Merge remote-tracking branch 'origin/dygraph' into dygraph

MANIFEST.in

@@ -5,5 +5,6 @@ recursive-include ppocr/utils *.txt utility.py logging.py network.py
 recursive-include ppocr/data *.py
 recursive-include ppocr/postprocess *.py
 recursive-include tools/infer *.py
+recursive-include tools __init__.py
 recursive-include ppocr/utils/e2e_utils *.py
 recursive-include ppstructure *.py
\ No newline at end of file

PPOCRLabel/README.md

@@ -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
     ```
 
+### 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
 
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
\ No newline at end of file

PPOCRLabel/README_ch.md

@@ -193,7 +193,23 @@ PPOCRLabel支持三种导出方式：
     ```
     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
 
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)

PPOCRLabel/gen_ocr_train_val_test.py

+# 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

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
   cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
   checkpoints:
   save_inference_dir:
   use_visualdl: False
@@ -106,4 +106,4 @@ Eval:
     shuffle: False
     drop_last: False
     batch_size_per_card: 1 # must be 1
-    num_workers: 2
\ No newline at end of file
+    num_workers: 2

configs/det/det_r50_vd_sast_totaltext.yml

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
   cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
   checkpoints: 
   save_inference_dir:
   use_visualdl: False

configs/table/table_mv3.yml

 Global:
   use_gpu: true
-  epoch_num: 50
+  epoch_num: 400
   log_smooth_window: 20
   print_batch_step: 5
   save_model_dir: ./output/table_mv3/
-  save_epoch_step: 5
+  save_epoch_step: 3
   # evaluation is run every 400 iterations after the 0th iteration
   eval_batch_step: [0, 400]
   cal_metric_during_train: True
-  pretrained_model: 
+  pretrained_model:
   checkpoints: 
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/table/table.jpg
   # for data or label process
   character_dict_path: ppocr/utils/dict/table_structure_dict.txt
   character_type: en
   max_text_length: 100
-  max_elem_length: 500
+  max_elem_length: 800
   max_cell_num: 500
   infer_mode: False
   process_total_num: 0
   process_cut_num: 0
 
-
 Optimizer:
   name: Adam
   beta1: 0.9
@@ -41,13 +40,15 @@ Architecture:
   Backbone:
     name: MobileNetV3
     scale: 1.0
-    model_name: small
-    disable_se: True
+    model_name: large
   Head:
     name: TableAttentionHead
     hidden_size: 256
     l2_decay: 0.00001
     loc_type: 2
+    max_text_length: 100
+    max_elem_length: 800
+    max_cell_num: 500
 
 Loss:
   name: TableAttentionLoss

deploy/lite/ocr_db_crnn.cc

@@ -307,21 +307,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
   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;
   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);
 
   std::shared_ptr<PaddlePredictor> predictor =
@@ -391,7 +380,7 @@ void check_params(int argc, char **argv) {
   if (strcmp(argv[1], "det") == 0) {
       if (argc < 9){
         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);
       }
   }
@@ -399,7 +388,7 @@ void check_params(int argc, char **argv) {
   if (strcmp(argv[1], "rec") == 0) {
       if (argc < 9){
         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);
       }
   }
@@ -407,7 +396,7 @@ void check_params(int argc, char **argv) {
   if (strcmp(argv[1], "system") == 0) {
       if (argc < 12){
         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);
       }
   }
@@ -417,15 +406,15 @@ void system(char **argv){
   std::string det_model_file = argv[2];
   std::string rec_model_file = argv[3];
   std::string cls_model_file = argv[4];
-  std::string precision = argv[5];
-  std::string num_threads = argv[6];
-  std::string batchsize = argv[7];
-  std::string power_mode = argv[8];
+  std::string runtime_device = argv[5];
+  std::string precision = argv[6];
+  std::string num_threads = argv[7];
+  std::string batchsize = argv[8];
   std::string img_dir = argv[9];
   std::string det_config_path = argv[10];
   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;
       exit(1);
   }
@@ -441,9 +430,9 @@ void system(char **argv){
   charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
   charactor_dict.push_back(" ");
 
-  auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads));
-  auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads));
-  auto cls_predictor = loadModel(cls_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, 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) {
     std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
@@ -477,14 +466,14 @@ void system(char **argv){
 
 void det(int argc, char **argv) {
   std::string det_model_file = argv[2];
-  std::string precision = argv[3];
-  std::string num_threads = argv[4];
-  std::string batchsize = argv[5];
-  std::string power_mode = argv[6];
+  std::string runtime_device = argv[3];
+  std::string precision = argv[4];
+  std::string num_threads = argv[5];
+  std::string batchsize = argv[6];
   std::string img_dir = argv[7];
   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;
       exit(1);
   }
@@ -495,7 +484,7 @@ void det(int argc, char **argv) {
   //// load config from txt file
   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};
   for (int i = 0; i < cv_all_img_names.size(); ++i) {
@@ -530,14 +519,11 @@ void det(int argc, char **argv) {
 
