Develop

.gitignore

@@ -14,3 +14,4 @@ fastdeploy/LICENSE*
 fastdeploy/ThirdPartyNotices*
 *.so*
 fastdeploy/libs/third_libs
+csrcs/fastdeploy/core/config.h

csrcs/fastdeploy/vision.h

@@ -16,6 +16,7 @@
 #include "fastdeploy/core/config.h"
 #ifdef ENABLE_VISION
 #include "fastdeploy/vision/deepcam/yolov5face.h"
+#include "fastdeploy/vision/linzaer/ultraface.h"
 #include "fastdeploy/vision/megvii/yolox.h"
 #include "fastdeploy/vision/meituan/yolov6.h"
 #include "fastdeploy/vision/ppcls/model.h"

csrcs/fastdeploy/vision/linzaer/linzaer_pybind.cc

@@ -0,0 +1,35 @@
+// 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 BindLinzaer(pybind11::module& m) {
+  auto linzaer_module = m.def_submodule(
+      "linzaer",
+      "https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB");
+  pybind11::class_<vision::linzaer::UltraFace, FastDeployModel>(linzaer_module,
+                                                                "UltraFace")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::linzaer::UltraFace& 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::linzaer::UltraFace::size);
+}
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/linzaer/ultraface.cc

@@ -0,0 +1,220 @@
+// 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/linzaer/ultraface.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace linzaer {
+
+UltraFace::UltraFace(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 UltraFace::Initialize() {
+  // parameters for preprocess
+  size = {320, 240};
+
+  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 UltraFace::Preprocess(
+    Mat* mat, FDTensor* output,
+    std::map<std::string, std::array<float, 2>>* im_info) {
+  // ultraface's preprocess steps
+  // 1. resize
+  // 2. BGR->RGB
+  // 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);
+  }
+
+  BGR2RGB::Run(mat);
+  // Compute `result = mat * alpha + beta` directly by channel
+  // Reference: detect_imgs_onnx.py#L73
+  std::vector<float> alpha = {1.0f / 128.0f, 1.0f / 128.0f, 1.0f / 128.0f};
+  std::vector<float> beta = {-127.0f * (1.0f / 128.0f),
+                             -127.0f * (1.0f / 128.0f),
+                             -127.0f * (1.0f / 128.0f)};  // RGB;
+  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 UltraFace::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) {
+  // ultraface has 2 output tensors, scores & boxes
+  FDASSERT(
+      (infer_result.size() == 2),
+      "The default number of output tensor must be 2 according to ultraface.");
+  FDTensor& scores_tensor = infer_result.at(0);  // (1,4420,2)
+  FDTensor& boxes_tensor = infer_result.at(1);   // (1,4420,4)
+  FDASSERT((scores_tensor.shape[0] == 1), "Only support batch =1 now.");
+  FDASSERT((boxes_tensor.shape[0] == 1), "Only support batch =1 now.");
+
+  result->Clear();
+  // must be setup landmarks_per_face before reserve.
+  // ultraface detector does not detect landmarks by default.
+  result->landmarks_per_face = 0;
+  if (scores_tensor.dtype != FDDataType::FP32) {
+    FDERROR << "Only support post process with float32 data." << std::endl;
+    return false;
+  }
+  if (boxes_tensor.dtype != FDDataType::FP32) {
+    FDERROR << "Only support post process with float32 data." << std::endl;
+    return false;
+  }
+
+  float* scores_ptr = static_cast<float*>(scores_tensor.Data());
+  float* boxes_ptr = static_cast<float*>(boxes_tensor.Data());
+  const size_t num_bboxes = boxes_tensor.shape[1];  // e.g 4420
+  // 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];
+
+  // decode bounding boxes
+  for (size_t i = 0; i < num_bboxes; ++i) {
+    float confidence = scores_ptr[2 * i + 1];
+    // filter boxes by conf_threshold
+    if (confidence <= conf_threshold) {
+      continue;
+    }
+    float x1 = boxes_ptr[4 * i + 0] * ipt_w;
+    float y1 = boxes_ptr[4 * i + 1] * ipt_h;
+    float x2 = boxes_ptr[4 * i + 2] * ipt_w;
+    float y2 = boxes_ptr[4 * i + 3] * ipt_h;
+    result->boxes.emplace_back(std::array<float, 4>{x1, y1, x2, y2});
+    result->scores.push_back(confidence);
+  }
+
+  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);
+  }
+  return true;
+}
+
+bool UltraFace::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 linzaer
+}  // namespace vision
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/linzaer/ultraface.h

