Add NanoDet-Plus Model support (#32)

* update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * Add NanoDet-Plus Model support Co-authored-by: Jason <jiangjiajun@baidu.com>
2025-10-18 22:44:39 +08:00 · 2022-07-22 09:49:55 +08:00
parent a8458e6729
commit e248781784
16 changed files with 907 additions and 7 deletions
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,3 +1,17 @@
+# 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.
+
 function(add_fastdeploy_executable FIELD CC_FILE)
  # temp target name/file var in function scope
  set(TEMP_TARGET_FILE ${CC_FILE})
--- a/examples/vision/rangilyu_nanodet_plus.cc
+++ b/examples/vision/rangilyu_nanodet_plus.cc
@@ -0,0 +1,53 @@
+// 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.h"
+
+int main() {
+  namespace vis = fastdeploy::vision;
+
+  std::string model_file = "../resources/models/nanodet-plus-m_320.onnx";
+  std::string img_path = "../resources/images/bus.jpg";
+  std::string vis_path =
+      "../resources/outputs/rangilyu_nanodet_plus_vis_result.jpg";
+
+  auto model = vis::rangilyu::NanoDetPlus(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed! Model: " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "Init Done! Model:" << model_file << std::endl;
+  }
+  model.EnableDebug();
+
+  cv::Mat im = cv::imread(img_path);
+  cv::Mat vis_im = im.clone();
+
+  vis::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  } else {
+    std::cout << "Prediction Done!" << std::endl;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisDetection(&vis_im, res);
+  cv::imwrite(vis_path, vis_im);
+  std::cout << "Detect Done! Saved: " << vis_path << std::endl;
+  return 0;
+}
--- a/fastdeploy/vision.h
+++ b/fastdeploy/vision.h
@@ -19,6 +19,7 @@
 #include "fastdeploy/vision/meituan/yolov6.h"
 #include "fastdeploy/vision/ppcls/model.h"
 #include "fastdeploy/vision/ppdet/ppyoloe.h"
+#include "fastdeploy/vision/rangilyu/nanodet_plus.h"
 #include "fastdeploy/vision/ppseg/model.h"
 #include "fastdeploy/vision/ultralytics/yolov5.h"
 #include "fastdeploy/vision/wongkinyiu/yolor.h"
--- a/fastdeploy/vision/init.py
+++ b/fastdeploy/vision/init.py
@@ -22,3 +22,4 @@ from . import meituan
 from . import megvii
 from . import visualize
 from . import wongkinyiu
+from . import rangilyu
--- a/fastdeploy/vision/megvii/init.py
+++ b/fastdeploy/vision/megvii/init.py
@@ -28,8 +28,8 @@ class YOLOX(FastDeployModel):
        # 初始化后的option保存在self._runtime_option
        super(YOLOX, self).__init__(runtime_option)

-        self._model = C.vision.megvii.YOLOX(
-            model_file, params_file, self._runtime_option, model_format)
+        self._model = C.vision.megvii.YOLOX(model_file, params_file,
+                                            self._runtime_option, model_format)
        # 通过self.initialized判断整个模型的初始化是否成功
        assert self.initialized, "YOLOX initialize failed."

@@ -80,7 +80,7 @@ class YOLOX(FastDeployModel):
        assert isinstance(
            value,
            bool), "The value to set `is_decode_exported` must be type of bool."
-        self._model.max_wh = value
+        self._model.is_decode_exported = value

    @downsample_strides.setter
    def downsample_strides(self, value):
--- a/fastdeploy/vision/rangilyu/init.py
+++ b/fastdeploy/vision/rangilyu/init.py
@@ -0,0 +1,105 @@
+# 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.
+
+from __future__ import absolute_import
+import logging
+from ... import FastDeployModel, Frontend
+from ... import fastdeploy_main as C
+
+
+class NanoDetPlus(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=Frontend.ONNX):
+        # 调用基函数进行backend_option的初始化
+        # 初始化后的option保存在self._runtime_option
+        super(NanoDetPlus, self).__init__(runtime_option)
+
+        self._model = C.vision.rangilyu.NanoDetPlus(
+            model_file, params_file, self._runtime_option, model_format)
+        # 通过self.initialized判断整个模型的初始化是否成功
+        assert self.initialized, "NanoDetPlus initialize failed."
