[Model] Add Solov2 For PaddleDetection (PaddlePaddle#1435)

* update solov2 * Repair note * update solov2 postprocess * update * update solov2 * update solov2 * fixed bug * fixed bug * update solov2 * update solov2 * fix build android bug * update docs * update docs * update docs * update * update * update arch and docs * update * update * update solov2 python --------- Co-authored-by: DefTruth <[email protected]>
Venonary · Mar 8, 2023 · 0687d3b · 0687d3b
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diff --git a/examples/vision/detection/paddledetection/jetson/README.md b/examples/vision/detection/paddledetection/jetson/README.md
@@ -0,0 +1,21 @@
+English | [简体中文](README_CN.md)
+
+# PaddleDetection Model Deployment
+
+FastDeploy supports the SOLOV2 model of [PaddleDetection version 2.6](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6).
+
+You can enter the following command to get the static diagram model of SOLOV2.
+
+```bash
+# install PaddleDetection
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+cd PaddleDetection
+
+python tools/export_model.py -c configs/solov2/solov2_r50_fpn_1x_coco.yml --output_dir=./inference_model \
+ -o weights=https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams
+```
+
+## Detailed Deployment Documents
+
+- [Python Deployment](python)
+- [C++ Deployment](cpp)
diff --git a/examples/vision/detection/paddledetection/jetson/README_CN.md b/examples/vision/detection/paddledetection/jetson/README_CN.md
@@ -0,0 +1,20 @@
+[English](README.md) | 简体中文
+# PaddleDetection模型部署
+
+FastDeploy支持[PaddleDetection 2.6](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6)版本的SOLOv2模型，
+
+你可以输入以下命令得到SOLOv2的静态图模型。
+
+```bash
+# install PaddleDetection
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+cd PaddleDetection
+
+python tools/export_model.py -c configs/solov2/solov2_r50_fpn_1x_coco.yml --output_dir=./inference_model \
+ -o weights=https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams
+```
+
+## 详细部署文档
+
+- [Python部署](python)
+- [C++部署](cpp)
diff --git a/examples/vision/detection/paddledetection/jetson/cpp/CMakeLists.txt b/examples/vision/detection/paddledetection/jetson/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_solov2_demo ${PROJECT_SOURCE_DIR}/infer_solov2.cc)
+target_link_libraries(infer_solov2_demo ${FASTDEPLOY_LIBS})
diff --git a/examples/vision/detection/paddledetection/jetson/cpp/README.md b/examples/vision/detection/paddledetection/jetson/cpp/README.md
@@ -0,0 +1,28 @@
+English | [简体中文](README_CN.md)
+# PaddleDetection C++ Deployment Example
+
+This directory provides examples that `infer_xxx.cc` fast finishes the deployment of PaddleDetection models, including SOLOv2 on CPU/GPU and GPU accelerated by TensorRT.
+
+Before deployment, two steps require confirmation
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+
+Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
+
+```bash
+mkdir build
+cd build
+
+# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+# CPU inference
+./infer_solov2_demo ./solov2_r50_fpn_1x_coco 000000014439.jpg 0
+# GPU inference
+./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
+```
diff --git a/examples/vision/detection/paddledetection/jetson/cpp/README_CN.md b/examples/vision/detection/paddledetection/jetson/cpp/README_CN.md
@@ -0,0 +1,29 @@
+[English](README.md) | 简体中文
+# PaddleDetection C++部署示例
+
+本目录下提供`infer_xxx.cc`快速完成PaddleDetection模型包括SOLOv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 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. 根据开发环境，下载预编译部署库和examples代码，参考[FastDeploy预编译库](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
+
+
+```bash
+mkdir build
+cd build
+
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+# CPU推理
+./infer_solov2_demo ./solov2_r50_fpn_1x_coco 000000014439.jpg 0
+# GPU推理
+./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
+```
diff --git a/examples/vision/detection/paddledetection/jetson/cpp/infer_solov2.cc b/examples/vision/detection/paddledetection/jetson/cpp/infer_solov2.cc
@@ -0,0 +1,96 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void CpuInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseCpu();
+  auto model = fastdeploy::vision::detection::SOLOv2(model_file, params_file,
+                                                     config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+void GpuInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::detection::SOLOv2(model_file, params_file,
+                                                     config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 4) {
+    std::cout
+        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
+           "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
+        << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with kunlunxin."
+              << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  }
+  return 0;
+}
diff --git a/examples/vision/detection/paddledetection/jetson/python/README.md b/examples/vision/detection/paddledetection/jetson/python/README.md
@@ -0,0 +1,96 @@
+English | [简体中文](README_CN.md)
+# PaddleDetection Python Deployment Example
+
+Before deployment, two steps require confirmation.
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+This directory provides examples that `infer_xxx.py` fast finishes the deployment of PPYOLOE/PicoDet models on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
+
+```bash
+# Download deployment example code
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/detection/paddledetection/python/
+
+# Download the PPYOLOE model file and test images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf ppyoloe_crn_l_300e_coco.tgz
+
+# CPU inference
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device cpu
+# GPU inference
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu
+# TensorRT inference on GPU  （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu --use_trt True
+# Kunlunxin XPU Inference
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device kunlunxin
+# Huawei Ascend Inference
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device ascend
+```
+
+The visualized result after running is as follows
+<div  align="center">  
+<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
+</div>
+
+## PaddleDetection Python Interface
+
+```python
+fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.MaskRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.SSD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PaddleYOLOv5(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PaddleYOLOv6(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PaddleYOLOv7(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.RTMDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.CascadeRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PSSDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.RetinaNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PPYOLOESOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.FCOS(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.TTFNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.TOOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.GFL(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.SOLOv2(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+```
+
+PaddleDetection model loading and initialization, among which model_file and params_file are the exported Paddle model format. config_file is the configuration yaml file exported by PaddleDetection simultaneously
+
+**Parameter**
+
+> * **model_file**(str): Model file path
+> * **params_file**(str): Parameter file path
+> * **config_file**(str): Inference configuration yaml file path
+> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
+> * **model_format**(ModelFormat): Model format. Paddle format by default
+
+### predict Function
+
+PaddleDetection models, including PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN, all provide the following member functions for image detection
+> ```python
+> PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
+> ```
+>
+> Model prediction interface. Input images and output results directly.
+>
+> **Parameter**
+>
+> > * **image_data**(np.ndarray): Input data in HWC or BGR format
+
+> **Return**
+>
+> > Return `fastdeploy.vision.DetectionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
+
+## Other Documents
+
+- [PaddleDetection Model Description](..)
+- [PaddleDetection C++ Deployment](../cpp)
+- [Model Prediction Results](../../../../../docs/api/vision_results/)
+- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)