   if (strcmp(argv[9], "True") == 0) {
     AutoLogger autolog(det_model_file, 
-                       0,
-                       0,
-                       0,
+                       runtime_device,
                        std::stoi(num_threads),
                        std::stoi(batchsize), 
                        "dynamic", 
                        precision, 
-                       power_mode,
                        time_info, 
                        cv_all_img_names.size());
     autolog.report();
@@ -546,14 +532,14 @@ void det(int argc, char **argv) {
 
 void rec(int argc, char **argv) {
   std::string rec_model_file = argv[2];
-  std::string precision = argv[3];
-  std::string num_threads = argv[4];
-  std::string batchsize = argv[5];
-  std::string power_mode = argv[6];
+  std::string runtime_device = argv[3];
+  std::string precision = argv[4];
+  std::string num_threads = argv[5];
+  std::string batchsize = argv[6];
   std::string img_dir = argv[7];
   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;
       exit(1);
   }
@@ -565,7 +551,7 @@ void rec(int argc, char **argv) {
   charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
   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;
 
@@ -603,14 +589,11 @@ void rec(int argc, char **argv) {
   // TODO: support autolog
   if (strcmp(argv[9], "True") == 0) {
     AutoLogger autolog(rec_model_file, 
-                       0,
-                       0,
-                       0,
+                       runtime_device,
                        std::stoi(num_threads),
                        std::stoi(batchsize), 
                        "dynamic", 
                        precision, 
-                       power_mode,
                        time_info, 
                        cv_all_img_names.size());
     autolog.report();

deploy/pdserving/pipeline_rpc_client.py

@@ -41,6 +41,6 @@ for img_file in os.listdir(test_img_dir):
         image_data = file.read()
     image = cv2_to_base64(image_data)
 
-for i in range(1):
-    ret = client.predict(feed_dict={"image": image}, fetch=["res"])
-    print(ret)
+    for i in range(1):
+        ret = client.predict(feed_dict={"image": image}, fetch=["res"])
+        print(ret)

deploy/slim/prune/export_prune_model.py

@@ -30,7 +30,7 @@ from ppocr.modeling.architectures import build_model
 
 from ppocr.postprocess import build_post_process
 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
 
 
@@ -89,7 +89,7 @@ def main(config, device, logger, vdl_writer):
     logger.info(f"FLOPs after pruning: {flops}")
 
     # 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,
                           eval_class)
     logger.info(f"metric['hmean']: {metric['hmean']}")

deploy/slim/prune/sensitivity_anal.py

@@ -32,7 +32,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 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
 
 dist.get_world_size()
@@ -94,7 +94,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # 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'.
                 format(len(train_dataloader), len(valid_dataloader)))

deploy/slim/quantization/export_model.py

@@ -28,7 +28,7 @@ from paddle.jit import to_static
 
 from ppocr.modeling.architectures import build_model
 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 tools.program import load_config, merge_config, ArgsParser
 from ppocr.metrics import build_metric
@@ -101,7 +101,7 @@ def main():
     quanter = QAT(config=quant_config)
     quanter.quantize(model)
 
-    init_model(config, model)
+    load_model(config, model)
     model.eval()
 
     # build metric

deploy/slim/quantization/quant.py

@@ -37,7 +37,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 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
 from paddleslim.dygraph.quant import QAT
 
@@ -137,7 +137,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # 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'.
                 format(len(train_dataloader), len(valid_dataloader)))

deploy/slim/quantization/quant_kl.py

@@ -37,7 +37,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 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 paddleslim
 from paddleslim.dygraph.quant import QAT

doc/doc_ch/detection.md

@@ -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
 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
- 
+
 # 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 \
      -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
 