+
+    def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
+        return self._model.predict(input_image, conf_threshold,
+                                   nms_iou_threshold)
+
+    # 一些跟NanoDetPlus模型有关的属性封装
+    # 多数是预处理相关，可通过修改如model.size = [416, 416]改变预处理时resize的大小（前提是模型支持）
+    @property
+    def size(self):
+        return self._model.size
+
+    @property
+    def padding_value(self):
+        return self._model.padding_value
+
+    @property
+    def keep_ratio(self):
+        return self._model.keep_ratio
+
+    @property
+    def downsample_strides(self):
+        return self._model.downsample_strides
+
+    @property
+    def max_wh(self):
+        return self._model.max_wh
+
+    @property
+    def reg_max(self):
+        return self._model.reg_max
+
+    @size.setter
+    def size(self, wh):
+        assert isinstance(wh, [list, tuple]),\
+            "The value to set `size` must be type of tuple or list."
+        assert len(wh) == 2,\
+            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
+            len(wh))
+        self._model.size = wh
+
+    @padding_value.setter
+    def padding_value(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `padding_value` must be type of list."
+        self._model.padding_value = value
+
+    @keep_ratio.setter
+    def keep_ratio(self, value):
+        assert isinstance(
+            value, bool), "The value to set `keep_ratio` must be type of bool."
+        self._model.keep_ratio = value
+
+    @downsample_strides.setter
+    def downsample_strides(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `downsample_strides` must be type of list."
+        self._model.downsample_strides = value
+
+    @max_wh.setter
+    def max_wh(self, value):
+        assert isinstance(
+            value, float), "The value to set `max_wh` must be type of float."
+        self._model.max_wh = value
+
+    @reg_max.setter
+    def reg_max(self, value):
+        assert isinstance(
+            value, int), "The value to set `reg_max` must be type of int."
+        self._model.reg_max = value
--- a/fastdeploy/vision/rangilyu/nanodet_plus.cc
+++ b/fastdeploy/vision/rangilyu/nanodet_plus.cc
@@ -0,0 +1,355 @@
+// 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/rangilyu/nanodet_plus.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace rangilyu {
+
+struct NanoDetPlusCenterPoint {
+  int grid0;
+  int grid1;
+  int stride;
+};
+
+void GenerateNanoDetPlusCenterPoints(
+    const std::vector<int>& size, const std::vector<int>& downsample_strides,
+    std::vector<NanoDetPlusCenterPoint>* center_points) {
+  // size: tuple of input (width, height), e.g (320, 320)
+  // downsample_strides: downsample strides in NanoDet and
+  // NanoDet-Plus, e.g (8, 16, 32, 64)
+  const int width = size[0];
+  const int height = size[1];
+  for (const auto& ds : downsample_strides) {
+    int num_grid_w = width / ds;
+    int num_grid_h = height / ds;
+    for (int g1 = 0; g1 < num_grid_h; ++g1) {
+      for (int g0 = 0; g0 < num_grid_w; ++g0) {
+        (*center_points).emplace_back(NanoDetPlusCenterPoint{g0, g1, ds});
+      }
+    }
+  }
+}
+
+void WrapAndResize(Mat* mat, std::vector<int> size, std::vector<float> color,
+                   bool keep_ratio = false) {
+  // Reference: nanodet/data/transform/warp.py#L139
+  // size: tuple of input (width, height)
+  // The default value of `keep_ratio` is `fasle` in
+  // `config/nanodet-plus-m-1.5x_320.yml` for both
+  // train and val processes. So, we just let this
+  // option default `false` according to the official
+  // implementation in NanoDet and NanoDet-Plus.
+  // Note, this function will apply a normal resize
+  // operation to input Mat if the keep_ratio option
+  // is fasle and the behavior will be the same as
+  // yolov5's letterbox if keep_ratio is true.
