add paddleclas draft doct

2025-10-05 16:48:03 +08:00 · 2022-08-10 07:19:47 +00:00
parent e91cfc9e43
commit bf5affb510
12 changed files with 410 additions and 25 deletions
--- a/csrc/fastdeploy/vision.h
+++ b/csrc/fastdeploy/vision.h
@@ -33,7 +33,7 @@
 #include "fastdeploy/vision/faceid/contrib/partial_fc.h"
 #include "fastdeploy/vision/faceid/contrib/vpl.h"
 #include "fastdeploy/vision/matting/contrib/modnet.h"
-#include "fastdeploy/vision/ppcls/model.h"
+#include "fastdeploy/vision/classification/ppcls/model.h"
 #include "fastdeploy/vision/detection/ppdet/model.h"
 #include "fastdeploy/vision/ppseg/model.h"
 #endif
--- a/csrc/fastdeploy/vision/classification/ppcls/model.cc
+++ b/csrc/fastdeploy/vision/classification/ppcls/model.cc
@@ -12,15 +12,15 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#include "fastdeploy/vision/ppcls/model.h"
+#include "fastdeploy/vision/classification/ppcls/model.h"
 #include "fastdeploy/vision/utils/utils.h"
 #include "yaml-cpp/yaml.h"

 namespace fastdeploy {
 namespace vision {
-namespace ppcls {
+namespace classification {

-Model::Model(const std::string& model_file, const std::string& params_file,
+PaddleClasModel::PaddleClasModel(const std::string& model_file, const std::string& params_file,
             const std::string& config_file, const RuntimeOption& custom_option,
             const Frontend& model_format) {
  config_file_ = config_file;
@@ -33,7 +33,7 @@ Model::Model(const std::string& model_file, const std::string& params_file,
  initialized = Initialize();
 }

-bool Model::Initialize() {
+bool PaddleClasModel::Initialize() {
  if (!BuildPreprocessPipelineFromConfig()) {
    FDERROR << "Failed to build preprocess pipeline from configuration file."
            << std::endl;
@@ -46,7 +46,7 @@ bool Model::Initialize() {
  return true;
 }

-bool Model::BuildPreprocessPipelineFromConfig() {
+bool PaddleClasModel::BuildPreprocessPipelineFromConfig() {
  processors_.clear();
  YAML::Node cfg;
  try {
@@ -91,7 +91,7 @@ bool Model::BuildPreprocessPipelineFromConfig() {
  return true;
 }

-bool Model::Preprocess(Mat* mat, FDTensor* output) {
+bool PaddleClasModel::Preprocess(Mat* mat, FDTensor* output) {
  for (size_t i = 0; i < processors_.size(); ++i) {
    if (!(*(processors_[i].get()))(mat)) {
      FDERROR << "Failed to process image data in " << processors_[i]->Name()
@@ -109,7 +109,7 @@ bool Model::Preprocess(Mat* mat, FDTensor* output) {
  return true;
 }

-bool Model::Postprocess(const FDTensor& infer_result, ClassifyResult* result,
+bool PaddleClasModel::Postprocess(const FDTensor& infer_result, ClassifyResult* result,
                        int topk) {
  int num_classes = infer_result.shape[1];
  const float* infer_result_buffer =
@@ -124,7 +124,7 @@ bool Model::Postprocess(const FDTensor& infer_result, ClassifyResult* result,
  return true;
 }

-bool Model::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
+bool PaddleClasModel::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
  Mat mat(*im);
  std::vector<FDTensor> processed_data(1);
  if (!Preprocess(&mat, &(processed_data[0]))) {
@@ -148,6 +148,6 @@ bool Model::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
  return true;
 }

-} // namespace ppcls
+} // namespace classification
 } // namespace vision
 } // namespace fastdeploy
--- a/csrc/fastdeploy/vision/classification/ppcls/model.h
+++ b/csrc/fastdeploy/vision/classification/ppcls/model.h
@@ -19,21 +19,21 @@

 namespace fastdeploy {
 namespace vision {
-namespace ppcls {
+namespace classification {

-class FASTDEPLOY_DECL Model : public FastDeployModel {
+class FASTDEPLOY_DECL PaddleClasModel : public FastDeployModel {
 public:
-  Model(const std::string& model_file, const std::string& params_file,
+  PaddleClasModel(const std::string& model_file, const std::string& params_file,
        const std::string& config_file,
        const RuntimeOption& custom_option = RuntimeOption(),
        const Frontend& model_format = Frontend::PADDLE);

-  std::string ModelName() const { return "ppclas-classify"; }
+  virtual std::string ModelName() const { return "PaddleClas/Model"; }

