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PaddleOCR
提交 55d71902 编写于 作者: qq_25193841's avatar qq_25193841
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Merge remote-tracking branch 'origin/dygraph' into dygraph

上级 1d03fd33 10b7e706
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MANIFEST.in
浏览文件 @ 55d71902
...@@ -5,5 +5,6 @@ recursive-include ppocr/utils *.txt utility.py logging.py network.py ...@@ -5,5 +5,6 @@ recursive-include ppocr/utils *.txt utility.py logging.py network.py
recursive-include ppocr/data *.py recursive-include ppocr/data *.py
recursive-include ppocr/postprocess *.py recursive-include ppocr/postprocess *.py
recursive-include tools/infer *.py recursive-include tools/infer *.py
recursive-include tools __init__.py
recursive-include ppocr/utils/e2e_utils *.py recursive-include ppocr/utils/e2e_utils *.py
recursive-include ppstructure *.py recursive-include ppstructure *.py
\ No newline at end of file
PPOCRLabel/README.md
浏览文件 @ 55d71902
...@@ -207,6 +207,24 @@ For some data that are difficult to recognize, the recognition results will not ...@@ -207,6 +207,24 @@ For some data that are difficult to recognize, the recognition results will not
pip install opencv-contrib-python-headless==4.2.0.32 pip install opencv-contrib-python-headless==4.2.0.32
``` ```
### Dataset division
- Enter the following command in the terminal to execute the dataset division script:
```
cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
```
- Parameter Description:
trainValTestRatio is the division ratio of the number of images in the training set, validation set, and test set, set according to your actual situation, the default is 6:2:2
labelRootPath is the storage path of the dataset labeled by PPOCRLabel, the default is ../train_data/label
detRootPath is the path where the text detection dataset is divided according to the dataset marked by PPOCRLabel. The default is ../train_data/det
recRootPath is the path where the character recognition dataset is divided according to the dataset marked by PPOCRLabel. The default is ../train_data/rec
### Related ### Related
1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg) 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
\ No newline at end of file
PPOCRLabel/README_ch.md
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...@@ -193,7 +193,23 @@ PPOCRLabel支持三种导出方式： ...@@ -193,7 +193,23 @@ PPOCRLabel支持三种导出方式：
``` ```
pip install opencv-contrib-python-headless==4.2.0.32 pip install opencv-contrib-python-headless==4.2.0.32
``` ```
### 数据集划分
- 在终端中输入以下命令执行数据集划分脚本：
```
cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
```
- 参数说明：
trainValTestRatio是训练集、验证集、测试集的图像数量划分比例，根据你的实际情况设定，默认是6:2:2
labelRootPath是PPOCRLabel标注的数据集存放路径，默认是../train_data/label
detRootPath是根据PPOCRLabel标注的数据集划分后的文本检测数据集存放的路径，默认是../train_data/det
recRootPath是根据PPOCRLabel标注的数据集划分后的字符识别数据集存放的路径，默认是../train_data/rec
### 4. 参考资料 ### 4. 参考资料
1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg) 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
PPOCRLabel/gen_ocr_train_val_test.py 0 → 100644
浏览文件 @ 55d71902
# coding:utf8
import os
import shutil
import random
import argparse
# 删除划分的训练集、验证集、测试集文件夹，重新创建一个空的文件夹
def isCreateOrDeleteFolder(path, flag):
flagPath = os.path.join(path, flag)
if os.path.exists(flagPath):
shutil.rmtree(flagPath)
os.makedirs(flagPath)
flagAbsPath = os.path.abspath(flagPath)
return flagAbsPath
def splitTrainVal(root, dir, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
# 按照指定的比例划分训练集、验证集、测试集
labelPath = os.path.join(root, dir)
labelAbsPath = os.path.abspath(labelPath)
if flag == "det":
labelFilePath = os.path.join(labelAbsPath, args.detLabelFileName)
elif flag == "rec":
labelFilePath = os.path.join(labelAbsPath, args.recLabelFileName)
labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
labelFileContent = labelFileRead.readlines()
random.shuffle(labelFileContent)
labelRecordLen = len(labelFileContent)
for index, labelRecordInfo in enumerate(labelFileContent):
imageRelativePath = labelRecordInfo.split('\t')[0]
imageLabel = labelRecordInfo.split('\t')[1]
imageName = os.path.basename(imageRelativePath)
if flag == "det":
imagePath = os.path.join(labelAbsPath, imageName)
elif flag == "rec":
imagePath = os.path.join(labelAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
# 按预设的比例划分训练集、验证集、测试集
trainValTestRatio = args.trainValTestRatio.split(":")
trainRatio = eval(trainValTestRatio[0]) / 10
valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
curRatio = index / labelRecordLen
if curRatio < trainRatio:
imageCopyPath = os.path.join(absTrainRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
elif curRatio >= trainRatio and curRatio < valRatio:
imageCopyPath = os.path.join(absValRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
else:
imageCopyPath = os.path.join(absTestRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
testTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
# 删掉存在的文件
def removeFile(path):
if os.path.exists(path):
os.remove(path)
def genDetRecTrainVal(args):
detAbsTrainRootPath = isCreateOrDeleteFolder(args.detRootPath, "train")
detAbsValRootPath = isCreateOrDeleteFolder(args.detRootPath, "val")
detAbsTestRootPath = isCreateOrDeleteFolder(args.detRootPath, "test")
recAbsTrainRootPath = isCreateOrDeleteFolder(args.recRootPath, "train")
recAbsValRootPath = isCreateOrDeleteFolder(args.recRootPath, "val")
recAbsTestRootPath = isCreateOrDeleteFolder(args.recRootPath, "test")
removeFile(os.path.join(args.detRootPath, "train.txt"))
removeFile(os.path.join(args.detRootPath, "val.txt"))
removeFile(os.path.join(args.detRootPath, "test.txt"))
removeFile(os.path.join(args.recRootPath, "train.txt"))
removeFile(os.path.join(args.recRootPath, "val.txt"))
removeFile(os.path.join(args.recRootPath, "test.txt"))
detTrainTxt = open(os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8")
detValTxt = open(os.path.join(args.detRootPath, "val.txt"), "a", encoding="UTF-8")
detTestTxt = open(os.path.join(args.detRootPath, "test.txt"), "a", encoding="UTF-8")
recTrainTxt = open(os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8")
recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
recTestTxt = open(os.path.join(args.recRootPath, "test.txt"), "a", encoding="UTF-8")
for root, dirs, files in os.walk(args.labelRootPath):
for dir in dirs:
splitTrainVal(root, dir, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
detTestTxt, "det")
splitTrainVal(root, dir, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
recTestTxt, "rec")
break
if __name__ == "__main__":
# 功能描述：分别划分检测和识别的训练集、验证集、测试集
# 说明：可以根据自己的路径和需求调整参数，图像数据往往多人合作分批标注，每一批图像数据放在一个文件夹内用PPOCRLabel进行标注，
# 如此会有多个标注好的图像文件夹汇总并划分训练集、验证集、测试集的需求
parser = argparse.ArgumentParser()
parser.add_argument(
"--trainValTestRatio",
type=str,
default="6:2:2",
help="ratio of trainset:valset:testset")
parser.add_argument(
"--labelRootPath",
type=str,
default="../train_data/label",
help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
)
parser.add_argument(
"--detRootPath",
type=str,
default="../train_data/det",
help="the path where the divided detection dataset is placed")
parser.add_argument(
"--recRootPath",
type=str,
default="../train_data/rec",
help="the path where the divided recognition dataset is placed"
)
parser.add_argument(
"--detLabelFileName",
type=str,
default="Label.txt",
help="the name of the detection annotation file")
parser.add_argument(
"--recLabelFileName",
type=str,
default="rec_gt.txt",
help="the name of the recognition annotation file"
)
parser.add_argument(
"--recImageDirName",
type=str,
default="crop_img",
help="the name of the folder where the cropped recognition dataset is located"
)
args = parser.parse_args()
genDetRecTrainVal(args)
configs/det/det_r50_vd_sast_icdar15.yml
浏览文件 @ 55d71902
...@@ -8,7 +8,7 @@ Global: ...@@ -8,7 +8,7 @@ Global:
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints: checkpoints:
save_inference_dir: save_inference_dir:
use_visualdl: False use_visualdl: False
...@@ -106,4 +106,4 @@ Eval: ...@@ -106,4 +106,4 @@ Eval:
shuffle: False shuffle: False
drop_last: False drop_last: False
batch_size_per_card: 1 # must be 1 batch_size_per_card: 1 # must be 1
num_workers: 2 num_workers: 2
\ No newline at end of file
configs/det/det_r50_vd_sast_totaltext.yml
浏览文件 @ 55d71902
...@@ -8,7 +8,7 @@ Global: ...@@ -8,7 +8,7 @@ Global:
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints: checkpoints:
save_inference_dir: save_inference_dir:
use_visualdl: False use_visualdl: False
......
configs/table/table_mv3.yml
浏览文件 @ 55d71902
Global: Global:
use_gpu: true use_gpu: true
epoch_num: 50 epoch_num: 400
log_smooth_window: 20 log_smooth_window: 20
print_batch_step: 5 print_batch_step: 5
save_model_dir: ./output/table_mv3/ save_model_dir: ./output/table_mv3/
save_epoch_step: 5 save_epoch_step: 3
# evaluation is run every 400 iterations after the 0th iteration # evaluation is run every 400 iterations after the 0th iteration
eval_batch_step: [0, 400] eval_batch_step: [0, 400]
cal_metric_during_train: True cal_metric_during_train: True
pretrained_model: pretrained_model:
checkpoints: checkpoints:
save_inference_dir: save_inference_dir:
use_visualdl: False use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg infer_img: doc/table/table.jpg
# for data or label process # for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_dict_path: ppocr/utils/dict/table_structure_dict.txt
character_type: en character_type: en
max_text_length: 100 max_text_length: 100
max_elem_length: 500 max_elem_length: 800
max_cell_num: 500 max_cell_num: 500
infer_mode: False infer_mode: False
process_total_num: 0 process_total_num: 0
process_cut_num: 0 process_cut_num: 0
Optimizer: Optimizer:
name: Adam name: Adam
beta1: 0.9 beta1: 0.9
...@@ -41,13 +40,15 @@ Architecture: ...@@ -41,13 +40,15 @@ Architecture:
Backbone: Backbone:
name: MobileNetV3 name: MobileNetV3
scale: 1.0 scale: 1.0
model_name: small model_name: large
disable_se: True
Head: Head:
name: TableAttentionHead name: TableAttentionHead
hidden_size: 256 hidden_size: 256
l2_decay: 0.00001 l2_decay: 0.00001
loc_type: 2 loc_type: 2
max_text_length: 100
max_elem_length: 800
max_cell_num: 500
Loss: Loss:
name: TableAttentionLoss name: TableAttentionLoss
......