+
+  // with keep_ratio = false (default)
+  if (!keep_ratio) {
+    int resize_h = size[1];
+    int resize_w = size[0];
+    if (resize_h != mat->Height() || resize_w != mat->Width()) {
+      Resize::Run(mat, resize_w, resize_h);
+    }
+    return;
+  }
+  // with keep_ratio = true, same as yolov5's letterbox
+  float r = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()),
+                     size[0] * 1.0f / static_cast<float>(mat->Width()));
+
+  int resize_h = int(round(static_cast<float>(mat->Height()) * r));
+  int resize_w = int(round(static_cast<float>(mat->Width()) * r));
+
+  if (resize_h != mat->Height() || resize_w != mat->Width()) {
+    Resize::Run(mat, resize_w, resize_h);
+  }
+
+  int pad_w = size[0] - resize_w;
+  int pad_h = size[1] - resize_h;
+  if (pad_h > 0 || pad_w > 0) {
+    float half_h = pad_h * 1.0 / 2;
+    int top = int(round(half_h - 0.1));
+    int bottom = int(round(half_h + 0.1));
+    float half_w = pad_w * 1.0 / 2;
+    int left = int(round(half_w - 0.1));
+    int right = int(round(half_w + 0.1));
+    Pad::Run(mat, top, bottom, left, right, color);
+  }
+}
+
+void GFLRegression(const float* logits, size_t reg_num, float* offset) {
+  // Hint: reg_num = reg_max + 1
+  FDASSERT(((nullptr != logits) && (reg_num != 0)),
+           "NanoDetPlus: logits is nullptr or reg_num is 0 in GFLRegression.");
+  // softmax
+  float total_exp = 0.f;
+  std::vector<float> softmax_probs(reg_num);
+  for (size_t i = 0; i < reg_num; ++i) {
+    softmax_probs[i] = std::exp(logits[i]);
+    total_exp += softmax_probs[i];
+  }
+  for (size_t i = 0; i < reg_num; ++i) {
+    softmax_probs[i] = softmax_probs[i] / total_exp;
+  }
+  // gfl regression -> offset
+  for (size_t i = 0; i < reg_num; ++i) {
+    (*offset) += static_cast<float>(i) * softmax_probs[i];
+  }
+}
+
+NanoDetPlus::NanoDetPlus(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 NanoDetPlus::Initialize() {
+  // parameters for preprocess
+  size = {320, 320};
+  padding_value = {0.0f, 0.0f, 0.0f};
+  keep_ratio = false;
+  downsample_strides = {8, 16, 32, 64};
+  max_wh = 4096.0f;
+  reg_max = 7;
+
+  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 NanoDetPlus::Preprocess(
+    Mat* mat, FDTensor* output,
+    std::map<std::string, std::array<float, 2>>* im_info) {
+  // NanoDet-Plus preprocess steps
+  // 1. WrapAndResize
+  // 2. HWC->CHW
+  // 3. Normalize or Convert (keep BGR order)
+  WrapAndResize(mat, size, padding_value, keep_ratio);
+  // Record output shape of preprocessed image
+  (*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
+                                static_cast<float>(mat->Width())};
+
+  // Compute `result = mat * alpha + beta` directly by channel
+  // Reference: /config/nanodet-plus-m-1.5x_320.yml#L89
+  // from mean: [103.53, 116.28, 123.675], std: [57.375, 57.12, 58.395]
+  // x' = (x - mean) / std to x'= x * alpha + beta.
+  // e.g alpha[0] = 0.017429f = 1.0f / 57.375f
+  // e.g beta[0] = -103.53f * 0.0174291f
+  std::vector<float> alpha = {0.017429f, 0.017507f, 0.017125f};
+  std::vector<float> beta = {-103.53f * 0.0174291f, -116.28f * 0.0175070f,
+                             -123.675f * 0.0171247f};  // BGR order
+  Convert::Run(mat, alpha, beta);
+
+  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 NanoDetPlus::Postprocess(
+    FDTensor& infer_result, DetectionResult* result,
+    const std::map<std::string, std::array<float, 2>>& im_info,
+    float conf_threshold, float nms_iou_threshold) {
+  FDASSERT(infer_result.shape[0] == 1, "Only support batch =1 now.");
+  result->Clear();
+  result->Reserve(infer_result.shape[1]);
+  if (infer_result.dtype != FDDataType::FP32) {
+    FDERROR << "Only support post process with float32 data." << std::endl;
+    return false;
+  }
+  // generate center points with dowmsample strides
+  std::vector<NanoDetPlusCenterPoint> center_points;
+  GenerateNanoDetPlusCenterPoints(size, downsample_strides, &center_points);
+
+  // infer_result shape might look like (1,2125,112)
+  const int num_cls_reg = infer_result.shape[2];            // e.g 112
+  const int num_classes = num_cls_reg - (reg_max + 1) * 4;  // e.g 80
+  float* data = static_cast<float*>(infer_result.Data());
+  for (size_t i = 0; i < infer_result.shape[1]; ++i) {
+    float* scores = data + i * num_cls_reg;
+    float* max_class_score = std::max_element(scores, scores + num_classes);
+    float confidence = (*max_class_score);
+    // filter boxes by conf_threshold
+    if (confidence <= conf_threshold) {
+      continue;
+    }
+    int32_t label_id = std::distance(scores, max_class_score);
+    // fetch i-th center point
+    float grid0 = static_cast<float>(center_points.at(i).grid0);
+    float grid1 = static_cast<float>(center_points.at(i).grid1);
+    float downsample_stride = static_cast<float>(center_points.at(i).stride);
+    // apply gfl regression to get offsets (l,t,r,b)
+    float* logits = data + i * num_cls_reg + num_classes;  // 32|44...
