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Merge branch 'dygraph' into service_for_ci

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xiaoting 2021-09-01 15:46:23 +08:00 committed by GitHub
parent a2bd540c90 073a591cee
commit 8bff085239
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56 changed files with 2290 additions and 360 deletions
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102
configs/rec/rec_mtb_nrtr.yml Normal file
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@ -0,0 +1,102 @@
Global:
use_gpu: True
epoch_num: 21
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/nrtr/
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: EN_symbol
max_text_length: 25
infer_mode: False
use_space_char: True
save_res_path: ./output/rec/predicts_nrtr.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.99
clip_norm: 5.0
lr:
name: Cosine
learning_rate: 0.0005
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0.
Architecture:
model_type: rec
algorithm: NRTR
in_channels: 1
Transform:
Backbone:
name: MTB
cnn_num: 2
Head:
name: Transformer
d_model: 512
num_encoder_layers: 6
beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation.
Loss:
name: NRTRLoss
smoothing: True
PostProcess:
name: NRTRLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- NRTRDecodeImage: # load image
img_mode: BGR
channel_first: False
- NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg:
image_shape: [100, 32]
resize_type: PIL # PIL or OpenCV
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 512
drop_last: True
num_workers: 8
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- NRTRDecodeImage: # load image
img_mode: BGR
channel_first: False
- NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg:
image_shape: [100, 32]
resize_type: PIL # PIL or OpenCV
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 1
use_shared_memory: False

6
deploy/cpp_infer/CMakeLists.txt
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@ -1,4 +1,5 @@
project(ppocr CXX C)
cmake_minimum_required(VERSION 3.14)
option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON)
option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." OFF)
@ -206,9 +207,12 @@ endif()
set(DEPS ${DEPS} ${OpenCV_LIBS})
include(FetchContent)
include(external-cmake/auto-log.cmake)
include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
AUX_SOURCE_DIRECTORY(./src SRCS)
add_executable(${DEMO_NAME} ${SRCS})
target_link_libraries(${DEMO_NAME} ${DEPS})
if (WIN32 AND WITH_MKL)

13
deploy/cpp_infer/external-cmake/auto-log.cmake Normal file
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@ -0,0 +1,13 @@
find_package(Git REQUIRED)
include(FetchContent)
set(FETCHCONTENT_BASE_DIR "${CMAKE_CURRENT_BINARY_DIR}/third-party")
FetchContent_Declare(
extern_Autolog
PREFIX autolog
GIT_REPOSITORY https://github.com/LDOUBLEV/AutoLog.git
GIT_TAG main
)
FetchContent_MakeAvailable(extern_Autolog)

6
deploy/cpp_infer/include/ocr_cls.h
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@ -42,7 +42,7 @@ public:
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const double &cls_thresh,
const bool &use_tensorrt, const bool &use_fp16) {
const bool &use_tensorrt, const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
@ -51,7 +51,7 @@ public:
this->cls_thresh = cls_thresh;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->precision_ = precision;
LoadModel(model_dir);
}
@ -75,7 +75,7 @@ private:
std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
bool is_scale_ = true;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::string precision_ = "fp32";
// pre-process
ClsResizeImg resize_op_;
Normalize normalize_op_;

8
deploy/cpp_infer/include/ocr_det.h
View File

@ -46,7 +46,7 @@ public:
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 bool &use_fp16) {
const bool &use_tensorrt, const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
@ -62,7 +62,7 @@ public:
this->visualize_ = visualize;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->precision_ = precision;
LoadModel(model_dir);
}
@ -71,7 +71,7 @@ public:
void LoadModel(const std::string &model_dir);
// Run predictor
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes);
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes, std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
@ -91,7 +91,7 @@ private:
bool visualize_ = true;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::string precision_ = "fp32";
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};

11
deploy/cpp_infer/include/ocr_rec.h
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@ -44,14 +44,14 @@ public:
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const string &label_path,
const bool &use_tensorrt, const bool &use_fp16) {
const bool &use_tensorrt, const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->precision_ = precision;
this->label_list_ = Utility::ReadDict(label_path);
this->label_list_.insert(this->label_list_.begin(),
@ -64,7 +64,7 @@ public:
// Load Paddle inference model
void LoadModel(const std::string &model_dir);
void Run(cv::Mat &img);
void Run(cv::Mat &img, std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
@ -81,7 +81,7 @@ private:
std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
bool is_scale_ = true;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::string precision_ = "fp32";
// pre-process
CrnnResizeImg resize_op_;
Normalize normalize_op_;
@ -90,9 +90,6 @@ private:
// post-process
PostProcessor post_processor_;
cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box);
}; // class CrnnRecognizer
} // namespace PaddleOCR

3
deploy/cpp_infer/include/utility.h
View File

@ -47,6 +47,9 @@ public:
static void GetAllFiles(const char *dir_name,
std::vector<std::string> &all_inputs);
static cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box);
};
} // namespace PaddleOCR

281
deploy/cpp_infer/src/main.cpp
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@ -31,17 +31,21 @@
#include <include/ocr_det.h>
#include <include/ocr_cls.h>
#include <include/ocr_rec.h>
#include <include/utility.h>
#include <sys/stat.h>
#include <gflags/gflags.h>
#include "auto_log/autolog.h"
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
DEFINE_int32(cpu_math_library_num_threads, 10, "Num of threads with CPU.");
DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU.");
DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
DEFINE_bool(use_fp16, false, "Whether use fp16 when use tensorrt.");
DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
DEFINE_bool(benchmark, true, "Whether use benchmark.");
DEFINE_string(save_log_path, "./log_output/", "Save benchmark log path.");
// detection related
DEFINE_string(image_dir, "", "Dir of input image.");
DEFINE_string(det_model_dir, "", "Path of det inference model.");
@ -57,6 +61,7 @@ DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
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, 1, "rec_batch_num.");
DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
@ -76,87 +81,14 @@ static bool PathExists(const std::string& path){
}
cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box) {
cv::Mat image;
srcimage.copyTo(image);
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
int right = int(*std::max_element(x_collect, x_collect + 4));
int top = int(*std::min_element(y_collect, y_collect + 4));
int bottom = int(*std::max_element(y_collect, y_collect + 4));
cv::Mat img_crop;
image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M,
cv::Size(img_crop_width, img_crop_height),
cv::BORDER_REPLICATE);
if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
cv::transpose(dst_img, srcCopy);
cv::flip(srcCopy, srcCopy, 0);
return srcCopy;
} else {
return dst_img;
}
}
int main_det(int argc, char **argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
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 (!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;
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_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
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_use_fp16);
auto start = std::chrono::system_clock::now();
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];
@ -167,46 +99,38 @@ int main_det(int argc, char **argv) {
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
std::vector<double> det_times;
det.Run(srcimg, boxes);
det.Run(srcimg, boxes, &det_times);
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "s" << std::endl;
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();
}
return 0;
}
int main_rec(int argc, char **argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
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 (!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;
int main_rec(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0};
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_char_list_file,
FLAGS_use_tensorrt, FLAGS_use_fp16);
auto start = std::chrono::system_clock::now();
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_char_list_file,
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];
@ -217,65 +141,38 @@ int main_rec(int argc, char **argv) {
exit(1);
}
rec.Run(srcimg);
std::vector<double> rec_times;
rec.Run(srcimg, &rec_times);
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "s" << std::endl;
time_info[0] += rec_times[0];
time_info[1] += rec_times[1];
time_info[2] += rec_times[2];
}
return 0;
}
int main_system(int argc, char **argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
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 (!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;
int main_system(std::vector<cv::String> cv_all_img_names) {
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
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_use_fp16);
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_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_cls_thresh,
FLAGS_use_tensorrt, FLAGS_use_fp16);
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_cls_thresh,
FLAGS_use_tensorrt, FLAGS_precision);
}
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_char_list_file,
FLAGS_use_tensorrt, FLAGS_use_fp16);
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_char_list_file,
FLAGS_use_tensorrt, FLAGS_precision);
auto start = std::chrono::system_clock::now();
@ -288,17 +185,19 @@ int main_system(int argc, char **argv) {
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.Run(srcimg, boxes, &det_times);
cv::Mat crop_img;
for (int j = 0; j < boxes.size(); j++) {
crop_img = GetRotateCropImage(srcimg, boxes[j]);
crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
}
rec.Run(crop_img);
rec.Run(crop_img, &rec_times);
}
auto end = std::chrono::system_clock::now();
@ -315,22 +214,70 @@ int main_system(int argc, char **argv) {
}
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, "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 (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0) {
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 (strcmp(argv[1], "det")==0) {
return main_det(argc, argv);
}
if (strcmp(argv[1], "rec")==0) {
return main_rec(argc, argv);
}
if (strcmp(argv[1], "system")==0) {
return main_system(argc, argv);
// 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);
}
// return 0;
}

10
deploy/cpp_infer/src/ocr_cls.cpp
View File

@ -77,10 +77,16 @@ void Classifier::LoadModel(const std::string &model_dir) {
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
precision,
false, false);
}
} else {