@@ -0,0 +1,84 @@
+// 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.
+
+#pragma once
+#include "fastdeploy/fastdeploy_model.h"
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace linzaer {
+
+class FASTDEPLOY_DECL UltraFace : public FastDeployModel {
+ public:
+  // 当model_format为ONNX时，无需指定params_file
+  // 当model_format为Paddle时，则需同时指定model_file & params_file
+  UltraFace(const std::string& model_file, const std::string& params_file = "",
+            const RuntimeOption& custom_option = RuntimeOption(),
+            const Frontend& model_format = Frontend::ONNX);
+
+  // 定义模型的名称
+  std::string ModelName() const {
+    return "Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB";
+  }
+
+  // 模型预测接口，即用户调用的接口
+  // im 为用户的输入数据，目前对于CV均定义为cv::Mat
+  // result 为模型预测的输出结构体
+  // conf_threshold 为后处理的参数
+  // nms_iou_threshold 为后处理的参数
+  virtual bool Predict(cv::Mat* im, FaceDetectionResult* result,
+                       float conf_threshold = 0.7f,
+                       float nms_iou_threshold = 0.3f);
+
+  // 以下为模型在预测时的一些参数，基本是前后处理所需
+  // 用户在创建模型后，可根据模型的要求，以及自己的需求
+  // 对参数进行修改
+  // tuple of (width, height), default (320, 240)
+  std::vector<int> size;
+
+ private:
+  // 初始化函数，包括初始化后端，以及其它模型推理需要涉及的操作
+  bool Initialize();
+
+  // 输入图像预处理操作
+  // Mat为FastDeploy定义的数据结构
+  // FDTensor为预处理后的Tensor数据，传给后端进行推理
+  // im_info为预处理过程保存的数据，在后处理中需要用到
+  bool Preprocess(Mat* mat, FDTensor* outputs,
+                  std::map<std::string, std::array<float, 2>>* im_info);
+
+  // 后端推理结果后处理，输出给用户
+  // infer_result 为后端推理后的输出Tensor
+  // result 为模型预测的结果
+  // im_info 为预处理记录的信息，后处理用于还原box
+  // conf_threshold 后处理时过滤box的置信度阈值
+  // nms_iou_threshold 后处理时NMS设定的iou阈值
+  bool 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);
+
+  // 查看输入是否为动态维度的 不建议直接使用 不同模型的逻辑可能不一致
+  bool IsDynamicInput() const { return is_dynamic_input_; }
+
+  bool is_dynamic_input_;
+};
+
+}  // namespace linzaer
+}  // namespace vision
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/vision_pybind.cc

@@ -25,6 +25,7 @@ void BindMeituan(pybind11::module& m);
 void BindMegvii(pybind11::module& m);
 void BindDeepCam(pybind11::module& m);
 void BindRangiLyu(pybind11::module& m);
+void BindLinzaer(pybind11::module& m);
 #ifdef ENABLE_VISION_VISUALIZE
 void BindVisualize(pybind11::module& m);
 #endif
@@ -69,6 +70,7 @@ void BindVision(pybind11::module& m) {
   BindMegvii(m);
   BindDeepCam(m);
   BindRangiLyu(m);
+  BindLinzaer(m);
 #ifdef ENABLE_VISION_VISUALIZE
   BindVisualize(m);
 #endif