+    std::vector<float> offsets(4);
+    for (size_t j = 0; j < 4; ++j) {
+      GFLRegression(logits + j * (reg_max + 1), reg_max + 1, &offsets[j]);
+    }
+    // convert from offsets to [x1, y1, x2, y2]
+    float l = offsets[0];  // left
+    float t = offsets[1];  // top
+    float r = offsets[2];  // right
+    float b = offsets[3];  // bottom
+
+    float x1 = (grid0 - l) * downsample_stride;  // cx - l x1
+    float y1 = (grid1 - t) * downsample_stride;  // cy - t y1
+    float x2 = (grid0 + r) * downsample_stride;  // cx + r x2
+    float y2 = (grid1 + b) * downsample_stride;  // cy + b y2
+
+    result->boxes.emplace_back(
+        std::array<float, 4>{x1 + label_id * max_wh, y1 + label_id * max_wh,
+                             x2 + label_id * max_wh, y2 + label_id * max_wh});
+    // label_id * max_wh for multi classes NMS
+    result->label_ids.push_back(label_id);
+    result->scores.push_back(confidence);
+  }
+  utils::NMS(result, nms_iou_threshold);
+
+  // scale the boxes to the origin image shape
+  auto iter_out = im_info.find("output_shape");
+  auto iter_ipt = im_info.find("input_shape");
+  FDASSERT(iter_out != im_info.end() && iter_ipt != im_info.end(),
+           "Cannot find input_shape or output_shape from im_info.");
+  float out_h = iter_out->second[0];
+  float out_w = iter_out->second[1];
+  float ipt_h = iter_ipt->second[0];
+  float ipt_w = iter_ipt->second[1];
+  // without keep_ratio
+  if (!keep_ratio) {
+    // x' = (x / out_w) * ipt_w = x / (out_w / ipt_w)
+    // y' = (y / out_h) * ipt_h = y / (out_h / ipt_h)
+    float r_w = out_w / ipt_w;
+    float r_h = out_h / ipt_h;
+    for (size_t i = 0; i < result->boxes.size(); ++i) {
+      int32_t label_id = (result->label_ids)[i];
+      // clip box
+      result->boxes[i][0] = result->boxes[i][0] - max_wh * label_id;
+      result->boxes[i][1] = result->boxes[i][1] - max_wh * label_id;
+      result->boxes[i][2] = result->boxes[i][2] - max_wh * label_id;
+      result->boxes[i][3] = result->boxes[i][3] - max_wh * label_id;
+      result->boxes[i][0] = std::max(result->boxes[i][0] / r_w, 0.0f);
+      result->boxes[i][1] = std::max(result->boxes[i][1] / r_h, 0.0f);
+      result->boxes[i][2] = std::max(result->boxes[i][2] / r_w, 0.0f);
+      result->boxes[i][3] = std::max(result->boxes[i][3] / r_h, 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;
+  }
+  // with keep_ratio
+  float r = std::min(out_h / ipt_h, out_w / ipt_w);
+  float pad_h = (out_h - ipt_h * r) / 2;
+  float pad_w = (out_w - ipt_w * r) / 2;
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    int32_t label_id = (result->label_ids)[i];
+    // clip box
+    result->boxes[i][0] = result->boxes[i][0] - max_wh * label_id;
+    result->boxes[i][1] = result->boxes[i][1] - max_wh * label_id;
+    result->boxes[i][2] = result->boxes[i][2] - max_wh * label_id;
+    result->boxes[i][3] = result->boxes[i][3] - max_wh * label_id;
+    result->boxes[i][0] = std::max((result->boxes[i][0] - pad_w) / r, 0.0f);
+    result->boxes[i][1] = std::max((result->boxes[i][1] - pad_h) / r, 0.0f);
+    result->boxes[i][2] = std::max((result->boxes[i][2] - pad_w) / r, 0.0f);
+    result->boxes[i][3] = std::max((result->boxes[i][3] - pad_h) / r, 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 NanoDetPlus::Predict(cv::Mat* im, DetectionResult* 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[0], 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 rangilyu
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/rangilyu/nanodet_plus.h
+++ b/fastdeploy/vision/rangilyu/nanodet_plus.h
@@ -0,0 +1,101 @@
+// 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 rangilyu {
+
+class FASTDEPLOY_DECL NanoDetPlus : 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
