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Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph

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LDOUBLEV
2022-03-09 17:15:17 +08:00
parent 571341d2e8 d9e3832b3a
commit 322efb3d71
43 changed files with 378 additions and 308 deletions
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PPOCRLabel/README.md
+12 -2
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@@ -54,7 +54,10 @@ PPOCRLabel can be started in two ways: whl package and Python script. The whl pa
```bash
pip install PPOCRLabel # install
PPOCRLabel # run
# Select label mode and run
PPOCRLabel # [Normal mode] for [detection + recognition] labeling
PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
> If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file using http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely.
@@ -67,13 +70,18 @@ PPOCRLabel # run
```bash
pip3 install PPOCRLabel
pip3 install trash-cli
PPOCRLabel
# Select label mode and run
PPOCRLabel # [Normal mode] for [detection + recognition] labeling
PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
#### MacOS
```bash
pip3 install PPOCRLabel
pip3 install opencv-contrib-python-headless==4.2.0.32
# Select label mode and run
PPOCRLabel # [Normal mode] for [detection + recognition] labeling
PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
@@ -90,6 +98,8 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
```bash
cd ./PPOCRLabel # Switch to the PPOCRLabel directory
# Select label mode and run
python PPOCRLabel.py # [Normal mode] for [detection + recognition] labeling
python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
PPOCRLabel/README_ch.md
+12 -2
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@@ -57,7 +57,10 @@ PPOCRLabel可通过whl包与Python脚本两种方式启动，whl包形式启动
```bash
pip install PPOCRLabel # 安装
PPOCRLabel --lang ch # 运行
# 选择标签模式来启动
PPOCRLabel --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
> 注意：通过whl包安装PPOCRLabel会自动下载 `paddleocr` whl包，其中shapely依赖可能会出现 `[winRrror 126] 找不到指定模块的问题。` 的错误，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载并安装
##### Ubuntu Linux
@@ -65,13 +68,18 @@ PPOCRLabel --lang ch # 运行
```bash
pip3 install PPOCRLabel
pip3 install trash-cli
PPOCRLabel --lang ch
# 选择标签模式来启动
PPOCRLabel --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
##### MacOS
```bash
pip3 install PPOCRLabel
pip3 install opencv-contrib-python-headless==4.2.0.32 # 如果下载过慢请添加"-i https://mirror.baidu.com/pypi/simple"
# 选择标签模式来启动
PPOCRLabel --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
@@ -92,6 +100,8 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.
```bash
cd ./PPOCRLabel # 切换到PPOCRLabel目录
# 选择标签模式来启动
python PPOCRLabel.py --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
python PPOCRLabel.py --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
deploy/cpp_infer/include/ocr_det.h
+7 -3
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@@ -45,8 +45,9 @@ public:
const double &det_db_thresh,
const double &det_db_box_thresh,
const double &det_db_unclip_ratio,
const bool &use_polygon_score, const bool &visualize,
const bool &use_tensorrt, const std::string &precision) {
const bool &use_polygon_score, const bool &use_dilation,
const bool &visualize, const bool &use_tensorrt,
const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
@@ -59,6 +60,7 @@ public:
this->det_db_box_thresh_ = det_db_box_thresh;
this->det_db_unclip_ratio_ = det_db_unclip_ratio;
this->use_polygon_score_ = use_polygon_score;
this->use_dilation_ = use_dilation;
this->visualize_ = visualize;
this->use_tensorrt_ = use_tensorrt;
@@ -71,7 +73,8 @@ public:
void LoadModel(const std::string &model_dir);
// Run predictor
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes, std::vector<double> *times);
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes,
std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
@@ -88,6 +91,7 @@ private:
double det_db_box_thresh_ = 0.5;
double det_db_unclip_ratio_ = 2.0;
bool use_polygon_score_ = false;
bool use_dilation_ = false;
bool visualize_ = true;
bool use_tensorrt_ = false;
deploy/cpp_infer/readme.md
+16 -12
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@@ -4,16 +4,20 @@
C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
PaddleOCR模型部署。
* [1. 准备环境](#1)
+ [1.0 运行准备](#10)
+ [1.1 编译opencv库](#11)
+ [1.2 下载或者编译Paddle预测库](#12)
- [1.2.1 直接下载安装](#121)
- [1.2.2 预测库源码编译](#122)
* [2 开始运行](#2)
+ [2.1 将模型导出为inference model](#21)
+ [2.2 编译PaddleOCR C++预测demo](#22)
+ [2.3运行demo](#23)
- [服务器端C++预测](#服务器端c预测)
- [1. 准备环境](#1-准备环境)
- [1.0 运行准备](#10-运行准备)
- [1.1 编译opencv库](#11-编译opencv库)
- [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库)
- [1.2.1 直接下载安装](#121-直接下载安装)
- [1.2.2 预测库源码编译](#122-预测库源码编译)
- [2 开始运行](#2-开始运行)
