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简体中文 | [English](README_EN.md)
English | [简体中文](README_CN.md)
# YOLOv7 Python部署示例
在部署前，需确认以下两个步骤
# YOLOv7 Python Deployment Demo
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
Two steps before deployment:
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
- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
This doc provides a quick `infer.py` demo of YOLOv7 deployment on CPU/GPU, and accelerated GPU deployment by TensorRT. Run the following command:
```bash
#下载部署示例代码
# Download sample deployment code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/detection/yolov7/python/
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_infer.tar
tar -xf yolov7_infer.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU推理
# CPU
python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device cpu
# GPU推理
# GPU
python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
# 昆仑芯XPU推理
# KunlunXin XPU
python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device kunlunxin
# 华为昇腾推理
# Huawei Ascend
python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device ascend
```
如果想要验证ONNX模型的推理，可以参考如下命令：
If you want to test ONNX model:
```bash
#下载yolov7模型文件和测试图片
# Download yolov7 model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU推理
# CPU Inference
python infer.py --model yolov7.onnx --image 000000014439.jpg --device cpu
# GPU推理
# GPU
python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu
# GPU上使用TensorRT推理
# Infer with TensorRT on GPU
python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_trt True
```
运行完成可视化结果如下图所示
The visualisation of the results is as follows.
<img width="640" src="https://user-images.githubusercontent.com/67993288/183847558-abcd9a57-9cd9-4891-b09a-710963c99b74.jpg">
## YOLOv7 Python接口
## YOLOv7 Python Interface
```python
fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式
YOLOv7 model loading and initialisation, with model_file being the exported ONNX model format.
**参数**
**Parameters**
> * **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**(ModelFormat): 模型格式，默认为ONNX
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. If the model format is ONNX, the parameter can be filled with an empty string.
> * **runtime_option**(RuntimeOption): Back-end inference configuration. The default is None, i.e. the default is applied
> * **model_format**(ModelFormat): Model format. The default is ONNX format
### predict函数
### Predict Function
> ```python
> 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。
> Model prediction interface with direct output of detection results from the image input.
>
> **参数**
> **Parameters**
>
> > * **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
> > * **image_data**(np.ndarray): Input image. Images need to be in HWC or BGR format
> > * **conf_threshold**(float): Filter threshold for detection box confidence
> > * **nms_iou_threshold**(float): iou thresholds during NMS processing
> **返回**
> **Return**
>
> > 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/)
> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/api/vision_results/)
### 类成员属性
#### 预处理参数
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
### Class Member Variables
> > * **size**(list[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]
> > * **padding_value**(list[float]): 通过此参数可以修改图片在resize时候做填充(padding)的值, 包含三个浮点型元素, 分别表示三个通道的值, 默认值为[114, 114, 114]
> > * **is_no_pad**(bool): 通过此参数让图片是否通过填充的方式进行resize, `is_no_pad=True` 表示不使用填充的方式，默认值为`is_no_pad=False`
> > * **is_mini_pad**(bool): 通过此参数可以将resize之后图像的宽高这是为最接近`size`成员变量的值, 并且满足填充的像素大小是可以被`stride`成员变量整除的。默认值为`is_mini_pad=False`
> > * **stride**(int): 配合`stris_mini_padide`成员变量使用, 默认值为`stride=32`
#### Pre-processing parameters
Users can modify the following pre-processing parameters for their needs. This will affect the final reasoning and deployment results
> > * **size**(list[int]): This parameter modifies the 'resize' during preprocessing and contains two integer elements representing [width, height]. The default value is [640, 640].
> > * **padding_value**(list[float]): This parameter modifies the value of the padding when resizing the image. It contains three floating-point elements, representing the values of the three channels. The default value is [114, 114, 114].
> > * **is_no_pad**(bool): This parameter determines whether the image is resized by padding, `is_no_pad=ture` means no padding is used. The default value is `is_no_pad=false`.
> > * **is_mini_pad**(bool): This parameter allows the width and height of the image after resize to be the closest value to the `size` member variable, which the pixel size of the padding can be divided by the `stride` member variable. The default value is `is_mini_pad=false`.
> > * **stride**(int): Used with`stris_mini_pad` member value. The default value is`stride=32`
## 其它文档
## Related files
- [YOLOv7 模型介绍](..)
- [YOLOv7 C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [YOLOv7 Model Introduction](..)
- [YOLOv7 C++ Deployment](../cpp)
- [Vision Model Results](../../../../../docs/api/vision_results/)
- [how to change inference backend](../../../../../docs/en/faq/how_to_change_backend.md)