fix conflict

applications/PCB字符识别/gen_data/corpus/text.txt

+5ZQ
+I4UL
+PWL
+SNOG
+ZL02
+1C30
+O3H
+YHRS
+N03S
+1U5Y
+JTK
+EN4F
+YKJ
+DWNH
+R42W
+X0V
+4OF5
+08AM
+Y93S
+GWE2
+0KR
+9U2A
+DBQ
+Y6J
+ROZ
+K06
+KIEY
+NZQJ
+UN1B
+6X4
\ No newline at end of file

applications/PCB字符识别/gen_data/gen.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/zcswdt/Color_OCR_image_generator
+"""
+import os
+import random
+from PIL import Image, ImageDraw, ImageFont
+import json
+import argparse
+
+
+def get_char_lines(txt_root_path):
+    """
+    desc:get corpus line
+    """
+    txt_files = os.listdir(txt_root_path)
+    char_lines = []
+    for txt in txt_files:
+        f = open(os.path.join(txt_root_path, txt), mode='r', encoding='utf-8')
+        lines = f.readlines()
+        f.close()
+        for line in lines:
+            char_lines.append(line.strip())
+        return char_lines
+
+
+def get_horizontal_text_picture(image_file, chars, fonts_list, cf):
+    """
+    desc:gen horizontal text picture
+    """
+    img = Image.open(image_file)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    img_w, img_h = img.size
+
+    # random choice font
+    font_path = random.choice(fonts_list)
+    # random choice font size
+    font_size = random.randint(cf.font_min_size, cf.font_max_size)
+    font = ImageFont.truetype(font_path, font_size)
+
+    ch_w = []
+    ch_h = []
+    for ch in chars:
+        wt, ht = font.getsize(ch)
+        ch_w.append(wt)
+        ch_h.append(ht)
+    f_w = sum(ch_w)
+    f_h = max(ch_h)
+
+    # add space
+    char_space_width = max(ch_w)
+    f_w += (char_space_width * (len(chars) - 1))
+
+    x1 = random.randint(0, img_w - f_w)
+    y1 = random.randint(0, img_h - f_h)
+    x2 = x1 + f_w
+    y2 = y1 + f_h
+
+    crop_y1 = y1
+    crop_x1 = x1
+    crop_y2 = y2
+    crop_x2 = x2
+
+    best_color = (0, 0, 0)
+    draw = ImageDraw.Draw(img)
+    for i, ch in enumerate(chars):
+        draw.text((x1, y1), ch, best_color, font=font)
+        x1 += (ch_w[i] + char_space_width)
+    crop_img = img.crop((crop_x1, crop_y1, crop_x2, crop_y2))
+    return crop_img, chars
+
+
+def get_vertical_text_picture(image_file, chars, fonts_list, cf):
+    """
+    desc:gen vertical text picture
+    """
+    img = Image.open(image_file)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    img_w, img_h = img.size
+    # random choice font
+    font_path = random.choice(fonts_list)
+    # random choice font size
+    font_size = random.randint(cf.font_min_size, cf.font_max_size)
+    font = ImageFont.truetype(font_path, font_size)
+
+    ch_w = []
+    ch_h = []
+    for ch in chars:
+        wt, ht = font.getsize(ch)
+        ch_w.append(wt)
+        ch_h.append(ht)
+    f_w = max(ch_w)
+    f_h = sum(ch_h)
+
+    x1 = random.randint(0, img_w - f_w)
+    y1 = random.randint(0, img_h - f_h)
+    x2 = x1 + f_w
+    y2 = y1 + f_h
+
+    crop_y1 = y1
+    crop_x1 = x1
+    crop_y2 = y2
+    crop_x2 = x2
+
+    best_color = (0, 0, 0)
+    draw = ImageDraw.Draw(img)
+    i = 0
+    for ch in chars:
+        draw.text((x1, y1), ch, best_color, font=font)
+        y1 = y1 + ch_h[i]
+        i = i + 1
+    crop_img = img.crop((crop_x1, crop_y1, crop_x2, crop_y2))
+    crop_img = crop_img.transpose(Image.ROTATE_90)
+    return crop_img, chars
+
+
+def get_fonts(fonts_path):
+    """
+    desc: get all fonts
+    """
+    font_files = os.listdir(fonts_path)
+    fonts_list=[]
+    for font_file in font_files:
+        font_path=os.path.join(fonts_path, font_file)
+        fonts_list.append(font_path)
+    return fonts_list
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--num_img', type=int, default=30, help="Number of images to generate")
+    parser.add_argument('--font_min_size', type=int, default=11)
+    parser.add_argument('--font_max_size', type=int, default=12,
+                        help="Help adjust the size of the generated text and the size of the picture")
+    parser.add_argument('--bg_path', type=str, default='./background',
+                        help='The generated text pictures will be pasted onto the pictures of this folder')
+    parser.add_argument('--det_bg_path', type=str, default='./det_background',
+                        help='The generated text pictures will use the pictures of this folder as the background')
+    parser.add_argument('--fonts_path', type=str, default='../../StyleText/fonts',
+                        help='The font used to generate the picture')
+    parser.add_argument('--corpus_path', type=str, default='./corpus',
+                        help='The corpus used to generate the text picture')
+    parser.add_argument('--output_dir', type=str, default='./output/', help='Images save dir')
+
+
+    cf = parser.parse_args()
+    # save path
+    if not os.path.exists(cf.output_dir):
+        os.mkdir(cf.output_dir)
+
+    # get corpus
+    txt_root_path = cf.corpus_path
+    char_lines = get_char_lines(txt_root_path=txt_root_path)
+
+    # get all fonts
+    fonts_path = cf.fonts_path
+    fonts_list = get_fonts(fonts_path)
+
+    # rec bg
+    img_root_path = cf.bg_path
+    imnames=os.listdir(img_root_path)
+    
+    # det bg
+    det_bg_path = cf.det_bg_path
+    bg_pics = os.listdir(det_bg_path)
+
+    # OCR det files
+    det_val_file = open(cf.output_dir + 'det_gt_val.txt', 'w', encoding='utf-8')
+    det_train_file = open(cf.output_dir + 'det_gt_train.txt', 'w', encoding='utf-8')
+    # det imgs
+    det_save_dir = 'imgs/'
+    if not os.path.exists(cf.output_dir + det_save_dir):
+        os.mkdir(cf.output_dir + det_save_dir)
+    det_val_save_dir = 'imgs_val/'
+    if not os.path.exists(cf.output_dir + det_val_save_dir):
+        os.mkdir(cf.output_dir + det_val_save_dir)
+
+    # OCR rec files
+    rec_val_file = open(cf.output_dir + 'rec_gt_val.txt', 'w', encoding='utf-8')
+    rec_train_file = open(cf.output_dir + 'rec_gt_train.txt', 'w', encoding='utf-8')
+    # rec imgs
+    rec_save_dir = 'rec_imgs/'
+    if not os.path.exists(cf.output_dir + rec_save_dir):
+        os.mkdir(cf.output_dir + rec_save_dir)
+    rec_val_save_dir = 'rec_imgs_val/'
+    if not os.path.exists(cf.output_dir + rec_val_save_dir):
+        os.mkdir(cf.output_dir + rec_val_save_dir)
+
+
+    val_ratio = cf.num_img * 0.2  # val dataset ratio
+
+    print('start generating...')
+    for i in range(0, cf.num_img):
+        imname = random.choice(imnames)
+        img_path = os.path.join(img_root_path, imname)
+
+        rnd = random.random()
+        # gen horizontal text picture
+        if rnd < 0.5:
+            gen_img, chars = get_horizontal_text_picture(img_path, char_lines[i], fonts_list, cf)
+            ori_w, ori_h = gen_img.size
+            gen_img = gen_img.crop((0, 3, ori_w, ori_h))
+        # gen vertical text picture
+        else:
+            gen_img, chars = get_vertical_text_picture(img_path, char_lines[i], fonts_list, cf)
+            ori_w, ori_h = gen_img.size
+            gen_img = gen_img.crop((3, 0, ori_w, ori_h))
+
+        ori_w, ori_h = gen_img.size
+
+        # rec imgs
+        save_img_name = str(i).zfill(4) + '.jpg'
+        if i < val_ratio:
+            save_dir = os.path.join(rec_val_save_dir, save_img_name)
+            line = save_dir + '\t' + char_lines[i] + '\n'
+            rec_val_file.write(line)
+        else:
+            save_dir = os.path.join(rec_save_dir, save_img_name)
+            line = save_dir + '\t' + char_lines[i] + '\n'
+            rec_train_file.write(line)
+        gen_img.save(cf.output_dir + save_dir, quality = 95, subsampling=0)
+
+        # det img
+        # random choice bg
+        bg_pic = random.sample(bg_pics, 1)[0]
+        det_img = Image.open(os.path.join(det_bg_path, bg_pic))
+        # the PCB position is fixed, modify it according to your own scenario
+        if bg_pic == '1.png':
+            x1 = 38
+            y1 = 3
+        else:
+            x1 = 34
+            y1 = 1
+
+        det_img.paste(gen_img, (x1, y1))
+        # text pos
+        chars_pos = [[x1, y1], [x1 + ori_w, y1], [x1 + ori_w, y1 + ori_h], [x1, y1 + ori_h]]
+        label = [{"transcription":char_lines[i], "points":chars_pos}]
+        if i < val_ratio:
+            save_dir = os.path.join(det_val_save_dir, save_img_name)
+            det_val_file.write(save_dir + '\t' + json.dumps(
+                    label, ensure_ascii=False) + '\n')
+        else:
+            save_dir = os.path.join(det_save_dir, save_img_name)
+            det_train_file.write(save_dir + '\t' + json.dumps(
+                    label, ensure_ascii=False) + '\n')
+        det_img.save(cf.output_dir + save_dir, quality = 95, subsampling=0)

configs/cls/ch_PP-OCRv3/ch_PP-OCRv3_rotnet.yml

@@ -63,8 +63,7 @@ Train:
     - DecodeImage:
         img_mode: BGR
         channel_first: false
-    - RecAug:
-        use_tia: False
+    - BaseDataAugmentation:
     - RandAugment:
     - SSLRotateResize:
         image_shape: [3, 48, 320]

configs/cls/cls_mv3.yml

@@ -60,8 +60,7 @@ Train:
           img_mode: BGR
           channel_first: False
       - ClsLabelEncode: # Class handling label
-      - RecAug:
-          use_tia: False
+      - BaseDataAugmentation:
       - RandAugment:
       - ClsResizeImg:
           image_shape: [3, 48, 192]

deploy/cpp_infer/CMakeLists.txt

@@ -92,6 +92,8 @@ include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
 include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
 include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+include_directories("${PADDLE_LIB}/third_party/install/onnxruntime/include")
+include_directories("${PADDLE_LIB}/third_party/install/paddle2onnx/include")
 include_directories("${PADDLE_LIB}/third_party/boost")
 include_directories("${PADDLE_LIB}/third_party/eigen3")
 
