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PaddleDetection
未验证 提交 7ebb6f29 编写于 作者: G Guanghua Yu 提交者： GitHub
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add static lite demo (#2899)

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deploy/lite/README.md
浏览文件 @ 7ebb6f29
......@@ -195,18 +195,19 @@ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
执行完成后，detection文件夹下将有如下文件格式：
```
demo/cxx/clas/
demo/cxx/detection/
|-- debug/
| |--ppyolo_tiny.nb 优化后的检测器模型文件
| |--000000014439.jpg 待测试图像
| |--coco_label_list.txt 类别映射文件
| |--libpaddle_light_api_shared.so C++预测库文件
| |--config_ppyolo_tiny.txt 分类预测超参数配置
|-- image_classfication.cpp 图像分类代码文件
| |--config_ppyolo_tiny.txt 检测模型预测超参数配置
|-- run_detection.cc 目标检测代码文件
|-- Makefile 编译文件
```
#### 注意：
**注意：**
* 上述文件中，`coco_label_list.txt` 是COCO数据集的类别映射文件，如果使用自定义的类别，需要更换该类别映射文件。
* `config_ppyolo_tiny.txt` 包含了检测器的超参数，如下：
......
static/deploy/lite/Makefile 0 → 100644
浏览文件 @ 7ebb6f29
ARM_ABI = arm8
export ARM_ABI
include ../Makefile.def
LITE_ROOT=../../../
THIRD_PARTY_DIR=${LITE_ROOT}/third_party
OPENCV_VERSION=opencv4.1.0
OPENCV_LIBS = ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgcodecs.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgproc.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_core.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtegra_hal.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjpeg-turbo.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibwebp.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibpng.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjasper.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibtiff.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libIlmImf.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtbb.a \
${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libcpufeatures.a
OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/arm64-v8a/include
CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
###############################################################
# How to use one of static libaray: #
# `libpaddle_api_full_bundled.a` #
# `libpaddle_api_light_bundled.a` #
###############################################################
# Note: default use lite's shared library. #
###############################################################
# 1. Comment above line using `libpaddle_light_api_shared.so`
# 2. Undo comment below line using `libpaddle_api_light_bundled.a`
#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
detect_system: fetch_opencv detect_system.o
$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) detect_system.o -o detect_system $(CXX_LIBS) $(LDFLAGS)
detect_system.o: run_detection.cc
$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o detect_system.o -c run_detection.cc
fetch_opencv:
@ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR}
@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
(echo "fetch opencv libs" && \
wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz)
@ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \
tar -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
.PHONY: clean
clean:
rm -f detect_system.o
rm -f detect_system
static/deploy/lite/README.md 0 → 100644
浏览文件 @ 7ebb6f29
# Paddle-Lite端侧部署
本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleDetection的**静态图**模型的详细步骤。
Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。
## 1. 准备环境
### 运行准备
- 电脑（编译Paddle Lite）
- 安卓手机（armv7或armv8）
### 1.1 准备交叉编译环境
交叉编译环境用于编译 Paddle Lite 和 PaddleDetection 的C++ demo。
支持多种开发环境，不同开发环境的编译流程请参考对应文档。