28
deploy/cpp_infer/src/ocr_det.cpp
View File

@ -26,10 +26,16 @@ void DBDetector::LoadModel(const std::string &model_dir) {
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
precision,
false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 50, 50}},
@ -91,13 +97,16 @@ void DBDetector::LoadModel(const std::string &model_dir) {
}
void DBDetector::Run(cv::Mat &img,
std::vector<std::vector<std::vector<int>>> &boxes) {
std::vector<std::vector<std::vector<int>>> &boxes,
std::vector<double> *times) {
float ratio_h{};
float ratio_w{};
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_);
@ -106,12 +115,15 @@ 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;
@ -123,7 +135,9 @@ 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];
int n = n2 * n3;
@ -151,8 +165,16 @@ void DBDetector::Run(cv::Mat &img,
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;
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;
times->push_back(double(postprocess_diff.count() * 1000));
//// visualization
if (this->visualize_) {
Utility::VisualizeBboxes(srcimg, boxes);

81
deploy/cpp_infer/src/ocr_rec.cpp
View File

@ -16,13 +16,13 @@
namespace PaddleOCR {
void CRNNRecognizer::Run(cv::Mat &img) {
void CRNNRecognizer::Run(cv::Mat &img, std::vector<double> *times) {
cv::Mat srcimg;
img.copyTo(srcimg);
cv::Mat resize_img;
float wh_ratio = float(srcimg.cols) / float(srcimg.rows);
auto preprocess_start = std::chrono::steady_clock::now();
this->resize_op_.Run(srcimg, resize_img, wh_ratio, this->use_tensorrt_);
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
@ -31,11 +31,13 @@ void CRNNRecognizer::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();
@ -49,8 +51,10 @@ void CRNNRecognizer::Run(cv::Mat &img) {
predict_batch.resize(out_num);
output_t->CopyToCpu(predict_batch.data());
auto inference_end = std::chrono::steady_clock::now();
// ctc decode
auto postprocess_start = std::chrono::steady_clock::now();
std::vector<std::string> str_res;
int argmax_idx;
int last_index = 0;
@ -73,11 +77,19 @@ void CRNNRecognizer::Run(cv::Mat &img) {
}
last_index = argmax_idx;
}
auto postprocess_end = std::chrono::steady_clock::now();
score /= count;
for (int i = 0; i < str_res.size(); i++) {
std::cout << str_res[i];
}
std::cout << "\tscore: " << score << std::endl;
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;
times->push_back(double(postprocess_diff.count() * 1000));
}
void CRNNRecognizer::LoadModel(const std::string &model_dir) {
@ -89,10 +101,16 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
precision,
false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 32, 10}}};
@ -126,59 +144,4 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
this->predictor_ = CreatePredictor(config);
}
cv::Mat CRNNRecognizer::GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box) {
cv::Mat image;
srcimage.copyTo(image);
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
int right = int(*std::max_element(x_collect, x_collect + 4));
int top = int(*std::min_element(y_collect, y_collect + 4));
int bottom = int(*std::max_element(y_collect, y_collect + 4));
cv::Mat img_crop;
image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M,
cv::Size(img_crop_width, img_crop_height),
cv::BORDER_REPLICATE);
if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
cv::transpose(dst_img, srcCopy);
cv::flip(srcCopy, srcCopy, 0);
return srcCopy;
} else {
return dst_img;
}
}
} // namespace PaddleOCR

55
deploy/cpp_infer/src/utility.cpp
View File

@ -92,4 +92,59 @@ void Utility::GetAllFiles(const char *dir_name,
}
}
cv::Mat Utility::GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box) {
cv::Mat image;
srcimage.copyTo(image);
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
int right = int(*std::max_element(x_collect, x_collect + 4));
int top = int(*std::min_element(y_collect, y_collect + 4));
int bottom = int(*std::max_element(y_collect, y_collect + 4));
cv::Mat img_crop;
image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M,
cv::Size(img_crop_width, img_crop_height),
cv::BORDER_REPLICATE);
if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
cv::transpose(dst_img, srcCopy);
cv::flip(srcCopy, srcCopy, 0);
return srcCopy;
} else {
return dst_img;
}
}
} // namespace PaddleOCR

19
deploy/slim/prune/sensitivity_anal.py
View File

@ -75,7 +75,7 @@ def main(config, device, logger, vdl_writer):
model = build_model(config['Architecture'])
flops = paddle.flops(model, [1, 3, 640, 640])
logger.info(f"FLOPs before pruning: {flops}")
logger.info("FLOPs before pruning: {}".format(flops))
from paddleslim.dygraph import FPGMFilterPruner
model.train()
@ -106,8 +106,8 @@ def main(config, device, logger, vdl_writer):
def eval_fn():
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class)
logger.info(f"metric['hmean']: {metric['hmean']}")
eval_class, False)
logger.info("metric['hmean']: {}".format(metric['hmean']))
return metric['hmean']
params_sensitive = pruner.sensitive(
@ -123,16 +123,17 @@ def main(config, device, logger, vdl_writer):
# calculate pruned params's ratio
params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
for key in params_sensitive.keys():
logger.info(f"{key}, {params_sensitive[key]}")
logger.info("{}, {}".format(key, params_sensitive[key]))
#params_sensitive = {}
#for param in model.parameters():
# if 'transpose' not in param.name and 'linear' not in param.name:
# params_sensitive[param.name] = 0.1
plan = pruner.prune_vars(params_sensitive, [0])
for param in model.parameters():
if ("weights" in param.name and "conv" in param.name) or (
"w_0" in param.name and "conv2d" in param.name):
logger.info(f"{param.name}: {param.shape}")
flops = paddle.flops(model, [1, 3, 640, 640])
logger.info(f"FLOPs after pruning: {flops}")
logger.info("FLOPs after pruning: {}".format(flops))
# start train

2
doc/doc_ch/algorithm_overview.md
View File

@ -44,6 +44,7 @@ PaddleOCR基于动态图开源的文本识别算法列表：
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
- [x] NRTR([paper](https://arxiv.org/abs/1806.00926v2))
参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
@ -58,6 +59,7 @@ PaddleOCR基于动态图开源的文本识别算法列表：
|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) |
|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。

7
doc/doc_ch/recognition.md
View File

@ -185,11 +185,11 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
<a name="数据增强"></a>
#### 2.1 数据增强
PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
PaddleOCR提供了多种数据增强方式，默认配置文件中已经添加了数据增广。
默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)。
默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)、TIA数据增广。
训练过程中每种扰动方式以50%的概率被选择，具体代码实现请参考：[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
训练过程中每种扰动方式以40%的概率被选择，具体代码实现请参考：[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
*由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*
@ -215,6 +215,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| rec_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：

2
doc/doc_en/algorithm_overview_en.md
View File

@ -46,6 +46,7 @@ PaddleOCR open-source text recognition algorithms list:
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
- [x] NRTR([paper](https://arxiv.org/abs/1806.00926v2))
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
@ -60,5 +61,6 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)

8
doc/doc_en/recognition_en.md
View File

@ -177,11 +177,11 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
<a name="Data_Augmentation"></a>
#### 2.1 Data Augmentation
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
PaddleOCR provides a variety of data augmentation methods. All the augmentation methods are enabled by default.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, TIA augmentation.
Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
Each disturbance method is selected with a 40% probability during the training process. For specific code implementation, please refer to: [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
<a name="Training"></a>
#### 2.2 Training
@ -207,7 +207,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
For training Chinese data, it is recommended to use
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:

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2
paddleocr.py
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@ -127,7 +127,7 @@ model_urls = {
}
SUPPORT_DET_MODEL = ['DB']
VERSION = '2.2'
VERSION = '2.2.0.1'
SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")

12
ppocr/data/__init__.py
View File

@ -49,14 +49,12 @@ def term_mp(sig_num, frame):
os.killpg(pgid, signal.SIGKILL)
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
def build_dataloader(config, mode, device, logger, seed=None):
config = copy.deepcopy(config)
support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet']
support_dict = [
'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet'
]
module_name = config[mode]['dataset']['name']
assert module_name in support_dict, Exception(
'DataSet only support {}'.format(support_dict))
@ -96,4 +94,8 @@ def build_dataloader(config, mode, device, logger, seed=None):
return_list=True,
use_shared_memory=use_shared_memory)
# support exit using ctrl+c
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
return data_loader

2
ppocr/data/imaug/__init__.py
View File

@ -21,7 +21,7 @@ from .make_border_map import MakeBorderMap
from .make_shrink_map import MakeShrinkMap
from .random_crop_data import EastRandomCropData, PSERandomCrop
from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg
from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg
from .randaugment import RandAugment
from .copy_paste import CopyPaste
from .operators import *

28
ppocr/data/imaug/label_ops.py
View File

@ -161,6 +161,34 @@ class BaseRecLabelEncode(object):
return text_list
class NRTRLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='EN_symbol',
use_space_char=False,
**kwargs):
super(NRTRLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
data['length'] = np.array(len(text))
text.insert(0, 2)
text.append(3)
text = text + [0] * (self.max_text_len - len(text))
data['label'] = np.array(text)
return data
def add_special_char(self, dict_character):
dict_character = ['blank','<unk>','<s>','</s>'] + dict_character
return dict_character
class CTCLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """

32
ppocr/data/imaug/operators.py
View File

@ -57,6 +57,38 @@ class DecodeImage(object):
return data
class NRTRDecodeImage(object):
""" decode image """
def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
self.img_mode = img_mode
self.channel_first = channel_first
def __call__(self, data):
img = data['image']
if six.PY2:
assert type(img) is str and len(
img) > 0, "invalid input 'img' in DecodeImage"
else:
assert type(img) is bytes and len(
img) > 0, "invalid input 'img' in DecodeImage"
img = np.frombuffer(img, dtype='uint8')
img = cv2.imdecode(img, 1)
if img is None:
return None
if self.img_mode == 'GRAY':
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif self.img_mode == 'RGB':
assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
img = img[:, :, ::-1]
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
if self.channel_first:
img = img.transpose((2, 0, 1))
data['image'] = img
return data
class NormalizeImage(object):
""" normalize image such as substract mean, divide std
"""

21
ppocr/data/imaug/rec_img_aug.py
View File

@ -16,7 +16,7 @@ import math
import cv2
import numpy as np
import random
from PIL import Image
from .text_image_aug import tia_perspective, tia_stretch, tia_distort
@ -43,6 +43,25 @@ class ClsResizeImg(object):
return data
class NRTRRecResizeImg(object):
def __init__(self, image_shape, resize_type, **kwargs):
self.image_shape = image_shape
self.resize_type = resize_type
def __call__(self, data):
img = data['image']
if self.resize_type == 'PIL':
image_pil = Image.fromarray(np.uint8(img))
img = image_pil.resize(self.image_shape, Image.ANTIALIAS)
img = np.array(img)
if self.resize_type == 'OpenCV':
img = cv2.resize(img, self.image_shape)
norm_img = np.expand_dims(img, -1)
norm_img = norm_img.transpose((2, 0, 1))
data['image'] = norm_img.astype(np.float32) / 128. - 1.
return data
class RecResizeImg(object):
def __init__(self,
image_shape,

5
ppocr/losses/__init__.py
View File

@ -25,7 +25,7 @@ from .det_sast_loss import SASTLoss
from .rec_ctc_loss import CTCLoss
from .rec_att_loss import AttentionLoss
from .rec_srn_loss import SRNLoss
from .rec_nrtr_loss import NRTRLoss
# cls loss
from .cls_loss import ClsLoss
@ -44,8 +44,9 @@ from .table_att_loss import TableAttentionLoss
def build_loss(config):
support_dict = [
'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
'SRNLoss', 'PGLoss', 'CombinedLoss', 'TableAttentionLoss'
'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss'
]
config = copy.deepcopy(config)
module_name = config.pop('name')
assert module_name in support_dict, Exception('loss only support {}'.format(

2
ppocr/losses/cls_loss.py
View File

@ -25,6 +25,6 @@ class ClsLoss(nn.Layer):
self.loss_func = nn.CrossEntropyLoss(reduction='mean')
def forward(self, predicts, batch):
label = batch[1]
label = batch[1].astype("int64")
loss = self.loss_func(input=predicts, label=label)
return {'loss': loss}

30
ppocr/losses/rec_nrtr_loss.py Normal file
View File

@ -0,0 +1,30 @@
import paddle
from paddle import nn
import paddle.nn.functional as F
class NRTRLoss(nn.Layer):
def __init__(self, smoothing=True, **kwargs):
super(NRTRLoss, self).__init__()
self.loss_func = nn.CrossEntropyLoss(reduction='mean', ignore_index=0)
self.smoothing = smoothing
def forward(self, pred, batch):
pred = pred.reshape([-1, pred.shape[2]])
max_len = batch[2].max()
tgt = batch[1][:, 1:2 + max_len]
tgt = tgt.reshape([-1])
if self.smoothing:
eps = 0.1
n_class = pred.shape[1]
one_hot = F.one_hot(tgt, pred.shape[1])
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
log_prb = F.log_softmax(pred, axis=1)
non_pad_mask = paddle.not_equal(
tgt, paddle.zeros(
tgt.shape, dtype='int64'))
loss = -(one_hot * log_prb).sum(axis=1)
loss = loss.masked_select(non_pad_mask).mean()
else:
loss = self.loss_func(pred, tgt)
return {'loss': loss}

1
ppocr/metrics/rec_metric.py
View File

@ -57,3 +57,4 @@ class RecMetric(object):
self.correct_num = 0
self.all_num = 0
self.norm_edit_dis = 0

1
ppocr/modeling/architectures/base_model.py
View File

@ -14,7 +14,6 @@
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import nn
from ppocr.modeling.transforms import build_transform
from ppocr.modeling.backbones import build_backbone

3
ppocr/modeling/backbones/__init__.py
View File

@ -26,8 +26,9 @@ def build_backbone(config, model_type):
from .rec_resnet_vd import ResNet
from .rec_resnet_fpn import ResNetFPN
from .rec_mv1_enhance import MobileNetV1Enhance
from .rec_nrtr_mtb import MTB
support_dict = [
"MobileNetV1Enhance", "MobileNetV3", "ResNet", "ResNetFPN"
'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB'
]
elif model_type == "e2e":
from .e2e_resnet_vd_pg import ResNet

46
ppocr/modeling/backbones/rec_nrtr_mtb.py Normal file
View File

@ -0,0 +1,46 @@
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from paddle import nn
class MTB(nn.Layer):
def __init__(self, cnn_num, in_channels):
super(MTB, self).__init__()
self.block = nn.Sequential()
self.out_channels = in_channels
self.cnn_num = cnn_num
if self.cnn_num == 2:
for i in range(self.cnn_num):
self.block.add_sublayer(
'conv_{}'.format(i),
nn.Conv2D(
in_channels=in_channels
if i == 0 else 32 * (2**(i - 1)),
out_channels=32 * (2**i),
kernel_size=3,
stride=2,
padding=1))
self.block.add_sublayer('relu_{}'.format(i), nn.ReLU())
self.block.add_sublayer('bn_{}'.format(i),
nn.BatchNorm2D(32 * (2**i)))
def forward(self, images):
x = self.block(images)
if self.cnn_num == 2:
# (b, w, h, c)
x = x.transpose([0, 3, 2, 1])
x_shape = x.shape
x = x.reshape([x_shape[0], x_shape[1], x_shape[2] * x_shape[3]])
return x

4
ppocr/modeling/heads/__init__.py
View File

@ -26,12 +26,14 @@ def build_head(config):
from .rec_ctc_head import CTCHead
from .rec_att_head import AttentionHead
from .rec_srn_head import SRNHead
from .rec_nrtr_head import Transformer
# cls head
from .cls_head import ClsHead
support_dict = [
'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
'SRNHead', 'PGHead', 'TableAttentionHead']
'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead'
]
#table head
from .table_att_head import TableAttentionHead

178
ppocr/modeling/heads/multiheadAttention.py Executable file
View File

@ -0,0 +1,178 @@
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle.nn import Linear
from paddle.nn.initializer import XavierUniform as xavier_uniform_
from paddle.nn.initializer import Constant as constant_
from paddle.nn.initializer import XavierNormal as xavier_normal_
zeros_ = constant_(value=0.)
ones_ = constant_(value=1.)
class MultiheadAttention(nn.Layer):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self,
embed_dim,
num_heads,
dropout=0.,
bias=True,
add_bias_kv=False,
add_zero_attn=False):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim**-0.5
self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
self._reset_parameters()
self.conv1 = paddle.nn.Conv2D(
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
self.conv2 = paddle.nn.Conv2D(
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
self.conv3 = paddle.nn.Conv2D(
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self,
query,
key,
value,
key_padding_mask=None,
incremental_state=None,
need_weights=True,
static_kv=False,
attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
tgt_len, bsz, embed_dim = query.shape
assert embed_dim == self.embed_dim
assert list(query.shape) == [tgt_len, bsz, embed_dim]
assert key.shape == value.shape
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose(
[1, 0, 2])
k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose(
[1, 0, 2])
v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose(
[1, 0, 2])
src_len = k.shape[1]
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == bsz
assert key_padding_mask.shape[1] == src_len
attn_output_weights = paddle.bmm(q, k.transpose([0, 2, 1]))
assert list(attn_output_weights.
shape) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = attn_output_weights.reshape(
[bsz, self.num_heads, tgt_len, src_len])
key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32')
y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf')
y = paddle.where(key == 0., key, y)
attn_output_weights += y
attn_output_weights = attn_output_weights.reshape(
[bsz * self.num_heads, tgt_len, src_len])
attn_output_weights = F.softmax(
attn_output_weights.astype('float32'),
axis=-1,
dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16
else attn_output_weights.dtype)
attn_output_weights = F.dropout(
attn_output_weights, p=self.dropout, training=self.training)
attn_output = paddle.bmm(attn_output_weights, v)
assert list(attn_output.
shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
attn_output = attn_output.transpose([1, 0, 2]).reshape(
[tgt_len, bsz, embed_dim])
attn_output = self.out_proj(attn_output)
if need_weights:
# average attention weights over heads
attn_output_weights = attn_output_weights.reshape(
[bsz, self.num_heads, tgt_len, src_len])
attn_output_weights = attn_output_weights.sum(
axis=1) / self.num_heads
else:
attn_output_weights = None
return attn_output, attn_output_weights
def _in_proj_q(self, query):
query = query.transpose([1, 2, 0])
query = paddle.unsqueeze(query, axis=2)
res = self.conv1(query)
res = paddle.squeeze(res, axis=2)
res = res.transpose([2, 0, 1])
return res
def _in_proj_k(self, key):
key = key.transpose([1, 2, 0])
key = paddle.unsqueeze(key, axis=2)
res = self.conv2(key)
res = paddle.squeeze(res, axis=2)
res = res.transpose([2, 0, 1])
return res
def _in_proj_v(self, value):
value = value.transpose([1, 2, 0]) #(1, 2, 0)
value = paddle.unsqueeze(value, axis=2)
res = self.conv3(value)
res = paddle.squeeze(res, axis=2)
res = res.transpose([2, 0, 1])
return res