+  NanoDetPlus(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 "RangiLyu/nanodet"; }
+
+  // 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, DetectionResult* result,
+                       float conf_threshold = 0.35f,
+                       float nms_iou_threshold = 0.5f);
+
+  // 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 input size (width, height), e.g (320, 320)
+  std::vector<int> size;
+  // padding value, size should be same with Channels
+  std::vector<float> padding_value;
+  // keep aspect ratio or not when perform resize operation.
+  // This option is set as `false` by default in NanoDet-Plus.
+  bool keep_ratio;
+  // downsample strides for NanoDet-Plus to generate anchors, will
+  // take (8, 16, 32, 64) as default values.
+  std::vector<int> downsample_strides;
+  // for offseting the boxes by classes when using NMS, default 4096.
+  float max_wh;
+  // reg_max for GFL regression, default 7
+  int reg_max;
+
+ 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* output,
+                  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(FDTensor& infer_result, DetectionResult* 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_; }
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
+  // or not.)
+  // RangiLyu/nanodet official 'export_onnx.py' script will export static ONNX
+  // by default.
+  // This value will auto check by fastdeploy after the internal Runtime
+  // initialized.
+  bool is_dynamic_input_;
+};
+
+}  // namespace rangilyu
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/rangilyu/rangilyu_pybind.cc
+++ b/fastdeploy/vision/rangilyu/rangilyu_pybind.cc
@@ -0,0 +1,41 @@
+// 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 BindRangiLyu(pybind11::module& m) {
+  auto rangilyu_module =
+      m.def_submodule("rangilyu", "https://github.com/RangiLyu/nanodet");
+  pybind11::class_<vision::rangilyu::NanoDetPlus, FastDeployModel>(
+      rangilyu_module, "NanoDetPlus")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::rangilyu::NanoDetPlus& self, pybind11::array& data,
+              float conf_threshold, float nms_iou_threshold) {
+             auto mat = PyArrayToCvMat(data);
+             vision::DetectionResult res;
+             self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
+             return res;
+           })
+      .def_readwrite("size", &vision::rangilyu::NanoDetPlus::size)
+      .def_readwrite("padding_value",
+                     &vision::rangilyu::NanoDetPlus::padding_value)
+      .def_readwrite("keep_ratio", &vision::rangilyu::NanoDetPlus::keep_ratio)
+      .def_readwrite("downsample_strides",
+                     &vision::rangilyu::NanoDetPlus::downsample_strides)
+      .def_readwrite("max_wh", &vision::rangilyu::NanoDetPlus::max_wh)
+      .def_readwrite("reg_max", &vision::rangilyu::NanoDetPlus::reg_max);
+}
+}  // namespace fastdeploy
--- a/fastdeploy/vision/vision_pybind.cc
+++ b/fastdeploy/vision/vision_pybind.cc
@@ -23,6 +23,7 @@ void BindPPSeg(pybind11::module& m);
 void BindUltralytics(pybind11::module& m);
 void BindMeituan(pybind11::module& m);
 void BindMegvii(pybind11::module& m);
+void BindRangiLyu(pybind11::module& m);
 #ifdef ENABLE_VISION_VISUALIZE
 void BindVisualize(pybind11::module& m);
 #endif
@@ -56,6 +57,7 @@ void BindVision(pybind11::module& m) {
  BindWongkinyiu(m);
  BindMeituan(m);
  BindMegvii(m);
+  BindRangiLyu(m);
 #ifdef ENABLE_VISION_VISUALIZE
  BindVisualize(m);
 #endif
--- a/model_zoo/vision/nanodet_plus/README.md
+++ b/model_zoo/vision/nanodet_plus/README.md
@@ -0,0 +1,46 @@
+# NanoDetPlus部署示例
+
+当前支持模型版本为：[NanoDetPlus v1.0.0-alpha-1](https://github.com/RangiLyu/nanodet/releases/tag/v1.0.0-alpha-1)
+
+本文档说明如何进行[NanoDetPlus](https://github.com/RangiLyu/nanodet)的快速部署推理。本目录结构如下
+```
+.
