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[feature][vision] Add YOLOv7 End2End model with TRT NMS (#157)
* [feature][vision] Add YOLOv7 End2End model with TRT NMS * [docs] update yolov7end2end_trt examples docs Co-authored-by: Jason <jiangjiajun@baidu.com>
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examples/vision/detection/yolov7end2end_trt/python/README.md
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# YOLOv7End2EndTRT Python部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/the%20software%20and%20hardware%20requirements.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)
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本目录下提供`infer.py`快速完成YOLOv7End2EndTRT在TensorRT加速部署的示例。执行如下脚本即可完成
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/detection/yolov7end2end_trt/python/
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#下载yolov7模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# TensorRT GPU推理
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python infer.py --model yolov7-end2end-trt-nms.onnx --image 000000014439.jpg --device gpu --use_trt True
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# 若安装的python包没有支持该类 则请自行从源码develop分支编译最新的FastDeploy Python Wheel包进行安装
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```
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运行完成可视化结果如下图所示
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<div align='center'>
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<img width="640" alt="image" src="https://user-images.githubusercontent.com/31974251/186605967-ad0c53f2-3ce8-4032-a90f-6f5c1238e7f4.png">
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</div>
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注意，YOLOv7End2EndTRT 是专门用于推理YOLOv7中导出模型带[TRT_NMS](https://github.com/WongKinYiu/yolov7/blob/main/models/experimental.py#L111) 版本的End2End模型，不带nms的模型推理请使用YOLOv7类，而 [ORT_NMS](https://github.com/WongKinYiu/yolov7/blob/main/models/experimental.py#L87) 版本的End2End模型请使用YOLOv7End2EndORT进行推理。
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## YOLOv7End2EndTRT Python接口
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```python
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fastdeploy.vision.detection.YOLOv7End2EndTRT(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
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```
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YOLOv7End2EndTRT 模型加载和初始化，其中model_file为导出的ONNX模型格式
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **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
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(Frontend): 模型格式，默认为ONNX
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### predict函数
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> ```python
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> YOLOv7End2EndTRT.predict(image_data, conf_threshold=0.25)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **conf_threshold**(float): 检测框置信度过滤阈值，但由于YOLOv7 End2End的模型在导出成ONNX时已经指定了score阈值，因此该参数只有在大于已经指定的阈值时才会有效。
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> **返回**
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>
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> > 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/)
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### 类成员属性
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#### 预处理参数
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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
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> > * **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]
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> > * **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]
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> > * **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`
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> > * **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`
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> > * **stride**(int): 配合`stris_mini_padide`成员变量使用, 默认值为`stride=32`
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## 其它文档
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- [YOLOv7End2EndTRT 模型介绍](..)
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- [YOLOv7End2EndTRT C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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53
examples/vision/detection/yolov7end2end_trt/python/infer.py
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examples/vision/detection/yolov7end2end_trt/python/infer.py
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import fastdeploy as fd
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import cv2
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of yolov7 end2end onnx model.")
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parser.add_argument(
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"--image", required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("images", [1, 3, 640, 640])
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.detection.YOLOv7End2EndTRT(
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args.model, runtime_option=runtime_option)
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# 预测图片检测结果
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im = cv2.imread(args.image)
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result = model.predict(im.copy())
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print(result)
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# 预测结果可视化
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vis_im = fd.vision.vis_detection(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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