844
ppocr/modeling/heads/rec_nrtr_head.py Normal file
View File

@ -0,0 +1,844 @@
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import math
import paddle
import copy
from paddle import nn
import paddle.nn.functional as F
from paddle.nn import LayerList
from paddle.nn.initializer import XavierNormal as xavier_uniform_
from paddle.nn import Dropout, Linear, LayerNorm, Conv2D
import numpy as np
from ppocr.modeling.heads.multiheadAttention import MultiheadAttention
from paddle.nn.initializer import Constant as constant_
from paddle.nn.initializer import XavierNormal as xavier_normal_
zeros_ = constant_(value=0.)
ones_ = constant_(value=1.)
class Transformer(nn.Layer):
"""A transformer model. User is able to modify the attributes as needed. The architechture
is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
Processing Systems, pages 6000-6010.
Args:
d_model: the number of expected features in the encoder/decoder inputs (default=512).
nhead: the number of heads in the multiheadattention models (default=8).
num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
custom_encoder: custom encoder (default=None).
custom_decoder: custom decoder (default=None).
"""
def __init__(self,
d_model=512,
nhead=8,
num_encoder_layers=6,
beam_size=0,
num_decoder_layers=6,
dim_feedforward=1024,
attention_dropout_rate=0.0,
residual_dropout_rate=0.1,
custom_encoder=None,
custom_decoder=None,
in_channels=0,
out_channels=0,
dst_vocab_size=99,
scale_embedding=True):
super(Transformer, self).__init__()
self.embedding = Embeddings(
d_model=d_model,
vocab=dst_vocab_size,
padding_idx=0,
scale_embedding=scale_embedding)
self.positional_encoding = PositionalEncoding(
dropout=residual_dropout_rate,
dim=d_model, )
if custom_encoder is not None:
self.encoder = custom_encoder
else:
if num_encoder_layers > 0:
encoder_layer = TransformerEncoderLayer(
d_model, nhead, dim_feedforward, attention_dropout_rate,
residual_dropout_rate)
self.encoder = TransformerEncoder(encoder_layer,
num_encoder_layers)
else:
self.encoder = None
if custom_decoder is not None:
self.decoder = custom_decoder
else:
decoder_layer = TransformerDecoderLayer(
d_model, nhead, dim_feedforward, attention_dropout_rate,
residual_dropout_rate)
self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers)
self._reset_parameters()
self.beam_size = beam_size
self.d_model = d_model
self.nhead = nhead
self.tgt_word_prj = nn.Linear(d_model, dst_vocab_size, bias_attr=False)
w0 = np.random.normal(0.0, d_model**-0.5,
(d_model, dst_vocab_size)).astype(np.float32)
self.tgt_word_prj.weight.set_value(w0)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Conv2D):
xavier_normal_(m.weight)
if m.bias is not None:
zeros_(m.bias)
def forward_train(self, src, tgt):
tgt = tgt[:, :-1]
tgt_key_padding_mask = self.generate_padding_mask(tgt)
tgt = self.embedding(tgt).transpose([1, 0, 2])
tgt = self.positional_encoding(tgt)
tgt_mask = self.generate_square_subsequent_mask(tgt.shape[0])
if self.encoder is not None:
src = self.positional_encoding(src.transpose([1, 0, 2]))
memory = self.encoder(src)
else:
memory = src.squeeze(2).transpose([2, 0, 1])
output = self.decoder(
tgt,
memory,
tgt_mask=tgt_mask,
memory_mask=None,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=None)
output = output.transpose([1, 0, 2])
logit = self.tgt_word_prj(output)
return logit
def forward(self, src, targets=None):
"""Take in and process masked source/target sequences.
Args:
src: the sequence to the encoder (required).
tgt: the sequence to the decoder (required).
Shape:
- src: :math:`(S, N, E)`.
- tgt: :math:`(T, N, E)`.
Examples:
>>> output = transformer_model(src, tgt)
"""
if self.training:
max_len = targets[1].max()
tgt = targets[0][:, :2 + max_len]
return self.forward_train(src, tgt)
else:
if self.beam_size > 0:
return self.forward_beam(src)
else:
return self.forward_test(src)
def forward_test(self, src):
bs = src.shape[0]
if self.encoder is not None:
src = self.positional_encoding(src.transpose([1, 0, 2]))
memory = self.encoder(src)
else:
memory = src.squeeze(2).transpose([2, 0, 1])
dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64)
for len_dec_seq in range(1, 25):
src_enc = memory.clone()
tgt_key_padding_mask = self.generate_padding_mask(dec_seq)
dec_seq_embed = self.embedding(dec_seq).transpose([1, 0, 2])
dec_seq_embed = self.positional_encoding(dec_seq_embed)
tgt_mask = self.generate_square_subsequent_mask(dec_seq_embed.shape[
0])
output = self.decoder(
dec_seq_embed,
src_enc,
tgt_mask=tgt_mask,
memory_mask=None,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=None)
dec_output = output.transpose([1, 0, 2])
dec_output = dec_output[:,
-1, :] # Pick the last step: (bh * bm) * d_h
word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1)
word_prob = word_prob.reshape([1, bs, -1])
preds_idx = word_prob.argmax(axis=2)
if paddle.equal_all(
preds_idx[-1],
paddle.full(
preds_idx[-1].shape, 3, dtype='int64')):
break
preds_prob = word_prob.max(axis=2)
dec_seq = paddle.concat(
[dec_seq, preds_idx.reshape([-1, 1])], axis=1)
return dec_seq
def forward_beam(self, images):
''' Translation work in one batch '''
def get_inst_idx_to_tensor_position_map(inst_idx_list):
''' Indicate the position of an instance in a tensor. '''
return {
inst_idx: tensor_position
for tensor_position, inst_idx in enumerate(inst_idx_list)
}
def collect_active_part(beamed_tensor, curr_active_inst_idx,
n_prev_active_inst, n_bm):
''' Collect tensor parts associated to active instances. '''
_, *d_hs = beamed_tensor.shape
n_curr_active_inst = len(curr_active_inst_idx)
new_shape = (n_curr_active_inst * n_bm, *d_hs)
beamed_tensor = beamed_tensor.reshape([n_prev_active_inst, -1])
beamed_tensor = beamed_tensor.index_select(
paddle.to_tensor(curr_active_inst_idx), axis=0)
beamed_tensor = beamed_tensor.reshape([*new_shape])
return beamed_tensor
def collate_active_info(src_enc, inst_idx_to_position_map,
active_inst_idx_list):
# Sentences which are still active are collected,
# so the decoder will not run on completed sentences.
n_prev_active_inst = len(inst_idx_to_position_map)
active_inst_idx = [
inst_idx_to_position_map[k] for k in active_inst_idx_list
]
active_inst_idx = paddle.to_tensor(active_inst_idx, dtype='int64')
active_src_enc = collect_active_part(
src_enc.transpose([1, 0, 2]), active_inst_idx,
n_prev_active_inst, n_bm).transpose([1, 0, 2])
active_inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(
active_inst_idx_list)
return active_src_enc, active_inst_idx_to_position_map
def beam_decode_step(inst_dec_beams, len_dec_seq, enc_output,
inst_idx_to_position_map, n_bm,
memory_key_padding_mask):
''' Decode and update beam status, and then return active beam idx '''
def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq):
dec_partial_seq = [
b.get_current_state() for b in inst_dec_beams if not b.done
]
dec_partial_seq = paddle.stack(dec_partial_seq)
dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq])
return dec_partial_seq
def prepare_beam_memory_key_padding_mask(
inst_dec_beams, memory_key_padding_mask, n_bm):
keep = []
for idx in (memory_key_padding_mask):
if not inst_dec_beams[idx].done:
keep.append(idx)
memory_key_padding_mask = memory_key_padding_mask[
paddle.to_tensor(keep)]
len_s = memory_key_padding_mask.shape[-1]
n_inst = memory_key_padding_mask.shape[0]
memory_key_padding_mask = paddle.concat(
[memory_key_padding_mask for i in range(n_bm)], axis=1)
memory_key_padding_mask = memory_key_padding_mask.reshape(
[n_inst * n_bm, len_s]) #repeat(1, n_bm)
return memory_key_padding_mask
def predict_word(dec_seq, enc_output, n_active_inst, n_bm,
memory_key_padding_mask):
tgt_key_padding_mask = self.generate_padding_mask(dec_seq)
dec_seq = self.embedding(dec_seq).transpose([1, 0, 2])
dec_seq = self.positional_encoding(dec_seq)
tgt_mask = self.generate_square_subsequent_mask(dec_seq.shape[
0])
dec_output = self.decoder(
dec_seq,
enc_output,
tgt_mask=tgt_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask,
).transpose([1, 0, 2])
dec_output = dec_output[:,
-1, :] # Pick the last step: (bh * bm) * d_h
word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1)
word_prob = word_prob.reshape([n_active_inst, n_bm, -1])
return word_prob
def collect_active_inst_idx_list(inst_beams, word_prob,
inst_idx_to_position_map):
active_inst_idx_list = []
for inst_idx, inst_position in inst_idx_to_position_map.items():
is_inst_complete = inst_beams[inst_idx].advance(word_prob[
inst_position])
if not is_inst_complete:
active_inst_idx_list += [inst_idx]
return active_inst_idx_list
n_active_inst = len(inst_idx_to_position_map)
dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq)
memory_key_padding_mask = None
word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm,
memory_key_padding_mask)
# Update the beam with predicted word prob information and collect incomplete instances
active_inst_idx_list = collect_active_inst_idx_list(
inst_dec_beams, word_prob, inst_idx_to_position_map)
return active_inst_idx_list
def collect_hypothesis_and_scores(inst_dec_beams, n_best):
all_hyp, all_scores = [], []
for inst_idx in range(len(inst_dec_beams)):
scores, tail_idxs = inst_dec_beams[inst_idx].sort_scores()
all_scores += [scores[:n_best]]
hyps = [
inst_dec_beams[inst_idx].get_hypothesis(i)
for i in tail_idxs[:n_best]
]
all_hyp += [hyps]
return all_hyp, all_scores
with paddle.no_grad():
#-- Encode
if self.encoder is not None:
src = self.positional_encoding(images.transpose([1, 0, 2]))
src_enc = self.encoder(src).transpose([1, 0, 2])
else:
src_enc = images.squeeze(2).transpose([0, 2, 1])
#-- Repeat data for beam search
n_bm = self.beam_size
n_inst, len_s, d_h = src_enc.shape
src_enc = paddle.concat([src_enc for i in range(n_bm)], axis=1)
src_enc = src_enc.reshape([n_inst * n_bm, len_s, d_h]).transpose(
[1, 0, 2])
#-- Prepare beams
inst_dec_beams = [Beam(n_bm) for _ in range(n_inst)]
#-- Bookkeeping for active or not
active_inst_idx_list = list(range(n_inst))
inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(
active_inst_idx_list)
#-- Decode
for len_dec_seq in range(1, 25):
src_enc_copy = src_enc.clone()
active_inst_idx_list = beam_decode_step(
inst_dec_beams, len_dec_seq, src_enc_copy,
inst_idx_to_position_map, n_bm, None)
if not active_inst_idx_list:
break # all instances have finished their path to <EOS>
src_enc, inst_idx_to_position_map = collate_active_info(
src_enc_copy, inst_idx_to_position_map,
active_inst_idx_list)
batch_hyp, batch_scores = collect_hypothesis_and_scores(inst_dec_beams,
1)
result_hyp = []
for bs_hyp in batch_hyp:
bs_hyp_pad = bs_hyp[0] + [3] * (25 - len(bs_hyp[0]))
result_hyp.append(bs_hyp_pad)
return paddle.to_tensor(np.array(result_hyp), dtype=paddle.int64)
def generate_square_subsequent_mask(self, sz):
"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
Unmasked positions are filled with float(0.0).
"""
mask = paddle.zeros([sz, sz], dtype='float32')
mask_inf = paddle.triu(
paddle.full(
shape=[sz, sz], dtype='float32', fill_value='-inf'),
diagonal=1)
mask = mask + mask_inf
return mask
def generate_padding_mask(self, x):
padding_mask = x.equal(paddle.to_tensor(0, dtype=x.dtype))
return padding_mask
def _reset_parameters(self):
"""Initiate parameters in the transformer model."""
for p in self.parameters():
if p.dim() > 1:
xavier_uniform_(p)
class TransformerEncoder(nn.Layer):
