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add paddleclas draft doct
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jiangjiajun
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# PaddleClas 模型部署
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## 模型版本说明
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- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas)
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## 准备PaddleClas部署模型
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PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)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，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数。
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## 下载预训练模型
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为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
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| 模型 | 大小 |输入Shape | 精度 |
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|:---------------------------------------------------------------- |:----- |:----- | :----- |
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| [PPLCNet]() | 141MB | 224x224 |51.4% |
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| [PPLCNetv2]() | 10MB | 224x224 |51.4% |
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| [EfficientNet]() | | 224x224 | |
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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# YOLOv7 C++部署示例
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本目录下提供`infer.cc`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/compile/prebuilt_libraries.md)
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以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
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```
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mkdir build
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cd build
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wget https://xxx.tgz
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tar xvf fastdeploy-linux-x64-0.2.0.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.2.0
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make -j
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#下载官方转换好的yolov7模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000087038.jpg
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# CPU推理
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./infer_demo yolov7.onnx 000000087038.jpg 0
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# GPU推理
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./infer_demo yolov7.onnx 000000087038.jpg 1
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# GPU上TensorRT推理
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./infer_demo yolov7.onnx 000000087038.jpg 2
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```
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## YOLOv7 C++接口
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### YOLOv7类
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```
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fastdeploy::vision::detection::YOLOv7(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const Frontend& model_format = Frontend::ONNX)
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```
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YOLOv7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时，此参数传入空字符串即可
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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格式
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#### Predict函数
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> ```
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> YOLOv7::Predict(cv::Mat* im, DetectionResult* result,
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> float conf_threshold = 0.25,
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> float nms_iou_threshold = 0.5)
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> ```
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>
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> 模型预测接口，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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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > * **conf_threshold**: 检测框置信度过滤阈值
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> > * **nms_iou_threshold**: NMS处理过程中iou阈值
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### 类成员变量
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> > * **size**(vector<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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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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void CpuInfer(const std::string& model_file, const std::string& image_file) {
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auto model = fastdeploy::vision::detection::YOLOv7(model_file);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void GpuInfer(const std::string& model_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void TrtInfer(const std::string& model_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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option.SetTrtInputShape("images", {1, 3, 640, 640});
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auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
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"e.g ./infer_model ./yolov7.onnx ./test.jpeg 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[3]) == 0) {
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CpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 1) {
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GpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2]);
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}
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return 0;
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}
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# PaddleClas模型 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/quick_start/requirements.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md)
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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
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```
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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.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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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 examples/vison/detection/yolov7/python/
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# CPU推理
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python infer.py --model yolov7.onnx --image 000000087038.jpg --device cpu
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# GPU推理
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python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu
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# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu --use_trt True
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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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## YOLOv7 Python接口
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```
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
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```
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YOLOv7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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> ```
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> YOLOv7.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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> > * **conf_threshold**(float): 检测框置信度过滤阈值
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> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
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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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> > * **size**(list | tuple): 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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## 其它文档
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- [YOLOv7 模型介绍](..)
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- [YOLOv7 C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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@@ -0,0 +1,47 @@
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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 PaddleClas 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.classification.PaddleClasModel(args.model, runtime_option=runtime_option)
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# 预测图片分类结果
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im = cv2.imread(args.image)
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result = model.predict(im)
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print(result)
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