mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
d3845eb4e1
* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32 * Add diff compare for OCR * Add diff compare for OCR * rm ppocr pipeline * Add yolov5 diff compare * Add yolov5 diff compare * deal with comments * deal with comments * fixed bug * fixed bug
358 lines
13 KiB
C++
358 lines
13 KiB
C++
// 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.
|
|
|
|
#include "fastdeploy/vision/detection/contrib/yolov7end2end_trt.h"
|
|
|
|
#include "fastdeploy/utils/perf.h"
|
|
#include "fastdeploy/vision/utils/utils.h"
|
|
#ifdef WITH_GPU
|
|
#include "fastdeploy/vision/utils/cuda_utils.h"
|
|
#endif // WITH_GPU
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace detection {
|
|
|
|
void YOLOv7End2EndTRT::LetterBox(Mat* mat, const std::vector<int>& size,
|
|
const std::vector<float>& color, bool _auto,
|
|
bool scale_fill, bool scale_up, int stride) {
|
|
float scale =
|
|
std::min(size[1] * 1.0 / mat->Height(), size[0] * 1.0 / mat->Width());
|
|
if (!scale_up) {
|
|
scale = std::min(scale, 1.0f);
|
|
}
|
|
|
|
int resize_h = int(round(mat->Height() * scale));
|
|
int resize_w = int(round(mat->Width() * scale));
|
|
|
|
int pad_w = size[0] - resize_w;
|
|
int pad_h = size[1] - resize_h;
|
|
if (_auto) {
|
|
pad_h = pad_h % stride;
|
|
pad_w = pad_w % stride;
|
|
} else if (scale_fill) {
|
|
pad_h = 0;
|
|
pad_w = 0;
|
|
resize_h = size[1];
|
|
resize_w = size[0];
|
|
}
|
|
if (resize_h != mat->Height() || resize_w != mat->Width()) {
|
|
Resize::Run(mat, resize_w, resize_h);
|
|
}
|
|
if (pad_h > 0 || pad_w > 0) {
|
|
float half_h = pad_h * 1.0 / 2;
|
|
int top = int(round(half_h - 0.1));
|
|
int bottom = int(round(half_h + 0.1));
|
|
float half_w = pad_w * 1.0 / 2;
|
|
int left = int(round(half_w - 0.1));
|
|
int right = int(round(half_w + 0.1));
|
|
Pad::Run(mat, top, bottom, left, right, color);
|
|
}
|
|
}
|
|
|
|
YOLOv7End2EndTRT::YOLOv7End2EndTRT(const std::string& model_file,
|
|
const std::string& params_file,
|
|
const RuntimeOption& custom_option,
|
|
const ModelFormat& model_format) {
|
|
if (model_format == ModelFormat::ONNX) {
|
|
valid_cpu_backends = {}; // NO CPU
|
|
valid_gpu_backends = {Backend::TRT}; // NO ORT
|
|
} else {
|
|
valid_cpu_backends = {Backend::PDINFER};
|
|
valid_gpu_backends = {Backend::PDINFER};
|
|
}
|
|
runtime_option = custom_option;
|
|
runtime_option.model_format = model_format;
|
|
runtime_option.model_file = model_file;
|
|
if (runtime_option.device != Device::GPU) {
|
|
FDWARNING << runtime_option.device
|
|
<< " is not support for YOLOv7End2EndTRT,"
|
|
<< "will fallback to Device::GPU." << std::endl;
|
|
runtime_option.device = Device::GPU;
|
|
}
|
|
if (runtime_option.backend != Backend::UNKNOWN) {
|
|
if (runtime_option.backend != Backend::TRT) {
|
|
FDWARNING << runtime_option.backend
|
|
<< " is not support for YOLOv7End2EndTRT,"
|
|
<< "will fallback to Backend::TRT." << std::endl;
|
|
runtime_option.backend = Backend::TRT;
|
|
}
|
|
}
|
|
#ifdef WITH_GPU
|
|
cudaSetDevice(runtime_option.device_id);
|
|
cudaStream_t stream;
|
|
CUDA_CHECK(cudaStreamCreate(&stream));
|
|
cuda_stream_ = reinterpret_cast<void*>(stream);
|
|
runtime_option.SetExternalStream(cuda_stream_);
|
|
#endif // WITH_GPU
|
|
initialized = Initialize();
|
|
}
|
|
|
|
bool YOLOv7End2EndTRT::Initialize() {
|
|
// parameters for preprocess
|
|
size = {640, 640};
|
|
padding_value = {114.0, 114.0, 114.0};
|
|
is_mini_pad = false;
|
|
is_no_pad = false;
|
|
is_scale_up = false;
|
|
stride = 32;
|
|
reused_input_tensors_.resize(1);
|
|
|
|
if (!InitRuntime()) {
|
|
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