deploy/lite/ocr_db_crnn.cc
浏览文件 @ 55d71902
...@@ -307,21 +307,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img, ...@@ -307,21 +307,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
return filter_boxes; return filter_boxes;
} }
std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, std::string power_mode, int num_threads) { std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, int num_threads) {
MobileConfig config; MobileConfig config;
config.set_model_from_file(model_file); config.set_model_from_file(model_file);
if (power_mode == "LITE_POWER_HIGH"){
config.set_power_mode(LITE_POWER_HIGH);
} else {
if (power_mode == "LITE_POWER_LOW") {
config.set_power_mode(LITE_POWER_HIGH);
} else {
std::cerr << "Only support LITE_POWER_HIGH or LITE_POWER_HIGH." << std::endl;
exit(1);
}
}
config.set_threads(num_threads); config.set_threads(num_threads);
std::shared_ptr<PaddlePredictor> predictor = std::shared_ptr<PaddlePredictor> predictor =
...@@ -391,7 +380,7 @@ void check_params(int argc, char **argv) { ...@@ -391,7 +380,7 @@ void check_params(int argc, char **argv) {
if (strcmp(argv[1], "det") == 0) { if (strcmp(argv[1], "det") == 0) {
if (argc < 9){ if (argc < 9){
std::cerr << "[ERROR] usage:" << argv[0] std::cerr << "[ERROR] usage:" << argv[0]
<< " det det_model num_threads batchsize power_mode img_dir det_config lite_benchmark_value" << std::endl; << " det det_model runtime_device num_threads batchsize img_dir det_config lite_benchmark_value" << std::endl;
exit(1); exit(1);
} }
} }
...@@ -399,7 +388,7 @@ void check_params(int argc, char **argv) { ...@@ -399,7 +388,7 @@ void check_params(int argc, char **argv) {
if (strcmp(argv[1], "rec") == 0) { if (strcmp(argv[1], "rec") == 0) {
if (argc < 9){ if (argc < 9){
std::cerr << "[ERROR] usage:" << argv[0] std::cerr << "[ERROR] usage:" << argv[0]
<< " rec rec_model num_threads batchsize power_mode img_dir key_txt lite_benchmark_value" << std::endl; << " rec rec_model runtime_device num_threads batchsize img_dir key_txt lite_benchmark_value" << std::endl;
exit(1); exit(1);
} }
} }
...@@ -407,7 +396,7 @@ void check_params(int argc, char **argv) { ...@@ -407,7 +396,7 @@ void check_params(int argc, char **argv) {
if (strcmp(argv[1], "system") == 0) { if (strcmp(argv[1], "system") == 0) {
if (argc < 12){ if (argc < 12){
std::cerr << "[ERROR] usage:" << argv[0] std::cerr << "[ERROR] usage:" << argv[0]
<< " system det_model rec_model clas_model num_threads batchsize power_mode img_dir det_config key_txt lite_benchmark_value" << std::endl; << " system det_model rec_model clas_model runtime_device num_threads batchsize img_dir det_config key_txt lite_benchmark_value" << std::endl;
exit(1); exit(1);
} }
} }
...@@ -417,15 +406,15 @@ void system(char **argv){ ...@@ -417,15 +406,15 @@ void system(char **argv){
std::string det_model_file = argv[2]; std::string det_model_file = argv[2];
std::string rec_model_file = argv[3]; std::string rec_model_file = argv[3];
std::string cls_model_file = argv[4]; std::string cls_model_file = argv[4];
std::string precision = argv[5]; std::string runtime_device = argv[5];
std::string num_threads = argv[6]; std::string precision = argv[6];
std::string batchsize = argv[7]; std::string num_threads = argv[7];
std::string power_mode = argv[8]; std::string batchsize = argv[8];
std::string img_dir = argv[9]; std::string img_dir = argv[9];
std::string det_config_path = argv[10]; std::string det_config_path = argv[10];
std::string dict_path = argv[11]; std::string dict_path = argv[11];
if (strcmp(argv[5], "FP32") != 0 && strcmp(argv[5], "INT8") != 0) { if (strcmp(argv[6], "FP32") != 0 && strcmp(argv[6], "INT8") != 0) {
std::cerr << "Only support FP32 or INT8." << std::endl; std::cerr << "Only support FP32 or INT8." << std::endl;
exit(1); exit(1);
} }
...@@ -441,9 +430,9 @@ void system(char **argv){ ...@@ -441,9 +430,9 @@ void system(char **argv){
charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
charactor_dict.push_back(" "); charactor_dict.push_back(" ");
auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads)); auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads)); auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
auto cls_predictor = loadModel(cls_model_file, power_mode, std::stoi(num_threads)); auto cls_predictor = loadModel(cls_model_file, std::stoi(num_threads));
for (int i = 0; i < cv_all_img_names.size(); ++i) { for (int i = 0; i < cv_all_img_names.size(); ++i) {
std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
...@@ -477,14 +466,14 @@ void system(char **argv){ ...@@ -477,14 +466,14 @@ void system(char **argv){
void det(int argc, char **argv) { void det(int argc, char **argv) {
std::string det_model_file = argv[2]; std::string det_model_file = argv[2];
std::string precision = argv[3]; std::string runtime_device = argv[3];
std::string num_threads = argv[4]; std::string precision = argv[4];
std::string batchsize = argv[5]; std::string num_threads = argv[5];
std::string power_mode = argv[6]; std::string batchsize = argv[6];
std::string img_dir = argv[7]; std::string img_dir = argv[7];
std::string det_config_path = argv[8]; std::string det_config_path = argv[8];
if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) { if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) {
std::cerr << "Only support FP32 or INT8." << std::endl; std::cerr << "Only support FP32 or INT8." << std::endl;
exit(1); exit(1);
} }
...@@ -495,7 +484,7 @@ void det(int argc, char **argv) { ...@@ -495,7 +484,7 @@ void det(int argc, char **argv) {
//// load config from txt file //// load config from txt file
auto Config = LoadConfigTxt(det_config_path); auto Config = LoadConfigTxt(det_config_path);
auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads)); auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
std::vector<double> time_info = {0, 0, 0}; std::vector<double> time_info = {0, 0, 0};
for (int i = 0; i < cv_all_img_names.size(); ++i) { for (int i = 0; i < cv_all_img_names.size(); ++i) {
...@@ -530,14 +519,11 @@ void det(int argc, char **argv) { ...@@ -530,14 +519,11 @@ void det(int argc, char **argv) {
if (strcmp(argv[9], "True") == 0) { if (strcmp(argv[9], "True") == 0) {
AutoLogger autolog(det_model_file, AutoLogger autolog(det_model_file,
0, runtime_device,
0,
0,
std::stoi(num_threads), std::stoi(num_threads),
std::stoi(batchsize), std::stoi(batchsize),
"dynamic", "dynamic",
precision, precision,
power_mode,
time_info, time_info,
cv_all_img_names.size()); cv_all_img_names.size());
autolog.report(); autolog.report();
...@@ -546,14 +532,14 @@ void det(int argc, char **argv) { ...@@ -546,14 +532,14 @@ void det(int argc, char **argv) {
void rec(int argc, char **argv) { void rec(int argc, char **argv) {
std::string rec_model_file = argv[2]; std::string rec_model_file = argv[2];
std::string precision = argv[3]; std::string runtime_device = argv[3];
std::string num_threads = argv[4]; std::string precision = argv[4];
std::string batchsize = argv[5]; std::string num_threads = argv[5];
std::string power_mode = argv[6]; std::string batchsize = argv[6];
std::string img_dir = argv[7]; std::string img_dir = argv[7];
std::string dict_path = argv[8]; std::string dict_path = argv[8];
if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) { if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) {
std::cerr << "Only support FP32 or INT8." << std::endl; std::cerr << "Only support FP32 or INT8." << std::endl;
exit(1); exit(1);
} }
...@@ -565,7 +551,7 @@ void rec(int argc, char **argv) { ...@@ -565,7 +551,7 @@ void rec(int argc, char **argv) {
charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
charactor_dict.push_back(" "); charactor_dict.push_back(" ");
auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads)); auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
std::shared_ptr<PaddlePredictor> cls_predictor; std::shared_ptr<PaddlePredictor> cls_predictor;
...@@ -603,14 +589,11 @@ void rec(int argc, char **argv) { ...@@ -603,14 +589,11 @@ void rec(int argc, char **argv) {
// TODO: support autolog // TODO: support autolog
if (strcmp(argv[9], "True") == 0) { if (strcmp(argv[9], "True") == 0) {
AutoLogger autolog(rec_model_file, AutoLogger autolog(rec_model_file,
0, runtime_device,
0,
0,
std::stoi(num_threads), std::stoi(num_threads),
std::stoi(batchsize), std::stoi(batchsize),
"dynamic", "dynamic",
precision, precision,
power_mode,
time_info, time_info,
cv_all_img_names.size()); cv_all_img_names.size());
autolog.report(); autolog.report();
......
deploy/pdserving/pipeline_rpc_client.py
浏览文件 @ 55d71902
...@@ -41,6 +41,6 @@ for img_file in os.listdir(test_img_dir): ...@@ -41,6 +41,6 @@ for img_file in os.listdir(test_img_dir):
image_data = file.read() image_data = file.read()
image = cv2_to_base64(image_data) image = cv2_to_base64(image_data)
for i in range(1): for i in range(1):
ret = client.predict(feed_dict={"image": image}, fetch=["res"]) ret = client.predict(feed_dict={"image": image}, fetch=["res"])
print(ret) print(ret)
deploy/slim/prune/export_prune_model.py
浏览文件 @ 55d71902
...@@ -30,7 +30,7 @@ from ppocr.modeling.architectures import build_model ...@@ -30,7 +30,7 @@ from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
import tools.program as program import tools.program as program
...@@ -89,7 +89,7 @@ def main(config, device, logger, vdl_writer): ...@@ -89,7 +89,7 @@ def main(config, device, logger, vdl_writer):
logger.info(f"FLOPs after pruning: {flops}") logger.info(f"FLOPs after pruning: {flops}")
# load pretrain model # load pretrain model
pre_best_model_dict = init_model(config, model, logger, None) load_model(config, model)
metric = program.eval(model, valid_dataloader, post_process_class, metric = program.eval(model, valid_dataloader, post_process_class,
eval_class) eval_class)
logger.info(f"metric['hmean']: {metric['hmean']}") logger.info(f"metric['hmean']: {metric['hmean']}")
......
deploy/slim/prune/sensitivity_anal.py
浏览文件 @ 55d71902
...@@ -32,7 +32,7 @@ from ppocr.losses import build_loss ...@@ -32,7 +32,7 @@ from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
import tools.program as program import tools.program as program
dist.get_world_size() dist.get_world_size()
...@@ -94,7 +94,7 @@ def main(config, device, logger, vdl_writer): ...@@ -94,7 +94,7 @@ def main(config, device, logger, vdl_writer):
# build metric # build metric
eval_class = build_metric(config['Metric']) eval_class = build_metric(config['Metric'])
# load pretrain model # load pretrain model
pre_best_model_dict = init_model(config, model, logger, optimizer) pre_best_model_dict = load_model(config, model, optimizer)
logger.info('train dataloader has {} iters, valid dataloader has {} iters'. logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
format(len(train_dataloader), len(valid_dataloader))) format(len(train_dataloader), len(valid_dataloader)))
......
deploy/slim/quantization/export_model.py
浏览文件 @ 55d71902
...@@ -28,7 +28,7 @@ from paddle.jit import to_static ...@@ -28,7 +28,7 @@ from paddle.jit import to_static
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser from tools.program import load_config, merge_config, ArgsParser
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
...@@ -101,7 +101,7 @@ def main(): ...@@ -101,7 +101,7 @@ def main():
quanter = QAT(config=quant_config) quanter = QAT(config=quant_config)
quanter.quantize(model) quanter.quantize(model)
init_model(config, model) load_model(config, model)
model.eval() model.eval()
# build metric # build metric
......
deploy/slim/quantization/quant.py
浏览文件 @ 55d71902
...@@ -37,7 +37,7 @@ from ppocr.losses import build_loss ...@@ -37,7 +37,7 @@ from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
import tools.program as program import tools.program as program
from paddleslim.dygraph.quant import QAT from paddleslim.dygraph.quant import QAT
...@@ -137,7 +137,7 @@ def main(config, device, logger, vdl_writer): ...@@ -137,7 +137,7 @@ def main(config, device, logger, vdl_writer):
# build metric # build metric
eval_class = build_metric(config['Metric']) eval_class = build_metric(config['Metric'])
# load pretrain model # load pretrain model
pre_best_model_dict = init_model(config, model, logger, optimizer) pre_best_model_dict = load_model(config, model, optimizer)
logger.info('train dataloader has {} iters, valid dataloader has {} iters'. logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
format(len(train_dataloader), len(valid_dataloader))) format(len(train_dataloader), len(valid_dataloader)))
......
deploy/slim/quantization/quant_kl.py
浏览文件 @ 55d71902
...@@ -37,7 +37,7 @@ from ppocr.losses import build_loss ...@@ -37,7 +37,7 @@ from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
import tools.program as program import tools.program as program
import paddleslim import paddleslim
from paddleslim.dygraph.quant import QAT from paddleslim.dygraph.quant import QAT
......
doc/doc_ch/detection.md
浏览文件 @ 55d71902
...@@ -101,7 +101,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml \ ...@@ -101,7 +101,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml \
# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID # 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
# 多机多卡训练，通过 --ips 参数设置使用的机器IP地址，通过 --gpus 参数设置使用的GPU ID # 多机多卡训练，通过 --ips 参数设置使用的机器IP地址，通过 --gpus 参数设置使用的GPU ID
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
...@@ -109,14 +109,14 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1 ...@@ -109,14 +109,14 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1
上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。 上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
有关配置文件的详细解释，请参考[链接](./config.md)。 有关配置文件的详细解释，请参考[链接](./config.md)。
您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001 您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
```shell ```shell
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
``` ```
**注意:** 采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通。查看机器ip地址的命令为`ifconfig`。 **注意:** 采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通。另外，训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`。
如果您想进一步加快训练速度，可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html)， 以单机单卡为例，命令如下： 如果您想进一步加快训练速度，可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html)， 以单机单卡为例，命令如下：
```shell ```shell
python3 tools/train.py -c configs/det/det_mv3_db.yml \ python3 tools/train.py -c configs/det/det_mv3_db.yml \
......
doc/doc_ch/whl.md
浏览文件 @ 55d71902
...@@ -420,3 +420,5 @@ im_show.save('result.jpg') ...@@ -420,3 +420,5 @@ im_show.save('result.jpg')
| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE | | cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE |
| show_log | 是否打印det和rec等信息 | FALSE | | show_log | 是否打印det和rec等信息 | FALSE |
| type | 执行ocr或者表格结构化, 值可选['ocr','structure'] | ocr | | type | 执行ocr或者表格结构化, 值可选['ocr','structure'] | ocr |
| ocr_version | OCR模型版本，可选PP-OCRv2, PP-OCR。PP-OCRv2 目前仅支持中文的检测和识别模型，PP-OCR支持中文的检测，识别，多语种识别，方向分类器等模型 | PP-OCRv2 |
| structure_version | 表格结构化模型版本，可选 STRUCTURE。STRUCTURE支持表格结构化模型 | STRUCTURE |
doc/doc_en/detection_en.md
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...@@ -98,14 +98,14 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o \ ...@@ -98,14 +98,14 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o \
# multi-GPU training # multi-GPU training
# Set the GPU ID used by the '--gpus' parameter. # Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
# multi-Node, multi-GPU training # multi-Node, multi-GPU training
# Set the IPs of your nodes used by the '--ips' parameter. Set the GPU ID used by the '--gpus' parameter. # Set the IPs of your nodes used by the '--ips' parameter. Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
``` ```
**Note:** For multi-Node multi-GPU training, you need to replace the `ips` value in the preceding command with the address of your machine, and the machines must be able to ping each other. The command for viewing the IP address of the machine is `ifconfig`. **Note:** For multi-Node multi-GPU training, you need to replace the `ips` value in the preceding command with the address of your machine, and the machines must be able to ping each other. In addition, it requires activating commands separately on multiple machines when we start the training. The command for viewing the IP address of the machine is `ifconfig`.
