PaddlePaddle · jerrywgz · Apr 4, 2023 · Mar 14, 2023 · Mar 14, 2023 · Mar 14, 2023
diff --git a/deploy/fastdeploy/README.md b/deploy/fastdeploy/README.md
@@ -0,0 +1,93 @@
+# PaddleDetection高性能全场景模型部署方案—FastDeploy
+
+## 目录  
+- [FastDeploy介绍](#FastDeploy介绍)  
+- [PaddleDetection模型部署](#PaddleDetection模型部署)  
+- [常见问题](#常见问题)  
+
+## 1. FastDeploy介绍
+<div id="FastDeploy介绍"></div>  
+
+**[⚡️FastDeploy](https://github.com/PaddlePaddle/FastDeploy)**是一款**全场景**、**易用灵活**、**极致高效**的AI推理部署工具，支持**云边端**部署。使用FastDeploy可以简单高效的在X86 CPU、NVIDIA GPU、飞腾CPU、ARM CPU、Intel GPU、昆仑、昇腾、瑞芯微、晶晨、算能等10+款硬件上对PaddleDetection模型进行快速部署，并且支持Paddle Inference、Paddle Lite、TensorRT、OpenVINO、ONNXRuntime、RKNPU2、SOPHGO等多种推理后端。
+
+<div align="center">
+
+<img src="https://user-images.githubusercontent.com/31974251/224941235-d5ea4ed0-7626-4c62-8bbd-8e4fad1e72ad.png" >
+
+</div>  
+
+## 2. PaddleDetection模型部署  
+<div id="PaddleDetection模型部署"></div>  
+
+### 2.1 硬件支持列表
+
+|硬件类型|该硬件是否支持|使用指南|Python|C++|
+|:---:|:---:|:---:|:---:|:---:|
+|X86 CPU|✅|[链接](./cpu-gpu)|✅|✅|
+|NVIDIA GPU|✅|[链接](./cpu-gpu)|✅|✅|
+|飞腾CPU|✅|[链接](./cpu-gpu)|✅|✅|
+|ARM CPU|✅|[链接](./cpu-gpu)|✅|✅|
+|Intel GPU(集成显卡)|✅|[链接](./cpu-gpu)|✅|✅|  
+|Intel GPU(独立显卡)|✅|[链接](./cpu-gpu)|✅|✅|  
+|昆仑|✅|[链接](./kunlun)|✅|✅|
+|昇腾|✅|[链接](./ascend)|✅|✅|
+|瑞芯微|✅|[链接](./rockchip)|✅|✅|  
+|晶晨|✅|[链接](./amlogic)|--|✅|✅|  
+|算能|✅|[链接](./sophgo)|✅|✅|  
+
+### 2.2. 详细使用文档
+- X86 CPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- NVIDIA GPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- 飞腾CPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- ARM CPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- Intel GPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- 昆仑 XPU
+  - [部署模型准备](./kunlun)  
+  - [Python部署示例](./kunlun/python/)
+  - [C++部署示例](./kunlun/cpp/)
+- 昇腾 Ascend
+  - [部署模型准备](./ascend)  
+  - [Python部署示例](./ascend/python/)
+  - [C++部署示例](./ascend/cpp/)
+- 瑞芯微 Rockchip
+  - [部署模型准备](./rockchip/)  
+  - [Python部署示例](./rockchip/rknpu2/)
+  - [C++部署示例](./rockchip/rknpu2/)
+- 晶晨 Amlogic
+  - [部署模型准备](./amlogic/a311d/)  
+  - [C++部署示例](./amlogic/a311d/cpp/)  
+- 算能 Sophgo
+  - [部署模型准备](./sophgo/)  
+  - [Python部署示例](./sophgo/python/)
+  - [C++部署示例](./sophgo/cpp/)  
+
+### 2.3 更多部署方式
+
+- [Android ARM CPU部署](./android)  
+- [服务化Serving部署](./serving)  
+- [web部署](./web)  
+- [模型自动化压缩工具](./quantize)
+
+
+## 3. 常见问题
+<div id="常见问题"></div>  
+
+遇到问题可查看常见问题集合，搜索FastDeploy issue，*或给FastDeploy提交[issue](https://github.com/PaddlePaddle/FastDeploy/issues)*:
+
+[常见问题集合](https://github.com/PaddlePaddle/FastDeploy/tree/develop/docs/cn/faq)  
+[FastDeploy issues](https://github.com/PaddlePaddle/FastDeploy/issues)  
diff --git a/deploy/fastdeploy/amlogic/a311d/README.md b/deploy/fastdeploy/amlogic/a311d/README.md
diff --git a/deploy/fastdeploy/ascend/README.md b/deploy/fastdeploy/ascend/README.md
diff --git a/deploy/fastdeploy/cpu-gpu/README.md b/deploy/fastdeploy/cpu-gpu/README.md
@@ -0,0 +1,87 @@
+[English](README.md) | 简体中文
+
+# PaddleDetection高性能全场景模型部署方案—FastDeploy
+
+## 1. 说明
+PaddleDetection支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署检测模型
+
+## 2. 使用预导出的模型列表  
+为了方便开发者的测试，下面提供了PaddleDetection导出的各系列模型，开发者可直接下载使用。其中精度指标来源于PaddleDetection中对各模型的介绍，详情各参考PaddleDetection中的说明。
+
+| 模型                                                               | 参数大小    | 精度    | 备注 |
+|:---------------------------------------------------------------- |:----- |:----- | :------ |
