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https://github.com/PaddlePaddle/FastDeploy.git
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[Backend] Add pybind & PaddleDetection example for TVM (#1998)
* update * update * Update infer_ppyoloe_demo.cc --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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Zheng-Bicheng
@@ -4,7 +4,7 @@
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本目录下提供`infer_ppyoloe_demo.cc`快速完成PPDetection模型使用TVM加速部署的示例。
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## 转换模型并运行
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## 运行
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```bash
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# build example
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@@ -15,8 +15,8 @@
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#include "fastdeploy/vision.h"
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void TVMInfer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + "/tvm_model";
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auto params_file = "";
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auto model_file = model_dir + "/tvm_model.so";
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auto params_file = model_dir + "/tvm_model.params";
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auto config_file = model_dir + "/infer_cfg.yml";
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auto option = fastdeploy::RuntimeOption();
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@@ -54,4 +54,4 @@ int main(int argc, char* argv[]) {
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TVMInfer(argv[1], argv[2]);
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return 0;
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}
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}
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@@ -0,0 +1,80 @@
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[English](README.md) | 简体中文
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# PaddleDetection Python部署示例
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本目录下提供`infer_ppyoloe_demo.cc`快速完成PPDetection模型使用TVM加速部署的示例。
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## 运行
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```bash
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# copy model to example folder
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cp -r /path/to/model ./
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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python infer_ppyoloe.py --model_dir tvm_save --image 000000014439.jpg --device cpu
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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 src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
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</div>
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## PaddleDetection Python接口
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```python
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fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.MaskRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.SSD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PaddleYOLOv5(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PaddleYOLOv6(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PaddleYOLOv7(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.RTMDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.CascadeRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PSSDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.RetinaNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.PPYOLOESOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.FCOS(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.TTFNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.TOOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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fastdeploy.vision.detection.GFL(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleDetection模型加载和初始化，其中model_file， params_file为导出的Paddle部署模型格式, config_file为PaddleDetection同时导出的部署配置yaml文件
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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
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> * **config_file**(str): 推理配置yaml文件路径
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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**(ModelFormat): 模型格式，默认为Paddle
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### predict函数
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PaddleDetection中各个模型，包括PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN，均提供如下同样的成员函数用于进行图像的检测
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> ```python
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> PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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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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> **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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## 其它文档
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- [PaddleDetection 模型介绍](../..)
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- [PaddleDetection C++部署](../cpp)
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- [模型预测结果说明](../../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../../docs/cn/faq/how_to_change_backend.md)
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@@ -0,0 +1,68 @@
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import cv2
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import os
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import fastdeploy as fd
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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_dir",
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default=None,
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help="Path of PaddleDetection model directory")
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parser.add_argument(
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"--image", default=None, 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 'kunlunxin', '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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option.use_cpu()
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option.use_tvm_backend()
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return option
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args = parse_arguments()
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if args.model_dir is None:
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model_dir = fd.download_model(name='ppyoloe_crn_l_300e_coco')
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else:
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model_dir = args.model_dir
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model_file = os.path.join(model_dir, "tvm_model.so")
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params_file = os.path.join(model_dir, "tvm_model.params")
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config_file = os.path.join(model_dir, "infer_cfg.yml")
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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.PPYOLOE(
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model_file,
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params_file,
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config_file,
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.TVMFormat)
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model.postprocessor.apply_nms()
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# 预测图片检测结果
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if args.image is None:
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image = fd.utils.get_detection_test_image()
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else:
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image = args.image
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im = cv2.imread(image)
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result = model.predict(im)
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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, score_threshold=0.5)
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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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