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[Doc] Add tutorial of supporting new models (PaddlePaddle#418)
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* first commit for yolov7

* pybind for yolov7

* CPP README.md

* CPP README.md

* modified yolov7.cc

* README.md

* python file modify

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* README modified

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* move some helpers to private

* add examples for yolov7

* api.md modified

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* api.md modified

* YOLOv7

* yolov7 release link

* yolov7 release link

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* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* 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

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

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* first commit for yolor

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* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* 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

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
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* Yolor (#16)

* Develop (#11) (#12)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* 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

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

Co-authored-by: Jason <jiangjiajun@baidu.com>
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Co-authored-by: huangjianhui <852142024@qq.com>

* Develop (#13)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* 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

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* Develop (#14)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* 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

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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Co-authored-by: Jason <928090362@qq.com>

* add is_dynamic for YOLO series (#22)

* modify ppmatting backend and docs

* modify ppmatting docs

* fix the PPMatting size problem

* fix LimitShort's log

* retrigger ci

* modify PPMatting docs

* modify the way  for dealing with  LimitShort

* change develop_a_new_model.md dir

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Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>
Co-authored-by: Jason <928090362@qq.com>
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# FastDeploy集成新模型流程

进行中...
在FastDeploy里面新增一个模型,包括增加C++/Python的部署支持。 本文以torchvision v0.12.0中的ResNet50模型为例,介绍使用FastDeploy做外部[模型集成](#modelsupport),具体包括如下3步。

| 步骤 | 说明 | 创建或修改的文件 |
|:------:|:-------------------------------------:|:---------------------------------------------:|
| [1](#step2) | 在fastdeploy/vision相应任务模块增加模型实现 | resnet.h、resnet.cc、vision.h |
| [2](#step4) | 通过pybind完成Python接口绑定 | resnet_pybind.cc、classification_pybind.cc |
| [3](#step5) | 实现Python相应调用接口 | resnet.py、\_\_init\_\_.py |

在完成上述3步之后,一个外部模型就集成好了。
<br />
如果您想为FastDeploy贡献代码,还需要为新增模型添加测试代码、说明文档和代码注释,可在[测试](#test)中查看。
## 模型集成 <span id="modelsupport"></span>

### 模型准备 <span id="step1"></span>


在集成外部模型之前,先要将训练好的模型(.pt,.pdparams 等)转换成FastDeploy支持部署的模型格式(.onnx,.pdmodel)。多数开源仓库会提供模型转换脚本,可以直接利用脚本做模型的转换。由于torchvision没有提供转换脚本,因此手动编写转换脚本,本文中将 `torchvison.models.resnet50` 转换为 `resnet50.onnx`, 参考代码如下:

```python
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"}})
```
执行上述脚本将会得到 `resnet50.onnx` 文件。

### C++部分 <span id="step2"></span>
* 创建`resnet.h`文件
* 创建位置
* FastDeploy/fastdeploy/vision/classification/contrib/resnet.h (FastDeploy/C++代码存放位置/视觉模型/任务名称/外部模型/模型名.h)
* 创建内容
* 首先在resnet.h中创建 ResNet类并继承FastDeployModel父类,之后声明`Predict``Initialize``Preprocess``Postprocess``构造函数`,以及必要的变量,具体的代码细节请参考[resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e)

```C++
class FASTDEPLOY_DECL ResNet : public FastDeployModel {
public:
ResNet(...);
virtual bool Predict(...);
private:
bool Initialize();
bool Preprocess(...);
bool Postprocess(...);
};
```
* 创建`resnet.cc`文件
* 创建位置
* FastDeploy/fastdeploy/vision/classification/contrib/resnet.cc (FastDeploy/C++代码存放位置/视觉模型/任务名称/外部模型/模型名.cc)
* 创建内容
* 在`resnet.cc`中实现`resnet.h`中声明函数的具体逻辑,其中`PreProcess` 和 `PostProcess`需要参考源官方库的前后处理逻辑复现,ResNet每个函数具体逻辑如下,具体的代码请参考[resnet.cc](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-d229d702de28345253a53f2a5839fd2c638f3d32fffa6a7d04d23db9da13a871)。
```C++
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;
}
```
<span id="step3"></span>
*`vision.h`文件中加入新增模型文件
* 修改位置
* FastDeploy/fastdeploy/vision.h
* 修改内容