@@ -110,6 +112,8 @@ link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
 link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
 link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
 link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_LIB}/third_party/install/onnxruntime/lib")
+link_directories("${PADDLE_LIB}/third_party/install/paddle2onnx/lib")
 link_directories("${PADDLE_LIB}/paddle/lib")
 
 

deploy/cpp_infer/readme.md

@@ -208,7 +208,7 @@ Execute the built executable file:
 ./build/ppocr [--param1] [--param2] [...]
 ```
 
-**Note**:ppocr uses the `PP-OCRv3` model by default, and the input shape used by the recognition model is `3, 48, 320`, so if you use the recognition function, you need to add the parameter `--rec_img_h=48`, if you do not use the default `PP-OCRv3` model, you do not need to set this parameter.
+**Note**:ppocr uses the `PP-OCRv3` model by default, and the input shape used by the recognition model is `3, 48, 320`, if you want to use the old version model, you should add the parameter `--rec_img_h=32`.
 
 Specifically,
 
@@ -222,7 +222,6 @@ Specifically,
     --det=true \
     --rec=true \
     --cls=true \
-    --rec_img_h=48\
 ```
 
 ##### 2. det+rec：
@@ -234,7 +233,6 @@ Specifically,
     --det=true \
     --rec=true \
     --cls=false \
-    --rec_img_h=48\
 ```
 
 ##### 3. det
@@ -254,7 +252,6 @@ Specifically,
     --det=false \
     --rec=true \
     --cls=true \
-    --rec_img_h=48\
 ```
 
 ##### 5. rec
@@ -265,7 +262,6 @@ Specifically,
     --det=false \
     --rec=true \
     --cls=false \
-    --rec_img_h=48\
 ```
 
 ##### 6. cls
@@ -330,7 +326,7 @@ More parameters are as follows,
 |rec_model_dir|string|-|Address of recognition inference model|
 |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file|
 |rec_batch_num|int|6|batch size of recognition|
-|rec_img_h|int|32|image height of recognition|
+|rec_img_h|int|48|image height of recognition|
 |rec_img_w|int|320|image width of recognition|
 
 * Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `rec_char_dict_path` and `rec_model_dir`.

deploy/cpp_infer/readme_ch.md

@@ -213,7 +213,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 
 本demo支持系统串联调用，也支持单个功能的调用，如，只使用检测或识别功能。
 
-**注意** ppocr默认使用`PP-OCRv3`模型，识别模型使用的输入shape为`3,48,320`, 因此如果使用识别功能，需要添加参数`--rec_img_h=48`，如果不使用默认的`PP-OCRv3`模型，则无需设置该参数。
+**注意** ppocr默认使用`PP-OCRv3`模型，识别模型使用的输入shape为`3,48,320`, 如需使用旧版本的PP-OCR模型，则需要设置参数`--rec_img_h=32`。
 
 
 运行方式：  
@@ -232,7 +232,6 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=true \
     --rec=true \
     --cls=true \
-    --rec_img_h=48\
 ```
 
 ##### 2. 检测+识别：
@@ -244,7 +243,6 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=true \
     --rec=true \
     --cls=false \
-    --rec_img_h=48\
 ```
 
 ##### 3. 检测：
@@ -264,7 +262,6 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=false \
     --rec=true \
     --cls=true \
-    --rec_img_h=48\
 ```
 