1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
### 1.2 准备预测库
预测库有两种获取方式：
1. [**建议**]直接下载，预测库下载链接如下：
|平台|预测库下载链接|
|-|-|
|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.8/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.8/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)|
|iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.8/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.8/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
注：1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，注意选择`with_extra=ON，with_cv=ON`的下载链接。
2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
```shell
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
# 如果使用编译方式，建议使用develop分支编译预测库
git checkout develop
./lite/tools/build_android.sh --arch=armv8 --with_cv=ON --with_extra=ON
```
**注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html#id2)。
直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
预测库的文件目录如下：
```
inference_lite_lib.android.armv8/
|-- cxx C++ 预测库和头文件
| |-- include C++ 头文件
| | |-- paddle_api.h
| | |-- paddle_image_preprocess.h
| | |-- paddle_lite_factory_helper.h
| | |-- paddle_place.h
| | |-- paddle_use_kernels.h
| | |-- paddle_use_ops.h
| | `-- paddle_use_passes.h
| `-- lib C++预测库
| |-- libpaddle_api_light_bundled.a C++静态库
| `-- libpaddle_light_api_shared.so C++动态库
|-- java Java预测库
| |-- jar
| | `-- PaddlePredictor.jar
| |-- so
| | `-- libpaddle_lite_jni.so
| `-- src
|-- demo C++和Java示例代码
| |-- cxx C++ 预测库demo
| `-- java Java 预测库demo
```
## 2 开始运行
### 2.1 模型优化
Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-Lite的`opt`工具可以自动对inference模型进行优化，目前支持两种优化方式，优化后的模型更轻量，模型运行速度更快。
**注意**：如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。
#### 2.1.1 安装paddle_lite_opt工具
安装paddle_lite_opt工具有如下两种方法：
1. [**建议**]pip安装paddlelite并进行转换
```shell
pip install paddlelite
```
2. 源码编译Paddle-Lite生成opt工具
模型优化需要Paddle-Lite的`opt`可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下：
```shell
# 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
git checkout develop
# 启动编译
./lite/tools/build.sh build_optimize_tool
```
编译完成后，`opt`文件位于`build.opt/lite/api/`下，可通过如下方式查看`opt`的运行选项和使用方式；
```shell
cd build.opt/lite/api/
./opt
```
`opt`的使用方式与参数与上面的`paddle_lite_opt`完全一致。
之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下：
|选项|说明|
|-|-|
|--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径|
|--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径|
|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现，默认为naive_buffer|
|--optimize_out|优化模型的输出路径|
|--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm|
更详细的`paddle_lite_opt`工具使用说明请参考[使用opt转化模型文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/opt/opt_bin.html)
`--model_file`表示inference模型的model文件地址，`--param_file`表示inference模型的param文件地址；`optimize_out`用于指定输出文件的名称（不需要添加`.nb`的后缀）。直接在命令行中运行`paddle_lite_opt`，也可以查看所有参数及其说明。
#### 2.1.3 转换示例
下面以PaddleDetection中的 `PP-YOLO-tiny` 模型为例，介绍使用`paddle_lite_opt`完成预训练模型到inference模型，再到Paddle-Lite优化模型的转换。
```shell
# 进入PaddleDetection根目录
cd PaddleDetection_root_path
# 进入静态图模型文件夹
cd static
# 将预训练模型导出为inference模型
python tools/export_model.py -c configs/ppyolo/ppyolo_tiny.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams
# 将inference模型转化为Paddle-Lite优化模型
paddle_lite_opt --model_file=output/ppyolo_tiny/__model__ --param_file=output/ppyolo_tiny/__params__ --optimize_out=ppyolo_tiny
```
最终在当前文件夹下生成`ppyolo_tiny.nb`的文件。
**注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
### 2.2 与手机联调
首先需要进行一些准备工作。
1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI = arm7`。
2. 电脑上安装ADB工具，用于调试。 ADB安装方式如下：
2.1. MAC电脑安装ADB:
```shell