  // TODO(jiangjiajun) Batch is on the way
  virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);

- private:
+ protected:
  bool Initialize();

  bool BuildPreprocessPipelineFromConfig();
@@ -46,6 +46,20 @@ class FASTDEPLOY_DECL Model : public FastDeployModel {
  std::vector<std::shared_ptr<Processor>> processors_;
  std::string config_file_;
 };
-}  // namespace ppcls
+
+typedef PaddleClasModel PPLCNet;
+typedef PaddleClasModel PPLCNetv2;
+typedef PaddleClasModel EfficientNet;
+typedef PaddleClasModel GhostNet;
+typedef PaddleClasModel MobileNetv1;
+typedef PaddleClasModel MobileNetv2;
+typedef PaddleClasModel MobileNetv3;
+typedef PaddleClasModel ShuffleNetv2;
+typedef PaddleClasModel SqueezeNet;
+typedef PaddleClasModel Inceptionv3;
+typedef PaddleClasModel PPHGNet;
+typedef PaddleClasModel ResNet50vd;
+typedef PaddleClasModel SwinTransformer;
+}  // namespace classification
 }  // namespace vision
 }  // namespace fastdeploy
--- a/csrc/fastdeploy/vision/classification/ppcls/ppcls_pybind.cc
+++ b/csrc/fastdeploy/vision/classification/ppcls/ppcls_pybind.cc
@@ -15,12 +15,11 @@