 ##### 5. 识别：
@@ -275,7 +272,6 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=false \
     --rec=true \
     --cls=false \
-    --rec_img_h=48\
 ```
 
 ##### 6. 分类：
@@ -339,7 +335,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 |rec_model_dir|string|-|识别模型inference model地址|
 |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
 |rec_batch_num|int|6|识别模型batchsize|
-|rec_img_h|int|32|识别模型输入图像高度|
+|rec_img_h|int|48|识别模型输入图像高度|
 |rec_img_w|int|320|识别模型输入图像宽度|
 
 

deploy/cpp_infer/src/args.cpp

@@ -47,7 +47,7 @@ DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
 DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
 DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt",
               "Path of dictionary.");
-DEFINE_int32(rec_img_h, 32, "rec image height");
+DEFINE_int32(rec_img_h, 48, "rec image height");
 DEFINE_int32(rec_img_w, 320, "rec image width");
 
 // ocr forward related

deploy/cpp_infer/src/ocr_rec.cpp

@@ -83,7 +83,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
     int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
                                   std::multiplies<int>());
     predict_batch.resize(out_num);
-
+    // predict_batch is the result of Last FC with softmax
     output_t->CopyToCpu(predict_batch.data());
     auto inference_end = std::chrono::steady_clock::now();
     inference_diff += inference_end - inference_start;
@@ -98,9 +98,11 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
       float max_value = 0.0f;
 
       for (int n = 0; n < predict_shape[1]; n++) {
+        // get idx
         argmax_idx = int(Utility::argmax(
             &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
             &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+        // get score
         max_value = float(*std::max_element(
             &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
             &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
@@ -132,7 +134,9 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   paddle_infer::Config config;
   config.SetModel(model_dir + "/inference.pdmodel",
                   model_dir + "/inference.pdiparams");
-
+  std::cout << "In PP-OCRv3, default rec_img_h is 48,"
+            << "if you use other model, you should set the param rec_img_h=32"
+            << std::endl;
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
     if (this->use_tensorrt_) {

deploy/pdserving/README.md

@@ -136,7 +136,7 @@ The recognition model is the same.
 2. Run the following command to start the service.
     ```
     # Start the service and save the running log in log.txt
-    python3 web_service.py &>log.txt &
+    python3 web_service.py --config=config.yml &>log.txt &
     ```
     After the service is successfully started, a log similar to the following will be printed in log.txt
     ![](./imgs/start_server.png)
@@ -217,7 +217,7 @@ The C++ service deployment is the same as python in the environment setup and da
 2. Run the following command to start the service.
     ```
     # Start the service and save the running log in log.txt
-    python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
+    python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 8181 &>log.txt &
     ```
     After the service is successfully started, a log similar to the following will be printed in log.txt
     ![](./imgs/start_server.png)

deploy/pdserving/README_CN.md

@@ -135,7 +135,7 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
 2. 启动服务可运行如下命令：
     ```
     # 启动服务，运行日志保存在log.txt
-    python3 web_service.py &>log.txt &
+    python3 web_service.py --config=config.yml &>log.txt &
     ```
     成功启动服务后，log.txt中会打印类似如下日志
     ![](./imgs/start_server.png)
@@ -230,7 +230,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
 
     ```
     # 启动服务，运行日志保存在log.txt
-    python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
+    python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 8181 &>log.txt &
     ```
     成功启动服务后，log.txt中会打印类似如下日志
     ![](./imgs/start_server.png)

deploy/pdserving/ocr_cpp_client.py

@@ -22,15 +22,16 @@ import cv2
 from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from ocr_reader import OCRReader
+import codecs
 
 client = Client()
 # TODO:load_client need to load more than one client model.
 # this need to figure out some details.
 client.load_client_config(sys.argv[1:])
-client.connect(["127.0.0.1:9293"])
+client.connect(["127.0.0.1:8181"])
 
 import paddle
-test_img_dir = "../../doc/imgs/"
+test_img_dir = "../../doc/imgs/1.jpg"
 
 ocr_reader = OCRReader(char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
 
@@ -40,14 +41,43 @@ def cv2_to_base64(image):
         'utf8')  #data.tostring()).decode('utf8')
 
 
-for img_file in os.listdir(test_img_dir):
-    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+def _check_image_file(path):
+    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+    return any([path.lower().endswith(e) for e in img_end])
+
+
+test_img_list = []
+if os.path.isfile(test_img_dir) and _check_image_file(test_img_dir):
+    test_img_list.append(test_img_dir)
+elif os.path.isdir(test_img_dir):
+    for single_file in os.listdir(test_img_dir):
+        file_path = os.path.join(test_img_dir, single_file)
+        if os.path.isfile(file_path) and _check_image_file(file_path):
+            test_img_list.append(file_path)
+if len(test_img_list) == 0:
+    raise Exception("not found any img file in {}".format(test_img_dir))
+
+for img_file in test_img_list:
+    with open(img_file, 'rb') as file:
         image_data = file.read()
     image = cv2_to_base64(image_data)
     res_list = []
     fetch_map = client.predict(feed={"x": image}, fetch=[], batch=True)
-    one_batch_res = ocr_reader.postprocess(fetch_map, with_score=True)
-    for res in one_batch_res:
-        res_list.append(res[0])
-    res = {"res": str(res_list)}
-    print(res)
+    if fetch_map is None:
+        print('no results')
+    else:
+        if "text" in fetch_map:
+            for x in fetch_map["text"]:
+                x = codecs.encode(x)
+                words = base64.b64decode(x).decode('utf-8')
+                res_list.append(words)
+        else:
+            try:
+                one_batch_res = ocr_reader.postprocess(
+                    fetch_map, with_score=True)
+                for res in one_batch_res:
+                    res_list.append(res[0])
+            except:
+                print('no results')
+        res = {"res": str(res_list)}
+        print(res)

deploy/pdserving/ocr_reader.py

@@ -339,7 +339,7 @@ class CharacterOps(object):
 class OCRReader(object):
     def __init__(self,
                  algorithm="CRNN",
-                 image_shape=[3, 32, 320],
+                 image_shape=[3, 48, 320],
                  char_type="ch",
                  batch_num=1,
                  char_dict_path="./ppocr_keys_v1.txt"):
@@ -356,7 +356,7 @@ class OCRReader(object):
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         if self.character_type == "ch":
-            imgW = int(32 * max_wh_ratio)
+            imgW = int(imgH * max_wh_ratio)
         h = img.shape[0]
         w = img.shape[1]
         ratio = w / float(h)
@@ -377,7 +377,7 @@ class OCRReader(object):
     def preprocess(self, img_list):
         img_num = len(img_list)
         norm_img_batch = []
-        max_wh_ratio = 0
+        max_wh_ratio = 320/48.
         for ino in range(img_num):
             h, w = img_list[ino].shape[0:2]
             wh_ratio = w * 1.0 / h

deploy/pdserving/pipeline_http_client.py

@@ -36,11 +36,27 @@ def cv2_to_base64(image):
     return base64.b64encode(image).decode('utf8')
 
 
+def _check_image_file(path):
+    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+    return any([path.lower().endswith(e) for e in img_end])
+
+
 url = "http://127.0.0.1:9998/ocr/prediction"
 test_img_dir = args.image_dir
 
-for idx, img_file in enumerate(os.listdir(test_img_dir)):
-    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+test_img_list = []
+if os.path.isfile(test_img_dir) and _check_image_file(test_img_dir):
+    test_img_list.append(test_img_dir)
+elif os.path.isdir(test_img_dir):
+    for single_file in os.listdir(test_img_dir):
+        file_path = os.path.join(test_img_dir, single_file)
+        if os.path.isfile(file_path) and _check_image_file(file_path):
+            test_img_list.append(file_path)
+if len(test_img_list) == 0:
+    raise Exception("not found any img file in {}".format(test_img_dir))
+
+for idx, img_file in enumerate(test_img_list):
+    with open(img_file, 'rb') as file:
         image_data1 = file.read()
     # print file name
     print('{}{}{}'.format('*' * 10, img_file, '*' * 10))
@@ -70,4 +86,4 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)):
         print(
             "For details about error message, see PipelineServingLogs/pipeline.log"
         )
-print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
+print("==> total number of test imgs: ", len(test_img_list))

deploy/pdserving/serving_client_conf.prototxt

+feed_var {
+  name: "x"
+  alias_name: "x"
+  is_lod_tensor: false
+  feed_type: 20
+  shape: 1
+}
+fetch_var {
+  name: "save_infer_model/scale_0.tmp_1"
+  alias_name: "save_infer_model/scale_0.tmp_1"
+  is_lod_tensor: false
+  fetch_type: 1
+  shape: 1
+  shape: 640
+  shape: 640
+}

deploy/pdserving/web_service.py

@@ -19,7 +19,7 @@ import copy
 import cv2
 import base64
 # from paddle_serving_app.reader import OCRReader
-from ocr_reader import OCRReader, DetResizeForTest
+from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
 from paddle_serving_app.reader import Sequential, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
@@ -63,7 +63,6 @@ class DetOp(Op):
         dt_boxes_list = self.post_func(det_out, [ratio_list])
         dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
         out_dict = {"dt_boxes": dt_boxes, "image": self.raw_im}
-
         return out_dict, None, ""
 
 
@@ -86,7 +85,7 @@ class RecOp(Op):
         dt_boxes = copy.deepcopy(self.dt_list)
         feed_list = []
         img_list = []
-        max_wh_ratio = 0
+        max_wh_ratio = 320 / 48.
         ## Many mini-batchs, the type of feed_data is list.
         max_batch_size = 6  # len(dt_boxes)
 
@@ -150,7 +149,8 @@ class RecOp(Op):
         for i in range(dt_num):
             text = rec_list[i]
             dt_box = self.dt_list[i]
-            result_list.append([text, dt_box.tolist()])
+            if text[1] >= 0.5:
+                result_list.append([text, dt_box.tolist()])
         res = {"result": str(result_list)}
         return res, None, ""
 
@@ -163,5 +163,6 @@ class OcrService(WebService):
 
 
 uci_service = OcrService(name="ocr")
-uci_service.prepare_pipeline_config("config.yml")
+FLAGS = ArgsParser().parse_args()
+uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
 uci_service.run_service()

doc/doc_ch/FAQ.md

@@ -682,7 +682,7 @@ lr:
 
 #### Q: 关于dygraph分支中，文本识别模型训练，要使用数据增强应该如何设置？
 
-**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4.由于tia数据增强特殊性，默认不采用，可以通过添加use_tia设置，使tia数据增强生效。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。
+**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。
 
 #### Q: 训练过程中，训练程序意外退出/挂起，应该如何解决？
 
@@ -720,6 +720,13 @@ C++TensorRT预测需要使用支持TRT的预测库并在编译时打开[-DWITH_T
 
 注：建议使用TensorRT大于等于6.1.0.5以上的版本。
 
+#### Q: 为什么识别模型做预测的时候，预测图片的数量数量还会影响预测的精度
+**A**： 推理时识别模型默认的batch_size=6, 如预测图片长度变化大，可能影响预测效果。如果出现上述问题可在推理的时候设置识别bs=1，命令如下：
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --rec_batch_num=1
+```
+
 <a name="213"></a>
 
 ### 2.13 推理部署

doc/doc_ch/dataset/layout_datasets.md

@@ -15,8 +15,8 @@
 - **数据简介**：publaynet数据集的训练集合中包含35万张图像，验证集合中包含1.1万张图像。总共包含5个类别，分别是： `text, title, list, table, figure`。部分图像以及标注框可视化如下所示。
 
 <div align="center">
-    <img src="../datasets/publaynet_demo/gt_PMC3724501_00006.jpg" width="500">
-    <img src="../datasets/publaynet_demo/gt_PMC5086060_00002.jpg" width="500">
+    <img src="../../datasets/publaynet_demo/gt_PMC3724501_00006.jpg" width="500">
+    <img src="../../datasets/publaynet_demo/gt_PMC5086060_00002.jpg" width="500">
 </div>
 
 - **下载地址**：https://developer.ibm.com/exchanges/data/all/publaynet/
@@ -30,8 +30,8 @@
 - **数据简介**：CDLA据集的训练集合中包含5000张图像，验证集合中包含1000张图像。总共包含10个类别，分别是： `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`。部分图像以及标注框可视化如下所示。
 
 <div align="center">
-    <img src="../datasets/CDLA_demo/val_0633.jpg" width="500">
-    <img src="../datasets/CDLA_demo/val_0941.jpg" width="500">
+    <img src="../../datasets/CDLA_demo/val_0633.jpg" width="500">
+    <img src="../../datasets/CDLA_demo/val_0941.jpg" width="500">
 </div>
 
 - **下载地址**：https://github.com/buptlihang/CDLA
@@ -45,8 +45,8 @@
 - **数据简介**：TableBank数据集包含Latex（训练集187199张，验证集7265张，测试集5719张）与Word（训练集73383张，验证集2735张，测试集2281张）两种类别的文档。仅包含`Table` 1个类别。部分图像以及标注框可视化如下所示。
 
 <div align="center">
-    <img src="../datasets/tablebank_demo/004.png" height="700">
-    <img src="../datasets/tablebank_demo/005.png" height="700">
+    <img src="../../datasets/tablebank_demo/004.png" height="700">
+    <img src="../../datasets/tablebank_demo/005.png" height="700">
 </div>
 
 - **下载地址**：https://doc-analysis.github.io/tablebank-page/index.html

doc/doc_ch/detection.md

@@ -13,6 +13,7 @@
   - [2.5 分布式训练](#25-分布式训练)
   - [2.6 知识蒸馏训练](#26-知识蒸馏训练)
   - [2.7 其他训练环境](#27-其他训练环境)
+  - [2.8 模型微调](#28-模型微调)
 - [3. 模型评估与预测](#3-模型评估与预测)
   - [3.1 指标评估](#31-指标评估)
   - [3.2 测试检测效果](#32-测试检测效果)
@@ -141,7 +142,8 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml \
      Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
  ```
 
-<a name="26---fleet---"></a>
+<a name="25---fleet---"></a>
+
 ## 2.5 分布式训练
 
 多机多卡训练时，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：
@@ -151,7 +153,7 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1
      -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 ```
 
-**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`。
+**注意:** （1）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；（2）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`；（3）更多关于分布式训练的性能优势等信息，请参考：[分布式训练教程](./distributed_training.md)。
 
 
 <a name="26---distill---"></a>
@@ -177,6 +179,13 @@ Windows平台只支持`单卡`的训练与预测，指定GPU进行训练`set CUD
 - Linux DCU
 DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`，其余训练评估预测命令与Linux GPU完全相同。
 