brew cask install android-platform-tools
```
2.2. Linux安装ADB
```shell
sudo apt update
sudo apt install -y wget adb
```
2.3. Window安装ADB
win上安装需要去谷歌的安卓平台下载ADB软件包进行安装：[链接](https://developer.android.com/studio)
3. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入：
```shell
adb devices
```
如果有device输出，则表示安装成功，如下所示：
```
List of devices attached
744be294 device
```
4. 准备优化后的模型、预测库文件、测试图像和类别映射文件。
```shell
cd PaddleDetection_root_path
cd static/deploy/lite/
# 将预测库文件、测试图像和使用的类别字典文件放置在预测库中的demo/cxx/detection文件夹下
inference_lite_path=/{lite prediction library path}/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv/
mkdir -p $inference_lite_path/demo/cxx/detection/debug/
cp ../../ppyolo_tiny.nb $inference_lite_path/demo/cxx/detection/debug/
cp ./coco_label_list.txt $inference_lite_path/demo/cxx/detection/debug/
cp Makefile run_detection.cc $inference_lite_path/demo/cxx/detection/
cp ./config_ppyolo_tiny.txt $inference_lite_path/demo/cxx/detection/debug/
cp ../../demo/000000014439.jpg $inference_lite_path/demo/cxx/detection/debug/
# 进入lite demo的工作目录
cd /{lite prediction library path}/inference_lite_lib.android.armv8/
cd demo/cxx/detection/
# 将C++预测动态库so文件复制到debug文件夹中
cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
```
执行完成后，detection文件夹下将有如下文件格式：
```
demo/cxx/detection/
|-- debug/
| |--ppyolo_tiny.nb 优化后的检测器模型文件
| |--000000014439.jpg 待测试图像
| |--coco_label_list.txt 类别映射文件
| |--libpaddle_light_api_shared.so C++预测库文件
| |--config_ppyolo_tiny.txt 检测模型预测超参数配置
|-- run_detection.cc 目标检测代码文件
|-- Makefile 编译文件
```
**注意：**
* 上述文件中，`coco_label_list.txt` 是COCO数据集的类别映射文件，如果使用自定义的类别，需要更换该类别映射文件。
* `config_ppyolo_tiny.txt` 包含了检测器的超参数，如下：
```shell
model_file ./ppyolo_tiny.nb # 模型文件地址
label_path ./coco_label_list.txt # 类别映射文本文件
num_threads 1 # 线程数
enable_benchmark 1 # 是否运行benchmark
Resize 320,320 # resize图像尺寸
keep_ratio False # 是否keep ratio
mean 0.485,0.456,0.406 # 预处理均值
std 0.229,0.224,0.225 # 预处理方差
precision fp32 # 模型精度
```
5. 启动调试，上述步骤完成后就可以使用ADB将文件夹 `debug/` push到手机上运行，步骤如下：
```shell
# 执行编译，得到可执行文件detect_system
make
# 将编译得到的可执行文件移动到debug文件夹中
mv detect_system ./debug/
# 将上述debug文件夹push到手机上
adb push debug /data/local/tmp/
adb shell
cd /data/local/tmp/debug
export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
# detect_system可执行文件的使用方式为:
# ./detect_system 配置文件路径 测试图像路径
./detect_system ./config_ppyolo_tiny.txt ./000000014439.jpg
```
如果对代码做了修改，则需要重新编译并push到手机上。
运行效果如下：
<div align="center">
<img src="../../../docs/images/lite_demo.jpg" width="600">
</div>
## FAQ
Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？
A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模型文件即可，同时要注意修改下配置文件中的 `.nb` 文件路径以及类别映射文件（如有必要）。
Q2：换一个图测试怎么做？
A2：替换 debug 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。
static/deploy/lite/coco_label_list.txt 0 → 100644
浏览文件 @ 7ebb6f29
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
dining table
toilet
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
\ No newline at end of file
static/deploy/lite/config_ppyolo_tiny.txt 0 → 100644
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model_file ./ppyolo_tiny.nb
label_path ./coco_label_list.txt
num_threads 1
precision fp32
enable_benchmark 1
arch YOLO
image_shape 3,320,320
Resize 320,320
keep_ratio False
mean 0.485,0.456,0.406
std 0.229,0.224,0.225
PadStride 0
static/deploy/lite/run_detection.cc 0 → 100644
浏览文件 @ 7ebb6f29
// Copyright (c) 2021 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 <fstream>
#include <iostream>
#include <vector>
#include <chrono>
#include <numeric>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_api.h" // NOLINT