+| [picodet_l_320_coco_lcnet](https://bj.bcebos.com/paddlehub/fastdeploy/picodet_l_320_coco_lcnet.tgz) |23MB | Box AP 42.6% |
+| [ppyoloe_crn_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz) |200MB | Box AP 51.4% |
+| [ppyoloe_plus_crn_m_80e_coco](https://bj.bcebos.com/fastdeploy/models/ppyoloe_plus_crn_m_80e_coco.tgz) |83.3MB | Box AP 49.8% |
+| [ppyolo_r50vd_dcn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyolo_r50vd_dcn_1x_coco.tgz) | 180MB | Box AP 44.8% | 暂不支持TensorRT |
+| [ppyolov2_r101vd_dcn_365e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyolov2_r101vd_dcn_365e_coco.tgz) | 282MB | Box AP 49.7% | 暂不支持TensorRT |
+| [yolov3_darknet53_270e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov3_darknet53_270e_coco.tgz) |237MB | Box AP 39.1% | |
+| [yolox_s_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s_300e_coco.tgz) | 35MB | Box AP 40.4% | |
+| [faster_rcnn_r50_vd_fpn_2x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_r50_vd_fpn_2x_coco.tgz) | 160MB | Box AP 40.8%| 暂不支持TensorRT |
+| [mask_rcnn_r50_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/mask_rcnn_r50_1x_coco.tgz) | 128M | Box AP 37.4%, Mask AP 32.8%| 暂不支持TensorRT、ORT |
+| [ssd_mobilenet_v1_300_120e_voc](https://bj.bcebos.com/paddlehub/fastdeploy/ssd_mobilenet_v1_300_120e_voc.tgz) | 24.9M | Box AP 73.8%| 暂不支持TensorRT、ORT |
+| [ssd_vgg16_300_240e_voc](https://bj.bcebos.com/paddlehub/fastdeploy/ssd_vgg16_300_240e_voc.tgz) | 106.5M | Box AP 77.8%| 暂不支持TensorRT、ORT |
+| [ssdlite_mobilenet_v1_300_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ssdlite_mobilenet_v1_300_coco.tgz) | 29.1M | | 暂不支持TensorRT、ORT |
+| [rtmdet_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/rtmdet_l_300e_coco.tgz) | 224M | Box AP 51.2%|  |
+| [rtmdet_s_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/rtmdet_s_300e_coco.tgz) | 42M | Box AP 44.5%|  |
+| [yolov5_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5_l_300e_coco.tgz) | 183M | Box AP 48.9%|  |
+| [yolov5_s_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5_s_300e_coco.tgz) | 31M | Box AP 37.6%|  |
+| [yolov6_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6_l_300e_coco.tgz) | 229M | Box AP 51.0%|  |
+| [yolov6_s_400e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6_s_400e_coco.tgz) | 68M | Box AP 43.4%|  |
+| [yolov7_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_l_300e_coco.tgz) | 145M | Box AP 51.0%|  |
+| [yolov7_x_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_x_300e_coco.tgz) | 277M | Box AP 53.0%|  |
+| [cascade_rcnn_r50_fpn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/cascade_rcnn_r50_fpn_1x_coco.tgz) | 271M | Box AP 41.1%|  暂不支持TensorRT、ORT |
+| [cascade_rcnn_r50_vd_fpn_ssld_2x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.tgz) | 271M | Box AP 45.0%|  暂不支持TensorRT、ORT |
+| [faster_rcnn_enhance_3x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_enhance_3x_coco.tgz) | 119M | Box AP 41.5%|  暂不支持TensorRT、ORT |
+| [fcos_r50_fpn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/fcos_r50_fpn_1x_coco.tgz) | 129M | Box AP 39.6%|  暂不支持TensorRT |
+| [gfl_r50_fpn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/gfl_r50_fpn_1x_coco.tgz) | 128M | Box AP 41.0%|  暂不支持TensorRT |
+| [ppyoloe_crn_l_80e_sliced_visdrone_640_025](https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_80e_sliced_visdrone_640_025.tgz) | 200M | Box AP 31.9%|  |
+| [retinanet_r101_fpn_2x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/retinanet_r101_fpn_2x_coco.tgz) | 210M | Box AP 40.6%|  暂不支持TensorRT、ORT |
