mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 01:22:59 +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
293 lines
11 KiB
C++
293 lines
11 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/facedet/contrib/retinaface.h"
|
|
#include "fastdeploy/utils/perf.h"
|
|
#include "fastdeploy/vision/utils/utils.h"
|
|
|
|
namespace fastdeploy {
|
|
|
|
namespace vision {
|
|
|
|
namespace facedet {
|
|
|
|
struct RetinaAnchor {
|
|
float cx;
|
|
float cy;
|
|
float s_kx;
|
|
float s_ky;
|
|
};
|
|
|
|
void GenerateRetinaAnchors(const std::vector<int>& size,
|
|
const std::vector<int>& downsample_strides,
|
|
const std::vector<std::vector<int>>& min_sizes,
|
|
std::vector<RetinaAnchor>* anchors) {
|
|
// size: tuple of input (width, height)
|
|
// downsample_strides: downsample strides (steps), e.g (8,16,32)
|
|
// min_sizes: width and height for each anchor,
|
|
// e.g {{16, 32}, {64, 128}, {256, 512}}
|
|
int h = size[1];
|
|
int w = size[0];
|
|
std::vector<std::vector<int>> feature_maps;
|
|
for (auto s : downsample_strides) {
|
|
feature_maps.push_back(
|
|
{static_cast<int>(
|
|
std::ceil(static_cast<float>(h) / static_cast<float>(s))),
|
|
static_cast<int>(
|
|
std::ceil(static_cast<float>(w) / static_cast<float>(s)))});
|
|
}
|
|
|
|
(*anchors).clear();
|
|
const size_t num_feature_map = feature_maps.size();
|
|
// reference: layers/functions/prior_box.py#L21
|
|
for (size_t k = 0; k < num_feature_map; ++k) {
|
|
auto f_map = feature_maps.at(k); // e.g [640//8,640//8]
|
|
auto tmp_min_sizes = min_sizes.at(k); // e.g [8,16]
|
|
int f_h = f_map.at(0);
|
|
int f_w = f_map.at(1);
|
|
for (size_t i = 0; i < f_h; ++i) {
|
|
for (size_t j = 0; j < f_w; ++j) {
|
|
for (auto min_size : tmp_min_sizes) {
|
|
float s_kx =
|
|
static_cast<float>(min_size) / static_cast<float>(w); // e.g 16/w
|
|
float s_ky =
|
|
static_cast<float>(min_size) / static_cast<float>(h); // e.g 16/h
|
|
// (x + 0.5) * step / w normalized loc mapping to input width
|
|
// (y + 0.5) * step / h normalized loc mapping to input height
|
|
float s = static_cast<float>(downsample_strides.at(k));
|
|
float cx = (static_cast<float>(j) + 0.5f) * s / static_cast<float>(w);
|
|
float cy = (static_cast<float>(i) + 0.5f) * s / static_cast<float>(h);
|
|
(*anchors).emplace_back(
|
|
RetinaAnchor{cx, cy, s_kx, s_ky}); // without clip
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
RetinaFace::RetinaFace(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 = {Backend::ORT};
|
|
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
|
} else {
|
|
valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
|
|
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
|
}
|
|
runtime_option = custom_option;
|
|
runtime_option.model_format = model_format;
|
|
runtime_option.model_file = model_file;
|
|
runtime_option.params_file = params_file;
|
|
initialized = Initialize();
|
|
}
|
|
|
|
bool RetinaFace::Initialize() {
|
|
// parameters for preprocess
|
|
size = {640, 640};
|
|
variance = {0.1f, 0.2f};
|
|
downsample_strides = {8, 16, 32};
|
|
min_sizes = {{16, 32}, {64, 128}, {256, 512}};
|
|
landmarks_per_face = 5;
|
|
|
|
if (!InitRuntime()) {
|
|
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
|
return false;
|
|
}
|
|
// Check if the input shape is dynamic after Runtime already initialized,
|
|
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;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool RetinaFace::Preprocess(
|
|
Mat* mat, FDTensor* output,
|
|
std::map<std::string, std::array<float, 2>>* im_info) {
|
|
// retinaface's preprocess steps
|
|
// 1. Resize
|
|
// 2. Convert(opencv style) or Normalize
|
|
// 3. HWC->CHW
|
|
int resize_w = size[0];
|
|
int resize_h = size[1];
|
|
if (resize_h != mat->Height() || resize_w != mat->Width()) {
|
|
Resize::Run(mat, resize_w, resize_h);
|
|
}
|
|
|
|
// Compute `result = mat * alpha + beta` directly by channel
|
|
// Reference: detect.py#L94
|
|
std::vector<float> alpha = {1.f, 1.f, 1.f};
|
|
std::vector<float> beta = {-104.f, -117.f, -123.f}; // BGR;
|
|
Convert::Run(mat, alpha, beta);
|
|
|
|
// Record output shape of preprocessed image
|
|
(*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;
|
|
}
|
|
|
|
bool RetinaFace::Postprocess(
|
|
std::vector<FDTensor>& infer_result, FaceDetectionResult* result,
|
|
const std::map<std::string, std::array<float, 2>>& im_info,
|
|
float conf_threshold, float nms_iou_threshold) {
|
|
// retinaface has 3 output tensors, boxes & conf & landmarks
|
|
FDASSERT(
|
|
(infer_result.size() == 3),
|
|
"The default number of output tensor must be 3 according to retinaface.");
|
|
FDTensor& boxes_tensor = infer_result.at(0); // (1,n,4)
|
|
