Add RetinaFace Model support

csrcs/fastdeploy/vision.h

@@ -15,6 +15,7 @@
 
 #include "fastdeploy/core/config.h"
 #ifdef ENABLE_VISION
+#include "fastdeploy/vision/biubug6/retinaface.h"
 #include "fastdeploy/vision/deepcam/yolov5face.h"
 #include "fastdeploy/vision/linzaer/ultraface.h"
 #include "fastdeploy/vision/megvii/yolox.h"

csrcs/fastdeploy/vision/biubug6/biubug6_pybind.cc

@@ -0,0 +1,40 @@
+// 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/pybind/main.h"
+
+namespace fastdeploy {
+void BindBiubug6(pybind11::module& m) {
+  auto biubug6_module = m.def_submodule(
+      "biubug6", "https://github.com/biubug6/Pytorch_Retinaface");
+  pybind11::class_<vision::biubug6::RetinaFace, FastDeployModel>(biubug6_module,
+                                                                 "RetinaFace")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::biubug6::RetinaFace& self, pybind11::array& data,
+              float conf_threshold, float nms_iou_threshold) {
+             auto mat = PyArrayToCvMat(data);
+             vision::FaceDetectionResult res;
+             self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
+             return res;
+           })
+      .def_readwrite("size", &vision::biubug6::RetinaFace::size)
+      .def_readwrite("variance", &vision::biubug6::RetinaFace::variance)
+      .def_readwrite("downsample_strides",
+                     &vision::biubug6::RetinaFace::downsample_strides)
+      .def_readwrite("min_sizes", &vision::biubug6::RetinaFace::min_sizes)
+      .def_readwrite("landmarks_per_face",
+                     &vision::biubug6::RetinaFace::landmarks_per_face);
+}
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/biubug6/retinaface.cc

@@ -0,0 +1,310 @@
+// 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/biubug6/retinaface.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace biubug6 {
+
+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 Frontend& model_format) {
+  if (model_format == Frontend::ONNX) {
+    valid_cpu_backends = {Backend::ORT};  // 指定可用的CPU后端
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};  // 指定可用的GPU后端
+  } 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, h, w, c
+  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) {
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_START(0)
+#endif
+
+  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;
+  }
+
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(0, "Preprocess")
+  TIMERECORD_START(1)
+#endif
+
+  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;
+  }
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(1, "Inference")
+  TIMERECORD_START(2)
+#endif
+
+  if (!Postprocess(output_tensors, result, im_info, conf_threshold,
+                   nms_iou_threshold)) {
+    FDERROR << "Failed to post process." << std::endl;
+    return false;
+  }
+
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(2, "Postprocess")
+#endif
+  return true;
+}
+
+}  // namespace biubug6
+}  // namespace vision
+}  // namespace fastdeploy