mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-11-01 12:22:53 +08:00
Merge branch 'develop' of https://github.com/PaddlePaddle/FastDeploy into huawei
This commit is contained in:
yunyaoXYY
14
examples/vision/detection/fastestdet/cpp/CMakeLists.txt
Normal file
14
examples/vision/detection/fastestdet/cpp/CMakeLists.txt
Normal file
@@ -0,0 +1,14 @@
|
||||
PROJECT(infer_demo C CXX)
|
||||
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||
|
||||
# Specifies the path to the fastdeploy library after you have downloaded it
|
||||
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||
|
||||
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||
|
||||
# Include the FastDeploy dependency header file
|
||||
include_directories(${FASTDEPLOY_INCS})
|
||||
|
||||
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||
# Add the FastDeploy library dependency
|
||||
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||||
87
examples/vision/detection/fastestdet/cpp/README.md
Normal file
87
examples/vision/detection/fastestdet/cpp/README.md
Normal file
@@ -0,0 +1,87 @@
|
||||
# FastestDet C++部署示例
|
||||
|
||||
本目录下提供`infer.cc`快速完成FastestDet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
|
||||
|
||||
```bash
|
||||
mkdir build
|
||||
cd build
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-1.0.3.tgz
|
||||
tar xvf fastdeploy-linux-x64-1.0.3.tgz
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-1.0.3
|
||||
make -j
|
||||
|
||||
#下载官方转换好的FastestDet模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
|
||||
|
||||
# CPU推理
|
||||
./infer_demo FastestDet.onnx 000000014439.jpg 0
|
||||
# GPU推理
|
||||
./infer_demo FastestDet.onnx 000000014439.jpg 1
|
||||
# GPU上TensorRT推理
|
||||
./infer_demo FastestDet.onnx 000000014439.jpg 2
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/44280887/206176291-61eb118b-391b-4431-b79e-a393b9452138.jpg">
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
## FastestDet C++接口
|
||||
|
||||
### FastestDet类
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::detection::FastestDet(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
```
|
||||
|
||||
FastestDet模型加载和初始化，其中model_file为导出的ONNX模型格式。
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **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
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为ONNX格式
|
||||
|
||||
#### Predict函数
|
||||
|
||||
> ```c++
|
||||
> FastestDet::Predict(cv::Mat* im, DetectionResult* result,
|
||||
> float conf_threshold = 0.65,
|
||||
> float nms_iou_threshold = 0.45)
|
||||
> ```
|
||||
>
|
||||
> 模型预测接口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **im**: 输入图像，注意需为HWC，BGR格式
|
||||
> > * **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/)
|
||||
> > * **conf_threshold**: 检测框置信度过滤阈值
|
||||
> > * **nms_iou_threshold**: NMS处理过程中iou阈值
|
||||
|
||||
### 类成员变量
|
||||
#### 预处理参数
|
||||
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
|
||||
|
||||
> > * **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[352, 352]
|
||||
|
||||
- [模型介绍](../../)
|
||||
- [Python部署](../python)
|
||||
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
105
examples/vision/detection/fastestdet/cpp/infer.cc
Normal file
105
examples/vision/detection/fastestdet/cpp/infer.cc
Normal file
@@ -0,0 +1,105 @@
|
||||
// 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"
|
||||
|
||||
void CpuInfer(const std::string& model_file, const std::string& image_file) {
|
||||
auto model = fastdeploy::vision::detection::FastestDet(model_file);
|
||||
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);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& model_file, const std::string& image_file) {
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseGpu();
|
||||
auto model = fastdeploy::vision::detection::FastestDet(model_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);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& model_file, const std::string& image_file) {
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseGpu();
|
||||
option.UseTrtBackend();
|
||||
option.SetTrtInputShape("images", {1, 3, 352, 352});
|
||||
auto model = fastdeploy::vision::detection::FastestDet(model_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);
|
||||
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 path/to/image run_option, "
|
||||
"e.g ./infer_model ./FastestDet.onnx ./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 gpu and use tensorrt backend."