 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)。
- 
+
 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
 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：
 ```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml \

doc/doc_ch/whl.md

@@ -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                    |
 | 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                    |
+| 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

@@ -98,14 +98,14 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o   \
 # multi-GPU training
 # 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
- 
+
 # 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.
 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
 ```
-**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:
 ```
 python3 tools/train.py -c configs/det/det_mv3_db.yml \

doc/doc_en/whl_en.md

@@ -366,4 +366,6 @@ im_show.save('result.jpg')
 | 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                    |
 | 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                    |
\ No newline at end of file
+| type                     | Perform ocr or table structuring, the value is selected in ['ocr','structure']                                                                                                                                                                                             | ocr                    |
+| 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

@@ -16,6 +16,9 @@ import os
 import sys
 
 __dir__ = os.path.dirname(__file__)
+
+import paddle
+
 sys.path.append(os.path.join(__dir__, ''))
 
 import cv2
@@ -29,7 +32,7 @@ from ppocr.utils.logging import get_logger
 logger = get_logger()
 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 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.predict_system import OCRSystem, save_structure_res
 
@@ -39,130 +42,137 @@ __all__ = [
 ]
 
 SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.2.1'
+VERSION = '2.3.0.1'
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
-DEFAULT_MODEL_VERSION = '2.0'
+DEFAULT_OCR_MODEL_VERSION = 'PP-OCR'
+DEFAULT_STRUCTURE_MODEL_VERSION = 'STRUCTURE'
 MODEL_URLS = {
-    '2.1': {
-        'det': {
-            'ch': {
-                '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',
+    'OCR': {
+        'PP-OCRv2': {
+            'det': {
+                'ch': {
+                    'url':
+                    'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar',
+                },
             },
-            'structure': {
-                'url':
-                'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_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'
+                }
             }
         },
-        'rec': {
-            'ch': {
-                'url':
-                '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'
+        DEFAULT_OCR_MODEL_VERSION: {
+            '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': {
+                    'url':
+                    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
+                }
             },
-            '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'
+            'rec': {
+                'ch': {
+                    'url':
+                    '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': {
-                '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'
+            'cls': {
+                'ch': {
+                    'url':
+                    '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',
-                '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'
-            }
-        },
-        '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'
+        }
+    },
+    'STRUCTURE': {
+        DEFAULT_STRUCTURE_MODEL_VERSION: {
+            '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):
     parser.add_argument("--det", type=str2bool, default=True)
     parser.add_argument("--rec", type=str2bool, default=True)
     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:
         if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
@@ -215,9 +238,9 @@ def parse_lang(lang):
         lang = "cyrillic"
     elif lang in devanagari_lang:
         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(
-            MODEL_URLS[DEFAULT_MODEL_VERSION]['rec'].keys(), lang)
+            MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang)
     if lang == "ch":
         det_lang = "ch"
     elif lang == 'structure':
@@ -227,33 +250,41 @@ def parse_lang(lang):
     return lang, det_lang
 
 
-def get_model_config(version, model_type, lang):
-    if version not in MODEL_URLS:
-        logger.warning('version {} not in {}, use version {} instead'.format(
-            version, MODEL_URLS.keys(), DEFAULT_MODEL_VERSION))
+def get_model_config(type, version, model_type, lang):
+    if type == 'OCR':
+        DEFAULT_MODEL_VERSION = DEFAULT_OCR_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
-    if model_type not in MODEL_URLS[version]:
-        if model_type in MODEL_URLS[DEFAULT_MODEL_VERSION]:
+    if model_type not in model_urls[version]:
+        if model_type in model_urls[DEFAULT_MODEL_VERSION]:
             logger.warning(
-                'version {} not support {} models, use version {} instead'.
+                'version {} not support {} models, auto switch to version {}'.
                 format(version, model_type, DEFAULT_MODEL_VERSION))
             version = DEFAULT_MODEL_VERSION
         else:
             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)
-    if lang not in MODEL_URLS[version][model_type]:
-        if lang in MODEL_URLS[DEFAULT_MODEL_VERSION][model_type]:
-            logger.warning('lang {} is not support in {}, use {} instead'.
-                           format(lang, version, DEFAULT_MODEL_VERSION))
+    if lang not in model_urls[version][model_type]:
+        if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]:
+            logger.warning(
+                'lang {} is not support in {}, auto switch to version {}'.
+                format(lang, version, DEFAULT_MODEL_VERSION))
             version = DEFAULT_MODEL_VERSION
         else:
             logger.error(
                 '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))
             sys.exit(-1)
-    return MODEL_URLS[version][model_type][lang]
+    return model_urls[version][model_type][lang]
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -265,23 +296,28 @@ class PaddleOCR(predict_system.TextSystem):
         """
         params = parse_args(mMain=False)
         params.__dict__.update(**kwargs)
+        params.use_gpu = check_gpu(params.use_gpu)
+
         if not params.show_log:
             logger.setLevel(logging.INFO)
         self.use_angle_cls = params.use_angle_cls
         lang, det_lang = parse_lang(params.lang)
 
         # 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,
             os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
             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,
             os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
             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,
             os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
@@ -362,22 +398,27 @@ class PPStructure(OCRSystem):
     def __init__(self, **kwargs):
         params = parse_args(mMain=False)
         params.__dict__.update(**kwargs)
+        params.use_gpu = check_gpu(params.use_gpu)
+
         if not params.show_log:
             logger.setLevel(logging.INFO)
         lang, det_lang = parse_lang(params.lang)
 
         # 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,
             os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
             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,
             os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
             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,
             os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),

ppocr/modeling/architectures/distillation_model.py

@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone
 from ppocr.modeling.necks import build_neck
 from ppocr.modeling.heads import build_head
 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']
 

ppocr/modeling/heads/table_att_head.py

@@ -23,32 +23,40 @@ import numpy as np
 
 
 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__()
         self.input_size = in_channels[-1]
         self.hidden_size = hidden_size
         self.elem_num = 30
-        self.max_text_length = 100
-        self.max_elem_length = 500
-        self.max_cell_num = 500
+        self.max_text_length = max_text_length
+        self.max_elem_length = max_elem_length
+        self.max_cell_num = max_cell_num
 
         self.structure_attention_cell = AttentionGRUCell(
             self.input_size, hidden_size, self.elem_num, use_gru=False)
         self.structure_generator = nn.Linear(hidden_size, self.elem_num)
         self.loc_type = loc_type
         self.in_max_len = in_max_len
-        
+
         if self.loc_type == 1:
             self.loc_generator = nn.Linear(hidden_size, 4)
         else:
             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:
-                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:
-                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)
-            
+
     def _char_to_onehot(self, input_char, onehot_dim):
         input_ont_hot = F.one_hot(input_char, onehot_dim)
         return input_ont_hot
@@ -60,16 +68,16 @@ class TableAttentionHead(nn.Layer):
         if len(fea.shape) == 3:
             pass
         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 = fea.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
         batch_size = fea.shape[0]
-        
+
         hidden = paddle.zeros((batch_size, self.hidden_size))
         output_hiddens = []
         if self.training and targets is not None:
             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(
                     structure[:, i], onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -96,7 +104,7 @@ class TableAttentionHead(nn.Layer):
             alpha = None
             max_elem_length = paddle.to_tensor(self.max_elem_length)
             i = 0
-            while i < max_elem_length+1:
+            while i < max_elem_length + 1:
                 elem_onehots = self._char_to_onehot(
                     temp_elem, onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -105,7 +113,7 @@ class TableAttentionHead(nn.Layer):
                 structure_probs_step = self.structure_generator(outputs)
                 temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
                 i += 1
-                
+
             output = paddle.concat(output_hiddens, axis=1)
             structure_probs = self.structure_generator(output)
             structure_probs = F.softmax(structure_probs)
@@ -119,9 +127,9 @@ class TableAttentionHead(nn.Layer):
                 loc_concat = paddle.concat([output, loc_fea], axis=2)
                 loc_preds = self.loc_generator(loc_concat)
                 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):
     def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
         super(AttentionGRUCell, self).__init__()

ppocr/utils/network.py

@@ -24,15 +24,17 @@ from ppocr.utils.logging import get_logger
 def download_with_progressbar(url, save_path):
     logger = get_logger()
     response = requests.get(url, stream=True)
-    total_size_in_bytes = int(response.headers.get('content-length', 0))
-    block_size = 1024  # 1 Kibibyte
-    progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
-    with open(save_path, 'wb') as file:
-        for data in response.iter_content(block_size):
-            progress_bar.update(len(data))
-            file.write(data)
-    progress_bar.close()
-    if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
+    if response.status_code == 200:
+        total_size_in_bytes = int(response.headers.get('content-length', 1))
+        block_size = 1024  # 1 Kibibyte
+        progress_bar = tqdm(
+            total=total_size_in_bytes, unit='iB', unit_scale=True)
+        with open(save_path, 'wb') as file:
+            for data in response.iter_content(block_size):
+                progress_bar.update(len(data))
+                file.write(data)
+        progress_bar.close()
+    else:
         logger.error("Something went wrong while downloading models")
         sys.exit(0)
 
@@ -45,7 +47,7 @@ def maybe_download(model_storage_directory, url):
     if not os.path.exists(
             os.path.join(model_storage_directory, 'inference.pdiparams')
     ) 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'
         tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
         print('download {} to {}'.format(url, tmp_path))

ppocr/utils/save_load.py

@@ -25,7 +25,7 @@ import paddle
 
 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):
@@ -44,7 +44,7 @@ def _mkdir_if_not_exist(path, logger):
                 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
     """
@@ -54,15 +54,14 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
     pretrained_model = global_config.get('pretrained_model')
     best_model_dict = {}
     if checkpoints:
-        assert os.path.exists(checkpoints + ".pdparams"), \
-            "Given dir {}.pdparams not exist.".format(checkpoints)
+        if checkpoints.endswith('pdparams'):
+            checkpoints = checkpoints.replace('.pdparams', '')
         assert os.path.exists(checkpoints + ".pdopt"), \
-            "Given dir {}.pdopt not exist.".format(checkpoints)
-        para_dict = paddle.load(checkpoints + '.pdparams')
-        opti_dict = paddle.load(checkpoints + '.pdopt')
-        model.set_state_dict(para_dict)
+            f"The {checkpoints}.pdopt does not exists!"
+        load_pretrained_params(model, checkpoints)
+        optim_dict = paddle.load(checkpoints + '.pdopt')
         if optimizer is not None:
-            optimizer.set_state_dict(opti_dict)
+            optimizer.set_state_dict(optim_dict)
 
         if os.path.exists(checkpoints + '.states'):
             with open(checkpoints + '.states', 'rb') as f:
@@ -73,70 +72,31 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
                 best_model_dict['start_epoch'] = states_dict['epoch'] + 1
         logger.info("resume from {}".format(checkpoints))
     elif pretrained_model:
-        if not isinstance(pretrained_model, list):
-            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))
+        load_pretrained_params(model, pretrained_model)
     else:
         logger.info('train from scratch')
     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):
-    if path is None:
-        return False
-    if not os.path.exists(path) and not os.path.exists(path + ".pdparams"):
-        print(f"The pretrained_model {path} does not exists!")
-        return False
-
-    path = path if path.endswith('.pdparams') else path + '.pdparams'
-    params = paddle.load(path)
+    logger = get_logger()
+    if path.endswith('pdparams'):
+        path = path.replace('.pdparams', '')
+    assert os.path.exists(path + ".pdparams"), \
+        f"The {path}.pdparams does not exists!"
+
+    params = paddle.load(path + '.pdparams')
     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:
-            print(
+            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)
-    print(f"load pretrain successful from {path}")
+    logger.info(f"load pretrain successful from {path}")
     return model
 
 

test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================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

-===========================train_params===========================
+===========================infer_params===========================
 model_name:ocr_det
 python:python
-gpu_list:null
-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_model:./inference/ch_ppocr_mobile_v2.0_det_infer
 infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py

test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt

+===========================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

+===========================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

+===========================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

+===========================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

 ===========================train_params===========================
 model_name:ocr_det
 python:python3.7
-gpu_list:xx.xx.xx.xx,xx.xx.xx.xx;0,1
-Global.use_gpu:True|True
-Global.auto_cast:null|amp
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+Global.use_gpu:True
+Global.auto_cast:fp32|amp
 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
@@ -49,62 +49,3 @@ inference:tools/infer/predict_det.py
 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_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

@@ -49,62 +49,3 @@ inference:tools/infer/predict_det.py
 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_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

-===========================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

+===========================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

+===========================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

@@ -13,21 +13,21 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
 trainer:norm_train|pact_train|fpgm_export
-norm_train:tools/train.py -c tests/configs/det_r50_vd_db.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_r50_vd_db.yml -o 
-fpgm_export:deploy/slim/prune/export_prune_model.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 test_tipc/configs/ppocr_det_server/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
 null:null
 null:null
 ##
 ===========================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
 ##
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 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 
 fpgm_export:null
 distill_export:null
@@ -48,37 +48,4 @@ inference:tools/infer/predict_det.py
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 --save_log_path:null
 --benchmark:True
-null:null
-===========================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 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
+null:null
\ No newline at end of file

test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================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

+===========================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

-===========================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

+===========================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

+===========================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

-===========================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

-===========================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

-===========================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

 ## 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：
 - CUDA 10.1/10.2

test_tipc/docs/test_inference_cpp.md

@@ -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。
 