If you want to further speed up the training, you can use [automatic mixed precision training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_en.html). for single card training, the command is as follows: If you want to further speed up the training, you can use [automatic mixed precision training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_en.html). for single card training, the command is as follows:
``` ```
python3 tools/train.py -c configs/det/det_mv3_db.yml \ python3 tools/train.py -c configs/det/det_mv3_db.yml \
......
doc/doc_en/whl_en.md
浏览文件 @ 55d71902
...@@ -366,4 +366,6 @@ im_show.save('result.jpg') ...@@ -366,4 +366,6 @@ im_show.save('result.jpg')
| rec | Enable recognition when `ppocr.ocr` func exec | TRUE | | rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE | | cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
| show_log | Whether to print log in det and rec | FALSE | | show_log | Whether to print log in det and rec | FALSE |
| type | Perform ocr or table structuring, the value is selected in ['ocr','structure'] | ocr | | type | Perform ocr or table structuring, the value is selected in ['ocr','structure'] | ocr |
\ No newline at end of file | ocr_version | OCR Model version number, the current model support list is as follows: PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv2 |
| structure_version | table structure Model version number, the current model support list is as follows: STRUCTURE support english table structure model | STRUCTURE |
paddleocr.py
浏览文件 @ 55d71902
...@@ -16,6 +16,9 @@ import os ...@@ -16,6 +16,9 @@ import os
import sys import sys
__dir__ = os.path.dirname(__file__) __dir__ = os.path.dirname(__file__)
import paddle
sys.path.append(os.path.join(__dir__, '')) sys.path.append(os.path.join(__dir__, ''))
import cv2 import cv2
...@@ -29,7 +32,7 @@ from ppocr.utils.logging import get_logger ...@@ -29,7 +32,7 @@ from ppocr.utils.logging import get_logger
logger = get_logger() logger = get_logger()
from ppocr.utils.utility import check_and_read_gif, get_image_file_list from ppocr.utils.utility import check_and_read_gif, get_image_file_list
from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
from tools.infer.utility import draw_ocr, str2bool from tools.infer.utility import draw_ocr, str2bool, check_gpu
from ppstructure.utility import init_args, draw_structure_result from ppstructure.utility import init_args, draw_structure_result
from ppstructure.predict_system import OCRSystem, save_structure_res from ppstructure.predict_system import OCRSystem, save_structure_res
...@@ -39,130 +42,137 @@ __all__ = [ ...@@ -39,130 +42,137 @@ __all__ = [
] ]
SUPPORT_DET_MODEL = ['DB'] SUPPORT_DET_MODEL = ['DB']
VERSION = '2.2.1' VERSION = '2.3.0.1'
SUPPORT_REC_MODEL = ['CRNN'] SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/") BASE_DIR = os.path.expanduser("~/.paddleocr/")
DEFAULT_MODEL_VERSION = '2.0' DEFAULT_OCR_MODEL_VERSION = 'PP-OCR'
DEFAULT_STRUCTURE_MODEL_VERSION = 'STRUCTURE'
MODEL_URLS = { MODEL_URLS = {
'2.1': { 'OCR': {
'det': { 'PP-OCRv2': {
'ch': { 'det': {
'url': 'ch': {
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar', 'url':
}, 'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar',
}, },
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
}
}
},
'2.0': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
}, },
'structure': { 'rec': {
'url': 'ch': {
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar' 'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
}
} }
}, },
'rec': { DEFAULT_OCR_MODEL_VERSION: {
'ch': { 'det': {
'url': 'ch': {
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar', 'url':
'dict_path': './ppocr/utils/ppocr_keys_v1.txt' 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
}, },
'en': { 'en': {
'url': 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar', 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt' },
}, 'structure': {
'french': { 'url':
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar', }
'dict_path': './ppocr/utils/dict/french_dict.txt'
},
'german': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/german_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
}, },
'te': { 'rec': {
'url': 'ch': {
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar', 'url':
'dict_path': './ppocr/utils/dict/te_dict.txt' 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'french': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/french_dict.txt'
},
'german': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/german_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
'structure': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
'dict_path': 'ppocr/utils/dict/table_dict.txt'
}
}, },
'ka': { 'cls': {
'url': 'ch': {
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar', 'url':
'dict_path': './ppocr/utils/dict/ka_dict.txt' 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
}, },
'latin': { }
'url': },
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar', 'STRUCTURE': {
'dict_path': './ppocr/utils/dict/latin_dict.txt' DEFAULT_STRUCTURE_MODEL_VERSION: {
}, 'table': {
'arabic': { 'en': {
'url': 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar', 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt' 'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
}, }
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
'structure': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
'dict_path': 'ppocr/utils/dict/table_dict.txt'
}
},
'cls': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
},
'table': {
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
} }
} }
} }
...@@ -177,7 +187,20 @@ def parse_args(mMain=True): ...@@ -177,7 +187,20 @@ def parse_args(mMain=True):
parser.add_argument("--det", type=str2bool, default=True) parser.add_argument("--det", type=str2bool, default=True)
parser.add_argument("--rec", type=str2bool, default=True) parser.add_argument("--rec", type=str2bool, default=True)
parser.add_argument("--type", type=str, default='ocr') parser.add_argument("--type", type=str, default='ocr')
parser.add_argument("--version", type=str, default='2.1') parser.add_argument(
"--ocr_version",
type=str,
default='PP-OCRv2',
help='OCR Model version, the current model support list is as follows: '
'1. PP-OCRv2 Support Chinese detection and recognition model. '
'2. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.'
)
parser.add_argument(
"--structure_version",
type=str,
default='STRUCTURE',
help='Model version, the current model support list is as follows:'
' 1. STRUCTURE Support en table structure model.')
for action in parser._actions: for action in parser._actions:
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']: if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
...@@ -215,9 +238,9 @@ def parse_lang(lang): ...@@ -215,9 +238,9 @@ def parse_lang(lang):
lang = "cyrillic" lang = "cyrillic"
elif lang in devanagari_lang: elif lang in devanagari_lang:
lang = "devanagari" lang = "devanagari"
assert lang in MODEL_URLS[DEFAULT_MODEL_VERSION][ assert lang in MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION][
'rec'], 'param lang must in {}, but got {}'.format( 'rec'], 'param lang must in {}, but got {}'.format(
MODEL_URLS[DEFAULT_MODEL_VERSION]['rec'].keys(), lang) MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang)
if lang == "ch": if lang == "ch":
det_lang = "ch" det_lang = "ch"
elif lang == 'structure': elif lang == 'structure':
...@@ -227,33 +250,41 @@ def parse_lang(lang): ...@@ -227,33 +250,41 @@ def parse_lang(lang):
return lang, det_lang return lang, det_lang
def get_model_config(version, model_type, lang): def get_model_config(type, version, model_type, lang):
if version not in MODEL_URLS: if type == 'OCR':
logger.warning('version {} not in {}, use version {} instead'.format( DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION
version, MODEL_URLS.keys(), DEFAULT_MODEL_VERSION)) elif type == 'STRUCTURE':
DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION
else:
raise NotImplementedError
model_urls = MODEL_URLS[type]
if version not in model_urls:
logger.warning('version {} not in {}, auto switch to version {}'.format(
version, model_urls.keys(), DEFAULT_MODEL_VERSION))
version = DEFAULT_MODEL_VERSION version = DEFAULT_MODEL_VERSION
if model_type not in MODEL_URLS[version]: if model_type not in model_urls[version]:
if model_type in MODEL_URLS[DEFAULT_MODEL_VERSION]: if model_type in model_urls[DEFAULT_MODEL_VERSION]:
logger.warning( logger.warning(
'version {} not support {} models, use version {} instead'. 'version {} not support {} models, auto switch to version {}'.
format(version, model_type, DEFAULT_MODEL_VERSION)) format(version, model_type, DEFAULT_MODEL_VERSION))
version = DEFAULT_MODEL_VERSION version = DEFAULT_MODEL_VERSION
else: else:
logger.error('{} models is not support, we only support {}'.format( logger.error('{} models is not support, we only support {}'.format(
model_type, MODEL_URLS[DEFAULT_MODEL_VERSION].keys())) model_type, model_urls[DEFAULT_MODEL_VERSION].keys()))
sys.exit(-1) sys.exit(-1)
if lang not in MODEL_URLS[version][model_type]: if lang not in model_urls[version][model_type]:
if lang in MODEL_URLS[DEFAULT_MODEL_VERSION][model_type]: if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]:
logger.warning('lang {} is not support in {}, use {} instead'. logger.warning(
format(lang, version, DEFAULT_MODEL_VERSION)) 'lang {} is not support in {}, auto switch to version {}'.
format(lang, version, DEFAULT_MODEL_VERSION))
version = DEFAULT_MODEL_VERSION version = DEFAULT_MODEL_VERSION
else: else:
logger.error( logger.error(
'lang {} is not support, we only support {} for {} models'. 'lang {} is not support, we only support {} for {} models'.
format(lang, MODEL_URLS[DEFAULT_MODEL_VERSION][model_type].keys( format(lang, model_urls[DEFAULT_MODEL_VERSION][model_type].keys(
), model_type)) ), model_type))
sys.exit(-1) sys.exit(-1)
return MODEL_URLS[version][model_type][lang] return model_urls[version][model_type][lang]
class PaddleOCR(predict_system.TextSystem): class PaddleOCR(predict_system.TextSystem):
...@@ -265,23 +296,28 @@ class PaddleOCR(predict_system.TextSystem): ...@@ -265,23 +296,28 @@ class PaddleOCR(predict_system.TextSystem):
""" """
params = parse_args(mMain=False) params = parse_args(mMain=False)
params.__dict__.update(**kwargs) params.__dict__.update(**kwargs)
params.use_gpu = check_gpu(params.use_gpu)
if not params.show_log: if not params.show_log:
logger.setLevel(logging.INFO) logger.setLevel(logging.INFO)
self.use_angle_cls = params.use_angle_cls self.use_angle_cls = params.use_angle_cls
lang, det_lang = parse_lang(params.lang) lang, det_lang = parse_lang(params.lang)
# init model dir # init model dir
det_model_config = get_model_config(params.version, 'det', det_lang) det_model_config = get_model_config('OCR', params.ocr_version, 'det',
det_lang)
params.det_model_dir, det_url = confirm_model_dir_url( params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir, params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang), os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
det_model_config['url']) det_model_config['url'])
rec_model_config = get_model_config(params.version, 'rec', lang) rec_model_config = get_model_config('OCR', params.ocr_version, 'rec',
lang)
params.rec_model_dir, rec_url = confirm_model_dir_url( params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir, params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang), os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
rec_model_config['url']) rec_model_config['url'])
cls_model_config = get_model_config(params.version, 'cls', 'ch') cls_model_config = get_model_config('OCR', params.ocr_version, 'cls',
'ch')
params.cls_model_dir, cls_url = confirm_model_dir_url( params.cls_model_dir, cls_url = confirm_model_dir_url(
params.cls_model_dir, params.cls_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'), os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
...@@ -362,22 +398,27 @@ class PPStructure(OCRSystem): ...@@ -362,22 +398,27 @@ class PPStructure(OCRSystem):
def __init__(self, **kwargs): def __init__(self, **kwargs):
params = parse_args(mMain=False) params = parse_args(mMain=False)
params.__dict__.update(**kwargs) params.__dict__.update(**kwargs)
params.use_gpu = check_gpu(params.use_gpu)
if not params.show_log: if not params.show_log:
logger.setLevel(logging.INFO) logger.setLevel(logging.INFO)
lang, det_lang = parse_lang(params.lang) lang, det_lang = parse_lang(params.lang)
# init model dir # init model dir
det_model_config = get_model_config(params.version, 'det', det_lang) det_model_config = get_model_config('OCR', params.ocr_version, 'det',
det_lang)
params.det_model_dir, det_url = confirm_model_dir_url( params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir, params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang), os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
det_model_config['url']) det_model_config['url'])
rec_model_config = get_model_config(params.version, 'rec', lang) rec_model_config = get_model_config('OCR', params.ocr_version, 'rec',
lang)
params.rec_model_dir, rec_url = confirm_model_dir_url( params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir, params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang), os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
rec_model_config['url']) rec_model_config['url'])
table_model_config = get_model_config(params.version, 'table', 'en') table_model_config = get_model_config(
'STRUCTURE', params.structure_version, 'table', 'en')
params.table_model_dir, table_url = confirm_model_dir_url( params.table_model_dir, table_url = confirm_model_dir_url(
params.table_model_dir, params.table_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'table'), os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
......