|
||||
<< 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]);
|
||||
} else if (std::atoi(argv[3]) == 2) {
|
||||
TrtInfer(argv[1], argv[2]);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
74
examples/vision/detection/fastestdet/python/README.md
Normal file
74
examples/vision/detection/fastestdet/python/README.md
Normal file
@@ -0,0 +1,74 @@
|
||||
# FastestDet Python部署示例
|
||||
|
||||
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. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
本目录下提供`infer.py`快速完成FastestDet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
|
||||
|
||||
```bash
|
||||
#下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd examples/vision/detection/fastestdet/python/
|
||||
|
||||
#下载fastestdet模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
|
||||
# CPU推理
|
||||
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device cpu
|
||||
# GPU推理
|
||||
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device gpu
|
||||
# GPU上使用TensorRT推理
|
||||
python infer.py --model FastestDet.onnx --image 000000014439.jpg --device gpu --use_trt True
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/44280887/206176291-61eb118b-391b-4431-b79e-a393b9452138.jpg">
|
||||
|
||||
## FastestDet Python接口
|
||||
|
||||
```python
|
||||
fastdeploy.vision.detection.FastestDet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
||||
```
|
||||
|
||||
FastestDet模型加载和初始化，其中model_file为导出的ONNX模型格式
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **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
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为ONNX
|
||||
|
||||
### predict函数
|
||||
|
||||
> ```python
|
||||
> FastestDet.predict(image_data)
|
||||
> ```
|
||||
>
|
||||
> 模型预测接口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||
|
||||
> **返回**
|
||||
>
|
||||
> > 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/)
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
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
|
||||
|
||||
> > * **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[352, 352]
|
||||
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [FastestDet 模型介绍](..)
|
||||
- [FastestDet C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
51
examples/vision/detection/fastestdet/python/infer.py
Normal file
51
examples/vision/detection/fastestdet/python/infer.py
Normal file
@@ -0,0 +1,51 @@
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model", required=True, help="Path of FastestDet onnx model.")
|
||||
parser.add_argument(
|
||||
"--image", required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
|
||||
if args.use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("images", [1, 3, 352, 352])
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# Configure runtime and load model
|
||||
runtime_option = build_option(args)
|
||||
model = fd.vision.detection.FastestDet(args.model, runtime_option=runtime_option)
|
||||
|
||||
# Predict picture detection results
|
||||
im = cv2.imread(args.image)
|
||||
result = model.predict(im)
|
||||
|
||||
# Visualization of prediction results
|
||||
vis_im = fd.vision.vis_detection(im, result)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
@@ -17,16 +17,9 @@ cd FastDeploy/examples/vision/detection/paddledetection/python/serving
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
|
||||
tar xvf ppyoloe_crn_l_300e_coco.tgz
|
||||
|
||||
# 安装uvicorn
|
||||
pip install uvicorn
|
||||
|
||||
# 启动服务，可选择是否使用GPU和TensorRT，可根据uvicorn --help配置IP、端口号等
|
||||
# CPU
|
||||
MODEL_DIR=ppyoloe_crn_l_300e_coco DEVICE=cpu uvicorn server:app
|
||||
# GPU
|
||||
MODEL_DIR=ppyoloe_crn_l_300e_coco DEVICE=gpu uvicorn server:app
|
||||
# GPU上使用TensorRT （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）
|
||||
MODEL_DIR=ppyoloe_crn_l_300e_coco DEVICE=gpu USE_TRT=true uvicorn server:app
|
||||
# 启动服务，可修改server.py中的配置项来指定硬件、后端等
|
||||
# 可通过--host、--port指定IP和端口号
|
||||
fastdeploy simple_serving --app server:app
|
||||
```
|
||||
|
||||
客户端：
|
||||
|
||||
@@ -17,17 +17,9 @@ cd FastDeploy/examples/vision/detection/paddledetection/python/serving