 ```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:
-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
-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：

test_tipc/docs/test_lite.md → test_tipc/docs/test_lite_arm_cpu_cpp.md

-# 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
 
 目前Lite端的样本间支持以方式的组合：
 
 **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（FP16）
+- 模型类型：包括正常模型（FP32）和量化模型（INT8）
 - 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
-- 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，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
+- 预测库来源：包括下载方式和编译方式
 
-| 模型类型 | batch-size | predictor数量 | 功耗模式 | 预测库来源 | 支持语言 |
-|  :----:   |  :----:   |  :----:  |  :----:  |  :----:  |  :----:  |
-| 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 |
+| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 |
+|  :----:   |  :----:  | :----:  |  :----:  |  :----:  |
+| 正常模型/量化模型 | 1 | 1/4 |  1 | 下载方式 |
 
 
 ## 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
 
 ### 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
 
 # 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:
-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
 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
 ...
 ```
@@ -52,7 +51,7 @@ Run successfully with command xxx
 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
 ...
 ```

test_tipc/docs/test_paddle2onnx.md

@@ -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。
 
 ```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:
-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

test_tipc/docs/test_serving.md

@@ -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。
 
 ```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:
-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

test_tipc/docs/test_train_inference_python.md

@@ -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；
 ```shell
-bash test_tipc/prepare.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_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/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；
 ```shell
-bash test_tipc/prepare.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_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/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;
 ```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:
-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
-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；
 ```shell
-bash test_tipc/prepare.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_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/train_infer_python.txt 'whole_train_whole_infer'
 ```  
 
 - 模式5：klquant_whole_infer，测试离线量化；
 ```shell
-bash test_tipc/prepare.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_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/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：

test_tipc/prepare.sh

 #!/bin/bash
+source test_tipc/common_func.sh
+
 FILENAME=$1
 
 # 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']
+#                 'cpp_infer', 'serving_infer']
 
 MODE=$2
 
@@ -12,30 +14,12 @@ dataline=$(cat ${FILENAME})
 # parser params
 IFS=$'\n'
 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
 model_name=$(func_parser_value "${lines[1]}")
 
 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
     # pretrain lite train data
@@ -87,7 +71,8 @@ elif [ ${MODE} = "whole_infer" ];then
         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/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
         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
@@ -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 ../
 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 [ ${MODE} = "paddle2onnx_infer" ];then
     # prepare serving env
     python_name=$(func_parser_value "${lines[2]}")