ppocr/modeling/architectures/distillation_model.py
浏览文件 @ 55d71902
...@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone ...@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone
from ppocr.modeling.necks import build_neck from ppocr.modeling.necks import build_neck
from ppocr.modeling.heads import build_head from ppocr.modeling.heads import build_head
from .base_model import BaseModel from .base_model import BaseModel
from ppocr.utils.save_load import init_model, load_pretrained_params from ppocr.utils.save_load import load_pretrained_params
__all__ = ['DistillationModel'] __all__ = ['DistillationModel']
......
ppocr/modeling/heads/table_att_head.py
浏览文件 @ 55d71902
...@@ -23,32 +23,40 @@ import numpy as np ...@@ -23,32 +23,40 @@ import numpy as np
class TableAttentionHead(nn.Layer): class TableAttentionHead(nn.Layer):
def __init__(self, in_channels, hidden_size, loc_type, in_max_len=488, **kwargs): def __init__(self,
in_channels,
hidden_size,
loc_type,
in_max_len=488,
max_text_length=100,
max_elem_length=800,
max_cell_num=500,
**kwargs):
super(TableAttentionHead, self).__init__() super(TableAttentionHead, self).__init__()
self.input_size = in_channels[-1] self.input_size = in_channels[-1]
self.hidden_size = hidden_size self.hidden_size = hidden_size
self.elem_num = 30 self.elem_num = 30
self.max_text_length = 100 self.max_text_length = max_text_length
self.max_elem_length = 500 self.max_elem_length = max_elem_length
self.max_cell_num = 500 self.max_cell_num = max_cell_num
self.structure_attention_cell = AttentionGRUCell( self.structure_attention_cell = AttentionGRUCell(
self.input_size, hidden_size, self.elem_num, use_gru=False) self.input_size, hidden_size, self.elem_num, use_gru=False)
self.structure_generator = nn.Linear(hidden_size, self.elem_num) self.structure_generator = nn.Linear(hidden_size, self.elem_num)
self.loc_type = loc_type self.loc_type = loc_type
self.in_max_len = in_max_len self.in_max_len = in_max_len
if self.loc_type == 1: if self.loc_type == 1:
self.loc_generator = nn.Linear(hidden_size, 4) self.loc_generator = nn.Linear(hidden_size, 4)
else: else:
if self.in_max_len == 640: if self.in_max_len == 640:
self.loc_fea_trans = nn.Linear(400, self.max_elem_length+1) self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1)
elif self.in_max_len == 800: elif self.in_max_len == 800:
self.loc_fea_trans = nn.Linear(625, self.max_elem_length+1) self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1)
else: else:
self.loc_fea_trans = nn.Linear(256, self.max_elem_length+1) self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1)
self.loc_generator = nn.Linear(self.input_size + hidden_size, 4) self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
def _char_to_onehot(self, input_char, onehot_dim): def _char_to_onehot(self, input_char, onehot_dim):
input_ont_hot = F.one_hot(input_char, onehot_dim) input_ont_hot = F.one_hot(input_char, onehot_dim)
return input_ont_hot return input_ont_hot
...@@ -60,16 +68,16 @@ class TableAttentionHead(nn.Layer): ...@@ -60,16 +68,16 @@ class TableAttentionHead(nn.Layer):
if len(fea.shape) == 3: if len(fea.shape) == 3:
pass pass
else: else:
last_shape = int(np.prod(fea.shape[2:])) # gry added last_shape = int(np.prod(fea.shape[2:])) # gry added
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape]) fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels) fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
batch_size = fea.shape[0] batch_size = fea.shape[0]
hidden = paddle.zeros((batch_size, self.hidden_size)) hidden = paddle.zeros((batch_size, self.hidden_size))
output_hiddens = [] output_hiddens = []
if self.training and targets is not None: if self.training and targets is not None:
structure = targets[0] structure = targets[0]
for i in range(self.max_elem_length+1): for i in range(self.max_elem_length + 1):
elem_onehots = self._char_to_onehot( elem_onehots = self._char_to_onehot(
structure[:, i], onehot_dim=self.elem_num) structure[:, i], onehot_dim=self.elem_num)
(outputs, hidden), alpha = self.structure_attention_cell( (outputs, hidden), alpha = self.structure_attention_cell(
...@@ -96,7 +104,7 @@ class TableAttentionHead(nn.Layer): ...@@ -96,7 +104,7 @@ class TableAttentionHead(nn.Layer):
alpha = None alpha = None
max_elem_length = paddle.to_tensor(self.max_elem_length) max_elem_length = paddle.to_tensor(self.max_elem_length)
i = 0 i = 0
while i < max_elem_length+1: while i < max_elem_length + 1:
elem_onehots = self._char_to_onehot( elem_onehots = self._char_to_onehot(
temp_elem, onehot_dim=self.elem_num) temp_elem, onehot_dim=self.elem_num)
(outputs, hidden), alpha = self.structure_attention_cell( (outputs, hidden), alpha = self.structure_attention_cell(
...@@ -105,7 +113,7 @@ class TableAttentionHead(nn.Layer): ...@@ -105,7 +113,7 @@ class TableAttentionHead(nn.Layer):
structure_probs_step = self.structure_generator(outputs) structure_probs_step = self.structure_generator(outputs)
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32") temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
i += 1 i += 1
output = paddle.concat(output_hiddens, axis=1) output = paddle.concat(output_hiddens, axis=1)
structure_probs = self.structure_generator(output) structure_probs = self.structure_generator(output)
structure_probs = F.softmax(structure_probs) structure_probs = F.softmax(structure_probs)
...@@ -119,9 +127,9 @@ class TableAttentionHead(nn.Layer): ...@@ -119,9 +127,9 @@ class TableAttentionHead(nn.Layer):
loc_concat = paddle.concat([output, loc_fea], axis=2) loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat) loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds) loc_preds = F.sigmoid(loc_preds)
return {'structure_probs':structure_probs, 'loc_preds':loc_preds} return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
class AttentionGRUCell(nn.Layer): class AttentionGRUCell(nn.Layer):
def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
super(AttentionGRUCell, self).__init__() super(AttentionGRUCell, self).__init__()
......
ppocr/utils/network.py
浏览文件 @ 55d71902
...@@ -24,15 +24,17 @@ from ppocr.utils.logging import get_logger ...@@ -24,15 +24,17 @@ from ppocr.utils.logging import get_logger
def download_with_progressbar(url, save_path): def download_with_progressbar(url, save_path):
logger = get_logger() logger = get_logger()
response = requests.get(url, stream=True) response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0)) if response.status_code == 200:
block_size = 1024 # 1 Kibibyte total_size_in_bytes = int(response.headers.get('content-length', 1))
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) block_size = 1024 # 1 Kibibyte
with open(save_path, 'wb') as file: progress_bar = tqdm(
for data in response.iter_content(block_size): total=total_size_in_bytes, unit='iB', unit_scale=True)
progress_bar.update(len(data)) with open(save_path, 'wb') as file:
file.write(data) for data in response.iter_content(block_size):
progress_bar.close() progress_bar.update(len(data))
if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes: file.write(data)
progress_bar.close()
else:
logger.error("Something went wrong while downloading models") logger.error("Something went wrong while downloading models")
sys.exit(0) sys.exit(0)
...@@ -45,7 +47,7 @@ def maybe_download(model_storage_directory, url): ...@@ -45,7 +47,7 @@ def maybe_download(model_storage_directory, url):
if not os.path.exists( if not os.path.exists(
os.path.join(model_storage_directory, 'inference.pdiparams') os.path.join(model_storage_directory, 'inference.pdiparams')
) or not os.path.exists( ) or not os.path.exists(
os.path.join(model_storage_directory, 'inference.pdmodel')): os.path.join(model_storage_directory, 'inference.pdmodel')):
assert url.endswith('.tar'), 'Only supports tar compressed package' assert url.endswith('.tar'), 'Only supports tar compressed package'
tmp_path = os.path.join(model_storage_directory, url.split('/')[-1]) tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
print('download {} to {}'.format(url, tmp_path)) print('download {} to {}'.format(url, tmp_path))
......
ppocr/utils/save_load.py
浏览文件 @ 55d71902
...@@ -25,7 +25,7 @@ import paddle ...@@ -25,7 +25,7 @@ import paddle
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
__all__ = ['init_model', 'save_model', 'load_dygraph_params'] __all__ = ['load_model']
def _mkdir_if_not_exist(path, logger): def _mkdir_if_not_exist(path, logger):
...@@ -44,7 +44,7 @@ def _mkdir_if_not_exist(path, logger): ...@@ -44,7 +44,7 @@ def _mkdir_if_not_exist(path, logger):
raise OSError('Failed to mkdir {}'.format(path)) raise OSError('Failed to mkdir {}'.format(path))
def init_model(config, model, optimizer=None, lr_scheduler=None): def load_model(config, model, optimizer=None):
""" """
load model from checkpoint or pretrained_model load model from checkpoint or pretrained_model
""" """
...@@ -54,15 +54,14 @@ def init_model(config, model, optimizer=None, lr_scheduler=None): ...@@ -54,15 +54,14 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
pretrained_model = global_config.get('pretrained_model') pretrained_model = global_config.get('pretrained_model')
best_model_dict = {} best_model_dict = {}
if checkpoints: if checkpoints:
assert os.path.exists(checkpoints + ".pdparams"), \ if checkpoints.endswith('pdparams'):
"Given dir {}.pdparams not exist.".format(checkpoints) checkpoints = checkpoints.replace('.pdparams', '')
assert os.path.exists(checkpoints + ".pdopt"), \ assert os.path.exists(checkpoints + ".pdopt"), \
"Given dir {}.pdopt not exist.".format(checkpoints) f"The {checkpoints}.pdopt does not exists!"
para_dict = paddle.load(checkpoints + '.pdparams') load_pretrained_params(model, checkpoints)
opti_dict = paddle.load(checkpoints + '.pdopt') optim_dict = paddle.load(checkpoints + '.pdopt')
model.set_state_dict(para_dict)
if optimizer is not None: if optimizer is not None:
optimizer.set_state_dict(opti_dict) optimizer.set_state_dict(optim_dict)
if os.path.exists(checkpoints + '.states'): if os.path.exists(checkpoints + '.states'):
with open(checkpoints + '.states', 'rb') as f: with open(checkpoints + '.states', 'rb') as f:
...@@ -73,70 +72,31 @@ def init_model(config, model, optimizer=None, lr_scheduler=None): ...@@ -73,70 +72,31 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
best_model_dict['start_epoch'] = states_dict['epoch'] + 1 best_model_dict['start_epoch'] = states_dict['epoch'] + 1
logger.info("resume from {}".format(checkpoints)) logger.info("resume from {}".format(checkpoints))
elif pretrained_model: elif pretrained_model:
if not isinstance(pretrained_model, list): load_pretrained_params(model, pretrained_model)
pretrained_model = [pretrained_model]
for pretrained in pretrained_model:
if not (os.path.isdir(pretrained) or
os.path.exists(pretrained + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(pretrained))
param_state_dict = paddle.load(pretrained + '.pdparams')
model.set_state_dict(param_state_dict)
logger.info("load pretrained model from {}".format(
pretrained_model))
else: else:
logger.info('train from scratch') logger.info('train from scratch')
return best_model_dict return best_model_dict
def load_dygraph_params(config, model, logger, optimizer):
ckp = config['Global']['checkpoints']
if ckp and os.path.exists(ckp + ".pdparams"):
pre_best_model_dict = init_model(config, model, optimizer)
return pre_best_model_dict
else:
pm = config['Global']['pretrained_model']
if pm is None:
return {}
if not os.path.exists(pm) and not os.path.exists(pm + ".pdparams"):
logger.info(f"The pretrained_model {pm} does not exists!")
return {}
pm = pm if pm.endswith('.pdparams') else pm + '.pdparams'
params = paddle.load(pm)
state_dict = model.state_dict()
new_state_dict = {}
for k1, k2 in zip(state_dict.keys(), params.keys()):
if list(state_dict[k1].shape) == list(params[k2].shape):
new_state_dict[k1] = params[k2]
else:
logger.info(
f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
)
model.set_state_dict(new_state_dict)
logger.info(f"loaded pretrained_model successful from {pm}")
return {}
def load_pretrained_params(model, path): def load_pretrained_params(model, path):
if path is None: logger = get_logger()
return False if path.endswith('pdparams'):
if not os.path.exists(path) and not os.path.exists(path + ".pdparams"): path = path.replace('.pdparams', '')
print(f"The pretrained_model {path} does not exists!") assert os.path.exists(path + ".pdparams"), \
return False f"The {path}.pdparams does not exists!"
path = path if path.endswith('.pdparams') else path + '.pdparams' params = paddle.load(path + '.pdparams')
params = paddle.load(path)
state_dict = model.state_dict() state_dict = model.state_dict()
new_state_dict = {} new_state_dict = {}
for k1, k2 in zip(state_dict.keys(), params.keys()): for k1, k2 in zip(state_dict.keys(), params.keys()):
if list(state_dict[k1].shape) == list(params[k2].shape): if list(state_dict[k1].shape) == list(params[k2].shape):
new_state_dict[k1] = params[k2] new_state_dict[k1] = params[k2]
else: else:
print( logger.info(
f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !" f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
) )
model.set_state_dict(new_state_dict) model.set_state_dict(new_state_dict)
print(f"load pretrain successful from {path}") logger.info(f"load pretrain successful from {path}")
return model return model
......