"""TransformerEncoder is a stack of N encoder layers
Args:
encoder_layer: an instance of the TransformerEncoderLayer() class (required).
num_layers: the number of sub-encoder-layers in the encoder (required).
norm: the layer normalization component (optional).
"""
def __init__(self, encoder_layer, num_layers):
super(TransformerEncoder, self).__init__()
self.layers = _get_clones(encoder_layer, num_layers)
self.num_layers = num_layers
def forward(self, src):
"""Pass the input through the endocder layers in turn.
Args:
src: the sequnce to the encoder (required).
mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
"""
output = src
for i in range(self.num_layers):
output = self.layers[i](output,
src_mask=None,
src_key_padding_mask=None)
return output
class TransformerDecoder(nn.Layer):
"""TransformerDecoder is a stack of N decoder layers
Args:
decoder_layer: an instance of the TransformerDecoderLayer() class (required).
num_layers: the number of sub-decoder-layers in the decoder (required).
norm: the layer normalization component (optional).
"""
def __init__(self, decoder_layer, num_layers):
super(TransformerDecoder, self).__init__()
self.layers = _get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
def forward(self,
tgt,
memory,
tgt_mask=None,
memory_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None):
"""Pass the inputs (and mask) through the decoder layer in turn.
Args:
tgt: the sequence to the decoder (required).
memory: the sequnce from the last layer of the encoder (required).
tgt_mask: the mask for the tgt sequence (optional).
memory_mask: the mask for the memory sequence (optional).
tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
memory_key_padding_mask: the mask for the memory keys per batch (optional).
"""
output = tgt
for i in range(self.num_layers):
output = self.layers[i](
output,
memory,
tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
return output
class TransformerEncoderLayer(nn.Layer):
"""TransformerEncoderLayer is made up of self-attn and feedforward network.
This standard encoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self,
d_model,
nhead,
dim_feedforward=2048,
attention_dropout_rate=0.0,
residual_dropout_rate=0.1):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.conv1 = Conv2D(
in_channels=d_model,
out_channels=dim_feedforward,
kernel_size=(1, 1))
self.conv2 = Conv2D(
in_channels=dim_feedforward,
out_channels=d_model,
kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
def forward(self, src, src_mask=None, src_key_padding_mask=None):
"""Pass the input through the endocder layer.
Args:
src: the sequnce to the encoder layer (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
"""
src2 = self.self_attn(
src,
src,
src,
attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
src = src.transpose([1, 2, 0])
src = paddle.unsqueeze(src, 2)
src2 = self.conv2(F.relu(self.conv1(src)))
src2 = paddle.squeeze(src2, 2)
src2 = src2.transpose([2, 0, 1])
src = paddle.squeeze(src, 2)
src = src.transpose([2, 0, 1])
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
class TransformerDecoderLayer(nn.Layer):
"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
This standard decoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self,
d_model,
nhead,
dim_feedforward=2048,
attention_dropout_rate=0.0,
residual_dropout_rate=0.1):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.multihead_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.conv1 = Conv2D(
in_channels=d_model,
out_channels=dim_feedforward,
kernel_size=(1, 1))
self.conv2 = Conv2D(
in_channels=dim_feedforward,
out_channels=d_model,
kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
self.dropout3 = Dropout(residual_dropout_rate)
def forward(self,
tgt,
memory,
tgt_mask=None,
memory_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None):
"""Pass the inputs (and mask) through the decoder layer.
Args:
tgt: the sequence to the decoder layer (required).
memory: the sequnce from the last layer of the encoder (required).
tgt_mask: the mask for the tgt sequence (optional).
memory_mask: the mask for the memory sequence (optional).
tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
memory_key_padding_mask: the mask for the memory keys per batch (optional).
"""
tgt2 = self.self_attn(
tgt,
tgt,
tgt,
attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(
tgt,
memory,
memory,
attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
# default
tgt = tgt.transpose([1, 2, 0])
tgt = paddle.unsqueeze(tgt, 2)
tgt2 = self.conv2(F.relu(self.conv1(tgt)))
tgt2 = paddle.squeeze(tgt2, 2)
tgt2 = tgt2.transpose([2, 0, 1])
tgt = paddle.squeeze(tgt, 2)
tgt = tgt.transpose([2, 0, 1])
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
def _get_clones(module, N):
return LayerList([copy.deepcopy(module) for i in range(N)])
class PositionalEncoding(nn.Layer):
"""Inject some information about the relative or absolute position of the tokens
in the sequence. The positional encodings have the same dimension as
the embeddings, so that the two can be summed. Here, we use sine and cosine
functions of different frequencies.
.. math::
\text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
\text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
\text{where pos is the word position and i is the embed idx)
Args:
d_model: the embed dim (required).
dropout: the dropout value (default=0.1).
max_len: the max. length of the incoming sequence (default=5000).
Examples:
>>> pos_encoder = PositionalEncoding(d_model)
"""
def __init__(self, dropout, dim, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = paddle.zeros([max_len, dim])
position = paddle.arange(0, max_len, dtype=paddle.float32).unsqueeze(1)
div_term = paddle.exp(
paddle.arange(0, dim, 2).astype('float32') *
(-math.log(10000.0) / dim))
pe[:, 0::2] = paddle.sin(position * div_term)
pe[:, 1::2] = paddle.cos(position * div_term)
pe = pe.unsqueeze(0)
pe = pe.transpose([1, 0, 2])
self.register_buffer('pe', pe)
def forward(self, x):
"""Inputs of forward function
Args:
x: the sequence fed to the positional encoder model (required).
Shape:
x: [sequence length, batch size, embed dim]
output: [sequence length, batch size, embed dim]
Examples:
>>> output = pos_encoder(x)
"""
x = x + self.pe[:x.shape[0], :]
return self.dropout(x)
class PositionalEncoding_2d(nn.Layer):
"""Inject some information about the relative or absolute position of the tokens
in the sequence. The positional encodings have the same dimension as
the embeddings, so that the two can be summed. Here, we use sine and cosine
functions of different frequencies.
.. math::
\text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
\text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
\text{where pos is the word position and i is the embed idx)
Args:
d_model: the embed dim (required).
dropout: the dropout value (default=0.1).
max_len: the max. length of the incoming sequence (default=5000).
Examples:
>>> pos_encoder = PositionalEncoding(d_model)
"""
def __init__(self, dropout, dim, max_len=5000):
super(PositionalEncoding_2d, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = paddle.zeros([max_len, dim])
position = paddle.arange(0, max_len, dtype=paddle.float32).unsqueeze(1)
div_term = paddle.exp(
paddle.arange(0, dim, 2).astype('float32') *
(-math.log(10000.0) / dim))
pe[:, 0::2] = paddle.sin(position * div_term)
pe[:, 1::2] = paddle.cos(position * div_term)
pe = pe.unsqueeze(0).transpose([1, 0, 2])
self.register_buffer('pe', pe)
self.avg_pool_1 = nn.AdaptiveAvgPool2D((1, 1))
self.linear1 = nn.Linear(dim, dim)
self.linear1.weight.data.fill_(1.)
self.avg_pool_2 = nn.AdaptiveAvgPool2D((1, 1))
self.linear2 = nn.Linear(dim, dim)
self.linear2.weight.data.fill_(1.)
def forward(self, x):
"""Inputs of forward function
Args:
x: the sequence fed to the positional encoder model (required).
Shape:
x: [sequence length, batch size, embed dim]
output: [sequence length, batch size, embed dim]
Examples:
>>> output = pos_encoder(x)
"""
w_pe = self.pe[:x.shape[-1], :]
w1 = self.linear1(self.avg_pool_1(x).squeeze()).unsqueeze(0)
w_pe = w_pe * w1
w_pe = w_pe.transpose([1, 2, 0])
w_pe = w_pe.unsqueeze(2)
h_pe = self.pe[:x.shape[-2], :]
w2 = self.linear2(self.avg_pool_2(x).squeeze()).unsqueeze(0)
h_pe = h_pe * w2
h_pe = h_pe.transpose([1, 2, 0])
h_pe = h_pe.unsqueeze(3)
x = x + w_pe + h_pe
x = x.reshape(
[x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]).transpose(
[2, 0, 1])
return self.dropout(x)
class Embeddings(nn.Layer):
def __init__(self, d_model, vocab, padding_idx, scale_embedding):
super(Embeddings, self).__init__()
self.embedding = nn.Embedding(vocab, d_model, padding_idx=padding_idx)
w0 = np.random.normal(0.0, d_model**-0.5,
(vocab, d_model)).astype(np.float32)
self.embedding.weight.set_value(w0)
self.d_model = d_model
self.scale_embedding = scale_embedding
def forward(self, x):
if self.scale_embedding:
x = self.embedding(x)
return x * math.sqrt(self.d_model)
return self.embedding(x)
class Beam():
''' Beam search '''
def __init__(self, size, device=False):
self.size = size
self._done = False
# The score for each translation on the beam.
self.scores = paddle.zeros((size, ), dtype=paddle.float32)
self.all_scores = []
# The backpointers at each time-step.
self.prev_ks = []
# The outputs at each time-step.
self.next_ys = [paddle.full((size, ), 0, dtype=paddle.int64)]
self.next_ys[0][0] = 2
def get_current_state(self):
"Get the outputs for the current timestep."
return self.get_tentative_hypothesis()
def get_current_origin(self):
"Get the backpointers for the current timestep."
return self.prev_ks[-1]
@property
def done(self):
return self._done
def advance(self, word_prob):
"Update beam status and check if finished or not."
num_words = word_prob.shape[1]
# Sum the previous scores.
if len(self.prev_ks) > 0:
beam_lk = word_prob + self.scores.unsqueeze(1).expand_as(word_prob)
else:
beam_lk = word_prob[0]
flat_beam_lk = beam_lk.reshape([-1])
best_scores, best_scores_id = flat_beam_lk.topk(self.size, 0, True,
True) # 1st sort
self.all_scores.append(self.scores)
self.scores = best_scores
# bestScoresId is flattened as a (beam x word) array,
# so we need to calculate which word and beam each score came from
prev_k = best_scores_id // num_words
self.prev_ks.append(prev_k)
self.next_ys.append(best_scores_id - prev_k * num_words)
# End condition is when top-of-beam is EOS.
if self.next_ys[-1][0] == 3:
self._done = True
self.all_scores.append(self.scores)
return self._done
def sort_scores(self):
"Sort the scores."
return self.scores, paddle.to_tensor(
[i for i in range(self.scores.shape[0])], dtype='int32')
def get_the_best_score_and_idx(self):
"Get the score of the best in the beam."
scores, ids = self.sort_scores()
return scores[1], ids[1]
def get_tentative_hypothesis(self):
"Get the decoded sequence for the current timestep."
if len(self.next_ys) == 1:
dec_seq = self.next_ys[0].unsqueeze(1)
else:
_, keys = self.sort_scores()
hyps = [self.get_hypothesis(k) for k in keys]
hyps = [[2] + h for h in hyps]
dec_seq = paddle.to_tensor(hyps, dtype='int64')
return dec_seq
def get_hypothesis(self, k):
""" Walk back to construct the full hypothesis. """
hyp = []
for j in range(len(self.prev_ks) - 1, -1, -1):
hyp.append(self.next_ys[j + 1][k])
k = self.prev_ks[j][k]
return list(map(lambda x: x.item(), hyp[::-1]))

