << "Preprocessing with CUDA is only available when the arguments satisfy (is_mini_pad=false, is_no_pad=false, is_scale_up=true)." << std::endl;
return false;
}
// Record the shape of image and the shape of preprocessed image
(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
int src_img_buf_size = mat->Height() * mat->Width() * mat->Channels();
memcpy(input_img_cuda_buffer_host_, mat->Data(), src_img_buf_size);
CUDA_CHECK(cudaMemcpyAsync(input_img_cuda_buffer_device_,
input_img_cuda_buffer_host_,
src_img_buf_size, cudaMemcpyHostToDevice, stream));
utils::CudaYoloPreprocess(input_img_cuda_buffer_device_, mat->Width(),
mat->Height(), input_tensor_cuda_buffer_device_,
size[0], size[1], padding_value, stream);
cudaStreamSynchronize(stream);
cudaStreamDestroy(stream);
// Record output shape of preprocessed image
(*im_info)["output_shape"] = {static_cast<float>(size[0]), static_cast<float>(size[1])};
output->SetExternalData({mat->Channels(), size[0], size[1]}, FDDataType::FP32,
input_tensor_cuda_buffer_device_);
output->device = Device::GPU;
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
return true;
#else
FDERROR << "CUDA src code was not enabled." << std::endl;
return false;
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv7::Postprocess(
FDTensor& infer_result, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -227,9 +299,16 @@ bool YOLOv7::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
im_info["output_shape"] = {static_cast<float>(mat.Height()),
static_cast<float>(mat.Width())};
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
if (use_cuda_preprocessing_) {
if (!CudaPreprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
} else {
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
}
input_tensors[0].name = InputInfoOfRuntime(0).name;

15
fastdeploy/vision/detection/contrib/yolov7.h
View File

@@ -27,12 +27,16 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
const RuntimeOption& custom_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX);
~YOLOv7();
virtual std::string ModelName() const { return "yolov7"; }
virtual bool Predict(cv::Mat* im, DetectionResult* result,
float conf_threshold = 0.25,
float nms_iou_threshold = 0.5);
void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
// tuple of (width, height)
std::vector<int> size;
// padding value, size should be same with Channels
@@ -56,6 +60,9 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
bool Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool Postprocess(FDTensor& infer_result, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
float conf_threshold, float nms_iou_threshold);
@@ -71,6 +78,14 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
// value will
// auto check by fastdeploy after the internal Runtime already initialized.
bool is_dynamic_input_;
// CUDA host buffer for input image
uint8_t* input_img_cuda_buffer_host_ = nullptr;
// CUDA device buffer for input image
uint8_t* input_img_cuda_buffer_device_ = nullptr;
// CUDA device buffer for TRT input tensor
float* input_tensor_cuda_buffer_device_ = nullptr;
// Whether to use CUDA preprocessing
bool use_cuda_preprocessing_ = false;
};
} // namespace detection
} // namespace vision

4
fastdeploy/vision/detection/contrib/yolov7_pybind.cc
View File

@@ -27,6 +27,10 @@ void BindYOLOv7(pybind11::module& m) {
self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
return res;
})
.def("use_cuda_preprocessing",
[](vision::detection::YOLOv7& self, int max_image_size) {
self.UseCudaPreprocessing(max_image_size);
})
.def_readwrite("size", &vision::detection::YOLOv7::size)
.def_readwrite("padding_value", &vision::detection::YOLOv7::padding_value)
.def_readwrite("is_mini_pad", &vision::detection::YOLOv7::is_mini_pad)