using namespace paddle::lite_api; // NOLINT
using namespace std;
struct Object {
cv::Rect rec;
int class_id;
float prob;
};
// Object for storing all preprocessed data
struct ImageBlob {
// image width and height
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
const float* im_data_;
std::vector<float> mean_;
std::vector<float> scale_;
};
void PrintBenchmarkLog(std::vector<double> det_time,
std::map<std::string, std::string> config,
int img_num) {
std::cout << "----------------- Config info ------------------" << std::endl;
std::cout << "runtime_device: armv8" << std::endl;
std::cout << "precision: " << config.at("precision") << std::endl;
std::cout << "num_threads: " << config.at("num_threads") << std::endl;
std::cout << "---------------- Data info ---------------------" << std::endl;
std::cout << "batch_size: " << 1 << std::endl;
std::cout << "---------------- Model info --------------------" << std::endl;
std::cout << "Model_name: " << config.at("model_file") << std::endl;
std::cout << "---------------- Perf info ---------------------" << std::endl;
std::cout << "Total number of predicted data: " << img_num
<< " and total time spent(s): "
<< std::accumulate(det_time.begin(), det_time.end(), 0) << std::endl;
std::cout << "preproce_time(ms): " << det_time[0] / img_num
<< ", inference_time(ms): " << det_time[1] / img_num
<< ", postprocess_time(ms): " << det_time[2] << std::endl;
}
std::vector<std::string> LoadLabels(const std::string &path) {
std::ifstream file;
std::vector<std::string> labels;
file.open(path);
while (file) {
std::string line;
std::getline(file, line);
std::string::size_type pos = line.find(" ");
if (pos != std::string::npos) {
line = line.substr(pos);
}
labels.push_back(line);
}
file.clear();
file.close();
return labels;
}
std::vector<std::string> ReadDict(std::string path) {
std::ifstream in(path);
std::string filename;
std::string line;
std::vector<std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
}
} else {
std::cout << "no such file" << std::endl;
}
return m_vec;
}
std::vector<std::string> split(const std::string &str,
const std::string &delim) {
std::vector<std::string> res;
if ("" == str)
return res;
char *strs = new char[str.length() + 1];
std::strcpy(strs, str.c_str());
char *d = new char[delim.length() + 1];
std::strcpy(d, delim.c_str());
char *p = std::strtok(strs, d);
while (p) {
string s = p;
res.push_back(s);
p = std::strtok(NULL, d);
}
return res;
}
std::map<std::string, std::string> LoadConfigTxt(std::string config_path) {
auto config = ReadDict(config_path);
std::map<std::string, std::string> dict;
for (int i = 0; i < config.size(); i++) {
std::vector<std::string> res = split(config[i], " ");
dict[res[0]] = res[1];
}
return dict;
}
void PrintConfig(const std::map<std::string, std::string> &config) {
std::cout << "=======PaddleDetection lite demo config======" << std::endl;
for (auto iter = config.begin(); iter != config.end(); iter++) {
std::cout << iter->first << " : " << iter->second << std::endl;
}
std::cout << "===End of PaddleDetection lite demo config===" << std::endl;
}
// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
void neon_mean_scale(const float* din,
float* dout,
int size,
const std::vector<float> mean,
const std::vector<float> scale) {
if (mean.size() != 3 || scale.size() != 3) {
std::cerr << "[ERROR] mean or scale size must equal to 3\n";
exit(1);
}
float32x4_t vmean0 = vdupq_n_f32(mean[0]);
float32x4_t vmean1 = vdupq_n_f32(mean[1]);
float32x4_t vmean2 = vdupq_n_f32(mean[2]);
float32x4_t vscale0 = vdupq_n_f32(1.f / scale[0]);