+| [retinanet_r50_fpn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/retinanet_r50_fpn_1x_coco.tgz) | 136M | Box AP 37.5%|  暂不支持TensorRT、ORT |
+| [tood_r50_fpn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/tood_r50_fpn_1x_coco.tgz) | 130M | Box AP 42.5%|  暂不支持TensorRT、ORT |
+| [ttfnet_darknet53_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ttfnet_darknet53_1x_coco.tgz) | 178M | Box AP 33.5%|  暂不支持TensorRT、ORT |  
+| [yolov8_x_500e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8_x_500e_coco.tgz) | 265M | Box AP 53.8%
+| [yolov8_l_500e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8_l_500e_coco.tgz) | 173M | Box AP 52.8%
+| [yolov8_m_500e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8_m_500e_coco.tgz) | 99M | Box AP 50.2%
+| [yolov8_s_500e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8_s_500e_coco.tgz) | 43M | Box AP 44.9%
+| [yolov8_n_500e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8_n_500e_coco.tgz) | 13M | Box AP 37.3%
+
+
+## 3. 自行导出PaddleDetection部署模型  
+### 3.1 模型版本
+支持[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)大于等于2.4版本的PaddleDetection模型部署。目前FastDeploy测试过成功部署的模型:   
+
+- [PP-YOLOE(含PP-YOLOE+)系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppyoloe)
+- [PicoDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/picodet)
+- [PP-YOLO系列模型(含v2)](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppyolo)
+- [YOLOv3系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/yolov3)
+- [YOLOX系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/yolox)
+- [FasterRCNN系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/faster_rcnn)
+- [MaskRCNN系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/mask_rcnn)
+- [SSD系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ssd)
+- [YOLOv5系列模型](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5)
+- [YOLOv6系列模型](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov6)
+- [YOLOv7系列模型](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov7)  
+- [YOLOv8系列模型](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov8)
+- [RTMDet系列模型](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/rtmdet)
+- [CascadeRCNN系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/cascade_rcnn)
+- [PSSDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/rcnn_enhance)
+- [RetinaNet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/retinanet)
+- [PPYOLOESOD系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/smalldet)
+- [FCOS系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/fcos)
+- [TTFNet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ttfnet)
+- [TOOD系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/tood)
+- [GFL系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/gfl)
+
+### 3.2 模型导出   
+PaddleDetection模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.6/deploy/EXPORT_MODEL.md)，**注意**：PaddleDetection导出的模型包含`model.pdmodel`、`model.pdiparams`和`infer_cfg.yml`三个文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息
+
+### 3.3 导出须知  
+如果您是自行导出PaddleDetection推理模型，请注意以下问题：
+- 在导出模型时不要进行NMS的去除操作，正常导出即可  
+- 如果用于跑原生TensorRT后端（非Paddle Inference后端），不要添加--trt参数
+- 导出模型时，不要添加`fuse_normalize=True`参数
+
+## 4. 详细的部署示例  
+- [Python部署](python)
+- [C++部署](cpp)
diff --git a/deploy/fastdeploy/cpu-gpu/cpp/CMakeLists.txt b/deploy/fastdeploy/cpu-gpu/cpp/CMakeLists.txt
@@ -0,0 +1,11 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})