- [2.1 将模型导出为inference model](#21-将模型导出为inference-model)
- [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo)
- [2.3 运行demo](#23-运行demo)
- [1. 只调用检测：](#1-只调用检测)
- [2. 只调用识别：](#2-只调用识别)
- [3. 调用串联：](#3-调用串联)
<a name="1"></a>
@@ -103,7 +107,7 @@ opencv3/
#### 1.2.1 直接下载安装
* [Paddle预测库官网](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* 下载之后使用下面的方法解压。
@@ -249,7 +253,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
|gpu_id|int|0|GPU id，使用GPU时有效|
|gpu_mem|int|4000|申请的GPU内存|
|cpu_math_library_num_threads|int|10|CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快|
|use_mkldnn|bool|true|是否使用mkldnn库|
|enable_mkldnn|bool|true|是否使用mkldnn库|
- 检测模型相关
deploy/cpp_infer/readme_en.md
+2 -2
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@@ -78,7 +78,7 @@ opencv3/
#### 1.2.1 Direct download and installation
[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can review and select the appropriate version of the inference library on the official website.
[Paddle inference library official website](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux). You can review and select the appropriate version of the inference library on the official website.
* After downloading, use the following command to extract files.
@@ -231,7 +231,7 @@ More parameters are as follows,
|gpu_id|int|0|GPU id when use_gpu is true|
|gpu_mem|int|4000|GPU memory requested|
|cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
|use_mkldnn|bool|true|Whether to use mkdlnn library|
|enable_mkldnn|bool|true|Whether to use mkdlnn library|
- Detection related parameters
deploy/cpp_infer/src/main.cpp
+218 -238
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@@ -28,14 +28,14 @@
#include <numeric>
#include <glog/logging.h>
#include <include/ocr_det.h>
#include <include/ocr_cls.h>
#include <include/ocr_det.h>
#include <include/ocr_rec.h>
#include <include/utility.h>
#include <sys/stat.h>
#include <gflags/gflags.h>
#include "auto_log/autolog.h"
#include <gflags/gflags.h>
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
@@ -51,9 +51,10 @@ DEFINE_string(image_dir, "", "Dir of input image.");
DEFINE_string(det_model_dir, "", "Path of det inference model.");
DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
DEFINE_double(det_db_box_thresh, 0.5, "Threshold of det_db_box_thresh.");
DEFINE_double(det_db_unclip_ratio, 1.6, "Threshold of det_db_unclip_ratio.");
DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio.");
DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
DEFINE_bool(use_dilation, false, "Whether use the dilation on output map.");
DEFINE_bool(visualize, true, "Whether show the detection results.");
// classification related
DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
@@ -62,281 +63,260 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
// recognition related
DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt",
"Path of dictionary.");
using namespace std;
using namespace cv;
using namespace PaddleOCR;
static bool PathExists(const std::string& path){
static bool PathExists(const std::string &path) {
#ifdef _WIN32
struct _stat buffer;
return (_stat(path.c_str(), &buffer) == 0);
#else
struct stat buffer;
return (stat(path.c_str(), &buffer) == 0);
#endif // !_WIN32
#endif // !_WIN32
}
int main_det(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0};
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_precision);
for (int i = 0; i < cv_all_img_names.size(); ++i) {
// LOG(INFO) << "The predict img: " << cv_all_img_names[i];
std::vector<double> time_info = {0, 0, 0};
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_use_dilation, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_precision);
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
std::vector<double> det_times;
for (int i = 0; i < cv_all_img_names.size(); ++i) {
// LOG(INFO) << "The predict img: " << cv_all_img_names[i];
det.Run(srcimg, boxes, &det_times);
time_info[0] += det_times[0];
time_info[1] += det_times[1];
time_info[2] += det_times[2];
if (FLAGS_benchmark) {
cout << cv_all_img_names[i] << '\t';
for (int n = 0; n < boxes.size(); n++) {
for (int m = 0; m < boxes[n].size(); m++) {
cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' ';
}
}
cout << endl;
}
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: "
<< cv_all_img_names[i] << endl;
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
std::vector<double> det_times;
det.Run(srcimg, boxes, &det_times);
time_info[0] += det_times[0];
time_info[1] += det_times[1];
time_info[2] += det_times[2];
if (FLAGS_benchmark) {
AutoLogger autolog("ocr_det",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
1,
"dynamic",
FLAGS_precision,
time_info,
cv_all_img_names.size());
autolog.report();
cout << cv_all_img_names[i] << '\t';