+<a name="28-模型微调"></a>
+
+## 2.8 模型微调
+
+实际使用过程中，建议加载官方提供的预训练模型，在自己的数据集中进行微调，关于检测模型的微调方法，请参考：[模型微调教程](./finetune.md)。
+
+
 <a name="3--------"></a>
 # 3. 模型评估与预测
 
@@ -196,6 +205,7 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{pat
 ## 3.2 测试检测效果
 
 测试单张图像的检测效果：
+
 ```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"
 ```
@@ -226,14 +236,19 @@ python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.pretrained
 ```
 
 DB检测模型inference 模型预测：
+
 ```shell
 python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
 ```
 如果是其他检测，比如EAST模型，det_algorithm参数需要修改为EAST，默认为DB算法：
+
 ```shell
 python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
 ```
 
+更多关于推理超参数的配置与解释，请参考：[模型推理超参数解释教程](./inference_args.md)。
+
+
 <a name="5-faq"></a>
 # 5. FAQ
 

doc/doc_ch/distributed_training.md

@@ -41,11 +41,16 @@ python3 -m paddle.distributed.launch \
 
 ## 性能效果测试
 
-* 基于单机8卡P40，和2机8卡P40，在26W公开识别数据集(LSVT, RCTW, MTWI)上进行训练，最终耗时如下。
+* 在2机8卡P40的机器上，基于26W公开识别数据集(LSVT, RCTW, MTWI)上进行训练，最终耗时如下。
 
-|         模型             |     配置文件 |  机器数量    | 每台机器的GPU数量  |   训练时间    | 识别Acc    | 加速比 |
-| :----------------------: | :------------: | :------------: | :---------------: | :----------: | :-----------: | :-----------: |
-|          CRNN        |   configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml  | 1     |  8  |  60h  |  66.7% | - |
-|          CRNN        |   configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml   | 2   |  8  |  40h  |  67.0% | 150% |
+| 模型   | 配置  | 精度     | 单机8卡耗时 | 2机8卡耗时 | 加速比 |
+|------|-----|--------|--------|--------|-----|
+| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | 67.0% | 2.50d   | 1.67d  | **1.5** |
 
-可以看出，精度没有下降的情况下，训练时间由60h缩短为了40h，加速比可以达到60h/40h=150%，效率为60h/(40h*2)=75%。
+
+* 在4机8卡V100的机器上，基于全量数据训练，最终耗时如下
+
+
+| 模型   | 配置  | 精度     | 单机8卡耗时 | 4机8卡耗时 | 加速比 |
+|------|-----|--------|--------|--------|-----|
+| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | 74.0% | 10d   | 2.84d  | **3.5** |

doc/doc_ch/inference_args.md

+# PaddleOCR模型推理参数解释
+
+在使用PaddleOCR进行模型推理时，可以自定义修改参数，来修改模型、数据、预处理、后处理等内容（参数文件：[utility.py](../../tools/infer/utility.py)），详细的参数解释如下所示。
+
+* 全局信息
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  image_dir | str | 无，必须显式指定 | 图像或者文件夹路径 |
+|  vis_font_path | str | "./doc/fonts/simfang.ttf" | 用于可视化的字体路径 |
+|  drop_score | float | 0.5 | 识别得分小于该值的结果会被丢弃，不会作为返回结果 |
+|  use_pdserving | bool | False | 是否使用Paddle Serving进行预测 |
+|  warmup | bool | False | 是否开启warmup，在统计预测耗时的时候，可以使用这种方法 |
+|  draw_img_save_dir | str | "./inference_results" | 系统串联预测OCR结果的保存文件夹 |
+|  save_crop_res | bool | False  | 是否保存OCR的识别文本图像 |
+|  crop_res_save_dir | str | "./output" | 保存OCR识别出来的文本图像路径 |
+|  use_mp | bool | False | 是否开启多进程预测  |
+|  total_process_num | int | 6 | 开启的进城数，`use_mp`为`True`时生效  |
+|  process_id | int | 0 | 当前进程的id号，无需自己修改  |
+|  benchmark | bool | False | 是否开启benchmark，对预测速度、显存占用等进行统计  |
+|  save_log_path | str | "./log_output/" | 开启`benchmark`时，日志结果的保存文件夹 |
+|  show_log | bool | True | 是否显示预测中的日志信息  |
+|  use_onnx | bool | False | 是否开启onnx预测 |
+
+
+* 预测引擎相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  use_gpu | bool | True | 是否使用GPU进行预测 |
+|  ir_optim | bool | True | 是否对计算图进行分析与优化，开启后可以加速预测过程 |
+|  use_tensorrt | bool | False | 是否开启tensorrt |
+|  min_subgraph_size | int | 15 | tensorrt中最小子图size，当子图的size大于该值时，才会尝试对该子图使用trt engine计算 |
+|  precision | str | fp32 | 预测的精度，支持`fp32`, `fp16`, `int8` 3种输入 |
+|  enable_mkldnn | bool | True | 是否开启mkldnn |
+|  cpu_threads | int | 10 | 开启mkldnn时，cpu预测的线程数 |
+
+* 文本检测模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_algorithm | str | "DB" | 文本检测算法名称，目前支持`DB`, `EAST`, `SAST`, `PSE`  |
+|  det_model_dir | str | xx | 检测inference模型路径 |
+|  det_limit_side_len | int | 960 | 检测的图像边长限制 |
+|  det_limit_type | str | "max" | 检测的变成限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`det_limit_side_len`，`max`表示保证图像最长边不大于`det_limit_side_len` |
+
+其中，DB算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_db_thresh | float | 0.3 | DB输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点 |
+|  det_db_box_thresh | float | 0.6 | 检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域 |
+|  det_db_unclip_ratio | float | 1.5 | `Vatti clipping`算法的扩张系数，使用该方法对文字区域进行扩张 |
+|  max_batch_size | int | 10 | 预测的batch size |
+|  use_dilation | bool | False | 是否对分割结果进行膨胀以获取更优检测效果 |
+|  det_db_score_mode | str | "fast" | DB的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
+
+EAST算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_east_score_thresh | float | 0.8 | EAST后处理中score map的阈值 |
+|  det_east_cover_thresh | float | 0.1 | EAST后处理中文本框的平均得分阈值 |
+|  det_east_nms_thresh | float | 0.2 | EAST后处理中nms的阈值 |
+
+SAST算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_sast_score_thresh | float | 0.5 | SAST后处理中的得分阈值 |
+|  det_sast_nms_thresh | float | 0.5 | SAST后处理中nms的阈值 |
+|  det_sast_polygon | bool | False | 是否多边形检测，弯曲文本场景（如Total-Text）设置为True |
+
+PSE算法相关参数如下
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_pse_thresh | float | 0.0 | 对输出图做二值化的阈值 |
+|  det_pse_box_thresh | float | 0.85 | 对box进行过滤的阈值，低于此阈值的丢弃 |
+|  det_pse_min_area | float | 16 | box的最小面积，低于此阈值的丢弃 |
+|  det_pse_box_type | str | "box" | 返回框的类型，box:四点坐标，poly: 弯曲文本的所有点坐标 |
+|  det_pse_scale | int | 1 | 输入图像相对于进后处理的图的比例，如`640*640`的图像，网络输出为`160*160`，scale为2的情况下，进后处理的图片shape为`320*320`。这个值调大可以加快后处理速度，但是会带来精度的下降 |
+
+* 文本识别模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  rec_algorithm | str | "CRNN" | 文本识别算法名称，目前支持`CRNN`, `SRN`, `RARE`, `NETR`, `SAR` |
+|  rec_model_dir | str | 无，如果使用识别模型，该项是必填项 | 识别inference模型路径 |
+|  rec_image_shape | list | [3, 32, 320] | 识别时的图像尺寸， |
+|  rec_batch_num | int | 6 | 识别的batch size |
+|  max_text_length | int | 25 | 识别结果最大长度，在`SRN`中有效 |
+|  rec_char_dict_path | str | "./ppocr/utils/ppocr_keys_v1.txt" | 识别的字符字典文件 |
+|  use_space_char | bool | True | 是否包含空格，如果为`True`，则会在最后字符字典中补充`空格`字符 |
+
+
+* 端到端文本检测与识别模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  e2e_algorithm | str | "PGNet" | 端到端算法名称，目前支持`PGNet` |
+|  e2e_model_dir | str | 无，如果使用端到端模型，该项是必填项 | 端到端模型inference模型路径 |
+|  e2e_limit_side_len | int | 768 | 端到端的输入图像边长限制 |
+|  e2e_limit_type | str | "max" | 端到端的边长限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`e2e_limit_side_len`，`max`表示保证图像最长边不大于`e2e_limit_side_len` |
+|  e2e_pgnet_score_thresh | float | 0.5 | 端到端得分阈值，小于该阈值的结果会被丢弃 |
+|  e2e_char_dict_path | str | "./ppocr/utils/ic15_dict.txt" | 识别的字典文件路径 |
+|  e2e_pgnet_valid_set | str | "totaltext" | 验证集名称，目前支持`totaltext`, `partvgg`，不同数据集对应的后处理方式不同，与训练过程保持一致即可 |
+|  e2e_pgnet_mode | str | "fast" | PGNet的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
+
+
+* 方向分类器模型相关
+
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  use_angle_cls | bool | False | 是否使用方向分类器 |
+|  cls_model_dir | str | 无，如果需要使用，则必须显式指定路径 | 方向分类器inference模型路径 |
+|  cls_image_shape | list | [3, 48, 192] | 预测尺度 |
+|  label_list | list | ['0', '180'] | class id对应的角度值 |
+|  cls_batch_num | int | 6 | 方向分类器预测的batch size |
+|  cls_thresh | float | 0.9 | 预测阈值，模型预测结果为180度，且得分大于该阈值时，认为最终预测结果为180度，需要翻转 |

doc/doc_ch/ppocr_introduction.md

@@ -30,11 +30,11 @@ PP-OCR系统pipeline如下：
 
 PP-OCR系统在持续迭代优化，目前已发布PP-OCR和PP-OCRv2两个版本：
 
-PP-OCR从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941
+PP-OCR从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考[PP-OCR技术报告](https://arxiv.org/abs/2009.09941)。
 
 #### PP-OCRv2
 
-PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和[Enhanced CTC loss](./enhanced_ctc_loss.md)损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://arxiv.org/abs/2109.03144)。
+PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和[Enhanced CTC loss](./enhanced_ctc_loss.md)损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考[PP-OCRv2技术报告](https://arxiv.org/abs/2109.03144)。
 
 #### PP-OCRv3
 
@@ -48,7 +48,7 @@ PP-OCRv3系统pipeline如下：
     <img src="../ppocrv3_framework.png" width="800">
 </div>
 
-更多细节请参考PP-OCRv3[技术报告](./PP-OCRv3_introduction.md)。
+更多细节请参考[PP-OCRv3技术报告](https://arxiv.org/abs/2206.03001v2) 👉[中文简洁版](./PP-OCRv3_introduction.md)
 
 
 <a name="2"></a>

doc/doc_ch/quickstart.md

@@ -101,8 +101,17 @@ cd /path/to/ppocr_img
   ['韩国小馆', 0.994467]
   ```
 