6
ppocr/postprocess/__init__.py
View File

@ -24,18 +24,16 @@ __all__ = ['build_post_process']
from .db_postprocess import DBPostProcess, DistillationDBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, NRTRLabelDecode, \
TableLabelDecode
from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess
def build_post_process(config, global_config=None):
support_dict = [
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
'DistillationCTCLabelDecode', 'TableLabelDecode',
'DistillationDBPostProcess'
'DistillationCTCLabelDecode', 'NRTRLabelDecode', 'TableLabelDecode', 'DistillationDBPostProcess'
]
config = copy.deepcopy(config)

66
ppocr/postprocess/rec_postprocess.py
View File

@ -156,6 +156,69 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
return output
class NRTRLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='EN_symbol',
use_space_char=True,
**kwargs):
super(NRTRLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if preds.dtype == paddle.int64:
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
if preds[0][0]==2:
preds_idx = preds[:,1:]
else:
preds_idx = preds
text = self.decode(preds_idx)
if label is None:
return text
label = self.decode(label[:,1:])
else:
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:,1:])
return text, label
def add_special_char(self, dict_character):
dict_character = ['blank','<unk>','<s>','</s>'] + dict_character
return dict_character
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
batch_size = len(text_index)
for batch_idx in range(batch_size):
char_list = []
conf_list = []
for idx in range(len(text_index[batch_idx])):
if text_index[batch_idx][idx] == 3: # end
break
try:
char_list.append(self.character[int(text_index[batch_idx][idx])])
except:
continue
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:
conf_list.append(1)
text = ''.join(char_list)
result_list.append((text.lower(), np.mean(conf_list)))
return result_list
class AttnLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
@ -193,8 +256,7 @@ class AttnLabelDecode(BaseRecLabelDecode):
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
batch_idx][idx]:
continue
char_list.append(self.character[int(text_index[batch_idx][
idx])])
char_list.append(self.character[int(text_index[batch_idx][idx])])
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:

14
ppstructure/README.md
View File

@ -30,13 +30,13 @@ python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/
# CPU
python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
# For more，refer[Installation](https://www.paddlepaddle.org.cn/install/quick)。
```
For more，refer [Installation](https://www.paddlepaddle.org.cn/install/quick) .
- **(2) Install Layout-Parser**
```bash
pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
```
### 2.2 Install PaddleOCR（including PP-OCR and PP-Structure）
@ -180,10 +180,10 @@ OCR and table recognition model
|model name|description|model size|download|
| --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|en_ppocr_mobile_v2.0_table_det|Text detection of English table scenes trained on PubLayNet dataset|4.7M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|en_ppocr_mobile_v2.0_table_det|Text detection of English table scenes trained on PubLayNet dataset|4.7M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .

14
ppstructure/README_ch.md
View File

@ -30,13 +30,13 @@ python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/
# CPU安装
python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
# 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
```
更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
- **(2) 安装 Layout-Parser**
```bash
pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
```
### 2.2 安装PaddleOCR（包含PP-OCR和PP-Structure）
@ -179,10 +179,10 @@ OCR和表格识别模型
|模型名称|模型简介|推理模型大小|下载地址|
| --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
如需要使用其他模型，可以在 [model_list](../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到`det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。

2
ppstructure/table/README.md
View File

@ -41,7 +41,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# run
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --det_limit_side_len=736 --det_limit_type=min --output ../output/table
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`.