85
fastdeploy/vision/detection/contrib/yolov7end2end_trt.cc
View File

@@ -15,6 +15,9 @@
#include "fastdeploy/vision/detection/contrib/yolov7end2end_trt.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
#ifdef ENABLE_CUDA_PREPROCESS
#include "fastdeploy/vision/utils/cuda_utils.h"
#endif // ENABLE_CUDA_PREPROCESS
namespace fastdeploy {
namespace vision {
@@ -119,6 +122,16 @@ bool YOLOv7End2EndTRT::Initialize() {
return true;
}
YOLOv7End2EndTRT::~YOLOv7End2EndTRT() {
#ifdef ENABLE_CUDA_PREPROCESS
if (use_cuda_preprocessing_) {
CUDA_CHECK(cudaFreeHost(input_img_cuda_buffer_host_));
CUDA_CHECK(cudaFree(input_img_cuda_buffer_device_));
CUDA_CHECK(cudaFree(input_tensor_cuda_buffer_device_));
}
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv7End2EndTRT::Preprocess(
Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
@@ -150,6 +163,65 @@ bool YOLOv7End2EndTRT::Preprocess(
return true;
}
void YOLOv7End2EndTRT::UseCudaPreprocessing(int max_image_size) {
#ifdef ENABLE_CUDA_PREPROCESS
use_cuda_preprocessing_ = true;
is_scale_up = true;
if (input_img_cuda_buffer_host_ == nullptr) {
// prepare input data cache in GPU pinned memory
CUDA_CHECK(cudaMallocHost((void**)&input_img_cuda_buffer_host_, max_image_size * 3));
// prepare input data cache in GPU device memory
CUDA_CHECK(cudaMalloc((void**)&input_img_cuda_buffer_device_, max_image_size * 3));
CUDA_CHECK(cudaMalloc((void**)&input_tensor_cuda_buffer_device_, 3 * size[0] * size[1] * sizeof(float)));
}
#else
FDWARNING << "The FastDeploy didn't compile with BUILD_CUDA_SRC=ON."
<< std::endl;
use_cuda_preprocessing_ = false;
#endif
}
bool YOLOv7End2EndTRT::CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
#ifdef ENABLE_CUDA_PREPROCESS
if (is_mini_pad != false || is_no_pad != false || is_scale_up != true) {
FDERROR << "Preprocessing with CUDA is only available when the arguments satisfy (is_mini_pad=false, is_no_pad=false, is_scale_up=true)." << std::endl;
return false;
}
// Record the shape of image and the shape of preprocessed image
(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
static_cast<float>(mat->Width())};
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
int src_img_buf_size = mat->Height() * mat->Width() * mat->Channels();
memcpy(input_img_cuda_buffer_host_, mat->Data(), src_img_buf_size);
CUDA_CHECK(cudaMemcpyAsync(input_img_cuda_buffer_device_,
input_img_cuda_buffer_host_,
src_img_buf_size, cudaMemcpyHostToDevice, stream));
utils::CudaYoloPreprocess(input_img_cuda_buffer_device_, mat->Width(),
mat->Height(), input_tensor_cuda_buffer_device_,
size[0], size[1], padding_value, stream);
cudaStreamSynchronize(stream);
cudaStreamDestroy(stream);
// Record output shape of preprocessed image
(*im_info)["output_shape"] = {static_cast<float>(size[0]), static_cast<float>(size[1])};
output->SetExternalData({mat->Channels(), size[0], size[1]}, FDDataType::FP32,
input_tensor_cuda_buffer_device_);
output->device = Device::GPU;
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
return true;
#else
FDERROR << "CUDA src code was not enabled." << std::endl;
return false;
#endif // ENABLE_CUDA_PREPROCESS
}
bool YOLOv7End2EndTRT::Postprocess(
std::vector<FDTensor>& infer_results, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -242,9 +314,16 @@ bool YOLOv7End2EndTRT::Predict(cv::Mat* im, DetectionResult* result,
im_info["output_shape"] = {static_cast<float>(mat.Height()),
static_cast<float>(mat.Width())};
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
if (use_cuda_preprocessing_) {
if (!CudaPreprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
} else {
if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
FDERROR << "Failed to preprocess input image." << std::endl;
return false;
}
}
input_tensors[0].name = InputInfoOfRuntime(0).name;

15
fastdeploy/vision/detection/contrib/yolov7end2end_trt.h
View File

@@ -28,11 +28,15 @@ class FASTDEPLOY_DECL YOLOv7End2EndTRT : public FastDeployModel {
const RuntimeOption& custom_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX);
~YOLOv7End2EndTRT();
virtual std::string ModelName() const { return "yolov7end2end_trt"; }
virtual bool Predict(cv::Mat* im, DetectionResult* result,
float conf_threshold = 0.25);
void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
// tuple of (width, height)
std::vector<int> size;
// padding value, size should be same with Channels
@@ -54,6 +58,9 @@ class FASTDEPLOY_DECL YOLOv7End2EndTRT : public FastDeployModel {
bool Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool CudaPreprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info);
bool Postprocess(std::vector<FDTensor>& infer_results,
DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
@@ -65,6 +72,14 @@ class FASTDEPLOY_DECL YOLOv7End2EndTRT : public FastDeployModel {
int stride = 32);
bool is_dynamic_input_;
// CUDA host buffer for input image
uint8_t* input_img_cuda_buffer_host_ = nullptr;
// CUDA device buffer for input image
uint8_t* input_img_cuda_buffer_device_ = nullptr;
// CUDA device buffer for TRT input tensor
float* input_tensor_cuda_buffer_device_ = nullptr;
// Whether to use CUDA preprocessing
bool use_cuda_preprocessing_ = false;
};
} // namespace detection
} // namespace vision