+├── cpp                  # C++ 代码目录
+│   ├── CMakeLists.txt   # C++ 代码编译CMakeLists文件
+│   ├── README.md        # C++ 代码编译部署文档
+│   └── nanodet_plus.cc  # C++ 示例代码
+├── README.md            # YOLOX 部署文档
+└── nanodet_plus.py      # Python示例代码
+```
+
+## 安装FastDeploy
+
+使用如下命令安装FastDeploy，注意到此处安装的是`vision-cpu`，也可根据需求安装`vision-gpu`
+```
+# 安装fastdeploy-python工具
+pip install fastdeploy-python
+
+# 安装vision-cpu模块
+fastdeploy install vision-cpu
+```
+
+## Python部署
+
+执行如下代码即会自动下载NanoDetPlus模型和测试图片
+```
+python nanodet_plus.py
+```
+
+执行完成后会将可视化结果保存在本地`vis_result.jpg`，同时输出检测结果如下
+```
+DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
+5.710144,220.634033, 807.854370, 724.089111, 0.825635, 5
+45.646439,393.694061, 229.267044, 903.998413, 0.818263, 0
+218.289322,402.268829, 342.083252, 861.766479, 0.709301, 0
+698.587036,325.627197, 809.000000, 876.990967, 0.630235, 0
+```
+
+## 其它文档
+
+- [C++部署](./cpp/README.md)
+- [NanoDetPlus API文档](./api.md)
--- a/model_zoo/vision/nanodet_plus/api.md
+++ b/model_zoo/vision/nanodet_plus/api.md
@@ -0,0 +1,71 @@
+# NanoDetPlus API说明
+
+## Python API
+
+### NanoDetPlus类
+```
+fastdeploy.vision.rangilyu.NanoDetPlus(model_file, params_file=None, runtime_option=None, model_format=fd.Frontend.ONNX)
+```
+NanoDetPlus模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只需提供model_file，如`nanodet-plus-m_320.onnx`；当model_format为`fd.Frontend.PADDLE`时，则需同时提供model_file和params_file。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式
+
+#### predict函数
+> ```
+> NanoDetPlus.predict(image_data, conf_threshold=0.35, nms_iou_threshold=0.5)
+> ```
+> 模型预测结口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+> > * **conf_threshold**(float): 检测框置信度过滤阈值
+> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
+
+示例代码参考[nanodet_plus.py](./nanodet_plus.py)
+
+
+## C++ API
+
+### NanoDetPlus类
+```
+fastdeploy::vision::rangilyu::NanoDetPlus(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX)
+```
+NanoDetPlus模型加载和初始化，当model_format为`Frontend::ONNX`时，只需提供model_file，如`nanodet-plus-m_320.onnx`；当model_format为`Frontend::PADDLE`时，则需同时提供model_file和params_file。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式
+
+#### Predict函数
+> ```
+> NanoDetPlus::Predict(cv::Mat* im, DetectionResult* result,
+>                      float conf_threshold = 0.35,
+>                      float nms_iou_threshold = 0.5)
+> ```
+> 模型预测接口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **im**: 输入图像，注意需为HWC，BGR格式
+> > * **result**: 检测结果，包括检测框，各个框的置信度
+> > * **conf_threshold**: 检测框置信度过滤阈值
+> > * **nms_iou_threshold**: NMS处理过程中iou阈值
+
+示例代码参考[cpp/nanodet_plus.cc](cpp/nanodet_plus.cc)
+
+## 其它API使用
+
+- [模型部署RuntimeOption配置](../../../docs/api/runtime_option.md)
--- a/model_zoo/vision/nanodet_plus/cpp/CMakeLists.txt
+++ b/model_zoo/vision/nanodet_plus/cpp/CMakeLists.txt
@@ -0,0 +1,17 @@
+PROJECT(nanodet_plus_demo C CXX)