for (int n = 0; n < boxes.size(); n++) {
for (int m = 0; m < boxes[n].size(); m++) {
cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' ';
}
}
cout << endl;
}
return 0;
}
}
if (FLAGS_benchmark) {
AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
FLAGS_precision, time_info, cv_all_img_names.size());
autolog.report();
}
return 0;
}
int main_rec(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0};
std::string char_list_file = FLAGS_char_list_file;
if (FLAGS_benchmark)
char_list_file = FLAGS_char_list_file.substr(6);
cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, char_list_file,
FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
std::vector<double> time_info = {0, 0, 0};
std::vector<cv::Mat> img_list;
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i];
std::string char_list_file = FLAGS_char_list_file;
if (FLAGS_benchmark)
char_list_file = FLAGS_char_list_file.substr(6);
cout << "label file: " << char_list_file << endl;
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
exit(1);
}
img_list.push_back(srcimg);
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
FLAGS_rec_batch_num);
std::vector<cv::Mat> img_list;
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: "
<< cv_all_img_names[i] << endl;
exit(1);
}
std::vector<double> rec_times;
rec.Run(img_list, &rec_times);
time_info[0] += rec_times[0];
time_info[1] += rec_times[1];
time_info[2] += rec_times[2];
if (FLAGS_benchmark) {
AutoLogger autolog("ocr_rec",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
FLAGS_rec_batch_num,
"dynamic",
FLAGS_precision,
time_info,
cv_all_img_names.size());
autolog.report();
}
return 0;
img_list.push_back(srcimg);
}
std::vector<double> rec_times;
rec.Run(img_list, &rec_times);
time_info[0] += rec_times[0];
time_info[1] += rec_times[1];
time_info[2] += rec_times[2];
if (FLAGS_benchmark) {
AutoLogger autolog("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_enable_mkldnn, FLAGS_cpu_threads,
FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
time_info, cv_all_img_names.size());
autolog.report();
}
return 0;
}
int main_system(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info_det = {0, 0, 0};
std::vector<double> time_info_rec = {0, 0, 0};
std::vector<double> time_info_det = {0, 0, 0};
std::vector<double> time_info_rec = {0, 0, 0};
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_precision);
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_use_dilation, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_precision);
Classifier *cls = nullptr;
if (FLAGS_use_angle_cls) {
cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_cls_thresh,
FLAGS_use_tensorrt, FLAGS_precision);
Classifier *cls = nullptr;
if (FLAGS_use_angle_cls) {
cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision);
}
std::string char_list_file = FLAGS_char_list_file;
if (FLAGS_benchmark)
char_list_file = FLAGS_char_list_file.substr(6);
cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
FLAGS_rec_batch_num);
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: "
<< cv_all_img_names[i] << endl;
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
std::vector<double> det_times;
std::vector<double> rec_times;
det.Run(srcimg, boxes, &det_times);
time_info_det[0] += det_times[0];
time_info_det[1] += det_times[1];
time_info_det[2] += det_times[2];
std::vector<cv::Mat> img_list;
for (int j = 0; j < boxes.size(); j++) {
cv::Mat crop_img;
crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
}
img_list.push_back(crop_img);
}
std::string char_list_file = FLAGS_char_list_file;
if (FLAGS_benchmark)
char_list_file = FLAGS_char_list_file.substr(6);
cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, char_list_file,
FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
rec.Run(img_list, &rec_times);
time_info_rec[0] += rec_times[0];
time_info_rec[1] += rec_times[1];
time_info_rec[2] += rec_times[2];
}
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
std::vector<double> det_times;
std::vector<double> rec_times;
det.Run(srcimg, boxes, &det_times);
time_info_det[0] += det_times[0];
time_info_det[1] += det_times[1];
time_info_det[2] += det_times[2];
std::vector<cv::Mat> img_list;
for (int j = 0; j < boxes.size(); j++) {
cv::Mat crop_img;
crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
}
img_list.push_back(crop_img);
}
rec.Run(img_list, &rec_times);
time_info_rec[0] += rec_times[0];
time_info_rec[1] += rec_times[1];
time_info_rec[2] += rec_times[2];
}
if (FLAGS_benchmark) {
AutoLogger autolog_det("ocr_det",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
1,
"dynamic",
FLAGS_precision,
time_info_det,
cv_all_img_names.size());
AutoLogger autolog_rec("ocr_rec",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