2
ppstructure/table/README_ch.md
View File

@ -43,7 +43,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# 执行预测
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --det_limit_side_len=736 --det_limit_type=min --output ../output/table
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
运行完成后，每张图片的excel表格会保存到output字段指定的目录下

3
requirements.txt
View File

@ -8,3 +8,6 @@ numpy
visualdl
python-Levenshtein
opencv-contrib-python==4.4.0.46
lxml
premailer
openpyxl

133
tests/compare_results.py Normal file
View File

@ -0,0 +1,133 @@
import numpy as np
import os
import subprocess
import json
import argparse
import glob
def init_args():
parser = argparse.ArgumentParser()
# params for testing assert allclose
parser.add_argument("--atol", type=float, default=1e-3)
parser.add_argument("--rtol", type=float, default=1e-3)
parser.add_argument("--gt_file", type=str, default="")
parser.add_argument("--log_file", type=str, default="")
parser.add_argument("--precision", type=str, default="fp32")
return parser
def parse_args():
parser = init_args()
return parser.parse_args()
def run_shell_command(cmd):
p = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
out, err = p.communicate()
if p.returncode == 0:
return out.decode('utf-8')
else:
return None
def parser_results_from_log_by_name(log_path, names_list):
if not os.path.exists(log_path):
raise ValueError("The log file {} does not exists!".format(log_path))
if names_list is None or len(names_list) < 1:
return []
parser_results = {}
for name in names_list:
cmd = "grep {} {}".format(name, log_path)
outs = run_shell_command(cmd)
outs = outs.split("\n")[0]
result = outs.split("{}".format(name))[-1]
result = json.loads(result)
parser_results[name] = result
return parser_results
def load_gt_from_file(gt_file):
if not os.path.exists(gt_file):
raise ValueError("The log file {} does not exists!".format(gt_file))
with open(gt_file, 'r') as f:
data = f.readlines()
f.close()
parser_gt = {}
for line in data:
image_name, result = line.strip("\n").split("\t")
result = json.loads(result)
parser_gt[image_name] = result
return parser_gt
def load_gt_from_txts(gt_file):
gt_list = glob.glob(gt_file)
gt_collection = {}
for gt_f in gt_list:
gt_dict = load_gt_from_file(gt_f)
basename = os.path.basename(gt_f)
if "fp32" in basename:
gt_collection["fp32"] = [gt_dict, gt_f]
elif "fp16" in basename:
gt_collection["fp16"] = [gt_dict, gt_f]
elif "int8" in basename:
gt_collection["int8"] = [gt_dict, gt_f]
else:
continue
return gt_collection
def collect_predict_from_logs(log_path, key_list):
log_list = glob.glob(log_path)
pred_collection = {}
for log_f in log_list:
pred_dict = parser_results_from_log_by_name(log_f, key_list)
key = os.path.basename(log_f)
pred_collection[key] = pred_dict
return pred_collection
def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7):
for k in dict_x:
np.testing.assert_allclose(
np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol)
if __name__ == "__main__":
# Usage:
# python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt --log_file=./tests/output/infer_*.log
args = parse_args()
gt_collection = load_gt_from_txts(args.gt_file)
key_list = gt_collection["fp32"][0].keys()
pred_collection = collect_predict_from_logs(args.log_file, key_list)
for filename in pred_collection.keys():
if "fp32" in filename:
gt_dict, gt_filename = gt_collection["fp32"]
elif "fp16" in filename:
gt_dict, gt_filename = gt_collection["fp16"]
elif "int8" in filename:
gt_dict, gt_filename = gt_collection["int8"]
else:
continue
pred_dict = pred_collection[filename]
try:
testing_assert_allclose(
gt_dict, pred_dict, atol=args.atol, rtol=args.rtol)
print(
"Assert allclose passed! The results of {} and {} are consistent!".
format(filename, gt_filename))
except Exception as E:
print(E)
raise ValueError(
"The results of {} and the results of {} are inconsistent!".
format(filename, gt_filename))

23
tests/ocr_det_params.txt
View File

@ -4,7 +4,7 @@ python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.epoch_num:lite_train_infer=1|whole_train_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
Global.pretrained_model:null
@ -15,7 +15,7 @@ null:null
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
fpgm_train:null
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
distill_train:null
null:null
null:null
@ -29,7 +29,7 @@ Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py
fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o
distill_export:null
export1:null
export2:null
@ -49,7 +49,22 @@ inference:tools/infer/predict_det.py
--save_log_path:null
--benchmark:True
null:null
===========================deploy_params===========================
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr det
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
===========================serving_params===========================
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel

52
tests/ocr_det_server_params.txt Normal file
View File

@ -0,0 +1,52 @@
===========================train_params===========================
model_name:ocr_server_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=""
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
null:null

83
tests/prepare.sh
View File

@ -1,6 +1,6 @@
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
MODE=$2
dataline=$(cat ${FILENAME})
@ -34,11 +34,15 @@ MODE=$2
if [ ${MODE} = "lite_train_infer" ];then
# pretrain lite train data
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
rm -rf ./train_data/icdar2015
rm -rf ./train_data/ic15_data
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle
cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
@ -59,13 +63,17 @@ elif [ ${MODE} = "whole_infer" ];then
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
else
elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
if [ ${model_name} = "ocr_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
elif [ ${model_name} = "ocr_server_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
else
rm -rf ./train_data/ic15_data
eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
@ -86,3 +94,72 @@ wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppoc
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
if [ ${MODE} = "cpp_infer" ];then
cd deploy/cpp_infer
use_opencv=$(func_parser_value "${lines[52]}")
if [ ${use_opencv} = "True" ]; then
echo "################### build opencv ###################"
rm -rf 3.4.7.tar.gz opencv-3.4.7/
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xf 3.4.7.tar.gz
cd opencv-3.4.7/
install_path=$(pwd)/opencv-3.4.7/opencv3
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
cd ../
echo "################### build opencv finished ###################"
fi
echo "################### build PaddleOCR demo ####################"
if [ ${use_opencv} = "True" ]; then
OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
else
OPENCV_DIR=''
fi
LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DWITH_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DTENSORRT_DIR=${TENSORRT_DIR} \
make -j
echo "################### build PaddleOCR demo finished ###################"
fi