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
|
||||
tar xvf ppyoloe_crn_l_300e_coco.tgz
|
||||
|
||||
# Install uvicorn
|
||||
pip install uvicorn
|
||||
|
||||
# Launch server, it's configurable to use GPU and TensorRT,
|
||||
# and run 'uvicorn --help' to check how to specify IP and port, etc.
|
||||
# CPU
|
||||
MODEL_DIR=ppyoloe_crn_l_300e_coco DEVICE=cpu uvicorn server:app
|
||||
# GPU
|
||||
MODEL_DIR=ppyoloe_crn_l_300e_coco DEVICE=gpu uvicorn server:app
|
||||
# GPU and TensorRT
|
||||
MODEL_DIR=ppyoloe_crn_l_300e_coco DEVICE=gpu USE_TRT=true uvicorn server:app
|
||||
# Launch server, change the configurations in server.py to select hardware, backend, etc.
|
||||
# and use --host, --port to specify IP and port
|
||||
fastdeploy simple_serving --app server:app
|
||||
```
|
||||
|
||||
Client:
|
||||
|
||||
@@ -1,20 +1,15 @@
|
||||
import requests
|
||||
import json
|
||||
import cv2
|
||||
import base64
|
||||
import fastdeploy as fd
|
||||
from fastdeploy.serving.utils import cv2_to_base64
|
||||
|
||||
if __name__ == '__main__':
|
||||
url = "http://127.0.0.1:8000/fd/ppyoloe"
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
im = cv2.imread("000000014439.jpg")
|
||||
data = {
|
||||
"data": {
|
||||
"image": fd.serving.utils.cv2_to_base64(im)
|
||||
},
|
||||
"parameters": {}
|
||||
}
|
||||
data = {"data": {"image": cv2_to_base64(im)}, "parameters": {}}
|
||||
|
||||
resp = requests.post(url=url, headers=headers, data=json.dumps(data))
|
||||
if resp.status_code == 200:
|
||||
|
||||
@@ -1,18 +1,16 @@
|
||||
import fastdeploy as fd
|
||||
from fastdeploy.serving.server import SimpleServer
|
||||
import os
|
||||
import logging
|
||||
|
||||
logging.getLogger().setLevel(logging.INFO)
|
||||
|
||||
# Get arguments from envrionment variables
|
||||
model_dir = os.environ.get('MODEL_DIR')
|
||||
device = os.environ.get('DEVICE', 'cpu')
|
||||
use_trt = os.environ.get('USE_TRT', False)
|
||||
|
||||
# Prepare model, download from hub or use local dir
|
||||
if model_dir is None:
|
||||
model_dir = fd.download_model(name='ppyoloe_crn_l_300e_coco')
|
||||
# Configurations
|
||||
model_dir = 'ppyoloe_crn_l_300e_coco'
|
||||
device = 'cpu'
|
||||
use_trt = False
|
||||
|
||||
# Prepare model
|
||||
model_file = os.path.join(model_dir, "model.pdmodel")
|
||||
params_file = os.path.join(model_dir, "model.pdiparams")
|
||||
config_file = os.path.join(model_dir, "infer_cfg.yml")
|
||||
@@ -33,7 +31,7 @@ model_instance = fd.vision.detection.PPYOLOE(
|
||||
runtime_option=option)
|
||||
|
||||
# Create server, setup REST API
|
||||
app = fd.serving.SimpleServer()
|
||||
app = SimpleServer()
|
||||
app.register(
|
||||
task_name="fd/ppyoloe",
|
||||
model_handler=fd.serving.handler.VisionModelHandler,
|
||||
|
||||
@@ -20,19 +20,11 @@ install(TARGETS infer_demo DESTINATION ./)
|
||||
|
||||
install(DIRECTORY models DESTINATION ./)
|
||||
install(DIRECTORY images DESTINATION ./)
|
||||
# install(DIRECTORY run_with_adb.sh DESTINATION ./)
|
||||
|
||||
file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
|
||||
install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB OPENCV_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib/lib*)
|
||||
install(PROGRAMS ${OPENCV_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB PADDLELITE_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/lib*)
|
||||
install(PROGRAMS ${PADDLELITE_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB TIMVX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/verisilicon_timvx/*)
|
||||
install(PROGRAMS ${TIMVX_LIBS} DESTINATION lib)
|
||||
file(GLOB_RECURSE FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/lib*.so*)
|
||||
file(GLOB_RECURSE ALL_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/lib*.so*)
|
||||
list(APPEND ALL_LIBS ${FASTDEPLOY_LIBS})
|
||||
install(PROGRAMS ${ALL_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB ADB_TOOLS run_with_adb.sh)
|
||||
install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)
|
||||
|
||||
@@ -24,7 +24,7 @@ void InitAndInfer(const std::string& model_dir, const std::string& image_file) {
|
||||
auto params_file = model_dir + sep + "model.pdiparams";
|
||||
auto config_file = model_dir + sep + "infer_cfg.yml";
|
||||
auto subgraph_file = model_dir + sep + "subgraph.txt";
|
||||
|
||||
fastdeploy::vision::EnableFlyCV();
|
||||
fastdeploy::RuntimeOption option;
|
||||
option.UseTimVX();
|
||||
option.SetLiteSubgraphPartitionPath(subgraph_file);
|
||||
|
||||
@@ -37,13 +37,13 @@ cp models/runtime/ppyoloe_runtime_config.pbtxt models/runtime/config.pbtxt
|
||||
|
||||
# 拉取fastdeploy镜像(x.y.z为镜像版本号，需替换成fastdeploy版本数字)
|
||||
# GPU镜像
|
||||
docker pull paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
|
||||
docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