+**版本说明**
+paddleocr默认使用PP-OCRv3模型(`--ocr_version PP-OCRv3`)，如需使用其他版本可通过设置参数`--ocr_version`，具体版本说明如下：
+|  版本名称  |  版本说明 |
+|    ---    |   ---   |
+| PP-OCRv3 | 支持中、英文检测和识别，方向分类器，支持多语种识别 |
+| PP-OCRv2 | 支持中英文的检测和识别，方向分类器，多语言暂未更新 |
+| PP-OCR   | 支持中、英文检测和识别，方向分类器，支持多语种识别 |
 
-如需使用2.0模型，请指定参数`--ocr_version PP-OCR`，paddleocr默认使用PP-OCRv3模型(`--ocr_version PP-OCRv3`)。更多whl包使用可参考[whl包文档](./whl.md)
+如需新增自己训练的模型，可以在[paddleocr](../../paddleocr.py)中增加模型链接和字段，重新编译即可。
+
+更多whl包使用可参考[whl包文档](./whl.md)
 
 <a name="212"></a>
 

doc/doc_ch/recognition.md

@@ -18,6 +18,7 @@
   - [2.6. 知识蒸馏训练](#26-知识蒸馏训练)
   - [2.7. 多语言模型训练](#27-多语言模型训练)
   - [2.8. 其他训练环境](#28-其他训练环境)
+  - [2.9. 模型微调](#29-模型微调)
 - [3. 模型评估与预测](#3-模型评估与预测)
   - [3.1. 指标评估](#31-指标评估)
   - [3.2. 测试识别效果](#32-测试识别效果)
@@ -217,6 +218,30 @@ python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pre
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
 ```
 
+正常启动训练后，会看到以下log输出：
+
+```
+[2022/02/22 07:58:05] root INFO: epoch: [1/800], iter: 10, lr: 0.000000, loss: 0.754281, acc: 0.000000, norm_edit_dis: 0.000008, reader_cost: 0.55541 s, batch_cost: 0.91654 s, samples: 1408, ips: 153.62133
+[2022/02/22 07:58:13] root INFO: epoch: [1/800], iter: 20, lr: 0.000001, loss: 0.924677, acc: 0.000000, norm_edit_dis: 0.000008, reader_cost: 0.00236 s, batch_cost: 0.28528 s, samples: 1280, ips: 448.68599
+[2022/02/22 07:58:23] root INFO: epoch: [1/800], iter: 30, lr: 0.000002, loss: 0.967231, acc: 0.000000, norm_edit_dis: 0.000008, reader_cost: 0.14527 s, batch_cost: 0.42714 s, samples: 1280, ips: 299.66507
+[2022/02/22 07:58:31] root INFO: epoch: [1/800], iter: 40, lr: 0.000003, loss: 0.895318, acc: 0.000000, norm_edit_dis: 0.000008, reader_cost: 0.00173 s, batch_cost: 0.27719 s, samples: 1280, ips: 461.77252
+```
+
+log 中自动打印如下信息：
+
+|  字段   |   含义   |    
+| :----: | :------: | 
+|  epoch | 当前迭代轮次 |
+|  iter  | 当前迭代次数 |
+|  lr    | 当前学习率 |
+|  loss  | 当前损失函数 |
+|  acc   | 当前batch的准确率 |
+|  norm_edit_dis | 当前 batch 的编辑距离 |
+|  reader_cost | 当前 batch 数据处理耗时 |
+|  batch_cost | 当前 batch 总耗时 |
+|  samples  | 当前 batch 内的样本数 |
+|  ips  | 每秒处理图片的数量 |
+
 
 PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/en_PP-OCRv3_rec/best_accuracy` 。
 
@@ -363,7 +388,7 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1
      -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
 ```
 
-**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`。
+**注意:** （1）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；（2）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`；（3）更多关于分布式训练的性能优势等信息，请参考：[分布式训练教程](./distributed_training.md)。
 
 ## 2.6. 知识蒸馏训练
 
@@ -438,6 +463,11 @@ Windows平台只支持`单卡`的训练与预测，指定GPU进行训练`set CUD
 - Linux DCU
 DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`，其余训练评估预测命令与Linux GPU完全相同。
 
+## 2.9 模型微调
+
+实际使用过程中，建议加载官方提供的预训练模型，在自己的数据集中进行微调，关于识别模型的微调方法，请参考：[模型微调教程](./finetune.md)。
+
+
 # 3. 模型评估与预测
 
 ## 3.1. 指标评估
@@ -540,12 +570,13 @@ inference/en_PP-OCRv3_rec/
 
 - 自定义模型推理
 
-  如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径
+  如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，更多关于推理超参数的配置与解释，请参考：[模型推理超参数解释教程](./inference_args.md)。
 
   ```
   python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 48, 320" --rec_char_dict_path="your text dict path"
   ```
 
+
 # 5. FAQ
 
 Q1: 训练模型转inference 模型之后预测效果不一致？

doc/doc_en/PP-OCRv3_introduction_en.md

@@ -76,7 +76,7 @@ LK-PAN (Large Kernel PAN) is a lightweight [PAN](https://arxiv.org/pdf/1803.0153
 
 **(2) DML: Deep Mutual Learning Strategy for Teacher Model**
 
-[DML](https://arxiv.org/abs/1706.00384)(Collaborative Mutual Learning), as shown in the figure below, can effectively improve the accuracy of the text detection model by learning from each other with two models with the same structure. The DML strategy is adopted in the teacher model training, and the hmean is increased from 85% to 86%. By updating the teacher model of CML in PP-OCRv2 to the above-mentioned higher-precision one, the hmean of the student model can be further improved from 83.2% to 84.3%.
+[DML](https://arxiv.org/abs/1706.00384)(Deep Mutual Learning), as shown in the figure below, can effectively improve the accuracy of the text detection model by learning from each other with two models with the same structure. The DML strategy is adopted in the teacher model training, and the hmean is increased from 85% to 86%. By updating the teacher model of CML in PP-OCRv2 to the above-mentioned higher-precision one, the hmean of the student model can be further improved from 83.2% to 84.3%.
 
 
 <div align="center">
@@ -100,7 +100,7 @@ Considering that the features of some channels will be suppressed if the convolu
 
 The recognition module of PP-OCRv3 is optimized based on the text recognition algorithm [SVTR](https://arxiv.org/abs/2205.00159). RNN is abandoned in SVTR, and the context information of the text line image is more effectively mined by introducing the Transformers structure, thereby improving the text recognition ability.
 
-The recognition accuracy of SVTR_inty outperforms PP-OCRv2 recognition model by 5.3%, while the prediction speed nearly 11 times slower. It takes nearly 100ms to predict a text line on CPU. Therefore, as shown in the figure below, PP-OCRv3 adopts the following six optimization strategies to accelerate the recognition model.
+The recognition accuracy of SVTR_tiny outperforms PP-OCRv2 recognition model by 5.3%, while the prediction speed nearly 11 times slower. It takes nearly 100ms to predict a text line on CPU. Therefore, as shown in the figure below, PP-OCRv3 adopts the following six optimization strategies to accelerate the recognition model.
 