test_tipc/prepare_lite.sh

+#!/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

@@ -60,16 +60,20 @@
 ```shell
 test_tipc/
 ├── 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
-	├── det_r50_vd_db.yml            # 测试server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
-	├── 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
-	├── ppocr_det_mobile_params.txt     # 测试mobile版ppocr检测模型的参数配置文件
-	├── ppocr_rec_mobile_params.txt     # 测试mobile版ppocr识别模型的参数配置文件
-	├── 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_rec_server_params.txt     # 测试server版ppocr识别模型的参数配置文件
-	├── ...  
+    ├── ppocr_det_mobile             # ppocr_det_mobile模型的测试配置文件目录
+        ├── det_mv3_db.yml                  # 测试mobile版ppocr检测模型训练的yml文件
+        ├── 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
+        ├── model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt     # 测试Linux上c++预测的配置文件
+        ├── model_linux_gpu_normal_normal_infer_python_jetson.txt         # 测试Jetson上python预测的配置文件
+        ├── 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_det_server               # ppocr_det_server模型的测试配置文件目录
+        ├── ...  
+    ├── 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
 	├── 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
@@ -80,11 +84,22 @@ test_tipc/
 ├── 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_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
 └── 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
 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">
@@ -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_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_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>
 #### 更多教程
@@ -107,4 +123,5 @@ test_tipc/
 [test_train_inference_python 使用](docs/test_train_inference_python.md)  
 [test_inference_cpp 使用](docs/test_inference_cpp.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

@@ -2,38 +2,38 @@
 source test_tipc/common_func.sh
 
 FILENAME=$1
-dataline=$(awk 'NR==52, NR==66{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==16{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'
 lines=(${dataline})
 
 # parser cpp inference model 
-use_opencv=$(func_parser_value "${lines[1]}")
-cpp_infer_model_dir_list=$(func_parser_value "${lines[2]}")
-cpp_infer_is_quant=$(func_parser_value "${lines[3]}")
+model_name=$(func_parser_value "${lines[1]}")
+use_opencv=$(func_parser_value "${lines[2]}")
+cpp_infer_model_dir_list=$(func_parser_value "${lines[3]}")
+cpp_infer_is_quant=$(func_parser_value "${lines[4]}")
 # parser cpp inference 
-inference_cmd=$(func_parser_value "${lines[4]}")
-cpp_use_gpu_key=$(func_parser_key "${lines[5]}")
-cpp_use_gpu_list=$(func_parser_value "${lines[5]}")
-cpp_use_mkldnn_key=$(func_parser_key "${lines[6]}")
-cpp_use_mkldnn_list=$(func_parser_value "${lines[6]}")
-cpp_cpu_threads_key=$(func_parser_key "${lines[7]}")
-cpp_cpu_threads_list=$(func_parser_value "${lines[7]}")
-cpp_batch_size_key=$(func_parser_key "${lines[8]}")
-cpp_batch_size_list=$(func_parser_value "${lines[8]}")
-cpp_use_trt_key=$(func_parser_key "${lines[9]}")
-cpp_use_trt_list=$(func_parser_value "${lines[9]}")
-cpp_precision_key=$(func_parser_key "${lines[10]}")
-cpp_precision_list=$(func_parser_value "${lines[10]}")
-cpp_infer_model_key=$(func_parser_key "${lines[11]}")
-cpp_image_dir_key=$(func_parser_key "${lines[12]}")
-cpp_infer_img_dir=$(func_parser_value "${lines[12]}")
-cpp_infer_key1=$(func_parser_key "${lines[13]}")
-cpp_infer_value1=$(func_parser_value "${lines[13]}")
-cpp_benchmark_key=$(func_parser_key "${lines[14]}")
-cpp_benchmark_value=$(func_parser_value "${lines[14]}")
-
+inference_cmd=$(func_parser_value "${lines[5]}")
+cpp_use_gpu_key=$(func_parser_key "${lines[6]}")
+cpp_use_gpu_list=$(func_parser_value "${lines[6]}")
+cpp_use_mkldnn_key=$(func_parser_key "${lines[7]}")
+cpp_use_mkldnn_list=$(func_parser_value "${lines[7]}")
+cpp_cpu_threads_key=$(func_parser_key "${lines[8]}")
+cpp_cpu_threads_list=$(func_parser_value "${lines[8]}")
+cpp_batch_size_key=$(func_parser_key "${lines[9]}")
+cpp_batch_size_list=$(func_parser_value "${lines[9]}")
+cpp_use_trt_key=$(func_parser_key "${lines[10]}")
+cpp_use_trt_list=$(func_parser_value "${lines[10]}")
+cpp_precision_key=$(func_parser_key "${lines[11]}")
+cpp_precision_list=$(func_parser_value "${lines[11]}")
+cpp_infer_model_key=$(func_parser_key "${lines[12]}")
+cpp_image_dir_key=$(func_parser_key "${lines[13]}")
+cpp_infer_img_dir=$(func_parser_value "${lines[13]}")
+cpp_infer_key1=$(func_parser_key "${lines[14]}")
+cpp_infer_value1=$(func_parser_value "${lines[14]}")
+cpp_benchmark_key=$(func_parser_key "${lines[15]}")
+cpp_benchmark_value=$(func_parser_value "${lines[15]}")
 
 LOG_PATH="./test_tipc/output"
 mkdir -p ${LOG_PATH}

test_tipc/test_inference_jeston.sh

+#!/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

@@ -3,8 +3,7 @@ source ./common_func.sh
 export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
 
 FILENAME=$1
-dataline=$(awk 'NR==102, NR==111{print}'  $FILENAME)
-echo $dataline
+dataline=$(cat $FILENAME)
 # parser params
 IFS=$'\n'
 lines=(${dataline})
@@ -12,13 +11,14 @@ lines=(${dataline})
 # parser lite inference
 lite_inference_cmd=$(func_parser_value "${lines[1]}")
 lite_model_dir_list=$(func_parser_value "${lines[2]}")
-lite_cpu_threads_list=$(func_parser_value "${lines[3]}")
-lite_batch_size_list=$(func_parser_value "${lines[4]}")
-lite_power_mode_list=$(func_parser_value "${lines[5]}")
-lite_infer_img_dir_list=$(func_parser_value "${lines[6]}")