58
tests/readme.md Normal file
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@ -0,0 +1,58 @@
# 介绍
test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。
# 安装依赖
- 安装PaddlePaddle >= 2.0
- 安装PaddleOCR依赖
```
pip3 install -r ../requirements.txt
```
- 安装autolog
```
git clone https://github.com/LDOUBLEV/AutoLog
cd AutoLog
pip3 install -r requirements.txt
python3 setup.py bdist_wheel
pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
cd ../
```
# 目录介绍
```bash
tests/
├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件
├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件
└── prepare.sh # 完成test.sh运行所需要的数据和模型下载
└── test.sh # 根据
```
# 使用方法
test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```
bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
```
- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
```
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
```
- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
```
bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
用法1:
bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
```
模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度
```
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
```

49
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tests/results/det_results_gpu_trt_fp16.txt Normal file
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155
tests/test.sh
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@ -1,6 +1,6 @@
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
MODE=$2
dataline=$(cat ${FILENAME})
@ -145,30 +145,57 @@ benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
# parser serving
trans_model_py=$(func_parser_value "${lines[52]}")
infer_model_dir_key=$(func_parser_key "${lines[53]}")
infer_model_dir_value=$(func_parser_value "${lines[53]}")
model_filename_key=$(func_parser_key "${lines[54]}")
model_filename_value=$(func_parser_value "${lines[54]}")
params_filename_key=$(func_parser_key "${lines[55]}")
params_filename_value=$(func_parser_value "${lines[55]}")
serving_server_key=$(func_parser_key "${lines[56]}")
serving_server_value=$(func_parser_value "${lines[56]}")
serving_client_key=$(func_parser_key "${lines[57]}")
serving_client_value=$(func_parser_value "${lines[57]}")
serving_dir_value=$(func_parser_value "${lines[58]}")
web_service_py=$(func_parser_value "${lines[59]}")
web_use_gpu_key=$(func_parser_key "${lines[60]}")
web_use_gpu_list=$(func_parser_value "${lines[60]}")
web_use_mkldnn_key=$(func_parser_key "${lines[61]}")
web_use_mkldnn_list=$(func_parser_value "${lines[61]}")
web_cpu_threads_key=$(func_parser_key "${lines[62]}")
web_cpu_threads_list=$(func_parser_value "${lines[62]}")
web_use_trt_key=$(func_parser_key "${lines[63]}")
web_use_trt_list=$(func_parser_value "${lines[63]}")
web_precision_key=$(func_parser_key "${lines[64]}")
web_precision_list=$(func_parser_value "${lines[64]}")
pipeline_py=$(func_parser_value "${lines[65]}")
trans_model_py=$(func_parser_value "${lines[67]}")
infer_model_dir_key=$(func_parser_key "${lines[68]}")
infer_model_dir_value=$(func_parser_value "${lines[68]}")
model_filename_key=$(func_parser_key "${lines[69]}")
model_filename_value=$(func_parser_value "${lines[69]}")
params_filename_key=$(func_parser_key "${lines[70]}")
params_filename_value=$(func_parser_value "${lines[70]}")
serving_server_key=$(func_parser_key "${lines[71]}")
serving_server_value=$(func_parser_value "${lines[71]}")
serving_client_key=$(func_parser_key "${lines[72]}")
serving_client_value=$(func_parser_value "${lines[72]}")
serving_dir_value=$(func_parser_value "${lines[73]}")
web_service_py=$(func_parser_value "${lines[74]}")
web_use_gpu_key=$(func_parser_key "${lines[75]}")
web_use_gpu_list=$(func_parser_value "${lines[75]}")
web_use_mkldnn_key=$(func_parser_key "${lines[76]}")
web_use_mkldnn_list=$(func_parser_value "${lines[76]}")
web_cpu_threads_key=$(func_parser_key "${lines[77]}")
web_cpu_threads_list=$(func_parser_value "${lines[77]}")
web_use_trt_key=$(func_parser_key "${lines[78]}")
web_use_trt_list=$(func_parser_value "${lines[78]}")
web_precision_key=$(func_parser_key "${lines[79]}")
web_precision_list=$(func_parser_value "${lines[79]}")
pipeline_py=$(func_parser_value "${lines[80]}")
if [ ${MODE} = "cpp_infer" ]; then
# parser cpp inference model
cpp_infer_model_dir_list=$(func_parser_value "${lines[53]}")
cpp_infer_is_quant=$(func_parser_value "${lines[54]}")
# parser cpp inference
inference_cmd=$(func_parser_value "${lines[55]}")
cpp_use_gpu_key=$(func_parser_key "${lines[56]}")
cpp_use_gpu_list=$(func_parser_value "${lines[56]}")
cpp_use_mkldnn_key=$(func_parser_key "${lines[57]}")
cpp_use_mkldnn_list=$(func_parser_value "${lines[57]}")
cpp_cpu_threads_key=$(func_parser_key "${lines[58]}")
cpp_cpu_threads_list=$(func_parser_value "${lines[58]}")
cpp_batch_size_key=$(func_parser_key "${lines[59]}")
cpp_batch_size_list=$(func_parser_value "${lines[59]}")
cpp_use_trt_key=$(func_parser_key "${lines[60]}")
cpp_use_trt_list=$(func_parser_value "${lines[60]}")
cpp_precision_key=$(func_parser_key "${lines[61]}")
cpp_precision_list=$(func_parser_value "${lines[61]}")
cpp_infer_model_key=$(func_parser_key "${lines[62]}")
cpp_image_dir_key=$(func_parser_key "${lines[63]}")
cpp_infer_img_dir=$(func_parser_value "${lines[63]}")
cpp_save_log_key=$(func_parser_key "${lines[64]}")
cpp_benchmark_key=$(func_parser_key "${lines[65]}")
cpp_benchmark_value=$(func_parser_value "${lines[65]}")
fi
LOG_PATH="./tests/output"
@ -312,6 +339,63 @@ function func_serving(){
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
function func_cpp_inference(){
IFS='|'
_script=$1
_model_dir=$2
_log_path=$3
_img_dir=$4
_flag_quant=$5
# inference
for use_gpu in ${cpp_use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
continue
fi
for threads in ${cpp_cpu_threads_list[*]}; do
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
done
done
done
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
for use_trt in ${cpp_use_trt_list[*]}; do
for precision in ${cpp_precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
continue
fi
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
done
done
done
else
@ -320,7 +404,6 @@ function func_serving(){
done
}
if [ ${MODE} = "infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
@ -355,6 +438,25 @@ if [ ${MODE} = "infer" ]; then
Count=$(($Count + 1))
done
elif [ ${MODE} = "cpp_infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
# set CUDA_VISIBLE_DEVICES
eval $env
export Count=0
IFS="|"
infer_quant_flag=(${cpp_infer_is_quant})
for infer_model in ${cpp_infer_model_dir_list[*]}; do
#run inference
is_quant=${infer_quant_flag[Count]}
func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
Count=$(($Count + 1))
done
elif [ ${MODE} = "serving_infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
@ -368,6 +470,7 @@ elif [ ${MODE} = "serving_infer" ]; then
IFS="|"
#run serving
func_serving "${web_service_cmd}"
else
IFS="|"
export Count=0

16
tools/infer/predict_det.py
View File

@ -30,7 +30,7 @@ from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
import json
logger = get_logger()
@ -101,6 +101,7 @@ class TextDetector(object):
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="det",
model_precision=args.precision,
@ -110,7 +111,7 @@ class TextDetector(object):
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=0,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
@ -242,6 +243,7 @@ if __name__ == "__main__":
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
save_results = []
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
@ -255,8 +257,11 @@ if __name__ == "__main__":
if count > 0:
total_time += elapse
count += 1
logger.info("Predict time of {}: {}".format(image_file, elapse))
save_pred = os.path.basename(image_file) + "\t" + str(
json.dumps(np.array(dt_boxes).astype(np.int32).tolist())) + "\n"
save_results.append(save_pred)
logger.info(save_pred)
logger.info("The predict time of {}: {}".format(image_file, elapse))
src_im = utility.draw_text_det_res(dt_boxes, image_file)
img_name_pure = os.path.split(image_file)[-1]
img_path = os.path.join(draw_img_save,
@ -264,5 +269,8 @@ if __name__ == "__main__":
cv2.imwrite(img_path, src_im)
logger.info("The visualized image saved in {}".format(img_path))
with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
f.writelines(save_results)
f.close()
if args.benchmark:
text_detector.autolog.report()

2
tools/infer/predict_e2e.py
View File

@ -74,7 +74,7 @@ class TextE2E(object):
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
self.predictor, self.input_tensor, self.output_tensors, _ = utility.create_predictor(
args, 'e2e', logger) # paddle.jit.load(args.det_model_dir)
# self.predictor.eval()

7
tools/infer/predict_rec.py
View File

@ -68,6 +68,7 @@ class TextRecognizer(object):
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="rec",
model_precision=args.precision,
@ -77,7 +78,7 @@ class TextRecognizer(object):
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=0 if args.use_gpu else None,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
@ -87,7 +88,7 @@ class TextRecognizer(object):
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
assert imgC == img.shape[2]
if self.character_type == "ch":
max_wh_ratio = max(max_wh_ratio, imgW / imgH)
imgW = int((32 * max_wh_ratio))
h, w = img.shape[:2]
ratio = w / float(h)
@ -277,7 +278,7 @@ def main(args):
if args.warmup:
img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
for i in range(2):
res = text_recognizer([img])
res = text_recognizer([img] * int(args.rec_batch_num))
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)

21
tools/infer/utility.py
View File

@ -35,7 +35,7 @@ def init_args():
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--min_subgraph_size", type=int, default=10)
parser.add_argument("--min_subgraph_size", type=int, default=15)
parser.add_argument("--precision", type=str, default="fp32")
parser.add_argument("--gpu_mem", type=int, default=500)
@ -159,6 +159,11 @@ def create_predictor(args, mode, logger):
precision = inference.PrecisionType.Float32
if args.use_gpu:
gpu_id = get_infer_gpuid()
if gpu_id is None:
raise ValueError(
"Not found GPU in current device. Please check your device or set args.use_gpu as False"
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
@ -280,6 +285,20 @@ def create_predictor(args, mode, logger):
return predictor, input_tensor, output_tensors, config
def get_infer_gpuid():
cmd = "nvidia-smi"
res = os.popen(cmd).readlines()
if len(res) == 0:
return None
cmd = "env | grep CUDA_VISIBLE_DEVICES"
env_cuda = os.popen(cmd).readlines()
if len(env_cuda) == 0:
return 0
else:
gpu_id = env_cuda[0].strip().split("=")[1]
return int(gpu_id[0])
def draw_e2e_res(dt_boxes, strs, img_path):
src_im = cv2.imread(img_path)
for box, str in zip(dt_boxes, strs):

6
tools/program.py
View File

@ -186,6 +186,8 @@ def train(config,
model.train()
use_srn = config['Architecture']['algorithm'] == "SRN"
use_nrtr = config['Architecture']['algorithm'] == "NRTR"
try:
model_type = config['Architecture']['model_type']
except:
@ -213,7 +215,7 @@ def train(config,
images = batch[0]
if use_srn:
model_average = True
if use_srn or model_type == 'table':
if use_srn or model_type == 'table' or use_nrtr:
preds = model(images, data=batch[1:])
else:
preds = model(images)
@ -398,7 +400,7 @@ def preprocess(is_train=False):
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'TableAttn'
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn'
]
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'