FLAGS_rec_batch_num,
"dynamic",
FLAGS_precision,
time_info_rec,
cv_all_img_names.size());
autolog_det.report();
std::cout << endl;
autolog_rec.report();
}
return 0;
if (FLAGS_benchmark) {
AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
FLAGS_precision, time_info_det,
cv_all_img_names.size());
AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_enable_mkldnn, FLAGS_cpu_threads,
FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
time_info_rec, cv_all_img_names.size());
autolog_det.report();
std::cout << endl;
autolog_rec.report();
}
return 0;
}
void check_params(char* mode) {
if (strcmp(mode, "det")==0) {
if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
void check_params(char *mode) {
if (strcmp(mode, "det") == 0) {
if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[det]: ./ppocr "
"--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
if (strcmp(mode, "rec")==0) {
if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
}
if (strcmp(mode, "rec") == 0) {
if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[rec]: ./ppocr "
"--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
if (strcmp(mode, "system")==0) {
if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) ||
(FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
std::cout << "Usage[system without angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
std::cout << "Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--use_angle_cls=true "
<< "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
}
if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" && FLAGS_precision != "int8") {
cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
exit(1);
}
if (strcmp(mode, "system") == 0) {
if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() ||
FLAGS_image_dir.empty()) ||
(FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
std::cout << "Usage[system without angle cls]: ./ppocr "
"--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
std::cout << "Usage[system with angle cls]: ./ppocr "
"--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--use_angle_cls=true "
<< "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
}
if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
FLAGS_precision != "int8") {
cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
exit(1);
}
}
int main(int argc, char **argv) {
if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) {
std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
return -1;
}
std::cout << "mode: " << argv[1] << endl;
if (argc <= 1 ||
(strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 &&
strcmp(argv[1], "system") != 0)) {
std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
return -1;
}
std::cout << "mode: " << argv[1] << endl;
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
check_params(argv[1]);
if (!PathExists(FLAGS_image_dir)) {
std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << endl;
exit(1);
}
std::vector<cv::String> cv_all_img_names;
cv::glob(FLAGS_image_dir, cv_all_img_names);
std::cout << "total images num: " << cv_all_img_names.size() << endl;
if (strcmp(argv[1], "det")==0) {
return main_det(cv_all_img_names);
}
if (strcmp(argv[1], "rec")==0) {
return main_rec(cv_all_img_names);
}
if (strcmp(argv[1], "system")==0) {
return main_system(cv_all_img_names);
}
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
check_params(argv[1]);
if (!PathExists(FLAGS_image_dir)) {
std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
<< endl;
exit(1);
}
std::vector<cv::String> cv_all_img_names;
cv::glob(FLAGS_image_dir, cv_all_img_names);
std::cout << "total images num: " << cv_all_img_names.size() << endl;
if (strcmp(argv[1], "det") == 0) {
return main_det(cv_all_img_names);
}
if (strcmp(argv[1], "rec") == 0) {
return main_rec(cv_all_img_names);
}
if (strcmp(argv[1], "system") == 0) {
return main_system(cv_all_img_names);
}
}
deploy/cpp_infer/src/ocr_det.cpp
+21 -20
View File
@@ -14,7 +14,6 @@
#include <include/ocr_det.h>
namespace PaddleOCR {
void DBDetector::LoadModel(const std::string &model_dir) {
@@ -30,13 +29,10 @@ void DBDetector::LoadModel(const std::string &model_dir) {
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
precision,
false, false);
}
config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 50, 50}},
{"conv2d_92.tmp_0", {1, 96, 20, 20}},
@@ -105,7 +101,7 @@ void DBDetector::Run(cv::Mat &img,
cv::Mat srcimg;
cv::Mat resize_img;
img.copyTo(srcimg);
auto preprocess_start = std::chrono::steady_clock::now();
this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
this->use_tensorrt_);
@@ -116,16 +112,16 @@ void DBDetector::Run(cv::Mat &img,
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto preprocess_end = std::chrono::steady_clock::now();
// Inference.