float32x4_t vscale1 = vdupq_n_f32(1.f / scale[1]);
float32x4_t vscale2 = vdupq_n_f32(1.f / scale[2]);
float* dout_c0 = dout;
float* dout_c1 = dout + size;
float* dout_c2 = dout + size * 2;
int i = 0;
for (; i < size - 3; i += 4) {
float32x4x3_t vin3 = vld3q_f32(din);
float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
vst1q_f32(dout_c0, vs0);
vst1q_f32(dout_c1, vs1);
vst1q_f32(dout_c2, vs2);
din += 12;
dout_c0 += 4;
dout_c1 += 4;
dout_c2 += 4;
}
for (; i < size; i++) {
*(dout_c0++) = (*(din++) - mean[0]) * scale[0];
*(dout_c0++) = (*(din++) - mean[1]) * scale[1];
*(dout_c0++) = (*(din++) - mean[2]) * scale[2];
}
}
std::vector<Object> visualize_result(
const float* data,
int count,
float thresh,
cv::Mat& image,
const std::vector<std::string> &class_names) {
if (data == nullptr) {
std::cerr << "[ERROR] data can not be nullptr\n";
exit(1);
}
std::vector<Object> rect_out;
for (int iw = 0; iw < count; iw++) {
int oriw = image.cols;
int orih = image.rows;
if (data[1] > thresh) {
Object obj;
int x = static_cast<int>(data[2]);
int y = static_cast<int>(data[3]);
int w = static_cast<int>(data[4] - data[2] + 1);
int h = static_cast<int>(data[5] - data[3] + 1);
cv::Rect rec_clip =
cv::Rect(x, y, w, h) & cv::Rect(0, 0, image.cols, image.rows);
obj.class_id = static_cast<int>(data[0]);
obj.prob = data[1];
obj.rec = rec_clip;
if (w > 0 && h > 0 && obj.prob <= 1) {
rect_out.push_back(obj);
cv::rectangle(image, rec_clip, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
std::string str_prob = std::to_string(obj.prob);
std::string text = std::string(class_names[obj.class_id]) + ": " +
str_prob.substr(0, str_prob.find(".") + 4);
int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
double font_scale = 1.f;
int thickness = 1;
cv::Size text_size =
cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
float new_font_scale = w * 0.5 * font_scale / text_size.width;
text_size = cv::getTextSize(
text, font_face, new_font_scale, thickness, nullptr);
cv::Point origin;
origin.x = x + 3;
origin.y = y + text_size.height + 3;
cv::putText(image,
text,
origin,
font_face,
new_font_scale,
cv::Scalar(0, 255, 255),
thickness,
cv::LINE_AA);
std::cout << "detection, image size: " << image.cols << ", "
<< image.rows
<< ", detect object: " << class_names[obj.class_id]
<< ", score: " << obj.prob << ", location: x=" << x
<< ", y=" << y << ", width=" << w << ", height=" << h
<< std::endl;
}
}
data += 6;
}
return rect_out;
}
// Load Model and create model predictor
std::shared_ptr<PaddlePredictor> LoadModel(std::string model_file,
int num_theads) {
MobileConfig config;
config.set_threads(num_theads);
config.set_model_from_file(model_file);
std::shared_ptr<PaddlePredictor> predictor =
CreatePaddlePredictor<MobileConfig>(config);
return predictor;
}
ImageBlob prepare_imgdata(const cv::Mat& img,
std::map<std::string,
std::string> config) {
ImageBlob img_data;
std::vector<int> target_size_;
std::vector<std::string> size_str = split(config.at("Resize"), ",");
transform(size_str.begin(), size_str.end(), back_inserter(target_size_),
[](std::string const& s){return stoi(s);});
int width = target_size_[0];
int height = target_size_[1];
img_data.im_shape_ = {
static_cast<float>(target_size_[0]),
static_cast<float>(target_size_[1])
};
std::vector<float> mean_;
std::vector<float> scale_;
std::vector<std::string> mean_str = split(config.at("mean"), ",");
std::vector<std::string> std_str = split(config.at("std"), ",");
transform(mean_str.begin(), mean_str.end(), back_inserter(mean_),
[](std::string const& s){return stof(s);});