|
return false;
|
|
}
|
|
// Check if the input shape is dynamic after Runtime already initialized,
|
|
// Note that, We need to force is_mini_pad 'false' to keep static
|
|
// shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
|
|
is_dynamic_input_ = false;
|
|
auto shape = InputInfoOfRuntime(0).shape;
|
|
for (int i = 0; i < shape.size(); ++i) {
|
|
// if height or width is dynamic
|
|
if (i >= 2 && shape[i] <= 0) {
|
|
is_dynamic_input_ = true;
|
|
break;
|
|
}
|
|
}
|
|
if (!is_dynamic_input_) {
|
|
is_mini_pad = false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
YOLOv7End2EndTRT::~YOLOv7End2EndTRT() {
|
|
#ifdef WITH_GPU
|
|
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_));
|
|
CUDA_CHECK(cudaStreamDestroy(reinterpret_cast<cudaStream_t>(cuda_stream_)));
|
|
}
|
|
#endif // WITH_GPU
|
|
}
|
|
|
|
bool YOLOv7End2EndTRT::Preprocess(
|
|
Mat* mat, FDTensor* output,
|
|
std::map<std::string, std::array<float, 2>>* im_info) {
|
|
float ratio = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()),
|
|
size[0] * 1.0f / static_cast<float>(mat->Width()));
|
|
if (std::fabs(ratio - 1.0f) > 1e-06) {
|
|
int interp = cv::INTER_AREA;
|
|
if (ratio > 1.0) {
|
|
interp = cv::INTER_LINEAR;
|
|
}
|
|
int resize_h = int(mat->Height() * ratio);
|
|
int resize_w = int(mat->Width() * ratio);
|
|
Resize::Run(mat, resize_w, resize_h, -1, -1, interp);
|
|
}
|
|
YOLOv7End2EndTRT::LetterBox(mat, size, padding_value, is_mini_pad, is_no_pad,
|
|
is_scale_up, stride);
|
|
BGR2RGB::Run(mat);
|
|
std::vector<float> alpha = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
|
|
std::vector<float> beta = {0.0f, 0.0f, 0.0f};
|
|
Convert::Run(mat, alpha, beta);
|
|
|
|
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
|
|
static_cast<float>(mat->Width())};
|
|
|
|
HWC2CHW::Run(mat);
|
|
Cast::Run(mat, "float");
|
|
mat->ShareWithTensor(output);
|
|
output->shape.insert(output->shape.begin(), 1); // reshape to n, c, h, w
|
|
return true;
|
|
}
|
|
|
|
void YOLOv7End2EndTRT::UseCudaPreprocessing(int max_image_size) {
|
|
#ifdef WITH_GPU
|
|
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 WITH_GPU=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 WITH_GPU
|
|
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 = reinterpret_cast<cudaStream_t>(cuda_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);
|
|
|
|
// 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, c, h, w
|
|
return true;
|
|
#else
|
|
FDERROR << "CUDA src code was not enabled." << std::endl;
|
|
return false;
|
|
#endif // WITH_GPU
|
|
}
|
|
|
|
bool YOLOv7End2EndTRT::Postprocess(
|
|
std::vector<FDTensor>& infer_results, DetectionResult* result,
|
|
const std::map<std::string, std::array<float, 2>>& im_info,
|
|
float conf_threshold) {
|
|
FDASSERT(infer_results.size() == 4, "Output tensor size must be 4.");
|
|
FDTensor& num_tensor = infer_results.at(0); // INT32
|
|
FDTensor& boxes_tensor = infer_results.at(1); // FLOAT
|
|
FDTensor& scores_tensor = infer_results.at(2); // FLOAT
|
|
FDTensor& classes_tensor = infer_results.at(3); // INT32
|
|
FDASSERT(num_tensor.dtype == FDDataType::INT32,
|
|
"The dtype of num_dets must be INT32.");
|
|
FDASSERT(boxes_tensor.dtype == FDDataType::FP32,
|
|
"The dtype of det_boxes_tensor must be FP32.");
|
|
FDASSERT(scores_tensor.dtype == FDDataType::FP32,
|
|
"The dtype of det_scores_tensor must be FP32.");
|
|
FDASSERT(classes_tensor.dtype == FDDataType::INT32,
|
|
"The dtype of det_classes_tensor must be INT32.");
|
|
FDASSERT(num_tensor.shape[0] == 1, "Only support batch=1 now.");
|
|
// post-process for end2end yolov7 after trt nms.