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================cpp_infer_params===========================
model_name:ocr_det
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/
null:null
--benchmark:True
\ No newline at end of file
test_tipc/configs/jeston_ppocr_det_mobile_params.txt → test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt
浏览文件 @ 55d71902
===========================train_params=========================== ===========================infer_params===========================
model_name:ocr_det model_name:ocr_det
python:python python:python
gpu_list:null infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:null
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:null
null:null
##
trainer:null
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:null
norm_export:null
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/ch_ppocr_mobile_v2.0_det_infer
infer_export:null infer_export:null
infer_quant:False infer_quant:False
inference:tools/infer/predict_det.py inference:tools/infer/predict_det.py
......
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================lite_params===========================
inference:./ocr_db_crnn det
infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
runtime_device:ARM_CPU
--cpu_threads:1|4
--det_batch_size:1
--rec_batch_size:1
--system_batch_size:1
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
--benchmark:True
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================paddle2onnx_params===========================
model_name:ocr_det_mobile
python:python3.7
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/det_mobile_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_det.py
--use_gpu:True|False
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
\ No newline at end of file
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================serving_params===========================
model_name:ocr_det_mobile
python:python3.7|cpp
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs
\ No newline at end of file
test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 0 → 100644
浏览文件 @ 55d71902
===========================train_params===========================
model_name:ocr_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_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|fpgm_train
norm_train:tools/train.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ppocr_det_mobile/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
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
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/
null:null
--benchmark:True
null:null
\ No newline at end of file
test_tipc/configs/fleet_ppocr_det_mobile_params.txt → test_tipc/configs/ppocr_det_mobile/train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt
浏览文件 @ 55d71902
===========================train_params=========================== ===========================train_params===========================
model_name:ocr_det model_name:ocr_det
python:python3.7 python:python3.7
gpu_list:xx.xx.xx.xx,xx.xx.xx.xx;0,1 gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
Global.use_gpu:True|True Global.use_gpu:True
Global.auto_cast:null|amp Global.auto_cast:fp32|amp
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
Global.save_model_dir:./output/ Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
...@@ -49,62 +49,3 @@ inference:tools/infer/predict_det.py ...@@ -49,62 +49,3 @@ inference:tools/infer/predict_det.py
null:null null:null
--benchmark:True --benchmark:True
null:null null:null
===========================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/
null:null
--benchmark:True
===========================serving_params===========================
model_name:ocr_det
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py --image_dir=../../doc/imgs
===========================kl_quant_params===========================
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
infer_quant:True
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:int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
null:null
===========================lite_params===========================
inference:./ocr_db_crnn det
infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb
--cpu_threads:1|4
--batch_size:1
--power_mode:LITE_POWER_HIGH|LITE_POWER_LOW
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
--benchmark:True
test_tipc/configs/amp_ppocr_det_mobile_params.txt → test_tipc/configs/ppocr_det_mobile/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt
浏览文件 @ 55d71902
...@@ -49,62 +49,3 @@ inference:tools/infer/predict_det.py ...@@ -49,62 +49,3 @@ inference:tools/infer/predict_det.py
null:null null:null
--benchmark:True --benchmark:True
null:null null:null
===========================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/
null:null
--benchmark:True
===========================serving_params===========================
model_name:ocr_det
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py --image_dir=../../doc/imgs
===========================kl_quant_params===========================
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
infer_quant:True
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:int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
null:null
===========================lite_params===========================
inference:./ocr_db_crnn det
infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb
--cpu_threads:1|4
--batch_size:1
--power_mode:LITE_POWER_HIGH|LITE_POWER_LOW
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
--benchmark:True
test_tipc/configs/ppocr_det_mobile_params.txt 已删除 100644 → 0
浏览文件 @ 1d03fd33
===========================train_params===========================
model_name:ocr_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_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|fpgm_train
norm_train:tools/train.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/det_mv3_db.yml -o
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/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
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/det_mv3_db.yml -o
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
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/
null:null
--benchmark:True
null:null
===========================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/
null:null
--benchmark:True
===========================serving_params===========================
model_name:ocr_det
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs
===========================kl_quant_params===========================
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
infer_quant:True
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:int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
null:null
===========================lite_params===========================
inference:./ocr_db_crnn det
infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb
--cpu_threads:1|4
--batch_size:1
--power_mode:LITE_POWER_HIGH|LITE_POWER_LOW
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
--benchmark:True
===========================paddle2onnx_params===========================
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/det_mobile_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_det.py
--use_gpu:False
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
\ No newline at end of file
test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================paddle2onnx_params===========================
model_name:ocr_det_server
python:python3.7
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_server_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/det_server_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_det.py
--use_gpu:True|False
--det_model_dir:
--image_dir:./inference/det_inference
\ No newline at end of file
test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================serving_params===========================
model_name:ocr_det_server
python:python3.7|cpp
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_server_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_server_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_server_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs_words_en
\ No newline at end of file
test_tipc/configs/ppocr_det_server_params.txt → test_tipc/configs/ppocr_det_server/train_infer_python.txt
浏览文件 @ 55d71902
...@@ -13,21 +13,21 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/ ...@@ -13,21 +13,21 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null null:null
## ##
trainer:norm_train|pact_train|fpgm_export trainer:norm_train|pact_train|fpgm_export
norm_train:tools/train.py -c tests/configs/det_r50_vd_db.yml -o norm_train:tools/train.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_r50_vd_db.yml -o quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py -c tests/configs/det_r50_vd_db.yml -o fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o
distill_train:null distill_train:null
null:null null:null
null:null null:null
## ##
===========================eval_params=========================== ===========================eval_params===========================
eval:tools/eval.py -c tests/configs/det_r50_vd_db.yml -o eval:tools/eval.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o
null:null null:null
## ##
===========================infer_params=========================== ===========================infer_params===========================
Global.save_inference_dir:./output/ Global.save_inference_dir:./output/
Global.pretrained_model: Global.pretrained_model:
norm_export:tools/export_model.py -c tests/configs/det_r50_vd_db.yml -o norm_export:tools/export_model.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o
quant_export:null quant_export:null
fpgm_export:null fpgm_export:null
distill_export:null distill_export:null
...@@ -48,37 +48,4 @@ inference:tools/infer/predict_det.py ...@@ -48,37 +48,4 @@ inference:tools/infer/predict_det.py
--image_dir:./inference/ch_det_data_50/all-sum-510/ --image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null --save_log_path:null
--benchmark:True --benchmark:True
null:null null:null
===========================cpp_infer_params=========================== \ No newline at end of file
use_opencv:True
infer_model:./inference/ch_ppocr_server_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/
null:null
--benchmark:True
===========================serving_params===========================
model_name:ocr_det_server
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_server_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py|pipeline_rpc_client.py
--image_dir=../../doc/imgs
test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 0 → 100644
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===========================paddle2onnx_params===========================
model_name:ocr_rec_mobile
python:python3.7
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/rec_mobile_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--rec_model_dir:
--image_dir:./inference/rec_inference
\ No newline at end of file
test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================serving_params===========================
model_name:ocr_rec_mobile
python:python3.7|cpp
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
op.rec.local_service_conf.devices:null|0
op.rec.local_service_conf.use_mkldnn:True|False
op.rec.local_service_conf.thread_num:1|6
op.rec.local_service_conf.use_trt:False|True
op.rec.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs_words_en
\ No newline at end of file
test_tipc/configs/ppocr_rec_mobile_params.txt 已删除 100644 → 0
浏览文件 @ 1d03fd33
===========================train_params===========================
model_name:ocr_rec
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=128|whole_train_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o
pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr rec
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--rec_model_dir:
--image_dir:./inference/rec_inference/
null:null
--benchmark:True
===========================serving_params===========================
model_name:ocr_rec
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
op.rec.local_service_conf.devices:null|0
op.rec.local_service_conf.use_mkldnn:True|False
op.rec.local_service_conf.thread_num:1|6
op.rec.local_service_conf.use_trt:False|True
op.rec.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py|pipeline_rpc_client.py
--image_dir=../../doc/imgs_words_en
test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================paddle2onnx_params===========================
model_name:ocr_rec_server
python:python3.7
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/rec_server_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--rec_model_dir:
--image_dir:./inference/rec_inference
\ No newline at end of file
test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 55d71902
===========================serving_params===========================
model_name:ocr_rec_server
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
op.rec.local_service_conf.devices:null|0
op.rec.local_service_conf.use_mkldnn:True|False
op.rec.local_service_conf.thread_num:1|6
op.rec.local_service_conf.use_trt:False|True
op.rec.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs_words_en
\ No newline at end of file
test_tipc/configs/ppocr_rec_server_params.txt 已删除 100644 → 0
浏览文件 @ 1d03fd33
===========================train_params===========================
model_name:ocr_server_rec
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=128|whole_train_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c tests/configs/rec_icdar15_r34_train.yml -o
pact_train:deploy/slim/quantization/quant.py -c tests/configs/rec_icdar15_r34_train.yml -o
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c tests/configs/rec_icdar15_r34_train.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o
quant_export:deploy/slim/quantization/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1|6
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr rec
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--rec_model_dir:
--image_dir:./inference/rec_inference/
null:null
--benchmark:True
===========================serving_params===========================
model_name:ocr_server_rec
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
op.rec.local_service_conf.devices:null|0
op.rec.local_service_conf.use_mkldnn:True|False
op.rec.local_service_conf.thread_num:1|6
op.rec.local_service_conf.use_trt:False|True
op.rec.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py|pipeline_rpc_client.py
--image_dir=../../doc/imgs_words_en
test_tipc/configs/ppocr_sys_mobile_params.txt 已删除 100644 → 0
浏览文件 @ 1d03fd33
===========================train_params===========================
model_name:ocr_system_mobile
python:python3.7
gpu_list:null
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:null
train_infer_img_dir:null
null:null
##
trainer:
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:null
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_system.py
--use_gpu:True
--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
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
===========================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 system
--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/
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--benchmark:True
test_tipc/configs/ppocr_sys_server_params.txt 已删除 100644 → 0
浏览文件 @ 1d03fd33
===========================train_params===========================
model_name:ocr_system_server
python:python3.7
gpu_list:null
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:null
train_infer_img_dir:null
null:null
##
trainer:
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:null
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_system.py
--use_gpu:True
--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
--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr system
--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/
--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
--benchmark:True
\ No newline at end of file
test_tipc/docs/install.md
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## 1. 环境准备 ## 1. 环境准备
本教程适用于PTDN目录下基础功能测试的运行环境搭建。 本教程适用于test_tipc目录下基础功能测试的运行环境搭建。
推荐环境： 推荐环境：
- CUDA 10.1/10.2 - CUDA 10.1/10.2
......
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test_tipc/docs/test_inference_cpp.md
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...@@ -20,12 +20,12 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于 ...@@ -20,12 +20,12 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于
先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。 先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "cpp_infer" bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt "cpp_infer"
# 用法1: # 用法1:
bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt bash test_tipc/test_inference_cpp.sh test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt '1' bash test_tipc/test_inference_cpp.sh test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt '1'
``` ```
运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件： 运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件：
......
test_tipc/docs/test_lite.md → test_tipc/docs/test_lite_arm_cpu_cpp.md
浏览文件 @ 55d71902
# Lite预测功能测试 # Lite\_arm\_cpu\_cpp预测功能测试
Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预测库的模型推理功能。 Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以在ARM CPU上基于Lite预测库测试模型的C++推理功能。
## 1. 测试结论汇总 ## 1. 测试结论汇总
目前Lite端的样本间支持以方式的组合： 目前Lite端的样本间支持以方式的组合：
**字段说明：** **字段说明：**
- 输入设置：包括C++预测、python预测、java预测 - 模型类型：包括正常模型（FP32）和量化模型（INT8）
- 模型类型：包括正常模型（FP32）和量化模型（FP16）
- batch-size：包括1和4 - batch-size：包括1和4
- threads：包括1和4
- predictor数量：包括多predictor预测和单predictor预测 - predictor数量：包括多predictor预测和单predictor预测
- 功耗模式：包括高性能模式（LITE_POWER_HIGH）和省电模式（LITE_POWER_LOW） - 预测库来源：包括下载方式和编译方式
- 预测库来源：包括下载方式和编译方式，其中编译方式分为以下目标硬件：(1)ARM CPU;(2)Linux XPU;(3)OpenCL GPU;(4)Metal GPU
| 模型类型 | batch-size | predictor数量 | 功耗模式 | 预测库来源 | 支持语言 | | 模型类型 | batch-size | threads | predictor数量 | 预测库来源 |
| :----: | :----: | :----: | :----: | :----: | :----: | | :----: | :----: | :----: | :----: | :----: |
| 正常模型/量化模型 | 1 | 1 | 高性能模式/省电模式 | 下载方式 | C++预测 | | 正常模型/量化模型 | 1 | 1/4 | 1 | 下载方式 |
## 2. 测试流程 ## 2. 测试流程
...@@ -24,15 +23,15 @@ Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预 ...@@ -24,15 +23,15 @@ Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预
### 2.1 功能测试 ### 2.1 功能测试
先运行`prepare.sh`准备数据和模型，模型和数据会打包到test_lite.tar中，将test_lite.tar上传到手机上，解压后进`入test_lite`目录中，然后运行`test_lite.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。 先运行`prepare_lite.sh`，运行后会在当前路径下生成`test_lite.tar`，其中包含了测试数据、测试模型和用于预测的可执行文件。将`test_lite.tar`上传到被测试的手机上，在手机的终端解压该文件，进入`test_lite`目录中，然后运行`test_lite_arm_cpu_cpp.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
```shell ```shell
# 数据和模型准备 # 数据和模型准备
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "lite_infer" bash test_tipc/prepare_lite.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
# 手机端测试: # 手机端测试:
bash test_lite.sh ppocr_det_mobile_params.txt bash test_lite_arm_cpu_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
``` ```
...@@ -44,7 +43,7 @@ bash test_lite.sh ppocr_det_mobile_params.txt ...@@ -44,7 +43,7 @@ bash test_lite.sh ppocr_det_mobile_params.txt
运行成功时会输出： 运行成功时会输出：
``` ```
Run successfully with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1! Run successfully with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1 ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
Run successfully with command xxx Run successfully with command xxx
... ...