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


### Pybind部分 <span id="step4"></span>

* 创建Pybind文件
* 创建位置
* FastDeploy/fastdeploy/vision/classification/contrib/resnet_pybind.cc (FastDeploy/C++代码存放位置/视觉模型/任务名称/外部模型/模型名_pybind.cc)
* 创建内容
* 利用Pybind将C++中的函数变量绑定到Python中,具体代码请参考[resnet_pybind.cc](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-270af0d65720310e2cfbd5373c391b2110d65c0f4efa547f7b7eeffcb958bdec)
```C++
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);
}
```
* 调用Pybind函数
* 修改位置
* FastDeploy/fastdeploy/vision/classification/classification_pybind.cc (FastDeploy/C++代码存放位置/视觉模型/任务名称/任务名称}_pybind.cc)
* 修改内容
```C++
void BindResNet(pybind11::module& m);
void BindClassification(pybind11::module& m) {
auto classification_module =
m.def_submodule("classification", "Image classification models.");
BindResNet(classification_module);
}
```


### Python部分 <span id="step5"></span>


* 创建`resnet.py`文件
* 创建位置
* FastDeploy/python/fastdeploy/vision/classification/contrib/resnet.py (FastDeploy/Python代码存放位置/fastdeploy/视觉模型/任务名称/外部模型/模型名.py)
* 创建内容
* 创建ResNet类继承自FastDeployModel,实现 `\_\_init\_\_`、Pybind绑定的函数(如`predict()`)、以及`对Pybind绑定的全局变量进行赋值和获取的函数`,具体代码请参考[resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157)

```python
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):
...
```
<span id="step6"></span>
* 导入ResNet类
* 修改位置
* FastDeploy/python/fastdeploy/vision/classification/\_\_init\_\_.py (FastDeploy/Python代码存放位置/fastdeploy/视觉模型/任务名称/\_\_init\_\_.py)
* 修改内容

```Python
from .contrib.resnet import ResNet
```

## 测试 <span id="test"></span>
### 编译
* C++
* 位置:FastDeploy/

```
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
```

编译会得到 build/fastdeploy-0.0.3/。

* Python
* 位置:FastDeploy/python/

```
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
```

### 编写测试代码
* 创建位置: FastDeploy/examples/vision/classification/resnet/ (FastDeploy/示例目录/视觉模型/任务名称/模型名/)
* 创建目录结构

```
.
├── 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
```

* C++
* 编写CmakeLists文件、C++ 代码以及 README.md 内容请参考[cpp/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-afcbe607b796509581f89e38b84190717f1eeda2df0419a2ac9034197ead5f96)
* 编译 infer.cc
* 位置:FastDeploy/examples/vision/classification/resnet/cpp/

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

* Python
* Python 代码以及 README.md 内容请参考[python/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-5a0d6be8c603a8b81454ac14c17fb93555288d9adf92bbe40454449309700135)

### 为代码添加注释
为了方便用户理解代码,我们需要为新增代码添加注释,添加注释方法可参考如下示例。
- C++ 代码
您需要在resnet.h文件中为函数和变量增加注释,有如下三种注释方式,具体可参考[resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e)

```C++
/** \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();
```
- Python 代码
你需要为resnet.py文件中的函数和变量增加适当的注释,示例如下,具体可参考[resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157)。
```python
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)
```

对于集成模型过程中的其他文件,您也可以对实现的细节添加适当的注释说明。

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