4
fastdeploy/vision/detection/contrib/yolov7end2end_trt_pybind.cc
View File

@@ -28,6 +28,10 @@ void BindYOLOv7End2EndTRT(pybind11::module& m) {
self.Predict(&mat, &res, conf_threshold);
return res;
})
.def("use_cuda_preprocessing",
[](vision::detection::YOLOv7End2EndTRT& self, int max_image_size) {
self.UseCudaPreprocessing(max_image_size);
})
.def_readwrite("size", &vision::detection::YOLOv7End2EndTRT::size)
.def_readwrite("padding_value",
&vision::detection::YOLOv7End2EndTRT::padding_value)

42
fastdeploy/vision/utils/cuda_utils.h Normal file
View File

@@ -0,0 +1,42 @@
// Copyright (c) 2022 PaddlePaddle Authors. 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.
#pragma once
#include <cuda_runtime.h>
#include <cstdint>
#include <vector>
#ifndef CUDA_CHECK
#define CUDA_CHECK(callstr)\
{\
cudaError_t error_code = callstr;\
if (error_code != cudaSuccess) {\
std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":";\
std::cerr << __LINE__;\
assert(0);\
}\
}
#endif // CUDA_CHECK
namespace fastdeploy {
namespace vision {
namespace utils {
void CudaYoloPreprocess(uint8_t* src, int src_width, int src_height,
float* dst, int dst_width, int dst_height,
const std::vector<float> padding_value,
cudaStream_t stream);
} // namespace utils
} // namespace vision
} // namespace fastdeploy

146
fastdeploy/vision/utils/yolo_preprocess.cu Normal file
View File

@@ -0,0 +1,146 @@
// Copyright (c) 2022 PaddlePaddle Authors. 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.
//
// Part of the following code in this file refs to
// https://github.com/wang-xinyu/tensorrtx/blob/yolov5-v6.0/yolov5/preprocess.cu
//
// Copyright (c) 2022 tensorrtx
// Licensed under The MIT License
// \file preprocess.cu
// \brief
// \author Qi Liu, Xinyu Wang
#include "fastdeploy/vision/utils/cuda_utils.h"
#include <opencv2/opencv.hpp>
namespace fastdeploy {
namespace vision {
namespace utils {
struct AffineMatrix {
float value[6];
};
__global__ void YoloPreprocessCudaKernel(
uint8_t* src, int src_line_size, int src_width,
int src_height, float* dst, int dst_width,
int dst_height, uint8_t padding_color_b,
uint8_t padding_color_g, uint8_t padding_color_r,
AffineMatrix d2s, int edge) {
int position = blockDim.x * blockIdx.x + threadIdx.x;
if (position >= edge) return;
float m_x1 = d2s.value[0];
float m_y1 = d2s.value[1];
float m_z1 = d2s.value[2];
float m_x2 = d2s.value[3];
float m_y2 = d2s.value[4];
float m_z2 = d2s.value[5];
int dx = position % dst_width;
int dy = position / dst_width;
float src_x = m_x1 * dx + m_y1 * dy + m_z1 + 0.5f;
float src_y = m_x2 * dx + m_y2 * dy + m_z2 + 0.5f;
float c0, c1, c2;
if (src_x <= -1 || src_x >= src_width || src_y <= -1 || src_y >= src_height) {
// out of range
c0 = padding_color_b;
c1 = padding_color_g;
c2 = padding_color_r;
} else {
int y_low = floorf(src_y);
int x_low = floorf(src_x);
int y_high = y_low + 1;
int x_high = x_low + 1;
uint8_t const_value[] = {padding_color_b, padding_color_g, padding_color_r};
float ly = src_y - y_low;
float lx = src_x - x_low;
float hy = 1 - ly;
float hx = 1 - lx;
float w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
uint8_t* v1 = const_value;
uint8_t* v2 = const_value;
uint8_t* v3 = const_value;
uint8_t* v4 = const_value;
if (y_low >= 0) {
if (x_low >= 0) v1 = src + y_low * src_line_size + x_low * 3;
if (x_high < src_width) v2 = src + y_low * src_line_size + x_high * 3;
}
if (y_high < src_height) {
if (x_low >= 0) v3 = src + y_high * src_line_size + x_low * 3;
if (x_high < src_width) v4 = src + y_high * src_line_size + x_high * 3;
}
c0 = w1 * v1[0] + w2 * v2[0] + w3 * v3[0] + w4 * v4[0];
c1 = w1 * v1[1] + w2 * v2[1] + w3 * v3[1] + w4 * v4[1];
c2 = w1 * v1[2] + w2 * v2[2] + w3 * v3[2] + w4 * v4[2];
}
// bgr to rgb
float t = c2;
c2 = c0;
c0 = t;
// normalization
c0 = c0 / 255.0f;
c1 = c1 / 255.0f;
c2 = c2 / 255.0f;
// rgbrgbrgb to rrrgggbbb
int area = dst_width * dst_height;
float* pdst_c0 = dst + dy * dst_width + dx;
float* pdst_c1 = pdst_c0 + area;
float* pdst_c2 = pdst_c1 + area;
*pdst_c0 = c0;
*pdst_c1 = c1;
*pdst_c2 = c2;
}
void CudaYoloPreprocess(
uint8_t* src, int src_width, int src_height,
float* dst, int dst_width, int dst_height,
const std::vector<float> padding_value, cudaStream_t stream) {
AffineMatrix s2d, d2s;
float scale = std::min(dst_height / (float)src_height, dst_width / (float)src_width);
s2d.value[0] = scale;
s2d.value[1] = 0;
s2d.value[2] = -scale * src_width * 0.5 + dst_width * 0.5;
s2d.value[3] = 0;
s2d.value[4] = scale;
s2d.value[5] = -scale * src_height * 0.5 + dst_height * 0.5;
cv::Mat m2x3_s2d(2, 3, CV_32F, s2d.value);
cv::Mat m2x3_d2s(2, 3, CV_32F, d2s.value);
cv::invertAffineTransform(m2x3_s2d, m2x3_d2s);
memcpy(d2s.value, m2x3_d2s.ptr<float>(0), sizeof(d2s.value));
int jobs = dst_height * dst_width;
int threads = 256;
int blocks = ceil(jobs / (float)threads);
YoloPreprocessCudaKernel<<<blocks, threads, 0, stream>>>(
src, src_width * 3, src_width,
src_height, dst, dst_width,
dst_height, padding_value[0], padding_value[1], padding_value[2], d2s, jobs);
}
} // namespace utils
} // namespace vision
} // namespace fastdeploy