-lite_config_dir=$(func_parser_value "${lines[7]}")
-lite_rec_dict_dir=$(func_parser_value "${lines[8]}")
-lite_benchmark_value=$(func_parser_value "${lines[9]}")
+runtime_device=$(func_parser_value "${lines[3]}")
+lite_cpu_threads_list=$(func_parser_value "${lines[4]}")
+lite_batch_size_list=$(func_parser_value "${lines[5]}")
+lite_infer_img_dir_list=$(func_parser_value "${lines[8]}")
+lite_config_dir=$(func_parser_value "${lines[9]}")
+lite_rec_dict_dir=$(func_parser_value "${lines[10]}")
+lite_benchmark_value=$(func_parser_value "${lines[11]}")
+
 
 LOG_PATH="./output"
 mkdir -p ${LOG_PATH}
@@ -37,23 +37,14 @@ function func_lite(){
     else
         precision="FP32"
     fi
-    is_single_img=$(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
     for num_threads in ${lite_cpu_threads_list[*]}; do
-        for power_mode in ${lite_power_mode_list[*]}; do
-            for batchsize in ${lite_batch_size_list[*]}; do
-                model_name=$(echo $lite_model | awk -F "/" '{print $NF}')
-                _save_log_path="${_log_path}/lite_${model_name}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}_powermode_${power_mode}_singleimg_${single_img}.log"
-                command="${_script} ${lite_model} ${precision} ${num_threads} ${batchsize} ${power_mode} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
-                eval ${command}
-                status_check $? "${command}" "${status_log}"
-            done
+	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_${num_threads}.log"
+            command="${_script} ${_lite_model} ${runtime_device} ${precision} ${num_threads} ${batchsize}  ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
+            eval ${command}
+            status_check $? "${command}" "${status_log}"
         done
     done
 }
@@ -64,6 +55,6 @@ IFS="|"
 for lite_model in ${lite_model_dir_list[*]}; do
     #run lite inference
     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

test_tipc/test_paddle2onnx.sh

@@ -11,31 +11,33 @@ python=$(func_parser_value "${lines[2]}")
 
 
 # parser params
-dataline=$(awk 'NR==111, NR==123{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==12{print}'  $FILENAME)
 IFS=$'\n'
 lines=(${dataline})
 
 # parser paddle2onnx
-padlle2onnx_cmd=$(func_parser_value "${lines[1]}")
-infer_model_dir_key=$(func_parser_key "${lines[2]}")
-infer_model_dir_value=$(func_parser_value "${lines[2]}")
-model_filename_key=$(func_parser_key "${lines[3]}")
-model_filename_value=$(func_parser_value "${lines[3]}")
-params_filename_key=$(func_parser_key "${lines[4]}")
-params_filename_value=$(func_parser_value "${lines[4]}")
-save_file_key=$(func_parser_key "${lines[5]}")
-save_file_value=$(func_parser_value "${lines[5]}")
-opset_version_key=$(func_parser_key "${lines[6]}")
-opset_version_value=$(func_parser_value "${lines[6]}")
-enable_onnx_checker_key=$(func_parser_key "${lines[7]}")
-enable_onnx_checker_value=$(func_parser_value "${lines[7]}")
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+padlle2onnx_cmd=$(func_parser_value "${lines[3]}")
+infer_model_dir_key=$(func_parser_key "${lines[4]}")
+infer_model_dir_value=$(func_parser_value "${lines[4]}")
+model_filename_key=$(func_parser_key "${lines[5]}")
+model_filename_value=$(func_parser_value "${lines[5]}")
+params_filename_key=$(func_parser_key "${lines[6]}")
+params_filename_value=$(func_parser_value "${lines[6]}")
+save_file_key=$(func_parser_key "${lines[7]}")
+save_file_value=$(func_parser_value "${lines[7]}")
+opset_version_key=$(func_parser_key "${lines[8]}")
+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 
-inference_py=$(func_parser_value "${lines[8]}")
-use_gpu_key=$(func_parser_key "${lines[9]}")
-use_gpu_value=$(func_parser_value "${lines[9]}")
-det_model_key=$(func_parser_key "${lines[10]}")
-image_dir_key=$(func_parser_key "${lines[11]}")
-image_dir_value=$(func_parser_value "${lines[11]}")
+inference_py=$(func_parser_value "${lines[10]}")
+use_gpu_key=$(func_parser_key "${lines[11]}")
+use_gpu_value=$(func_parser_value "${lines[11]}")
+det_model_key=$(func_parser_key "${lines[12]}")
+image_dir_key=$(func_parser_key "${lines[13]}")
+image_dir_value=$(func_parser_value "${lines[13]}")
 
 
 LOG_PATH="./test_tipc/output"

test_tipc/test_serving.sh

@@ -2,7 +2,7 @@
 source test_tipc/common_func.sh
 
 FILENAME=$1
-dataline=$(awk 'NR==67, NR==84{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==18{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'

test_tipc/test_train_inference_python.sh

@@ -244,7 +244,7 @@ else
     export Count=0
     USE_GPU_KEY=(${train_use_gpu_value})
     for gpu in ${gpu_list[*]}; do
-        use_gpu=${USE_GPU_KEY[Count]}
+        train_use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
         ips=""
         if [ ${gpu} = "-1" ];then
@@ -302,11 +302,20 @@ else
                 set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_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_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
-                save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-                
+                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
+                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
-                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}"
                 fi
 
@@ -325,7 +334,7 @@ else
 
                 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 
-                if [ ${trainer} = ${trainer_norm} ]; then