+CMAKE_MINIMUM_REQUIRED(VERSION 3.16)
+
+# 在低版本ABI环境中，通过如下代码进行兼容性编译
+# add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+
+# 指定下载解压后的fastdeploy库路径
+set(FASTDEPLOY_INSTALL_DIR ${PROJECT_SOURCE_DIR}/fastdeploy-linux-x64-0.0.3/)
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(nanodet_plus_demo ${PROJECT_SOURCE_DIR}/nanodet_plus.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(nanodet_plus_demo ${FASTDEPLOY_LIBS})
--- a/model_zoo/vision/nanodet_plus/cpp/README.md
+++ b/model_zoo/vision/nanodet_plus/cpp/README.md
@@ -0,0 +1,30 @@
+# 编译NanoDetPlus示例
+
+当前支持模型版本为：[NanoDetPlus v1.0.0-alpha-1](https://github.com/RangiLyu/nanodet/releases/tag/v1.0.0-alpha-1)
+
+```
+# 下载和解压预测库
+wget https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz
+tar xvf fastdeploy-linux-x64-0.0.3.tgz
+
+# 编译示例代码
+mkdir build & cd build
+cmake ..
+make -j
+
+# 下载模型和图片
+wget https://github.com/RangiLyu/nanodet/releases/download/v1.0.0-alpha-1/nanodet-plus-m_320.onnx
+wget https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg
+
+# 执行
+./nanodet_plus_demo
+```
+
+执行完后可视化的结果保存在本地`vis_result.jpg`，同时会将检测框输出在终端，如下所示
+```
+DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
+5.710144,220.634033, 807.854370, 724.089111, 0.825635, 5
+45.646439,393.694061, 229.267044, 903.998413, 0.818263, 0
+218.289322,402.268829, 342.083252, 861.766479, 0.709301, 0
+698.587036,325.627197, 809.000000, 876.990967, 0.630235, 0
+```
--- a/model_zoo/vision/nanodet_plus/cpp/nanodet_plus.cc
+++ b/model_zoo/vision/nanodet_plus/cpp/nanodet_plus.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/vision.h"
+
+int main() {
+  namespace vis = fastdeploy::vision;
+  auto model = vis::rangilyu::NanoDetPlus("nanodet-plus-m_320.onnx");
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed." << std::endl;
+    return -1;
+  }
+  cv::Mat im = cv::imread("bus.jpg");
+  cv::Mat vis_im = im.clone();
+
+  vis::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisDetection(&vis_im, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  return 0;
+}
--- a/model_zoo/vision/nanodet_plus/nanodet_plus.py
+++ b/model_zoo/vision/nanodet_plus/nanodet_plus.py
@@ -0,0 +1,23 @@
+import fastdeploy as fd
+import cv2
+
+# 下载模型和测试图片
+model_url = "https://github.com/RangiLyu/nanodet/releases/download/v1.0.0-alpha-1/nanodet-plus-m_320.onnx"
+test_jpg_url = "https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg"
+fd.download(model_url, ".", show_progress=True)
+fd.download(test_jpg_url, ".", show_progress=True)
+
+# 加载模型
+model = fd.vision.rangilyu.NanoDetPlus("nanodet-plus-m_320.onnx")
+
+# 预测图片
+im = cv2.imread("bus.jpg")
+result = model.predict(im, conf_threshold=0.35, nms_iou_threshold=0.5)
+
+# 可视化结果
+fd.vision.visualize.vis_detection(im, result)
+cv2.imwrite("vis_result.jpg", im)
+
+# 输出预测结果
+print(result)
+print(model.runtime_option)