transform(std_str.begin(), std_str.end(), back_inserter(scale_),
[](std::string const& s){return stof(s);});
img_data.mean_ = mean_;
img_data.scale_ = scale_;
return img_data;
}
void preprocess(const cv::Mat& img, const ImageBlob img_data, float* data) {
cv::Mat rgb_img;
cv::cvtColor(img, rgb_img, cv::COLOR_BGR2RGB);
cv::resize(
rgb_img, rgb_img, cv::Size(img_data.im_shape_[0],img_data.im_shape_[1]),
0.f, 0.f, cv::INTER_CUBIC);
cv::Mat imgf;
rgb_img.convertTo(imgf, CV_32FC3, 1 / 255.f);
const float* dimg = reinterpret_cast<const float*>(imgf.data);
neon_mean_scale(
dimg, data, int(img_data.im_shape_[0] * img_data.im_shape_[1]),
img_data.mean_, img_data.scale_);
}
void RunModel(std::map<std::string, std::string> config,
std::string img_path,
const int repeats,
std::vector<double>* times) {
std::string model_file = config.at("model_file");
std::string label_path = config.at("label_path");
// Load Labels
std::vector<std::string> class_names = LoadLabels(label_path);
auto predictor = LoadModel(model_file, stoi(config.at("num_threads")));
cv::Mat img = imread(img_path, cv::IMREAD_COLOR);
auto img_data = prepare_imgdata(img, config);
auto preprocess_start = std::chrono::steady_clock::now();
// 1. Prepare input data from image
// input 0
std::unique_ptr<Tensor> input_tensor0(std::move(predictor->GetInput(0)));
input_tensor0->Resize({1, 3, img_data.im_shape_[0], img_data.im_shape_[1]});
auto* data0 = input_tensor0->mutable_data<float>();
preprocess(img, img_data, data0);
// input1
std::unique_ptr<Tensor> input_tensor1(std::move(predictor->GetInput(1)));
input_tensor1->Resize({1, 2});
auto* data1 = input_tensor1->mutable_data<int>();
data1[0] = img_data.im_shape_[0];
data1[1] = img_data.im_shape_[1];
auto preprocess_end = std::chrono::steady_clock::now();
// 2. Run predictor
// warm up
for (int i = 0; i < repeats / 2; i++)
{
predictor->Run();
}
auto inference_start = std::chrono::steady_clock::now();
for (int i = 0; i < repeats; i++)
{
predictor->Run();
}
auto inference_end = std::chrono::steady_clock::now();
// 3. Get output and post process
auto postprocess_start = std::chrono::steady_clock::now();
std::unique_ptr<const Tensor> output_tensor(
std::move(predictor->GetOutput(0)));
const float* outptr = output_tensor->data<float>();
auto shape_out = output_tensor->shape();
int64_t cnt = 1;
for (auto& i : shape_out) {
cnt *= i;
}
auto rec_out = visualize_result(
outptr, static_cast<int>(cnt / 6), 0.5f, img, class_names);
std::string result_name =
img_path.substr(0, img_path.find(".")) + "_result.jpg";
cv::imwrite(result_name, img);
auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> prep_diff = preprocess_end - preprocess_start;
times->push_back(double(prep_diff.count() * 1000));
std::chrono::duration<float> infer_diff = inference_end - inference_start;
times->push_back(double(infer_diff.count() / repeats * 1000));
std::chrono::duration<float> post_diff = postprocess_end - postprocess_start;
times->push_back(double(post_diff.count() * 1000));
}
int main(int argc, char** argv) {
if (argc < 3) {
std::cerr << "[ERROR] usage: " << argv[0] << " config_path image_path\n";
exit(1);
}
std::string config_path = argv[1];
std::string img_path = argv[2];
// load config
auto config = LoadConfigTxt(config_path);
PrintConfig(config);
bool enable_benchmark = bool(stoi(config.at("enable_benchmark")));
int repeats = enable_benchmark ? 50 : 1;
std::vector<double> det_times;
RunModel(config, img_path, repeats, &det_times);
PrintBenchmarkLog(det_times, config, 1);
return 0;
}
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