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yolov5 servitization optimization (#262)

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* yolov5 split pre and post process

* yolov5 postprocess

* yolov5 postprocess
This commit is contained in:
heliqi
2022-09-21 05:22:39 -05:00
committed by GitHub
parent 463ee0a088
commit a8e447f225
9 changed files with 194 additions and 77 deletions
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74
fastdeploy/vision/detection/contrib/yolov5.cc
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@@ -13,6 +13,7 @@
// limitations under the License.
#include "fastdeploy/vision/detection/contrib/yolov5.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
@@ -74,14 +75,14 @@ YOLOv5::YOLOv5(const std::string& model_file, const std::string& params_file,
bool YOLOv5::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;
max_wh = 7680.0;
multi_label = true;
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;
max_wh_ = 7680.0;
multi_label_ = true;
if (!InitRuntime()) {
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
@@ -90,23 +91,34 @@ bool YOLOv5::Initialize() {
// 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;
}
// TODO(qiuyanjun): remove
// 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;
}
bool YOLOv5::Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
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) {
// 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())};
// process after image load
double ratio = (size[0] * 1.0) / std::max(static_cast<float>(mat->Height()),
static_cast<float>(mat->Width()));
@@ -145,9 +157,11 @@ bool YOLOv5::Preprocess(Mat* mat, FDTensor* output,
}
bool YOLOv5::Postprocess(
FDTensor& infer_result, DetectionResult* result,
std::vector<FDTensor>& infer_results, DetectionResult* result,
const std::map<std::string, std::array<float, 2>>& im_info,
float conf_threshold, float nms_iou_threshold, bool multi_label) {
float conf_threshold, float nms_iou_threshold, bool multi_label,
float max_wh) {
auto& infer_result = infer_results[0];
FDASSERT(infer_result.shape[0] == 1, "Only support batch =1 now.");
result->Clear();
if (multi_label) {
@@ -251,13 +265,9 @@ bool YOLOv5::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
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 (!Preprocess(&mat, &input_tensors[0], &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;
}
@@ -278,8 +288,8 @@ bool YOLOv5::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
TIMERECORD_START(2)
#endif
if (!Postprocess(output_tensors[0], result, im_info, conf_threshold,
nms_iou_threshold, multi_label)) {
if (!Postprocess(output_tensors, result, im_info, conf_threshold,
nms_iou_threshold, multi_label_)) {
FDERROR << "Failed to post process." << std::endl;
return false;
}