 <div align="center">
     <img src="../ppocr_v3/v3_rec_pipeline.png" width=800>

doc/doc_en/algorithm_rec_starnet.md

+# STAR-Net
+
+- [1. Introduction](#1)
+- [2. Environment](#2)
+- [3. Model Training / Evaluation / Prediction](#3)
+    - [3.1 Training](#3-1)
+    - [3.2 Evaluation](#3-2)
+    - [3.3 Prediction](#3-3)
+- [4. Inference and Deployment](#4)
+    - [4.1 Python Inference](#4-1)
+    - [4.2 C++ Inference](#4-2)
+    - [4.3 Serving](#4-3)
+    - [4.4 More](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. Introduction
+
+Paper information:
+> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf)
+> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han.
+> BMVC, pages 43.1-43.13, 2016
+
+Refer to [DTRB](https://arxiv.org/abs/1904.01906) text Recognition Training and Evaluation Process . Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
+
+|Models|Backbone Networks|Avg Accuracy|Configuration Files|Download Links|
+| --- | --- | --- | --- | --- |
+|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
+|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[ trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
+
+
+<a name="2"></a>
+## 2. Environment
+Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code.
+
+<a name="3"></a>
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example:
+
+<a name="3-1"></a>
+### 3.1 Training
+After the data preparation is complete, the training can be started. The training command is as follows:
+
+````
+#Single card training (long training period, not recommended)
+python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml #Multi-card training, specify the card number through the --gpus parameter
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml
+ ````
+
+<a name="3-2"></a>
+### 3.2 Evaluation
+
+````
+# GPU evaluation, Global.pretrained_model is the model to be evaluated
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+ ````
+
+<a name="3-3"></a>
+### 3.3 Prediction
+
+````
+# The configuration file used for prediction must match the training
+python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+ ````
+
+<a name="4"></a>
+## 4. Inference
+
+<a name="4-1"></a>
+### 4.1 Python Inference
+First, convert the model saved during the STAR-Net text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example [Model download address]( https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) , which can be converted using the following command:
+
+```shell
+python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/rec_starnet
+ ````
+
+STAR-Net text recognition model inference, you can execute the following commands:
+
+```shell
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
+ ````
+
+![](../imgs_words_en/word_336.png)
+
+The inference results are as follows:
+
+
+```bash
+Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
+```
+
+**Attention** Since the above model refers to the [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the ultra-lightweight Chinese recognition model training in two aspects:
+
+- The image resolutions used during training are different. The image resolutions used for training the above models are [3, 32, 100], while for Chinese model training, in order to ensure the recognition effect of long texts, the image resolutions used during training are [ 3, 32, 320]. The default shape parameter of the predictive inference program is the image resolution used for training Chinese, i.e. [3, 32, 320]. Therefore, when inferring the above English model here, it is necessary to set the shape of the recognized image through the parameter rec_image_shape.
+
+- Character list, the experiment in the DTRB paper is only for 26 lowercase English letters and 10 numbers, a total of 36 characters. All uppercase and lowercase characters are converted to lowercase characters, and characters not listed above are ignored and considered spaces. Therefore, there is no input character dictionary here, but a dictionary is generated by the following command. Therefore, the parameter rec_char_dict_path needs to be set during inference, which is specified as an English dictionary "./ppocr/utils/ic15_dict.txt".
+
+```
+self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+dict_character = list(self.character_str)
+
+
+ ```
+
+<a name="4-2"></a>
+### 4.2 C++ Inference
+
+After preparing the inference model, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial to operate.
+
+<a name="4-3"></a>
+### 4.3 Serving
+
+After preparing the inference model, refer to the [pdserving](../../deploy/pdserving/) tutorial for Serving deployment, including two modes: Python Serving and C++ Serving.
+
+<a name="4-4"></a>
+### 4.4 More
+
+The STAR-Net model also supports the following inference deployment methods:
+
+- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
+
+<a name="5"></a>
+## 5. FAQ
+
+## Quote
+
+```bibtex
+@inproceedings{liu2016star,
+  title={STAR-Net: a spatial attention residue network for scene text recognition.},
+  author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
+  booktitle={BMVC},
+  volume={2},
+  pages={7},
+  year={2016}
+}
+```
+
+

doc/doc_en/detection_en.md

@@ -159,7 +159,7 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1
      -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 ```
 
-**Note:** When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. In addition, training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`.
+**Note:** (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`. (3) For more details about the distributed training speedup ratio, please refer to [Distributed Training Tutorial](./distributed_training_en.md).
 
 ### 2.6 Training with knowledge distillation
 

doc/doc_en/distributed_training.md → doc/doc_en/distributed_training_en.md

@@ -40,11 +40,17 @@ python3 -m paddle.distributed.launch \
 
 ## Performance comparison
 
-* Based on 26W public recognition dataset (LSVT, rctw, mtwi), training on single 8-card P40 and dual 8-card P40, the final time consumption is as follows.
+* On two 8-card P40 graphics cards, the final time consumption and speedup ratio for public recognition dataset (LSVT, RCTW, MTWI) containing 260k images are as follows.
 
-|   Model   |   Config file  |  Number of machines |   Number of GPUs per machine   |   Training time      | Recognition acc  | Speedup ratio |
-| :-------: | :------------: |  :----------------: | :----------------------------: | :------------------: | :--------------: | :-----------: |
-|   CRNN    |   configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml   |   1          |  8  |  60h  |  66.7% | - |
-|   CRNN    |   configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml   |   2          |  8  |  40h  |  67.0% | 150% |
 
-It can be seen that the training time is shortened from 60h to 40h, the speedup ratio can reach 150% (60h / 40h), and the efficiency is 75% (60h / (40h * 2)).
+| Model   | Config file  | Recognition acc     | single 8-card training time | two 8-card training time | Speedup ratio |
+|------|-----|--------|--------|--------|-----|
+| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | 67.0% | 2.50d   | 1.67d  | **1.5** |
+
+
+* On four 8-card V100 graphics cards, the final time consumption and speedup ratio for full data are as follows.
+
+
+| Model   | Config file  | Recognition acc     | single 8-card training time | four 8-card training time | Speedup ratio |
+|------|-----|--------|--------|--------|-----|
+| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | 74.0% | 10d   | 2.84d  | **3.5** |

doc/doc_en/ppocr_introduction_en.md

@@ -29,10 +29,10 @@ PP-OCR pipeline is as follows:
 
 PP-OCR system is in continuous optimization. At present, PP-OCR and PP-OCRv2 have been released:
 
-PP-OCR adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941).
+PP-OCR adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to [PP-OCR technical report](https://arxiv.org/abs/2009.09941).
 
 #### PP-OCRv2
-On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (https://arxiv.org/abs/2109.03144).
+On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to [PP-OCRv2 technical report](https://arxiv.org/abs/2109.03144).
 
 #### PP-OCRv3
 
@@ -46,7 +46,7 @@ PP-OCRv3 pipeline is as follows:
     <img src="../ppocrv3_framework.png" width="800">
 </div>
 
-For more details, please refer to [PP-OCRv3 technical report](./PP-OCRv3_introduction_en.md).
+For more details, please refer to [PP-OCRv3 technical report](https://arxiv.org/abs/2206.03001v2).
 
 <a name="2"></a>
 ## 2. Features

doc/doc_en/quickstart_en.md

@@ -119,7 +119,18 @@ If you do not use the provided test image, you can replace the following `--imag
   ['PAIN', 0.9934559464454651]
   ```
 
-If you need to use the 2.0 model, please specify the parameter `--ocr_version PP-OCR`, paddleocr uses the PP-OCRv3 model by default(`--ocr_version PP-OCRv3`). More whl package usage can be found in [whl package](./whl_en.md)
+**Version**
+paddleocr uses the PP-OCRv3 model by default(`--ocr_version PP-OCRv3`). If you want to use other versions, you can set the parameter `--ocr_version`, the specific version description is as follows:
+|  version name |  description |
+|    ---    |   ---   |
+| PP-OCRv3 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
+| PP-OCRv2 | only supports Chinese and English detection and recognition, direction classifier, multilingual model is not updated |
+| PP-OCR   | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
+
+If you want to add your own trained model, you can add model links and keys in [paddleocr](../../paddleocr.py) and recompile.
+
+More whl package usage can be found in [whl package](./whl_en.md)
+
 <a name="212-multi-language-model"></a>
 
 #### 2.1.2 Multi-language Model

doc/doc_en/recognition_en.md

@@ -306,7 +306,7 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1
      -o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train
 ```
 
-**Note:** When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. In addition, training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`.
+**Note:** (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`. (3) For more details about the distributed training speedup ratio, please refer to [Distributed Training Tutorial](./distributed_training_en.md).
 
 <a name="kd"></a>
 ### 2.6 Training with Knowledge Distillation

paddleocr.py

@@ -154,7 +154,13 @@ MODEL_URLS = {
                     'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar',
                     'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
                 }
-            }
+            },
+            'cls': {
+                'ch': {
+                    'url':
+                    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
+                }
+            },
         },
         'PP-OCR': {
             'det': {

ppocr/data/imaug/__init__.py

@@ -22,7 +22,7 @@ from .make_shrink_map import MakeShrinkMap
 from .random_crop_data import EastRandomCropData, RandomCropImgMask
 from .make_pse_gt import MakePseGt
 
-from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
+from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
     SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg
 from .ssl_img_aug import SSLRotateResize
 from .randaugment import RandAugment

ppocr/data/imaug/rec_img_aug.py

@@ -22,13 +22,74 @@ from .text_image_aug import tia_perspective, tia_stretch, tia_distort
 
 
 class RecAug(object):
-    def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
-        self.use_tia = use_tia
-        self.aug_prob = aug_prob
+    def __init__(self,
+                 tia_prob=0.4,
+                 crop_prob=0.4,
+                 reverse_prob=0.4,
+                 noise_prob=0.4,
+                 jitter_prob=0.4,
+                 blur_prob=0.4,
+                 hsv_aug_prob=0.4,
+                 **kwargs):
+        self.tia_prob = tia_prob
+        self.bda = BaseDataAugmentation(crop_prob, reverse_prob, noise_prob,
+                                        jitter_prob, blur_prob, hsv_aug_prob)
 
     def __call__(self, data):
         img = data['image']
-        img = warp(img, 10, self.use_tia, self.aug_prob)
+        h, w, _ = img.shape
+
+        # tia
+        if random.random() <= self.tia_prob:
+            if h >= 20 and w >= 20:
+                img = tia_distort(img, random.randint(3, 6))
+                img = tia_stretch(img, random.randint(3, 6))
+            img = tia_perspective(img)
+
+        # bda
+        data['image'] = img
+        data = self.bda(data)
+        return data
+
+
+class BaseDataAugmentation(object):
+    def __init__(self,
+                 crop_prob=0.4,
+                 reverse_prob=0.4,
+                 noise_prob=0.4,
+                 jitter_prob=0.4,
+                 blur_prob=0.4,
+                 hsv_aug_prob=0.4,
+                 **kwargs):
+        self.crop_prob = crop_prob
+        self.reverse_prob = reverse_prob
+        self.noise_prob = noise_prob
+        self.jitter_prob = jitter_prob
+        self.blur_prob = blur_prob
+        self.hsv_aug_prob = hsv_aug_prob
+
+    def __call__(self, data):
+        img = data['image']
+        h, w, _ = img.shape
+
+        if random.random() <= self.crop_prob and h >= 20 and w >= 20:
+            img = get_crop(img)
+
+        if random.random() <= self.blur_prob:
+            img = blur(img)
+
+        if random.random() <= self.hsv_aug_prob:
+            img = hsv_aug(img)
+
+        if random.random() <= self.jitter_prob:
+            img = jitter(img)
+
+        if random.random() <= self.noise_prob:
+            img = add_gasuss_noise(img)
+
+        if random.random() <= self.reverse_prob:
+            img = 255 - img
+
         data['image'] = img
         return data
 