+                if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
                     load_norm_train_model=${set_eval_pretrain}
                 fi
                 # run eval 

tools/__init__.py

+# 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

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 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
 import tools.program as program
 
@@ -60,7 +60,7 @@ def main():
     else:
         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):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/export_center.py

@@ -27,7 +27,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 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
 import tools.program as program
 
@@ -57,7 +57,7 @@ def main():
 
     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):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/export_model.py

@@ -26,7 +26,7 @@ from paddle.jit import to_static
 
 from ppocr.modeling.architectures import build_model
 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 tools.program import load_config, merge_config, ArgsParser
 
@@ -107,7 +107,7 @@ def main():
         else:  # base rec model
             config["Architecture"]["Head"]["out_channels"] = char_num
     model = build_model(config["Architecture"])
-    init_model(config, model)
+    load_model(config, model)
     model.eval()
 
     save_path = config["Global"]["save_inference_dir"]

tools/infer/utility.py

@@ -17,7 +17,7 @@ import os
 import sys
 import cv2
 import numpy as np
-import json
+import paddle
 from PIL import Image, ImageDraw, ImageFont
 import math
 from paddle import inference
@@ -205,7 +205,7 @@ def create_predictor(args, mode, logger):
                     "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
                 }
                 max_input_shape = {
-                    "x": [1, 3, 2000, 2000],
+                    "x": [1, 3, 1280, 1280],
                     "conv2d_92.tmp_0": [1, 120, 400, 400],
                     "conv2d_91.tmp_0": [1, 24, 200, 200],
                     "conv2d_59.tmp_0": [1, 96, 400, 400],
@@ -255,16 +255,16 @@ def create_predictor(args, mode, logger):
                 opt_input_shape.update(opt_pact_shape)
             elif mode == "rec":
                 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]}
             elif mode == "cls":
                 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]}
             else:
                 min_input_shape = {"x": [1, 3, 10, 10]}
-                max_input_shape = {"x": [1, 3, 1000, 1000]}
-                opt_input_shape = {"x": [1, 3, 500, 500]}
+                max_input_shape = {"x": [1, 3, 512, 512]}
+                opt_input_shape = {"x": [1, 3, 256, 256]}
             config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
                                               opt_input_shape)
 
@@ -601,5 +601,12 @@ def get_rotate_crop_image(img, points):
     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__':
     pass

tools/infer_cls.py

@@ -32,7 +32,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 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
 import tools.program as program
 
@@ -47,7 +47,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []

tools/infer_det.py

@@ -34,7 +34,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 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
 import tools.program as program
 
@@ -59,7 +59,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    _ = load_dygraph_params(config, model, logger, None)
+    load_model(config, model)
     # build post process
     post_process_class = build_post_process(config['PostProcess'])
 

tools/infer_e2e.py

@@ -34,7 +34,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 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
 import tools.program as program
 
@@ -68,7 +68,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'],

tools/infer_rec.py

@@ -33,7 +33,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 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
 import tools.program as program
 
@@ -58,7 +58,7 @@ def main():
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -75,9 +75,7 @@ def main():
                     'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
                 ]
             elif config['Architecture']['algorithm'] == "SAR":
-                op[op_name]['keep_keys'] = [
-                    'image', 'valid_ratio'
-                ]
+                op[op_name]['keep_keys'] = ['image', 'valid_ratio']
             else:
                 op[op_name]['keep_keys'] = ['image']
         transforms.append(op)

tools/infer_table.py

@@ -34,11 +34,12 @@ from paddle.jit import to_static
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 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
 import tools.program as program
 import cv2
 
+
 def main(config, device, logger, vdl_writer):
     global_config = config['Global']
 
@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer):
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer):
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess()
     main(config, device, logger, vdl_writer)
-

tools/train.py

@@ -35,7 +35,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 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
 
 dist.get_world_size()
@@ -97,7 +97,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # 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)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(