 namespace fastdeploy {
 void BindPPCls(pybind11::module& m) {
-  auto ppcls_module = m.def_submodule("ppcls", "Module to deploy PaddleClas.");
-  pybind11::class_<vision::ppcls::Model, FastDeployModel>(ppcls_module, "Model")
+  pybind11::class_<vision::classification::PaddleClasModel, FastDeployModel>(m, "PaddleClasModel")
      .def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
                          Frontend>())
      .def("predict",
-           [](vision::ppcls::Model& self, pybind11::array& data, int topk = 1) {
+           [](vision::classification::PaddleClasModel& self, pybind11::array& data, int topk = 1) {
             auto mat = PyArrayToCvMat(data);
             vision::ClassifyResult res;
             self.Predict(&mat, &res, topk);
--- a/examples/vision/classification/paddleclas/README.md
+++ b/examples/vision/classification/paddleclas/README.md
@@ -0,0 +1,28 @@
+# PaddleClas 模型部署
+
+## 模型版本说明
+
+- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas)
+
+## 准备PaddleClas部署模型
+
+PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
+
+注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数。
+
+
+## 下载预训练模型
+
+为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
+
+| 模型                                                               | 大小    |输入Shape |  精度    |
+|:---------------------------------------------------------------- |:----- |:----- | :----- |
+| [PPLCNet]() | 141MB | 224x224 |51.4% |
+| [PPLCNetv2]()  | 10MB  | 224x224 |51.4% |
+| [EfficientNet]() |     | 224x224 |     |
+
+
+## 详细部署文档
+
+- [Python部署](python)
+- [C++部署](cpp)
--- a/examples/vision/classification/paddleclas/cpp/CMakeLists.txt
+++ b/examples/vision/classification/paddleclas/cpp/CMakeLists.txt
@@ -0,0 +1,14 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
--- a/examples/vision/classification/paddleclas/cpp/README.md
+++ b/examples/vision/classification/paddleclas/cpp/README.md
@@ -0,0 +1,77 @@
+# YOLOv7 C++部署示例
+
+本目录下提供`infer.cc`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)  
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/compile/prebuilt_libraries.md)
+
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
+
+```
+mkdir build
+cd build
+wget https://xxx.tgz
+tar xvf fastdeploy-linux-x64-0.2.0.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.2.0
+make -j
+
+#下载官方转换好的yolov7模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000087038.jpg
+
+
+# CPU推理
+./infer_demo yolov7.onnx 000000087038.jpg 0
+# GPU推理
+./infer_demo yolov7.onnx 000000087038.jpg 1
+# GPU上TensorRT推理
+./infer_demo yolov7.onnx 000000087038.jpg 2
+```
+
+## YOLOv7 C++接口
+
+### YOLOv7类
+
+```
+fastdeploy::vision::detection::YOLOv7(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX)
+```
+
+YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
+> * **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，默认为ONNX格式
+
+#### Predict函数
+
+> ```
+> YOLOv7::Predict(cv::Mat* im, DetectionResult* result,
+>                 float conf_threshold = 0.25,
+>                 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, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+> > * **conf_threshold**: 检测框置信度过滤阈值
+> > * **nms_iou_threshold**: NMS处理过程中iou阈值
+
+### 类成员变量
+
+> > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
+
+- [模型介绍](../../)
+- [Python部署](../python)
+- [视觉模型预测结果](../../../../../docs/api/vision_results/)
--- a/examples/vision/classification/paddleclas/cpp/infer.cc
+++ b/examples/vision/classification/paddleclas/cpp/infer.cc
@@ -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.
+
+#include "fastdeploy/vision.h"
+
+void CpuInfer(const std::string& model_file, const std::string& image_file) {
+  auto model = fastdeploy::vision::detection::YOLOv7(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+void GpuInfer(const std::string& model_file, const std::string& image_file) {
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+void TrtInfer(const std::string& model_file, const std::string& image_file) {
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  option.SetTrtInputShape("images", {1, 3, 640, 640});
+  auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 4) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
+                 "e.g ./infer_model ./yolov7.onnx ./test.jpeg 0"
+              << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with gpu and use tensorrt backend."
+              << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 2) {
+    TrtInfer(argv[1], argv[2]);
+  }
+  return 0;
+}
--- a/examples/vision/classification/paddleclas/python/README.md
+++ b/examples/vision/classification/paddleclas/python/README.md
@@ -0,0 +1,71 @@
+# PaddleClas模型 Python部署示例
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)  
+- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md)
+
+本目录下提供`infer.py`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+
+```
+# 下载yolov7模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd examples/vison/detection/yolov7/python/
+
+# CPU推理
+python infer.py --model yolov7.onnx --image 000000087038.jpg --device cpu
+# GPU推理
+python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu --use_trt True
+```
+
+运行完成可视化结果如下图所示
+
+## YOLOv7 Python接口
+
+```
+fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
+```
+
+YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
+> * **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，默认为ONNX
+
+### predict函数
+
+> ```
+> YOLOv7.predict(image_data, conf_threshold=0.25, 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
+
+> **返回**
+> 
+> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+### 类成员属性
+
+> > * **size**(list | tuple): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
+
+## 其它文档
+
+- [YOLOv7 模型介绍](..)
+- [YOLOv7 C++部署](../cpp)
+- [模型预测结果说明](../../../../../docs/api/vision_results/)
--- a/examples/vision/classification/paddleclas/python/infer.py
+++ b/examples/vision/classification/paddleclas/python/infer.py
@@ -0,0 +1,47 @@
+import fastdeploy as fd
+import cv2
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("images", [1, 3, 640, 640])
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+model = fd.vision.classification.PaddleClasModel(args.model, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im)
+print(result)
--- a/fastdeploy/vision/classification/init.py
+++ b/fastdeploy/vision/classification/init.py
@@ -0,0 +1,30 @@
+# 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
+
+from .ppcls import PaddleClasModel
+
+PPLCNet = PaddleClasModel
+PPLCNetv2 = PaddleClasModel
+EfficientNet = PaddleClasModel
+GhostNet = PaddleClasModel
+MobileNetv1 = PaddleClasModel
+MobileNetv2 = PaddleClasModel
+MobileNetv3 = PaddleClasModel
+ShuffleNetv2 = PaddleClasModel
+SqueezeNet = PaddleClasModel
+Inceptionv3 = PaddleClasModel
+PPHGNet = PaddleClasModel
+ResNet50vd = PaddleClasModel
+SwinTransformer = PaddleClasModel
--- a/fastdeploy/vision/classification/ppcls/init.py
+++ b/fastdeploy/vision/classification/ppcls/init.py
@@ -14,21 +14,21 @@

 from __future__ import absolute_import
 import logging
-from ... import FastDeployModel, Frontend
-from ... import c_lib_wrap as C
+from .... import FastDeployModel, Frontend
+from .... import c_lib_wrap as C


-class Model(FastDeployModel):
+class PaddleClasModel(FastDeployModel):
    def __init__(self,
                 model_file,
                 params_file,
                 config_file,
                 backend_option=None,
                 model_format=Frontend.PADDLE):
-        super(Model, self).__init__(backend_option)
+        super(PaddleClasModel, self).__init__(backend_option)

-        assert model_format == Frontend.PADDLE, "PaddleClas only support model format of Frontend.Paddle now."
-        self._model = C.vision.ppcls.Model(model_file, params_file,
+        assert model_format == Frontend.PADDLE, "PaddleClasModel only support model format of Frontend.Paddle now."
+        self._model = C.vision.classification.PaddleClasModel(model_file, params_file,
                                           config_file, self._runtime_option,
                                           model_format)
        assert self.initialized, "PaddleClas model initialize failed."