FDTensor& conf_tensor = infer_result.at(1); // (1,n,2)
|
|
FDTensor& landmarks_tensor = infer_result.at(2); // (1,n,10)
|
|
FDASSERT((boxes_tensor.shape[0] == 1), "Only support batch =1 now.");
|
|
if (boxes_tensor.dtype != FDDataType::FP32) {
|
|
FDERROR << "Only support post process with float32 data." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
result->Clear();
|
|
// must be setup landmarks_per_face before reserve
|
|
result->landmarks_per_face = landmarks_per_face;
|
|
result->Reserve(boxes_tensor.shape[1]);
|
|
|
|
float* boxes_ptr = static_cast<float*>(boxes_tensor.Data());
|
|
float* conf_ptr = static_cast<float*>(conf_tensor.Data());
|
|
float* landmarks_ptr = static_cast<float*>(landmarks_tensor.Data());
|
|
const size_t num_bboxes = boxes_tensor.shape[1]; // n
|
|
// fetch original image shape
|
|
auto iter_ipt = im_info.find("input_shape");
|
|
FDASSERT((iter_ipt != im_info.end()),
|
|
"Cannot find input_shape from im_info.");
|
|
float ipt_h = iter_ipt->second[0];
|
|
float ipt_w = iter_ipt->second[1];
|
|
|
|
// generate anchors with dowmsample strides
|
|
std::vector<RetinaAnchor> anchors;
|
|
GenerateRetinaAnchors(size, downsample_strides, min_sizes, &anchors);
|
|
|
|
// decode bounding boxes
|
|
for (size_t i = 0; i < num_bboxes; ++i) {
|
|
float confidence = conf_ptr[2 * i + 1];
|
|
// filter boxes by conf_threshold
|
|
if (confidence <= conf_threshold) {
|
|
continue;
|
|
}
|
|
float prior_cx = anchors.at(i).cx;
|
|
float prior_cy = anchors.at(i).cy;
|
|
float prior_s_kx = anchors.at(i).s_kx;
|
|
float prior_s_ky = anchors.at(i).s_ky;
|
|
|
|
// fetch offsets (dx,dy,dw,dh)
|
|
float dx = boxes_ptr[4 * i + 0];
|
|
float dy = boxes_ptr[4 * i + 1];
|
|
float dw = boxes_ptr[4 * i + 2];
|
|
float dh = boxes_ptr[4 * i + 3];
|
|
// reference: Pytorch_Retinaface/utils/box_utils.py
|
|
float x = prior_cx + dx * variance[0] * prior_s_kx;
|
|
float y = prior_cy + dy * variance[0] * prior_s_ky;
|
|
float w = prior_s_kx * std::exp(dw * variance[1]);
|
|
float h = prior_s_ky * std::exp(dh * variance[1]); // (0.~1.)
|
|
// from (x,y,w,h) to (x1,y1,x2,y2)
|
|
float x1 = (x - w / 2.f) * ipt_w;
|
|
float y1 = (y - h / 2.f) * ipt_h;
|
|
float x2 = (x + w / 2.f) * ipt_w;
|
|
float y2 = (y + h / 2.f) * ipt_h;
|
|
result->boxes.emplace_back(std::array<float, 4>{x1, y1, x2, y2});
|
|
result->scores.push_back(confidence);
|
|
// decode landmarks (default 5 landmarks)
|
|
if (landmarks_per_face > 0) {
|
|
// reference: utils/box_utils.py#L241
|
|
for (size_t j = 0; j < landmarks_per_face * 2; j += 2) {
|
|
float ldx = landmarks_ptr[i * (landmarks_per_face * 2) + (j + 0)];
|
|
float ldy = landmarks_ptr[i * (landmarks_per_face * 2) + (j + 1)];
|
|
float lx = (prior_cx + ldx * variance[0] * prior_s_kx) * ipt_w;
|
|
float ly = (prior_cy + ldy * variance[0] * prior_s_ky) * ipt_h;
|
|
result->landmarks.emplace_back(std::array<float, 2>{lx, ly});
|
|
}
|
|
}
|
|
}
|
|
|
|
if (result->boxes.size() == 0) {
|
|
return true;
|
|
}
|
|
|
|
utils::NMS(result, nms_iou_threshold);
|
|
|
|
// scale and clip box
|
|
for (size_t i = 0; i < result->boxes.size(); ++i) {
|
|
result->boxes[i][0] = std::max(result->boxes[i][0], 0.0f);
|
|
result->boxes[i][1] = std::max(result->boxes[i][1], 0.0f);
|
|
result->boxes[i][2] = std::max(result->boxes[i][2], 0.0f);
|
|
result->boxes[i][3] = std::max(result->boxes[i][3], 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);
|
|
}
|
|
// scale and clip landmarks
|
|
for (size_t i = 0; i < result->landmarks.size(); ++i) {
|
|
result->landmarks[i][0] = std::max(result->landmarks[i][0], 0.0f);
|
|
result->landmarks[i][1] = std::max(result->landmarks[i][1], 0.0f);
|
|
result->landmarks[i][0] = std::min(result->landmarks[i][0], ipt_w - 1.0f);
|
|
result->landmarks[i][1] = std::min(result->landmarks[i][1], ipt_h - 1.0f);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool RetinaFace::Predict(cv::Mat* im, FaceDetectionResult* result,
|
|
float conf_threshold, float nms_iou_threshold) {
|
|
Mat mat(*im);
|
|
std::vector<FDTensor> input_tensors(1);
|
|
|
|
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)) {
|
|
FDERROR << "Failed to preprocess input image." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
input_tensors[0].name = InputInfoOfRuntime(0).name;
|
|
std::vector<FDTensor> output_tensors;
|
|
if (!Infer(input_tensors, &output_tensors)) {
|
|
FDERROR << "Failed to inference." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
if (!Postprocess(output_tensors, result, im_info, conf_threshold,
|
|
nms_iou_threshold)) {
|
|
FDERROR << "Failed to post process." << std::endl;
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
} // namespace facedet
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|