@@ -359,7 +420,7 @@ def flag():
     return 1 if random.random() > 0.5000001 else -1
 
 
-def cvtColor(img):
+def hsv_aug(img):
     """
     cvtColor
     """
@@ -427,50 +488,6 @@ def get_crop(image):
     return crop_img
 
 
-class Config:
-    """
-    Config
-    """
-
-    def __init__(self, use_tia):
-        self.anglex = random.random() * 30
-        self.angley = random.random() * 15
-        self.anglez = random.random() * 10
-        self.fov = 42
-        self.r = 0
-        self.shearx = random.random() * 0.3
-        self.sheary = random.random() * 0.05
-        self.borderMode = cv2.BORDER_REPLICATE
-        self.use_tia = use_tia
-
-    def make(self, w, h, ang):
-        """
-        make
-        """
-        self.anglex = random.random() * 5 * flag()
-        self.angley = random.random() * 5 * flag()
-        self.anglez = -1 * random.random() * int(ang) * flag()
-        self.fov = 42
-        self.r = 0
-        self.shearx = 0
-        self.sheary = 0
-        self.borderMode = cv2.BORDER_REPLICATE
-        self.w = w
-        self.h = h
-
-        self.perspective = self.use_tia
-        self.stretch = self.use_tia
-        self.distort = self.use_tia
-
-        self.crop = True
-        self.affine = False
-        self.reverse = True
-        self.noise = True
-        self.jitter = True
-        self.blur = True
-        self.color = True
-
-
 def rad(x):
     """
     rad
@@ -554,48 +571,3 @@ def get_warpAffine(config):
     rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
                    [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
     return rz
-
-
-def warp(img, ang, use_tia=True, prob=0.4):
-    """
-    warp
-    """
-    h, w, _ = img.shape
-    config = Config(use_tia=use_tia)
-    config.make(w, h, ang)
-    new_img = img
-
-    if config.distort:
-        img_height, img_width = img.shape[0:2]
-        if random.random() <= prob and img_height >= 20 and img_width >= 20:
-            new_img = tia_distort(new_img, random.randint(3, 6))
-
-    if config.stretch:
-        img_height, img_width = img.shape[0:2]
-        if random.random() <= prob and img_height >= 20 and img_width >= 20:
-            new_img = tia_stretch(new_img, random.randint(3, 6))
-
-    if config.perspective:
-        if random.random() <= prob:
-            new_img = tia_perspective(new_img)
-
-    if config.crop:
-        img_height, img_width = img.shape[0:2]
-        if random.random() <= prob and img_height >= 20 and img_width >= 20:
-            new_img = get_crop(new_img)
-
-    if config.blur:
-        if random.random() <= prob:
-            new_img = blur(new_img)
-    if config.color:
-        if random.random() <= prob:
-            new_img = cvtColor(new_img)
-    if config.jitter:
-        new_img = jitter(new_img)
-    if config.noise:
-        if random.random() <= prob:
-            new_img = add_gasuss_noise(new_img)
-    if config.reverse:
-        if random.random() <= prob:
-            new_img = 255 - new_img
-    return new_img

ppocr/data/simple_dataset.py

@@ -33,7 +33,7 @@ class SimpleDataSet(Dataset):
         self.delimiter = dataset_config.get('delimiter', '\t')
         label_file_list = dataset_config.pop('label_file_list')
         data_source_num = len(label_file_list)
-        ratio_list = dataset_config.get("ratio_list", [1.0])
+        ratio_list = dataset_config.get("ratio_list", 1.0)
         if isinstance(ratio_list, (float, int)):
             ratio_list = [float(ratio_list)] * int(data_source_num)
 

ppocr/losses/rec_aster_loss.py

@@ -27,12 +27,12 @@ class CosineEmbeddingLoss(nn.Layer):
         self.epsilon = 1e-12
 
     def forward(self, x1, x2, target):
-        similarity = paddle.fluid.layers.reduce_sum(
+        similarity = paddle.sum(
             x1 * x2, dim=-1) / (paddle.norm(
                 x1, axis=-1) * paddle.norm(
                     x2, axis=-1) + self.epsilon)
         one_list = paddle.full_like(target, fill_value=1)
-        out = paddle.fluid.layers.reduce_mean(
+        out = paddle.mean(
             paddle.where(
                 paddle.equal(target, one_list), 1. - similarity,
                 paddle.maximum(

ppocr/losses/table_att_loss.py

@@ -19,7 +19,6 @@ from __future__ import print_function
 import paddle
 from paddle import nn
 from paddle.nn import functional as F
-from paddle import fluid
 
 class TableAttentionLoss(nn.Layer):
     def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs):
@@ -36,13 +35,13 @@ class TableAttentionLoss(nn.Layer):
         :param bbox:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,]
         :return: loss
         '''
-        ix1 = fluid.layers.elementwise_max(preds[:, 0], bbox[:, 0])
-        iy1 = fluid.layers.elementwise_max(preds[:, 1], bbox[:, 1])
-        ix2 = fluid.layers.elementwise_min(preds[:, 2], bbox[:, 2])
-        iy2 = fluid.layers.elementwise_min(preds[:, 3], bbox[:, 3])
+        ix1 = paddle.maximum(preds[:, 0], bbox[:, 0])
+        iy1 = paddle.maximum(preds[:, 1], bbox[:, 1])
+        ix2 = paddle.minimum(preds[:, 2], bbox[:, 2])
+        iy2 = paddle.minimum(preds[:, 3], bbox[:, 3])
 
-        iw = fluid.layers.clip(ix2 - ix1 + 1e-3, 0., 1e10)
-        ih = fluid.layers.clip(iy2 - iy1 + 1e-3, 0., 1e10)
+        iw = paddle.clip(ix2 - ix1 + 1e-3, 0., 1e10)
+        ih = paddle.clip(iy2 - iy1 + 1e-3, 0., 1e10)
 
         # overlap
         inters = iw * ih
@@ -55,12 +54,12 @@ class TableAttentionLoss(nn.Layer):
         # ious
         ious = inters / uni
 
-        ex1 = fluid.layers.elementwise_min(preds[:, 0], bbox[:, 0])
-        ey1 = fluid.layers.elementwise_min(preds[:, 1], bbox[:, 1])
-        ex2 = fluid.layers.elementwise_max(preds[:, 2], bbox[:, 2])
-        ey2 = fluid.layers.elementwise_max(preds[:, 3], bbox[:, 3])
-        ew = fluid.layers.clip(ex2 - ex1 + 1e-3, 0., 1e10)
-        eh = fluid.layers.clip(ey2 - ey1 + 1e-3, 0., 1e10)
+        ex1 = paddle.minimum(preds[:, 0], bbox[:, 0])
+        ey1 = paddle.minimum(preds[:, 1], bbox[:, 1])
+        ex2 = paddle.maximum(preds[:, 2], bbox[:, 2])
+        ey2 = paddle.maximum(preds[:, 3], bbox[:, 3])
+        ew = paddle.clip(ex2 - ex1 + 1e-3, 0., 1e10)
+        eh = paddle.clip(ey2 - ey1 + 1e-3, 0., 1e10)
 
         # enclose erea
         enclose = ew * eh + eps

ppocr/modeling/backbones/kie_unet_sdmgr.py

@@ -175,12 +175,7 @@ class Kie_backbone(nn.Layer):
             img, relations, texts, gt_bboxes, tag, img_size)
         x = self.img_feat(img)
         boxes, rois_num = self.bbox2roi(gt_bboxes)
-        feats = paddle.fluid.layers.roi_align(
-            x,
-            boxes,
-            spatial_scale=1.0,
-            pooled_height=7,
-            pooled_width=7,
-            rois_num=rois_num)
+        feats = paddle.vision.ops.roi_align(
+            x, boxes, spatial_scale=1.0, output_size=7, boxes_num=rois_num)
         feats = self.maxpool(feats).squeeze(-1).squeeze(-1)
         return [relations, texts, feats]

ppocr/modeling/backbones/rec_resnet_fpn.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 
 from paddle import nn, ParamAttr
 from paddle.nn import functional as F
-import paddle.fluid as fluid
 import paddle
 import numpy as np
 

ppocr/modeling/heads/rec_srn_head.py

@@ -20,13 +20,11 @@ import math
 import paddle
 from paddle import nn, ParamAttr
 from paddle.nn import functional as F
-import paddle.fluid as fluid
 import numpy as np
 from .self_attention import WrapEncoderForFeature
 from .self_attention import WrapEncoder
 from paddle.static import Program
 from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
-import paddle.fluid.framework as framework
 
 from collections import OrderedDict
 gradient_clip = 10

ppocr/modeling/heads/self_attention.py

@@ -22,7 +22,6 @@ import paddle
 from paddle import ParamAttr, nn
 from paddle import nn, ParamAttr
 from paddle.nn import functional as F
-import paddle.fluid as fluid
 import numpy as np
 gradient_clip = 10
 