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto inference_start = std::chrono::steady_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
std::vector<float> out_data;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
@@ -136,7 +132,7 @@ void DBDetector::Run(cv::Mat &img,
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
int n2 = output_shape[2];
int n3 = output_shape[3];
@@ -157,24 +153,29 @@ void DBDetector::Run(cv::Mat &img,
const double maxvalue = 255;
cv::Mat bit_map;
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
cv::Mat dilation_map;
cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(bit_map, dilation_map, dila_ele);
if (this->use_dilation_) {
cv::Mat dila_ele =
cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(bit_map, bit_map, dila_ele);
}
boxes = post_processor_.BoxesFromBitmap(
pred_map, dilation_map, this->det_db_box_thresh_,
this->det_db_unclip_ratio_, this->use_polygon_score_);
pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
this->use_polygon_score_);
boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
auto postprocess_end = std::chrono::steady_clock::now();
std::cout << "Detected boxes num: " << boxes.size() << endl;
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = inference_end - inference_start;
times->push_back(double(inference_diff.count() * 1000));
std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
//// visualization
if (this->visualize_) {
Utility::VisualizeBboxes(srcimg, boxes);
doc/doc_ch/detection.md
+10
View File
@@ -10,6 +10,7 @@
* [2.1 启动训练](#21-----)
* [2.2 断点训练](#22-----)
* [2.3 更换Backbone 训练](#23---backbone---)
* [2.4 知识蒸馏训练](#24---distill---)
- [3. 模型评估与预测](#3--------)
* [3.1 指标评估](#31-----)
* [3.2 测试检测效果](#32-------)
@@ -182,6 +183,15 @@ args1: args1
**注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
<a name="24---distill---"></a>
## 2.4 知识蒸馏训练
PaddleOCR支持了基于知识蒸馏的检测模型训练过程，更多内容可以参考[知识蒸馏说明文档](./knowledge_distillation.md)。
<a name="3--------"></a>
# 3. 模型评估与预测
doc/doc_ch/recognition.md
+8
View File
@@ -11,6 +11,7 @@
- [2.1 数据增强](#数据增强)
- [2.2 通用模型训练](#通用模型训练)
- [2.3 多语言模型训练](#多语言模型训练)
- [2.4 知识蒸馏训练](#知识蒸馏训练)
- [3 评估](#评估)
- [4 预测](#预测)
- [5 转Inference模型测试](#Inference)
@@ -368,6 +369,13 @@ Eval:
label_file_list: ["./train_data/french_val.txt"]
...
```
<a name="知识蒸馏训练"></a>
### 2.4 知识蒸馏训练
PaddleOCR支持了基于知识蒸馏的文本识别模型训练过程，更多内容可以参考[知识蒸馏说明文档](./knowledge_distillation.md)。
<a name="评估"></a>
## 3 评估
doc/doc_en/detection_en.md
+6
View File
@@ -9,6 +9,7 @@ This section uses the icdar2015 dataset as an example to introduce the training,
* [2.1 Start Training](#21-start-training)
* [2.2 Load Trained Model and Continue Training](#22-load-trained-model-and-continue-training)
* [2.3 Training with New Backbone](#23-training-with-new-backbone)
* [2.4 Training with knowledge distillation](#24)
- [3. Evaluation and Test](#3-evaluation-and-test)
* [3.1 Evaluation](#31-evaluation)
* [3.2 Test](#32-test)
@@ -174,6 +175,11 @@ After adding the four-part modules of the network, you only need to configure th
**NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md).
### 2.4 Training with knowledge distillation
Knowledge distillation is supported in PaddleOCR for text detection training process. For more details, please refer to [doc](./knowledge_distillation_en.md).