|
|
float* boxes_data = static_cast<float*>(boxes_tensor.Data()); // (1,100,4)
|
|
float* scores_data = static_cast<float*>(scores_tensor.Data()); // (1,100)
|
|
int32_t* classes_data =
|
|
static_cast<int32_t*>(classes_tensor.Data()); // (1,100)
|
|
int32_t num_dets_after_trt_nms = static_cast<int32_t*>(num_tensor.Data())[0];
|
|
if (num_dets_after_trt_nms == 0) {
|
|
return true;
|
|
}
|
|
result->Clear();
|
|
result->Reserve(num_dets_after_trt_nms);
|
|
for (size_t i = 0; i < num_dets_after_trt_nms; ++i) {
|
|
float confidence = scores_data[i];
|
|
if (confidence <= conf_threshold) {
|
|
continue;
|
|
}
|
|
int32_t label_id = classes_data[i];
|
|
float x1 = boxes_data[(i * 4) + 0];
|
|
float y1 = boxes_data[(i * 4) + 1];
|
|
float x2 = boxes_data[(i * 4) + 2];
|
|
float y2 = boxes_data[(i * 4) + 3];
|
|
|
|
result->boxes.emplace_back(std::array<float, 4>{x1, y1, x2, y2});
|
|
result->label_ids.push_back(label_id);
|
|
result->scores.push_back(confidence);
|
|
}
|
|
|
|
if (result->boxes.size() == 0) {
|
|
return true;
|
|
}
|
|
|
|
// scale the boxes to the origin image shape
|
|
auto iter_out = im_info.find("output_shape");
|
|
auto iter_ipt = im_info.find("input_shape");
|
|
FDASSERT(iter_out != im_info.end() && iter_ipt != im_info.end(),
|
|
"Cannot find input_shape or output_shape from im_info.");
|
|
float out_h = iter_out->second[0];
|
|
float out_w = iter_out->second[1];
|
|
float ipt_h = iter_ipt->second[0];
|
|
float ipt_w = iter_ipt->second[1];
|
|
float scale = std::min(out_h / ipt_h, out_w / ipt_w);
|
|
float pad_h = (out_h - ipt_h * scale) / 2.0f;
|
|
float pad_w = (out_w - ipt_w * scale) / 2.0f;
|
|
if (is_mini_pad) {
|
|
pad_h = static_cast<float>(static_cast<int>(pad_h) % stride);
|
|
pad_w = static_cast<float>(static_cast<int>(pad_w) % stride);
|
|
}
|
|
for (size_t i = 0; i < result->boxes.size(); ++i) {
|
|
int32_t label_id = (result->label_ids)[i];
|
|
result->boxes[i][0] = std::max((result->boxes[i][0] - pad_w) / scale, 0.0f);
|
|
result->boxes[i][1] = std::max((result->boxes[i][1] - pad_h) / scale, 0.0f);
|
|
result->boxes[i][2] = std::max((result->boxes[i][2] - pad_w) / scale, 0.0f);
|
|
result->boxes[i][3] = std::max((result->boxes[i][3] - pad_h) / scale, 0.0f);
|
|
result->boxes[i][0] = std::min(result->boxes[i][0], ipt_w - 1.0f);
|
|
result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h - 1.0f);
|
|
result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w - 1.0f);
|
|
result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h - 1.0f);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool YOLOv7End2EndTRT::Predict(cv::Mat* im, DetectionResult* result,
|
|
float conf_threshold) {
|
|
Mat mat(*im);
|
|
|
|
std::map<std::string, std::array<float, 2>> im_info;
|
|
|
|
// 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())};
|
|
|
|
if (use_cuda_preprocessing_) {
|
|
if (!CudaPreprocess(&mat, &reused_input_tensors_[0], &im_info)) {
|
|
FDERROR << "Failed to preprocess input image." << std::endl;
|
|
return false;
|
|
}
|
|
} else {
|
|
if (!Preprocess(&mat, &reused_input_tensors_[0], &im_info)) {
|
|
FDERROR << "Failed to preprocess input image." << std::endl;
|
|
return false;
|
|
}
|
|
}
|
|
|
|
reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
|
|
if (!Infer()) {
|
|
FDERROR << "Failed to inference." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
if (!Postprocess(reused_output_tensors_, result, im_info, conf_threshold)) {
|
|
FDERROR << "Failed to post process." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
} // namespace detection
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|