|
||||
# CPU镜像
|
||||
docker pull paddlepaddle/fastdeploy:z.y.z-cpu-only-21.10
|
||||
|
||||
|
||||
# 运行容器.容器名字为 fd_serving, 并挂载当前目录为容器的 /serving 目录
|
||||
nvidia-docker run -it --net=host --name fd_serving --shm-size="1g" -v `pwd`/:/serving paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash
|
||||
nvidia-docker run -it --net=host --name fd_serving --shm-size="1g" -v `pwd`/:/serving registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash
|
||||
|
||||
# 启动服务(不设置CUDA_VISIBLE_DEVICES环境变量，会拥有所有GPU卡的调度权限)
|
||||
CUDA_VISIBLE_DEVICES=0 fastdeployserver --model-repository=/serving/models
|
||||
|
||||
1
examples/vision/detection/yolov5/python/serving/README.md
Symbolic link
1
examples/vision/detection/yolov5/python/serving/README.md
Symbolic link
@@ -0,0 +1 @@
|
||||
README_CN.md
|
||||
36
examples/vision/detection/yolov5/python/serving/README_CN.md
Normal file
36
examples/vision/detection/yolov5/python/serving/README_CN.md
Normal file
@@ -0,0 +1,36 @@
|
||||
简体中文 | [English](README_EN.md)
|
||||
|
||||
# YOLOv5 Python轻量服务化部署示例
|
||||
|
||||
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. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
服务端：
|
||||
```bash
|
||||
# 下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/detection/yolov5/python/serving
|
||||
|
||||
# 下载模型文件
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_infer.tar
|
||||
tar xvf yolov5s_infer.tar
|
||||
|
||||
# 启动服务，可修改server.py中的配置项来指定硬件、后端等
|
||||
# 可通过--host、--port指定IP和端口号
|
||||
fastdeploy simple_serving --app server:app
|
||||
```
|
||||
|
||||
客户端：
|
||||
```bash
|
||||
# 下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/detection/yolov5/python/serving
|
||||
|
||||
# 下载测试图片
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
|
||||
# 请求服务，获取推理结果（如有必要，请修改脚本中的IP和端口号）
|
||||
python client.py
|
||||
```
|
||||
36
examples/vision/detection/yolov5/python/serving/README_EN.md
Normal file
36
examples/vision/detection/yolov5/python/serving/README_EN.md
Normal file
@@ -0,0 +1,36 @@
|
||||
English | [简体中文](README_CN.md)
|
||||
|
||||
# YOLOv5 Python Simple Serving Demo
|
||||
|
||||
|
||||
## Environment
|
||||
|
||||
- 1. Prepare environment and install FastDeploy Python whl, refer to [download_prebuilt_libraries](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
Server:
|
||||
```bash
|
||||
# Download demo code
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/detection/yolov5/python/serving
|
||||
|
||||
# Download model
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_infer.tar
|
||||
tar xvf yolov5s_infer.tar
|
||||
|
||||
# Launch server, change the configurations in server.py to select hardware, backend, etc.
|
||||
# and use --host, --port to specify IP and port
|
||||
fastdeploy simple_serving --app server:app
|
||||
```
|
||||
|
||||
Client:
|
||||
```bash
|
||||
# Download demo code
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/detection/yolov5/python/serving
|
||||
|
||||
# Download test image
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
|
||||
# Send request and get inference result (Please adapt the IP and port if necessary)
|
||||
python client.py
|
||||
```
|
||||
23
examples/vision/detection/yolov5/python/serving/client.py
Normal file
23
examples/vision/detection/yolov5/python/serving/client.py
Normal file
@@ -0,0 +1,23 @@
|
||||
import requests
|
||||
import json
|
||||
import cv2
|
||||
import fastdeploy as fd
|
||||
from fastdeploy.serving.utils import cv2_to_base64
|
||||
|
||||
if __name__ == '__main__':
|
||||
url = "http://127.0.0.1:8000/fd/yolov5s"
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
im = cv2.imread("000000014439.jpg")
|
||||
data = {"data": {"image": cv2_to_base64(im)}, "parameters": {}}
|
||||
|
||||
resp = requests.post(url=url, headers=headers, data=json.dumps(data))
|
||||
if resp.status_code == 200:
|
||||
r_json = json.loads(resp.json()["result"])
|
||||
det_result = fd.vision.utils.json_to_detection(r_json)
|
||||
vis_im = fd.vision.vis_detection(im, det_result, score_threshold=0.5)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
else:
|
||||
print("Error code:", resp.status_code)
|
||||
print(resp.text)
|
||||
38
examples/vision/detection/yolov5/python/serving/server.py
Normal file
38
examples/vision/detection/yolov5/python/serving/server.py
Normal file
@@ -0,0 +1,38 @@
|
||||
import fastdeploy as fd
|
||||
from fastdeploy.serving.server import SimpleServer
|
||||
import os
|
||||
import logging
|
||||
|
||||
logging.getLogger().setLevel(logging.INFO)
|
||||
|
||||
# Configurations
|
||||
model_dir = 'yolov5s_infer'
|
||||
device = 'cpu'
|
||||
use_trt = False
|
||||
|
||||
# Prepare model
|
||||
model_file = os.path.join(model_dir, "model.pdmodel")
|
||||
params_file = os.path.join(model_dir, "model.pdiparams")