``` ```
...@@ -52,7 +51,7 @@ Run successfully with command xxx ...@@ -52,7 +51,7 @@ Run successfully with command xxx
运行失败时会输出： 运行失败时会输出：
``` ```
Run failed with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1! Run failed with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1 ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
Run failed with command xxx Run failed with command xxx
... ...
``` ```
......
test_tipc/docs/test_paddle2onnx.md
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...@@ -18,10 +18,10 @@ PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测 ...@@ -18,10 +18,10 @@ PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测
先运行`prepare.sh`准备数据和模型，然后运行`test_paddle2onnx.sh`进行测试，最终在```test_tipc/output```目录下生成`paddle2onnx_infer_*.log`后缀的日志文件。 先运行`prepare.sh`准备数据和模型，然后运行`test_paddle2onnx.sh`进行测试，最终在```test_tipc/output```目录下生成`paddle2onnx_infer_*.log`后缀的日志文件。
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "paddle2onnx_infer" bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt "paddle2onnx_infer"
# 用法: # 用法:
bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile_params.txt bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
``` ```
#### 运行结果 #### 运行结果
......
test_tipc/docs/test_serving.md
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...@@ -20,10 +20,10 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试 ...@@ -20,10 +20,10 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试
先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。 先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "serving_infer" bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt "serving_infer"
# 用法: # 用法:
bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile_params.txt bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
``` ```
#### 运行结果 #### 运行结果
......
test_tipc/docs/test_train_inference_python.md
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...@@ -64,35 +64,35 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho ...@@ -64,35 +64,35 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
- 模式1：lite_train_lite_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； - 模式1：lite_train_lite_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；
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_lite_infer' bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_lite_infer'
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_lite_infer' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_lite_infer'
``` ```
- 模式2：lite_train_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； - 模式2：lite_train_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；
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_whole_infer' bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_whole_infer'
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_whole_infer' bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_whole_infer'
``` ```
- 模式3：whole_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; - 模式3：whole_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;
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_infer'
# 用法1: # 用法1:
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_infer'
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' '1' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_infer' '1'
``` ```
- 模式4：whole_train_whole_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； - 模式4：whole_train_whole_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；
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_whole_infer' bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_train_whole_infer'
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_whole_infer' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_train_whole_infer'
``` ```
- 模式5：klquant_whole_infer，测试离线量化； - 模式5：klquant_whole_infer，测试离线量化；
```shell ```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_whole_infer' bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'klquant_whole_infer'
bash test_tipc/test_train_inference_python.sh test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_whole_infer' bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'klquant_whole_infer'
``` ```
运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件： 运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
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test_tipc/prepare.sh
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#!/bin/bash #!/bin/bash
source test_tipc/common_func.sh
FILENAME=$1 FILENAME=$1
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',
# 'whole_infer', 'klquant_whole_infer', # 'whole_infer', 'klquant_whole_infer',
# 'cpp_infer', 'serving_infer', 'lite_infer'] # 'cpp_infer', 'serving_infer']
MODE=$2 MODE=$2
...@@ -12,30 +14,12 @@ dataline=$(cat ${FILENAME}) ...@@ -12,30 +14,12 @@ dataline=$(cat ${FILENAME})
# parser params # parser params
IFS=$'\n' IFS=$'\n'
lines=(${dataline}) lines=(${dataline})
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
IFS=$'\n'
# The training params # The training params
model_name=$(func_parser_value "${lines[1]}") model_name=$(func_parser_value "${lines[1]}")
trainer_list=$(func_parser_value "${lines[14]}") trainer_list=$(func_parser_value "${lines[14]}")
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',
# 'whole_infer', 'klquant_whole_infer',
# 'cpp_infer', 'serving_infer', 'lite_infer']
MODE=$2
if [ ${MODE} = "lite_train_lite_infer" ];then if [ ${MODE} = "lite_train_lite_infer" ];then
# pretrain lite train data # pretrain lite train data
...@@ -87,7 +71,8 @@ elif [ ${MODE} = "whole_infer" ];then ...@@ -87,7 +71,8 @@ elif [ ${MODE} = "whole_infer" ];then
rm -rf ./train_data/icdar2015 rm -rf ./train_data/icdar2015
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../ wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ../
elif [ ${model_name} = "ocr_server_det" ]; then 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_train.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
...@@ -168,40 +153,6 @@ if [ ${MODE} = "serving_infer" ];then ...@@ -168,40 +153,6 @@ if [ ${MODE} = "serving_infer" ];then
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar && cd ../ cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar && cd ../
fi fi
if [ ${MODE} = "lite_infer" ];then
# prepare lite nb model and test data
current_dir=${PWD}
wget -nc -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb
wget -nc -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb
wget -nc -P ./test_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
cd ./test_data && tar -xf icdar2015_lite.tar && rm icdar2015_lite.tar && cd ../
# prepare lite env
export http_proxy=http://172.19.57.45:3128
export https_proxy=http://172.19.57.45:3128
paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
paddlelite_file=${paddlelite_zipfile:0:66}
wget ${paddlelite_url}
tar -xf ${paddlelite_zipfile}
mkdir -p ${paddlelite_file}/demo/cxx/ocr/test_lite
mv models test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
cp test_tipc/configs/ppocr_det_mobile_params.txt test_tipc/test_lite.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
cd ${paddlelite_file}/demo/cxx/ocr/
git clone https://github.com/LDOUBLEV/AutoLog.git
unset http_proxy
unset https_proxy
make -j
sleep 1
make -j
cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
fi
if [ ${MODE} = "paddle2onnx_infer" ];then if [ ${MODE} = "paddle2onnx_infer" ];then
# prepare serving env # prepare serving env
python_name=$(func_parser_value "${lines[2]}") python_name=$(func_parser_value "${lines[2]}")
......
test_tipc/prepare_lite.sh 0 → 100644
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#!/bin/bash
source ./test_tipc/common_func.sh
FILENAME=$1
dataline=$(cat ${FILENAME})
# parser params
IFS=$'\n'
lines=(${dataline})
IFS=$'\n'
lite_model_list=$(func_parser_value "${lines[2]}")
# prepare lite .nb model
pip install paddlelite==2.9
current_dir=${PWD}
IFS="|"
model_path=./inference_models
for model in ${lite_model_list[*]}; do
inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
inference_model=${inference_model_url##*/}
wget -nc -P ${model_path} ${inference_model_url}
cd ${model_path} && tar -xf ${inference_model} && cd ../
model_dir=${model_path}/${inference_model%.*}
model_file=${model_dir}/inference.pdmodel
param_file=${model_dir}/inference.pdiparams
paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=arm --optimize_out=${model_dir}_opt
done
# prepare test data
data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
model_path=./inference_models
inference_model=${inference_model_url##*/}
data_file=${data_url##*/}
wget -nc -P ./inference_models ${inference_model_url}
wget -nc -P ./test_data ${data_url}
cd ./inference_models && tar -xf ${inference_model} && cd ../
cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
# prepare lite env
paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
paddlelite_file=${paddlelite_zipfile:0:66}
wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
mkdir -p ${paddlelite_file}/demo/cxx/ocr/test_lite
cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ${FILENAME} test_tipc/test_lite_arm_cpu_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
cd ${paddlelite_file}/demo/cxx/ocr/
git clone https://github.com/cuicheng01/AutoLog.git
make -j
sleep 1
make -j
cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
rm -rf ${paddlelite_file}* && rm -rf ${model_path}
test_tipc/readme.md
浏览文件 @ 55d71902
...@@ -60,16 +60,20 @@ ...@@ -60,16 +60,20 @@
```shell ```shell
test_tipc/ test_tipc/
├── configs/ # 配置文件目录 ├── configs/ # 配置文件目录
├── det_mv3_db.yml # 测试mobile版ppocr检测模型训练的yml文件 ├── ppocr_det_mobile # ppocr_det_mobile模型的测试配置文件目录
├── det_r50_vd_db.yml # 测试server版ppocr检测模型训练的yml文件 ├── det_mv3_db.yml # 测试mobile版ppocr检测模型训练的yml文件
├── rec_icdar15_r34_train.yml # 测试server版ppocr识别模型训练的yml文件 ├── train_infer_python.txt.txt # 测试Linux上python训练预测（基础训练预测）的配置文件
├── ppocr_sys_mobile_params.txt # 测试mobile版ppocr检测+识别模型串联的参数配置文件 ├── model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt # 测试Linux上c++预测的配置文件
├── ppocr_det_mobile_params.txt # 测试mobile版ppocr检测模型的参数配置文件 ├── model_linux_gpu_normal_normal_infer_python_jetson.txt # 测试Jetson上python预测的配置文件
├── ppocr_rec_mobile_params.txt # 测试mobile版ppocr识别模型的参数配置文件 ├── train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt # 测试Linux上多机多卡、混合精度训练和python预测的配置文件
├── ppocr_sys_server_params.txt # 测试server版ppocr检测+识别模型串联的参数配置文件 ├── ...
├── ppocr_det_server_params.txt # 测试server版ppocr检测模型的参数配置文件 ├── ppocr_det_server # ppocr_det_server模型的测试配置文件目录
├── ppocr_rec_server_params.txt # 测试server版ppocr识别模型的参数配置文件 ├── ...
├── ... ├── ppocr_rec_mobile # ppocr_rec_mobile模型的测试配置文件目录
├── ...
├── ppocr_rec_server # ppocr_rec_server模型的测试配置文件目录
├── ...
├── ...
