FastDeploy集成新模型流程

import torch
import torchvision.models as models
model = models.resnet50(pretrained=True)
batch_size = 1  #批处理大小
input_shape = (3, 224, 224)   #输入数据,改成自己的输入shape
model.eval()
x = torch.randn(batch_size, *input_shape)	# 生成张量
export_onnx_file = "resnet50.onnx"			# 目的ONNX文件名
torch.onnx.export(model,
                    x,
                    export_onnx_file,
                    opset_version=12,
                    input_names=["input"],	# 输入名
                    output_names=["output"],	# 输出名
                    dynamic_axes={"input":{0:"batch_size"},  # 批处理变量
                                    "output":{0:"batch_size"}})

class FASTDEPLOY_DECL ResNet : public FastDeployModel {
 public:
  ResNet(...);
  virtual bool Predict(...);
 private:
  bool Initialize();
  bool Preprocess(...);
  bool Postprocess(...);
};

ResNet::ResNet(...) {
  // 构造函数逻辑
  // 1. 指定 Backend 2. 设置RuntimeOption 3. 调用Initialize()函数
}
bool ResNet::Initialize() {
  // 初始化逻辑
  // 1. 全局变量赋值 2. 调用InitRuntime()函数
  return true;
}
bool ResNet::Preprocess(Mat* mat, FDTensor* output) {
// 前处理逻辑
// 1. Resize 2. BGR2RGB 3. Normalize 4. HWC2CHW 5. 处理结果存入 FDTensor类中  
  return true;
}
bool ResNet::Postprocess(FDTensor& infer_result, ClassifyResult* result, int topk) {
  //后处理逻辑
  // 1. Softmax 2. Choose topk labels 3. 结果存入 ClassifyResult类
  return true;
}
bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
  Preprocess(...)
  Infer(...)
  Postprocess(...)
  return true;
}

#ifdef ENABLE_VISION
#include "fastdeploy/vision/classification/contrib/resnet.h"
#endif

void BindResNet(pybind11::module& m) {
  pybind11::class_<vision::classification::ResNet, FastDeployModel>(
      m, "ResNet")
      .def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
      .def("predict", ...)
      .def_readwrite("size", &vision::classification::ResNet::size)
      .def_readwrite("mean_vals", &vision::classification::ResNet::mean_vals)
      .def_readwrite("std_vals", &vision::classification::ResNet::std_vals);
}

void BindResNet(pybind11::module& m);
void BindClassification(pybind11::module& m) {
  auto classification_module =
      m.def_submodule("classification", "Image classification models.");
  BindResNet(classification_module);
}

class ResNet(FastDeployModel):
    def __init__(self, ...):
        self._model = C.vision.classification.ResNet(...)
    def predict(self, input_image, topk=1):
        return self._model.predict(input_image, topk)
    @property
    def size(self):
        return self._model.size
    @size.setter
    def size(self, wh):
        ...

from .contrib.resnet import ResNet

mkdir build & cd build
cmake .. -DENABLE_ORT_BACKEND=ON -DENABLE_VISION=ON -DCMAKE_INSTALL_PREFIX=${PWD/fastdeploy-0.0.3
-DENABLE_PADDLE_BACKEND=ON -DENABLE_TRT_BACKEND=ON -DWITH_GPU=ON -DTRT_DIRECTORY=/PATH/TO/TensorRT/
make -j8
make install

export TRT_DIRECTORY=/PATH/TO/TensorRT/    # 如果用TensorRT 需要填写TensorRT所在位置，并开启 ENABLE_TRT_BACKEND
export ENABLE_TRT_BACKEND=ON
export WITH_GPU=ON
export ENABLE_PADDLE_BACKEND=ON
export ENABLE_OPENVINO_BACKEND=ON
export ENABLE_VISION=ON
export ENABLE_ORT_BACKEND=ON
python setup.py build
python setup.py bdist_wheel
cd dist
pip install fastdeploy_gpu_python-版本号-cpxx-cpxxm-系统架构.whl

.
├── cpp
│   ├── CMakeLists.txt
│   ├── infer.cc    // C++ 版本测试代码
│   └── README.md   // C++版本使用文档
├── python
│   ├── infer.py    // Python 版本测试代码
│   └── README.md   // Python版本使用文档
└── README.md   // ResNet 模型集成说明文档

mkdir build & cd build
cmake .. -DFASTDEPLOY_INSTALL_DIR=/PATH/TO/FastDeploy/build/fastdeploy-0.0.3/
make

/** \brief Predict for the input "im", the result will be saved in "result".
*
* \param[in] im Input image for inference.
* \param[in] result Saving the inference result.
* \param[in] topk The length of return values, e.g., if topk==2, the result will include the 2 most possible class label for input image.
*/
virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
/// Tuple of (width, height)
std::vector<int> size;
/*! @brief Initialize for ResNet model, assign values to the global variables and call InitRuntime()
*/
bool Initialize();

  def predict(self, input_image, topk=1):
    """Classify an input image
    :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
    :param topk: (int)The topk result by the classify confidence score, default 1
    :return: ClassifyResult
    """
    return self._model.predict(input_image, topk)