|
||||
|
||||
# Setup runtime option to select hardware, backend, etc.
|
||||
option = fd.RuntimeOption()
|
||||
if device.lower() == 'gpu':
|
||||
option.use_gpu()
|
||||
if use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("images", [1, 3, 640, 640])
|
||||
option.set_trt_cache_file('yolov5s.trt')
|
||||
|
||||
# Create model instance
|
||||
model_instance = fd.vision.detection.YOLOv5(
|
||||
model_file,
|
||||
params_file,
|
||||
runtime_option=option,
|
||||
model_format=fd.ModelFormat.PADDLE)
|
||||
|
||||
# Create server, setup REST API
|
||||
app = SimpleServer()
|
||||
app.register(
|
||||
task_name="fd/yolov5s",
|
||||
model_handler=fd.serving.handler.VisionModelHandler,
|
||||
predictor=model_instance)
|
||||
@@ -21,17 +21,10 @@ install(TARGETS infer_demo DESTINATION ./)
|
||||
install(DIRECTORY models DESTINATION ./)
|
||||
install(DIRECTORY images DESTINATION ./)
|
||||
|
||||
file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
|
||||
install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB OPENCV_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib/lib*)
|
||||
install(PROGRAMS ${OPENCV_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB PADDLELITE_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/lib*)
|
||||
install(PROGRAMS ${PADDLELITE_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB TIMVX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/verisilicon_timvx/*)
|
||||
install(PROGRAMS ${TIMVX_LIBS} DESTINATION lib)
|
||||
file(GLOB_RECURSE FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/lib*.so*)
|
||||
file(GLOB_RECURSE ALL_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/lib*.so*)
|
||||
list(APPEND ALL_LIBS ${FASTDEPLOY_LIBS})
|
||||
install(PROGRAMS ${ALL_LIBS} DESTINATION lib)
|
||||
|
||||
file(GLOB ADB_TOOLS run_with_adb.sh)
|
||||
install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)
|
||||
|
||||
@@ -23,7 +23,7 @@ void InitAndInfer(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 subgraph_file = model_dir + sep + "subgraph.txt";
|
||||
|
||||
fastdeploy::vision::EnableFlyCV();
|
||||
fastdeploy::RuntimeOption option;
|
||||
option.UseTimVX();
|
||||
option.SetLiteSubgraphPartitionPath(subgraph_file);
|
||||
|
||||
@@ -20,12 +20,12 @@ mv yolov5s.onnx models/runtime/1/model.onnx
|
||||
|
||||
# 拉取fastdeploy镜像(x.y.z为镜像版本号，需参照serving文档替换为数字)
|
||||
# GPU镜像
|
||||
docker pull paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
|
||||
docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
|
||||
# CPU镜像
|
||||
docker pull paddlepaddle/fastdeploy:x.y.z-cpu-only-21.10
|
||||
docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-cpu-only-21.10
|
||||
|
||||
# 运行容器.容器名字为 fd_serving, 并挂载当前目录为容器的 /yolov5_serving 目录
|
||||
nvidia-docker run -it --net=host --name fd_serving -v `pwd`/:/yolov5_serving paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash
|
||||
nvidia-docker run -it --net=host --name fd_serving -v `pwd`/:/yolov5_serving registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash
|
||||
|
||||
# 启动服务(不设置CUDA_VISIBLE_DEVICES环境变量，会拥有所有GPU卡的调度权限)
|
||||
CUDA_VISIBLE_DEVICES=0 fastdeployserver --model-repository=/yolov5_serving/models --backend-config=python,shm-default-byte-size=10485760
|
||||
|
||||
Reference in New Issue
Block a user