├── results/ # 预先保存的预测结果，用于和实际预测结果进行精读比对 ├── results/ # 预先保存的预测结果，用于和实际预测结果进行精读比对
├── python_ppocr_det_mobile_results_fp32.txt # 预存的mobile版ppocr检测模型python预测fp32精度的结果 ├── python_ppocr_det_mobile_results_fp32.txt # 预存的mobile版ppocr检测模型python预测fp32精度的结果
├── python_ppocr_det_mobile_results_fp16.txt # 预存的mobile版ppocr检测模型python预测fp16精度的结果 ├── python_ppocr_det_mobile_results_fp16.txt # 预存的mobile版ppocr检测模型python预测fp16精度的结果
...@@ -80,11 +84,22 @@ test_tipc/ ...@@ -80,11 +84,22 @@ test_tipc/
├── test_train_inference_python.sh # 测试python训练预测的主程序 ├── test_train_inference_python.sh # 测试python训练预测的主程序
├── test_inference_cpp.sh # 测试c++预测的主程序 ├── test_inference_cpp.sh # 测试c++预测的主程序
├── test_serving.sh # 测试serving部署预测的主程序 ├── test_serving.sh # 测试serving部署预测的主程序
├── test_lite.sh # 测试lite部署预测的主程序 ├── test_lite_arm_cpu_cpp.sh # 测试lite在arm_cpu上部署的C++预测的主程序
├── compare_results.py # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内 ├── compare_results.py # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内
└── readme.md # 使用文档 └── readme.md # 使用文档
``` ```
### 配置文件命名规范
在`configs`目录下，按模型名称划分为子目录，子目录中存放所有该模型测试需要用到的配置文件，配置文件的命名遵循如下规范：
1. 基础训练预测配置简单命名为：`train_infer_python.txt`，表示**Linux环境下单机、不使用混合精度训练+python预测**，其完整命名对应`train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt`，由于本配置文件使用频率较高，这里进行了名称简化。
2. 其他带训练配置命名格式为：`train_训练硬件环境(linux_gpu/linux_dcu/…)_是否多机(fleet/normal)_是否混合精度(amp/normal)_预测模式(infer/lite/serving/js)_语言(cpp/python/java)_预测硬件环境(linux_gpu/mac/jetson/opencl_arm_gpu/...).txt`。如，linux gpu下多机多卡+混合精度链条测试对应配置 `train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt`，linux dcu下基础训练预测对应配置 `train_linux_dcu_normal_normal_infer_python_dcu.txt`。
3. 仅预测的配置（如serving、lite等）命名格式：`model_训练硬件环境(linux_gpu/linux_dcu/…)_是否多机(fleet/normal)_是否混合精度(amp/normal)_(infer/lite/serving/js)_语言(cpp/python/java)_预测硬件环境(linux_gpu/mac/jetson/opencl_arm_gpu/...).txt`，即，与2相比，仅第一个字段从train换为model，测试时模型直接下载获取，这里的“训练硬件环境”表示所测试的模型是在哪种环境下训练得到的。
根据上述命名规范，可以直接从配置文件名看出对应的测试场景和功能。
### 测试流程 ### 测试流程
使用本工具，可以测试不同功能的支持情况，以及预测结果是否对齐，测试流程如下： 使用本工具，可以测试不同功能的支持情况，以及预测结果是否对齐，测试流程如下：
<div align="center"> <div align="center">
...@@ -99,7 +114,8 @@ test_tipc/ ...@@ -99,7 +114,8 @@ test_tipc/
- `test_train_inference_python.sh`：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。 - `test_train_inference_python.sh`：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
- `test_inference_cpp.sh`：测试基于C++的模型推理。 - `test_inference_cpp.sh`：测试基于C++的模型推理。
- `test_serving.sh`：测试基于Paddle Serving的服务化部署功能。 - `test_serving.sh`：测试基于Paddle Serving的服务化部署功能。
- `test_lite.sh`：测试基于Paddle-Lite的端侧预测部署功能。 - `test_lite_arm_cpu_cpp.sh`：测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
- `test_paddle2onnx.sh`：测试Paddle2ONNX的模型转化功能，并验证正确性。
<a name="more"></a> <a name="more"></a>
#### 更多教程 #### 更多教程
...@@ -107,4 +123,5 @@ test_tipc/ ...@@ -107,4 +123,5 @@ test_tipc/
[test_train_inference_python 使用](docs/test_train_inference_python.md) [test_train_inference_python 使用](docs/test_train_inference_python.md)
[test_inference_cpp 使用](docs/test_inference_cpp.md) [test_inference_cpp 使用](docs/test_inference_cpp.md)
[test_serving 使用](docs/test_serving.md) [test_serving 使用](docs/test_serving.md)
[test_lite 使用](docs/test_lite.md) [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md)
[test_paddle2onnx 使用](docs/test_paddle2onnx.md)
test_tipc/test_inference_cpp.sh
浏览文件 @ 55d71902
...@@ -2,38 +2,38 @@ ...@@ -2,38 +2,38 @@
source test_tipc/common_func.sh source test_tipc/common_func.sh
FILENAME=$1 FILENAME=$1
dataline=$(awk 'NR==52, NR==66{print}' $FILENAME) dataline=$(awk 'NR==1, NR==16{print}' $FILENAME)
# parser params # parser params
IFS=$'\n' IFS=$'\n'
lines=(${dataline}) lines=(${dataline})
# parser cpp inference model # parser cpp inference model
use_opencv=$(func_parser_value "${lines[1]}") model_name=$(func_parser_value "${lines[1]}")
cpp_infer_model_dir_list=$(func_parser_value "${lines[2]}") use_opencv=$(func_parser_value "${lines[2]}")
cpp_infer_is_quant=$(func_parser_value "${lines[3]}") cpp_infer_model_dir_list=$(func_parser_value "${lines[3]}")
cpp_infer_is_quant=$(func_parser_value "${lines[4]}")
# parser cpp inference # parser cpp inference
inference_cmd=$(func_parser_value "${lines[4]}") inference_cmd=$(func_parser_value "${lines[5]}")
cpp_use_gpu_key=$(func_parser_key "${lines[5]}") cpp_use_gpu_key=$(func_parser_key "${lines[6]}")
cpp_use_gpu_list=$(func_parser_value "${lines[5]}") cpp_use_gpu_list=$(func_parser_value "${lines[6]}")
cpp_use_mkldnn_key=$(func_parser_key "${lines[6]}") cpp_use_mkldnn_key=$(func_parser_key "${lines[7]}")
cpp_use_mkldnn_list=$(func_parser_value "${lines[6]}") cpp_use_mkldnn_list=$(func_parser_value "${lines[7]}")
cpp_cpu_threads_key=$(func_parser_key "${lines[7]}") cpp_cpu_threads_key=$(func_parser_key "${lines[8]}")
cpp_cpu_threads_list=$(func_parser_value "${lines[7]}") cpp_cpu_threads_list=$(func_parser_value "${lines[8]}")
cpp_batch_size_key=$(func_parser_key "${lines[8]}") cpp_batch_size_key=$(func_parser_key "${lines[9]}")
cpp_batch_size_list=$(func_parser_value "${lines[8]}") cpp_batch_size_list=$(func_parser_value "${lines[9]}")
cpp_use_trt_key=$(func_parser_key "${lines[9]}") cpp_use_trt_key=$(func_parser_key "${lines[10]}")
cpp_use_trt_list=$(func_parser_value "${lines[9]}") cpp_use_trt_list=$(func_parser_value "${lines[10]}")
cpp_precision_key=$(func_parser_key "${lines[10]}") cpp_precision_key=$(func_parser_key "${lines[11]}")
cpp_precision_list=$(func_parser_value "${lines[10]}") cpp_precision_list=$(func_parser_value "${lines[11]}")
cpp_infer_model_key=$(func_parser_key "${lines[11]}") cpp_infer_model_key=$(func_parser_key "${lines[12]}")
cpp_image_dir_key=$(func_parser_key "${lines[12]}") cpp_image_dir_key=$(func_parser_key "${lines[13]}")
cpp_infer_img_dir=$(func_parser_value "${lines[12]}") cpp_infer_img_dir=$(func_parser_value "${lines[13]}")
cpp_infer_key1=$(func_parser_key "${lines[13]}") cpp_infer_key1=$(func_parser_key "${lines[14]}")
cpp_infer_value1=$(func_parser_value "${lines[13]}") cpp_infer_value1=$(func_parser_value "${lines[14]}")
cpp_benchmark_key=$(func_parser_key "${lines[14]}") cpp_benchmark_key=$(func_parser_key "${lines[15]}")
cpp_benchmark_value=$(func_parser_value "${lines[14]}") cpp_benchmark_value=$(func_parser_value "${lines[15]}")
LOG_PATH="./test_tipc/output" LOG_PATH="./test_tipc/output"
mkdir -p ${LOG_PATH} mkdir -p ${LOG_PATH}
......
test_tipc/test_inference_jeston.sh 0 → 100644
浏览文件 @ 55d71902
#!/bin/bash
source test_tipc/common_func.sh
source test_tipc/test_train_inference_python.sh
FILENAME=$1
# MODE be one of ['whole_infer']
MODE=$2
dataline=$(awk 'NR==1, NR==17{print}' $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
infer_model_dir_list=$(func_parser_value "${lines[3]}")
infer_export_list=$(func_parser_value "${lines[4]}")
infer_is_quant=$(func_parser_value "${lines[5]}")
# parser inference
inference_py=$(func_parser_value "${lines[6]}")
use_gpu_key=$(func_parser_key "${lines[7]}")
use_gpu_list=$(func_parser_value "${lines[7]}")
use_mkldnn_key=$(func_parser_key "${lines[8]}")
use_mkldnn_list=$(func_parser_value "${lines[8]}")
cpu_threads_key=$(func_parser_key "${lines[9]}")
cpu_threads_list=$(func_parser_value "${lines[9]}")
batch_size_key=$(func_parser_key "${lines[10]}")
batch_size_list=$(func_parser_value "${lines[10]}")
use_trt_key=$(func_parser_key "${lines[11]}")
use_trt_list=$(func_parser_value "${lines[11]}")
precision_key=$(func_parser_key "${lines[12]}")
precision_list=$(func_parser_value "${lines[12]}")
infer_model_key=$(func_parser_key "${lines[13]}")
image_dir_key=$(func_parser_key "${lines[14]}")
infer_img_dir=$(func_parser_value "${lines[14]}")
save_log_key=$(func_parser_key "${lines[15]}")
benchmark_key=$(func_parser_key "${lines[16]}")
benchmark_value=$(func_parser_value "${lines[16]}")
infer_key1=$(func_parser_key "${lines[17]}")
infer_value1=$(func_parser_value "${lines[17]}")
LOG_PATH="./test_tipc/output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log"
if [ ${MODE} = "whole_infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
# set CUDA_VISIBLE_DEVICES
eval $env
export Count=0
IFS="|"
infer_run_exports=(${infer_export_list})
infer_quant_flag=(${infer_is_quant})
for infer_model in ${infer_model_dir_list[*]}; do
# run export
if [ ${infer_run_exports[Count]} != "null" ];then
save_infer_dir=$(dirname $infer_model)
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
echo ${infer_run_exports[Count]}
echo $export_cmd
eval $export_cmd
status_export=$?
status_check $status_export "${export_cmd}" "${status_log}"
else
save_infer_dir=${infer_model}
fi
#run inference
is_quant=${infer_quant_flag[Count]}
if [ ${MODE} = "klquant_infer" ]; then
is_quant="True"
fi
func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
Count=$(($Count + 1))
done
fi
test_tipc/test_lite.sh → test_tipc/test_lite_arm_cpu_cpp.sh
浏览文件 @ 55d71902
...@@ -3,8 +3,7 @@ source ./common_func.sh ...@@ -3,8 +3,7 @@ source ./common_func.sh
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
FILENAME=$1 FILENAME=$1
dataline=$(awk 'NR==102, NR==111{print}' $FILENAME) dataline=$(cat $FILENAME)
echo $dataline
# parser params # parser params
IFS=$'\n' IFS=$'\n'
lines=(${dataline}) lines=(${dataline})
...@@ -12,13 +11,14 @@ lines=(${dataline}) ...@@ -12,13 +11,14 @@ lines=(${dataline})
# parser lite inference # parser lite inference
lite_inference_cmd=$(func_parser_value "${lines[1]}") lite_inference_cmd=$(func_parser_value "${lines[1]}")
lite_model_dir_list=$(func_parser_value "${lines[2]}") lite_model_dir_list=$(func_parser_value "${lines[2]}")
lite_cpu_threads_list=$(func_parser_value "${lines[3]}") runtime_device=$(func_parser_value "${lines[3]}")
lite_batch_size_list=$(func_parser_value "${lines[4]}") lite_cpu_threads_list=$(func_parser_value "${lines[4]}")
lite_power_mode_list=$(func_parser_value "${lines[5]}") lite_batch_size_list=$(func_parser_value "${lines[5]}")
lite_infer_img_dir_list=$(func_parser_value "${lines[6]}") lite_infer_img_dir_list=$(func_parser_value "${lines[8]}")
lite_config_dir=$(func_parser_value "${lines[7]}") lite_config_dir=$(func_parser_value "${lines[9]}")
lite_rec_dict_dir=$(func_parser_value "${lines[8]}") lite_rec_dict_dir=$(func_parser_value "${lines[10]}")
lite_benchmark_value=$(func_parser_value "${lines[9]}") lite_benchmark_value=$(func_parser_value "${lines[11]}")
LOG_PATH="./output" LOG_PATH="./output"
mkdir -p ${LOG_PATH} mkdir -p ${LOG_PATH}
...@@ -37,23 +37,14 @@ function func_lite(){ ...@@ -37,23 +37,14 @@ function func_lite(){
else else
precision="FP32" precision="FP32"
fi fi
is_single_img=$(echo $_img_dir | grep -E ".jpg|.jpeg|.png|.JPEG|.JPG")
if [[ "$is_single_img" != "" ]]; then
single_img="True"
else
single_img="False"
fi
# lite inference # lite inference
for num_threads in ${lite_cpu_threads_list[*]}; do for num_threads in ${lite_cpu_threads_list[*]}; do
for power_mode in ${lite_power_mode_list[*]}; do for batchsize in ${lite_batch_size_list[*]}; do
for batchsize in ${lite_batch_size_list[*]}; do _save_log_path="${_log_path}/lite_${_lite_model}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}.log"
model_name=$(echo $lite_model | awk -F "/" '{print $NF}') command="${_script} ${_lite_model} ${runtime_device} ${precision} ${num_threads} ${batchsize} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
_save_log_path="${_log_path}/lite_${model_name}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}_powermode_${power_mode}_singleimg_${single_img}.log" eval ${command}
command="${_script} ${lite_model} ${precision} ${num_threads} ${batchsize} ${power_mode} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1" status_check $? "${command}" "${status_log}"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done done
done done
} }
...@@ -64,6 +55,6 @@ IFS="|" ...@@ -64,6 +55,6 @@ IFS="|"
for lite_model in ${lite_model_dir_list[*]}; do for lite_model in ${lite_model_dir_list[*]}; do
#run lite inference #run lite inference
for img_dir in ${lite_infer_img_dir_list[*]}; do for img_dir in ${lite_infer_img_dir_list[*]}; do
func_lite "${lite_inference_cmd}" "${lite_model}" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}" func_lite "${lite_inference_cmd}" "${lite_model}_opt.nb" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
done done
done done
test_tipc/test_paddle2onnx.sh
浏览文件 @ 55d71902
...@@ -11,31 +11,33 @@ python=$(func_parser_value "${lines[2]}") ...@@ -11,31 +11,33 @@ python=$(func_parser_value "${lines[2]}")
# parser params # parser params
dataline=$(awk 'NR==111, NR==123{print}' $FILENAME) dataline=$(awk 'NR==1, NR==12{print}' $FILENAME)
IFS=$'\n' IFS=$'\n'
lines=(${dataline}) lines=(${dataline})
# parser paddle2onnx # parser paddle2onnx
padlle2onnx_cmd=$(func_parser_value "${lines[1]}") model_name=$(func_parser_value "${lines[1]}")
infer_model_dir_key=$(func_parser_key "${lines[2]}") python=$(func_parser_value "${lines[2]}")
infer_model_dir_value=$(func_parser_value "${lines[2]}") padlle2onnx_cmd=$(func_parser_value "${lines[3]}")
model_filename_key=$(func_parser_key "${lines[3]}") infer_model_dir_key=$(func_parser_key "${lines[4]}")
model_filename_value=$(func_parser_value "${lines[3]}") infer_model_dir_value=$(func_parser_value "${lines[4]}")
params_filename_key=$(func_parser_key "${lines[4]}") model_filename_key=$(func_parser_key "${lines[5]}")
params_filename_value=$(func_parser_value "${lines[4]}") model_filename_value=$(func_parser_value "${lines[5]}")
save_file_key=$(func_parser_key "${lines[5]}") params_filename_key=$(func_parser_key "${lines[6]}")
save_file_value=$(func_parser_value "${lines[5]}") params_filename_value=$(func_parser_value "${lines[6]}")
opset_version_key=$(func_parser_key "${lines[6]}") save_file_key=$(func_parser_key "${lines[7]}")
opset_version_value=$(func_parser_value "${lines[6]}") save_file_value=$(func_parser_value "${lines[7]}")
enable_onnx_checker_key=$(func_parser_key "${lines[7]}") opset_version_key=$(func_parser_key "${lines[8]}")
enable_onnx_checker_value=$(func_parser_value "${lines[7]}") opset_version_value=$(func_parser_value "${lines[8]}")
enable_onnx_checker_key=$(func_parser_key "${lines[9]}")
enable_onnx_checker_value=$(func_parser_value "${lines[9]}")
# parser onnx inference # parser onnx inference
inference_py=$(func_parser_value "${lines[8]}") inference_py=$(func_parser_value "${lines[10]}")
use_gpu_key=$(func_parser_key "${lines[9]}") use_gpu_key=$(func_parser_key "${lines[11]}")
use_gpu_value=$(func_parser_value "${lines[9]}") use_gpu_value=$(func_parser_value "${lines[11]}")
det_model_key=$(func_parser_key "${lines[10]}") det_model_key=$(func_parser_key "${lines[12]}")
image_dir_key=$(func_parser_key "${lines[11]}") image_dir_key=$(func_parser_key "${lines[13]}")
image_dir_value=$(func_parser_value "${lines[11]}") image_dir_value=$(func_parser_value "${lines[13]}")
LOG_PATH="./test_tipc/output" LOG_PATH="./test_tipc/output"
......