## 3. Evaluation and Test
### 3.1 Evaluation
doc/doc_en/recognition_en.md
+7
View File
@@ -10,6 +10,7 @@
- [2.1 Data Augmentation](#Data_Augmentation)
- [2.2 General Training](#Training)
- [2.3 Multi-language Training](#Multi_language)
- [2.4 Training with Knowledge Distillation](#kd)
- [3. Evaluation](#EVALUATION)
@@ -361,6 +362,12 @@ Eval:
...
```
<a name="kd"></a>
### 2.4 Training with Knowledge Distillation
Knowledge distillation is supported in PaddleOCR for text recognition training process. For more details, please refer to [doc](./knowledge_distillation_en.md).
<a name="EVALUATION"></a>
## 3. Evalution
ppocr/losses/basic_loss.py
+9 -4
View File
@@ -95,9 +95,15 @@ class DMLLoss(nn.Layer):
self.act = None
self.use_log = use_log
self.jskl_loss = KLJSLoss(mode="js")
def _kldiv(self, x, target):
eps = 1.0e-10
loss = target * (paddle.log(target + eps) - x)
# batch mean loss
loss = paddle.sum(loss) / loss.shape[0]
return loss
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1)
@@ -106,9 +112,8 @@ class DMLLoss(nn.Layer):
# for recognition distillation, log is needed for feature map
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
loss = (F.kl_div(
log_out1, out2, reduction='batchmean') + F.kl_div(
log_out2, out1, reduction='batchmean')) / 2.0
loss = (
self._kldiv(log_out1, out2) + self._kldiv(log_out2, out1)) / 2.0
else:
# for detection distillation log is not needed
loss = self.jskl_loss(out1, out2)
test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt
+2 -2
View File
@@ -49,5 +49,5 @@ inference:tools/infer/predict_rec.py
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,320]}]
test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt
+2 -2
View File
@@ -49,5 +49,5 @@ inference:tools/infer/predict_rec.py
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,320]}]
test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_infer_python.txt
+3 -1
View File
@@ -48,4 +48,6 @@ inference:tools/infer/predict_rec.py
--image_dir:./inference/rec_inference
null:null
--benchmark:True
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,320]}]
test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/train_infer_python.txt
+3 -1
View File
@@ -48,4 +48,6 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_ke
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,320]}]
test_tipc/configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_mtb_nrtr/train_infer_python.txt
+2 -1
View File
@@ -49,4 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/EN_symbo
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[1,32,100]}]
test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_mv3_none_none_ctc_v2.0/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt
+2 -1
View File
@@ -49,4 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_r31_sar/train_infer_python.txt
+2 -1
View File
@@ -49,4 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.t
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,48,48,160]}]
test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt
+2 -1
View File
@@ -49,4 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/train_infer_python.txt
+2
View File
@@ -49,3 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,100]}]
test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt
+2 -1
View File
@@ -49,4 +49,5 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dic
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[1,64,256]}]
tools/infer/predict_cls.py
+1 -1
View File
@@ -16,7 +16,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer/predict_det.py
+1 -1
View File
@@ -16,7 +16,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer/predict_e2e.py
+1 -1
View File
@@ -16,7 +16,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer/predict_rec.py
+1 -1
View File
@@ -16,7 +16,7 @@ import sys
from PIL import Image
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer/predict_system.py
+1 -1
View File
@@ -17,7 +17,7 @@ import subprocess
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_cls.py
+1 -1
View File
@@ -23,7 +23,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_det.py
+1 -1
View File
@@ -23,7 +23,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_e2e.py
+1 -1
View File
@@ -23,7 +23,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_kie.py
+1 -1
View File
@@ -24,7 +24,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_rec.py
+1 -1
View File
@@ -24,7 +24,7 @@ import json
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_table.py
+1 -1
View File
@@ -24,7 +24,7 @@ import json
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
tools/infer_vqa_token_ser.py
+1 -1
View File
@@ -23,7 +23,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
tools/infer_vqa_token_ser_re.py
+1 -1
View File
@@ -23,7 +23,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
tools/train.py
+1 -1
View File
@@ -21,7 +21,7 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
import yaml
import paddle