apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-10-19 23:16:15 +08:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

[Model] Add YOLOv5-seg (#988)

Browse Source 
* add onnx_ort_runtime demo

* rm in requirements

* support batch eval

* fixed MattingResults bug

* move assignment for DetectionResult

* integrated x2paddle

* add model convert readme

* update readme

* re-lint

* add processor api

* Add MattingResult Free

* change valid_cpu_backends order

* add ppocr benchmark

* mv bs from 64 to 32

* fixed quantize.md

* fixed quantize bugs

* Add Monitor for benchmark

* update mem monitor

* Set trt_max_batch_size default 1

* fixed ocr benchmark bug

* support yolov5 in serving

* Fixed yolov5 serving

* Fixed postprocess

* update yolov5 to 7.0

* add poros runtime demos

* update readme

* Support poros abi=1

* rm useless note

* deal with comments

* support pp_trt for ppseg

* fixed symlink problem

* Add is_mini_pad and stride for yolov5

* Add yolo series for paddle format

* fixed bugs

* fixed bug

* support yolov5seg

* fixed bug

* refactor yolov5seg

* fixed bug

* mv Mask int32 to uint8

* add yolov5seg example

* rm log info

* fixed code style

* add yolov5seg example in python

* fixed dtype bug

* update note

* deal with comments

* get sorted index

* add yolov5seg test case

* Add GPL-3.0 License

* add round func

* deal with comments

* deal with commens

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
WJJ1995
2023-01-11 15:36:32 +08:00
committed by GitHub
parent 60e6a12b93
commit aa6931bee9
28 changed files with 1607 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
fastdeploy/vision/detection/ppdet/postprocessor.cc Normal file → Executable file
View File

@@ -32,30 +32,30 @@ bool PaddleDetPostprocessor::ProcessMask(
int64_t out_mask_h = shape[1];
int64_t out_mask_w = shape[2];
int64_t out_mask_numel = shape[1] * shape[2];
const int32_t* data = reinterpret_cast<const int32_t*>(tensor.CpuData());
const uint8_t* data = reinterpret_cast<const uint8_t*>(tensor.CpuData());
int index = 0;
for (int i = 0; i < results->size(); ++i) {
(*results)[i].contain_masks = true;
(*results)[i].masks.resize((*results)[i].boxes.size());
for (int j = 0; j < (*results)[i].boxes.size(); ++j) {
int x1 = static_cast<int>((*results)[i].boxes[j][0]);
int y1 = static_cast<int>((*results)[i].boxes[j][1]);
int x2 = static_cast<int>((*results)[i].boxes[j][2]);
int y2 = static_cast<int>((*results)[i].boxes[j][3]);
int x1 = static_cast<int>(round((*results)[i].boxes[j][0]));
int y1 = static_cast<int>(round((*results)[i].boxes[j][1]));
int x2 = static_cast<int>(round((*results)[i].boxes[j][2]));
int y2 = static_cast<int>(round((*results)[i].boxes[j][3]));
int keep_mask_h = y2 - y1;
int keep_mask_w = x2 - x1;
int keep_mask_numel = keep_mask_h * keep_mask_w;
(*results)[i].masks[j].Resize(keep_mask_numel);
(*results)[i].masks[j].shape = {keep_mask_h, keep_mask_w};
const int32_t* current_ptr = data + index * out_mask_numel;
const uint8_t* current_ptr = data + index * out_mask_numel;
int32_t* keep_mask_ptr =
reinterpret_cast<int32_t*>((*results)[i].masks[j].Data());
uint8_t* keep_mask_ptr =
reinterpret_cast<uint8_t*>((*results)[i].masks[j].Data());
for (int row = y1; row < y2; ++row) {
size_t keep_nbytes_in_col = keep_mask_w * sizeof(int32_t);
const int32_t* out_row_start_ptr = current_ptr + row * out_mask_w + x1;
int32_t* keep_row_start_ptr = keep_mask_ptr + (row - y1) * keep_mask_w;
size_t keep_nbytes_in_col = keep_mask_w * sizeof(uint8_t);
const uint8_t* out_row_start_ptr = current_ptr + row * out_mask_w + x1;
uint8_t* keep_row_start_ptr = keep_mask_ptr + (row - y1) * keep_mask_w;
std::memcpy(keep_row_start_ptr, out_row_start_ptr, keep_nbytes_in_col);
}
index += 1;