test_tipc/test_serving.sh
浏览文件 @ 55d71902
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
source test_tipc/common_func.sh source test_tipc/common_func.sh
FILENAME=$1 FILENAME=$1
dataline=$(awk 'NR==67, NR==84{print}' $FILENAME) dataline=$(awk 'NR==1, NR==18{print}' $FILENAME)
# parser params # parser params
IFS=$'\n' IFS=$'\n'
......
test_tipc/test_train_inference_python.sh
浏览文件 @ 55d71902
...@@ -244,7 +244,7 @@ else ...@@ -244,7 +244,7 @@ else
export Count=0 export Count=0
USE_GPU_KEY=(${train_use_gpu_value}) USE_GPU_KEY=(${train_use_gpu_value})
for gpu in ${gpu_list[*]}; do for gpu in ${gpu_list[*]}; do
use_gpu=${USE_GPU_KEY[Count]} train_use_gpu=${USE_GPU_KEY[Count]}
Count=$(($Count + 1)) Count=$(($Count + 1))
ips="" ips=""
if [ ${gpu} = "-1" ];then if [ ${gpu} = "-1" ];then
...@@ -302,11 +302,20 @@ else ...@@ -302,11 +302,20 @@ else
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}") set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}") set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}") set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}" if [ ${#ips} -le 26 ];then
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
nodes=1
else
IFS=","
ips_array=(${ips})
IFS="|"
nodes=${#ips_array[@]}
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
fi
# load pretrain from norm training if current trainer is pact or fpgm trainer # load pretrain from norm training if current trainer is pact or fpgm trainer
if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
set_pretrain="${load_norm_train_model}" set_pretrain="${load_norm_train_model}"
fi fi
...@@ -325,7 +334,7 @@ else ...@@ -325,7 +334,7 @@ else
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training # save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ]; then if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
load_norm_train_model=${set_eval_pretrain} load_norm_train_model=${set_eval_pretrain}
fi fi
# run eval # run eval
......
tools/__init__.py 0 → 100644
浏览文件 @ 55d71902
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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.
tools/eval.py
浏览文件 @ 55d71902
...@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader ...@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model, load_dygraph_params from ppocr.utils.save_load import load_model
from ppocr.utils.utility import print_dict from ppocr.utils.utility import print_dict
import tools.program as program import tools.program as program
...@@ -60,7 +60,7 @@ def main(): ...@@ -60,7 +60,7 @@ def main():
else: else:
model_type = None model_type = None
best_model_dict = load_dygraph_params(config, model, logger, None) best_model_dict = load_model(config, model)
if len(best_model_dict): if len(best_model_dict):
logger.info('metric in ckpt ***************') logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items(): for k, v in best_model_dict.items():
......
tools/export_center.py
浏览文件 @ 55d71902
...@@ -27,7 +27,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) ...@@ -27,7 +27,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
from ppocr.data import build_dataloader from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model, load_dygraph_params from ppocr.utils.save_load import load_model
from ppocr.utils.utility import print_dict from ppocr.utils.utility import print_dict
import tools.program as program import tools.program as program
...@@ -57,7 +57,7 @@ def main(): ...@@ -57,7 +57,7 @@ def main():
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
best_model_dict = load_dygraph_params(config, model, logger, None) best_model_dict = load_model(config, model)
if len(best_model_dict): if len(best_model_dict):
logger.info('metric in ckpt ***************') logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items(): for k, v in best_model_dict.items():
......
tools/export_model.py
浏览文件 @ 55d71902
...@@ -26,7 +26,7 @@ from paddle.jit import to_static ...@@ -26,7 +26,7 @@ from paddle.jit import to_static
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser from tools.program import load_config, merge_config, ArgsParser
...@@ -107,7 +107,7 @@ def main(): ...@@ -107,7 +107,7 @@ def main():
else: # base rec model else: # base rec model
config["Architecture"]["Head"]["out_channels"] = char_num config["Architecture"]["Head"]["out_channels"] = char_num
model = build_model(config["Architecture"]) model = build_model(config["Architecture"])
init_model(config, model) load_model(config, model)
model.eval() model.eval()
save_path = config["Global"]["save_inference_dir"] save_path = config["Global"]["save_inference_dir"]
......
tools/infer/utility.py
浏览文件 @ 55d71902
...@@ -17,7 +17,7 @@ import os ...@@ -17,7 +17,7 @@ import os
import sys import sys
import cv2 import cv2
import numpy as np import numpy as np
import json import paddle
from PIL import Image, ImageDraw, ImageFont from PIL import Image, ImageDraw, ImageFont
import math import math
from paddle import inference from paddle import inference
...@@ -205,7 +205,7 @@ def create_predictor(args, mode, logger): ...@@ -205,7 +205,7 @@ def create_predictor(args, mode, logger):
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2] "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
} }
max_input_shape = { max_input_shape = {
"x": [1, 3, 2000, 2000], "x": [1, 3, 1280, 1280],
"conv2d_92.tmp_0": [1, 120, 400, 400], "conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 24, 200, 200], "conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400], "conv2d_59.tmp_0": [1, 96, 400, 400],
...@@ -255,16 +255,16 @@ def create_predictor(args, mode, logger): ...@@ -255,16 +255,16 @@ def create_predictor(args, mode, logger):
opt_input_shape.update(opt_pact_shape) opt_input_shape.update(opt_pact_shape)
elif mode == "rec": elif mode == "rec":
min_input_shape = {"x": [1, 3, 32, 10]} min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]} max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]} opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls": elif mode == "cls":
min_input_shape = {"x": [1, 3, 48, 10]} min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]} max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]} opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else: else:
min_input_shape = {"x": [1, 3, 10, 10]} min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 1000, 1000]} max_input_shape = {"x": [1, 3, 512, 512]}
opt_input_shape = {"x": [1, 3, 500, 500]} opt_input_shape = {"x": [1, 3, 256, 256]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape, config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape) opt_input_shape)
...@@ -601,5 +601,12 @@ def get_rotate_crop_image(img, points): ...@@ -601,5 +601,12 @@ def get_rotate_crop_image(img, points):
return dst_img return dst_img
def check_gpu(use_gpu):
if use_gpu and not paddle.is_compiled_with_cuda():
use_gpu = False
return use_gpu
if __name__ == '__main__': if __name__ == '__main__':
pass pass
tools/infer_cls.py
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...@@ -32,7 +32,7 @@ import paddle ...@@ -32,7 +32,7 @@ import paddle
from ppocr.data import create_operators, transform from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list from ppocr.utils.utility import get_image_file_list
import tools.program as program import tools.program as program
...@@ -47,7 +47,7 @@ def main(): ...@@ -47,7 +47,7 @@ def main():
# build model # build model
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
init_model(config, model) load_model(config, model)
# create data ops # create data ops
transforms = [] transforms = []
......
tools/infer_det.py
浏览文件 @ 55d71902
...@@ -34,7 +34,7 @@ import paddle ...@@ -34,7 +34,7 @@ import paddle
from ppocr.data import create_operators, transform from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model, load_dygraph_params from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list from ppocr.utils.utility import get_image_file_list
import tools.program as program import tools.program as program
...@@ -59,7 +59,7 @@ def main(): ...@@ -59,7 +59,7 @@ def main():
# build model # build model
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
_ = load_dygraph_params(config, model, logger, None) load_model(config, model)
# build post process # build post process
post_process_class = build_post_process(config['PostProcess']) post_process_class = build_post_process(config['PostProcess'])
......
tools/infer_e2e.py
浏览文件 @ 55d71902
...@@ -34,7 +34,7 @@ import paddle ...@@ -34,7 +34,7 @@ import paddle
from ppocr.data import create_operators, transform from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list from ppocr.utils.utility import get_image_file_list
import tools.program as program import tools.program as program
...@@ -68,7 +68,7 @@ def main(): ...@@ -68,7 +68,7 @@ def main():
# build model # build model
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
init_model(config, model) load_model(config, model)
# build post process # build post process
post_process_class = build_post_process(config['PostProcess'], post_process_class = build_post_process(config['PostProcess'],
......
tools/infer_rec.py
浏览文件 @ 55d71902
...@@ -33,7 +33,7 @@ import paddle ...@@ -33,7 +33,7 @@ import paddle
from ppocr.data import create_operators, transform from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list from ppocr.utils.utility import get_image_file_list
import tools.program as program import tools.program as program
...@@ -58,7 +58,7 @@ def main(): ...@@ -58,7 +58,7 @@ def main():
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
init_model(config, model) load_model(config, model)
# create data ops # create data ops
transforms = [] transforms = []
...@@ -75,9 +75,7 @@ def main(): ...@@ -75,9 +75,7 @@ def main():
'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2' 'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
] ]
elif config['Architecture']['algorithm'] == "SAR": elif config['Architecture']['algorithm'] == "SAR":
op[op_name]['keep_keys'] = [ op[op_name]['keep_keys'] = ['image', 'valid_ratio']
'image', 'valid_ratio'
]
else: else:
op[op_name]['keep_keys'] = ['image'] op[op_name]['keep_keys'] = ['image']
transforms.append(op) transforms.append(op)
......
tools/infer_table.py
浏览文件 @ 55d71902
...@@ -34,11 +34,12 @@ from paddle.jit import to_static ...@@ -34,11 +34,12 @@ from paddle.jit import to_static
from ppocr.data import create_operators, transform from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list from ppocr.utils.utility import get_image_file_list
import tools.program as program import tools.program as program
import cv2 import cv2
def main(config, device, logger, vdl_writer): def main(config, device, logger, vdl_writer):
global_config = config['Global'] global_config = config['Global']
...@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer): ...@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer):
model = build_model(config['Architecture']) model = build_model(config['Architecture'])
init_model(config, model, logger) load_model(config, model)
# create data ops # create data ops
transforms = [] transforms = []
...@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer): ...@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer):
if __name__ == '__main__': if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess() config, device, logger, vdl_writer = program.preprocess()
main(config, device, logger, vdl_writer) main(config, device, logger, vdl_writer)
tools/train.py
浏览文件 @ 55d71902
...@@ -35,7 +35,7 @@ from ppocr.losses import build_loss ...@@ -35,7 +35,7 @@ from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model, load_dygraph_params from ppocr.utils.save_load import load_model
import tools.program as program import tools.program as program
dist.get_world_size() dist.get_world_size()
...@@ -97,7 +97,7 @@ def main(config, device, logger, vdl_writer): ...@@ -97,7 +97,7 @@ def main(config, device, logger, vdl_writer):
# build metric # build metric
eval_class = build_metric(config['Metric']) eval_class = build_metric(config['Metric'])
# load pretrain model # load pretrain model
pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer) pre_best_model_dict = load_model(config, model, optimizer)
logger.info('train dataloader has {} iters'.format(len(train_dataloader))) logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
if valid_dataloader is not None: if valid_dataloader is not None:
logger.info('valid dataloader has {} iters'.format( logger.info('valid dataloader has {} iters'.format(
......
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