3
python/fastdeploy/vision/detection/contrib/yolov5.py
View File

@@ -37,6 +37,9 @@ class YOLOv5(FastDeployModel):
return self._model.predict(input_image, conf_threshold,
nms_iou_threshold)
def use_cuda_preprocessing(self, max_image_size=3840 * 2160):
return self._model.use_cuda_preprocessing(max_image_size)
@staticmethod
def preprocess(input_image,
size=[640, 640],

3
python/fastdeploy/vision/detection/contrib/yolov5lite.py
View File

@@ -37,6 +37,9 @@ class YOLOv5Lite(FastDeployModel):
return self._model.predict(input_image, conf_threshold,
nms_iou_threshold)
def use_cuda_preprocessing(self, max_image_size=3840 * 2160):
return self._model.use_cuda_preprocessing(max_image_size)
# 一些跟YOLOv5Lite模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
@property

3
python/fastdeploy/vision/detection/contrib/yolov6.py
View File

@@ -37,6 +37,9 @@ class YOLOv6(FastDeployModel):
return self._model.predict(input_image, conf_threshold,
nms_iou_threshold)
def use_cuda_preprocessing(self, max_image_size=3840 * 2160):
return self._model.use_cuda_preprocessing(max_image_size)
# 一些跟YOLOv6模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
@property

3
python/fastdeploy/vision/detection/contrib/yolov7.py
View File

@@ -37,6 +37,9 @@ class YOLOv7(FastDeployModel):
return self._model.predict(input_image, conf_threshold,
nms_iou_threshold)
def use_cuda_preprocessing(self, max_image_size=3840 * 2160):
return self._model.use_cuda_preprocessing(max_image_size)
# 一些跟YOLOv7模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
@property

3
python/fastdeploy/vision/detection/contrib/yolov7end2end_trt.py
View File

@@ -36,6 +36,9 @@ class YOLOv7End2EndTRT(FastDeployModel):
def predict(self, input_image, conf_threshold=0.25):
return self._model.predict(input_image, conf_threshold)
def use_cuda_preprocessing(self, max_image_size=3840 * 2160):
return self._model.use_cuda_preprocessing(max_image_size)
# 一些跟模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
@property