@@ -288,10 +287,10 @@ class PrePostProcessLayer(nn.Layer):
                         "layer_norm_%d" % len(self.sublayers()),
                         paddle.nn.LayerNorm(
                             normalized_shape=d_model,
-                            weight_attr=fluid.ParamAttr(
-                                initializer=fluid.initializer.Constant(1.)),
-                            bias_attr=fluid.ParamAttr(
-                                initializer=fluid.initializer.Constant(0.)))))
+                            weight_attr=paddle.ParamAttr(
+                                initializer=paddle.nn.initializer.Constant(1.)),
+                            bias_attr=paddle.ParamAttr(
+                                initializer=paddle.nn.initializer.Constant(0.)))))
             elif cmd == "d":  # add dropout
                 self.functors.append(lambda x: F.dropout(
                     x, p=dropout_rate, mode="downscale_in_infer")
@@ -324,7 +323,7 @@ class PrepareEncoder(nn.Layer):
 
     def forward(self, src_word, src_pos):
         src_word_emb = src_word
-        src_word_emb = fluid.layers.cast(src_word_emb, 'float32')
+        src_word_emb = paddle.cast(src_word_emb, 'float32')
         src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
         src_pos = paddle.squeeze(src_pos, axis=-1)
         src_pos_enc = self.emb(src_pos)
@@ -367,7 +366,7 @@ class PrepareDecoder(nn.Layer):
         self.dropout_rate = dropout_rate
 
     def forward(self, src_word, src_pos):
-        src_word = fluid.layers.cast(src_word, 'int64')
+        src_word = paddle.cast(src_word, 'int64')
         src_word = paddle.squeeze(src_word, axis=-1)
         src_word_emb = self.emb0(src_word)
         src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)

ppstructure/table/README.md

@@ -18,7 +18,7 @@ The table recognition mainly contains three models
 
 The table recognition flow chart is as follows
 
-![tableocr_pipeline](../../doc/table/tableocr_pipeline_en.jpg)
+![tableocr_pipeline](../docs/table/tableocr_pipeline_en.jpg)
 
 1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result.
 2. The table structure and cell coordinates is predicted by RARE model.

ppstructure/table/predict_table.py

@@ -28,6 +28,7 @@ import numpy as np
 import time
 import tools.infer.predict_rec as predict_rec
 import tools.infer.predict_det as predict_det
+import tools.infer.utility as utility
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
 from ppstructure.table.matcher import distance, compute_iou
@@ -59,11 +60,37 @@ class TableSystem(object):
         self.text_recognizer = predict_rec.TextRecognizer(
             args) if text_recognizer is None else text_recognizer
         self.table_structurer = predict_strture.TableStructurer(args)
+        self.benchmark = args.benchmark
+        self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
+            args, 'table', logger)
+        if args.benchmark:
+            import auto_log
+            pid = os.getpid()
+            gpu_id = utility.get_infer_gpuid()
+            self.autolog = auto_log.AutoLogger(
+                model_name="table",
+                model_precision=args.precision,
+                batch_size=1,
+                data_shape="dynamic",
+                save_path=None,  #args.save_log_path,
+                inference_config=self.config,
+                pids=pid,
+                process_name=None,
+                gpu_ids=gpu_id if args.use_gpu else None,
+                time_keys=[
+                    'preprocess_time', 'inference_time', 'postprocess_time'
+                ],
+                warmup=0,
+                logger=logger)
 
     def __call__(self, img, return_ocr_result_in_table=False):
         result = dict()
         ori_im = img.copy()
+        if self.benchmark:
+            self.autolog.times.start()
         structure_res, elapse = self.table_structurer(copy.deepcopy(img))
+        if self.benchmark:
+            self.autolog.times.stamp()
         dt_boxes, elapse = self.text_detector(copy.deepcopy(img))
         dt_boxes = sorted_boxes(dt_boxes)
         if return_ocr_result_in_table:
@@ -77,13 +104,11 @@ class TableSystem(object):
             box = [x_min, y_min, x_max, y_max]
             r_boxes.append(box)
         dt_boxes = np.array(r_boxes)
-
         logger.debug("dt_boxes num : {}, elapse : {}".format(
             len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
         img_crop_list = []
-
         for i in range(len(dt_boxes)):
             det_box = dt_boxes[i]
             x0, y0, x1, y1 = expand(2, det_box, ori_im.shape)
@@ -92,10 +117,14 @@ class TableSystem(object):
         rec_res, elapse = self.text_recognizer(img_crop_list)
         logger.debug("rec_res num  : {}, elapse : {}".format(
             len(rec_res), elapse))
+        if self.benchmark:
+            self.autolog.times.stamp()
         if return_ocr_result_in_table:
             result['rec_res'] = rec_res
         pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res)
         result['html'] = pred_html
+        if self.benchmark:
+            self.autolog.times.end(stamp=True)
         return result
 
     def rebuild_table(self, structure_res, dt_boxes, rec_res):
@@ -213,6 +242,8 @@ def main(args):
         logger.info('excel saved to {}'.format(excel_path))
         elapse = time.time() - starttime
         logger.info("Predict time : {:.3f}s".format(elapse))
+    if args.benchmark:
+        text_sys.autolog.report()
 
 
 if __name__ == "__main__":

test_tipc/build_server.sh

+#使用镜像：
+#registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82
+
+#编译Serving Server：
+
+#client和app可以直接使用release版本
+
+#server因为加入了自定义OP，需要重新编译
+
+apt-get update
+apt install -y libcurl4-openssl-dev libbz2-dev
+wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && tar xf centos_ssl.tar && rm -rf centos_ssl.tar && mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k && mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k && ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10 && ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10 && ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so && ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so
+
+# 安装go依赖
+rm -rf /usr/local/go
+wget -qO- https://paddle-ci.cdn.bcebos.com/go1.17.2.linux-amd64.tar.gz | tar -xz -C /usr/local
+export GOROOT=/usr/local/go
+export GOPATH=/root/gopath
+export PATH=$PATH:$GOPATH/bin:$GOROOT/bin
+go env -w GO111MODULE=on
+go env -w GOPROXY=https://goproxy.cn,direct
+go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
+go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
+go install github.com/golang/protobuf/protoc-gen-go@v1.4.3
+go install google.golang.org/grpc@v1.33.0
+go env -w GO111MODULE=auto
+
+# 下载opencv库
+wget https://paddle-qa.bj.bcebos.com/PaddleServing/opencv3.tar.gz && tar -xvf opencv3.tar.gz && rm -rf opencv3.tar.gz
+export OPENCV_DIR=$PWD/opencv3
+
+# clone Serving
+git clone https://github.com/PaddlePaddle/Serving.git -b develop --depth=1
+cd Serving
+export Serving_repo_path=$PWD
+git submodule update --init --recursive
+python -m pip install -r python/requirements.txt
+
+
+export PYTHON_INCLUDE_DIR=$(python -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
+export PYTHON_LIBRARIES=$(python -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))")
+export PYTHON_EXECUTABLE=`which python`
+
+export CUDA_PATH='/usr/local/cuda'
+export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
+export CUDA_CUDART_LIBRARY='/usr/local/cuda/lib64/'
+export TENSORRT_LIBRARY_PATH='/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/'
+
+# cp 自定义OP代码
+cp -rf ../deploy/pdserving/general_detection_op.cpp ${Serving_repo_path}/core/general-server/op
+
+# 编译Server, export SERVING_BIN
+mkdir server-build-gpu-opencv && cd server-build-gpu-opencv
+cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
+            -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
+            -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
+            -DCUDA_TOOLKIT_ROOT_DIR=${CUDA_PATH} \
+            -DCUDNN_LIBRARY=${CUDNN_LIBRARY} \
+            -DCUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY} \
+            -DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
+            -DOPENCV_DIR=${OPENCV_DIR} \
+            -DWITH_OPENCV=ON \
+            -DSERVER=ON \
+            -DWITH_GPU=ON ..
+make -j32
+
+python -m pip install python/dist/paddle*
+export SERVING_BIN=$PWD/core/general-server/serving
+cd ../../

test_tipc/common_func.sh

@@ -57,10 +57,11 @@ function status_check(){
     last_status=$1   # the exit code
     run_command=$2
     run_log=$3
+    model_name=$4
     if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+        echo -e "\033[33m Run successfully with command - ${model_name} - ${run_command}!  \033[0m" | tee -a ${run_log}
     else
-        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+        echo -e "\033[33m Run failed with command - ${model_name} - ${run_command}!  \033[0m" | tee -a ${run_log}
     fi
 }
 

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_img_h=32
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--rec_model_dir:./inference/ch_PP-OCRv2_rec_infer/
+--benchmark:True
+--det:True
+--rec:True
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -6,10 +6,10 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_system.py
 --use_gpu:False|True
---enable_mkldnn:False|True
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
+--use_tensorrt:False
 --precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ch_PP-OCRv2
+python:python3.7
+2onnx: paddle2onnx
+--det_model_dir:./inference/ch_PP-OCRv2_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_save_file:./inference/det_v2_onnx/model.onnx
+--rec_model_dir:./inference/ch_PP-OCRv2_rec_infer/
+--rec_save_file:./inference/rec_v2_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_system.py --rec_image_shape="3,32,320"
+--use_gpu:True|False
+--det_model_dir:
+--rec_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/00008790.jpg
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_client/
+--rec_dirname:./inference/ch_PP-OCRv2_rec_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_client/
+--rec_dirname:./inference/ch_PP-OCRv2_rec_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_client/
+serving_dir:./deploy/pdserving
+web_service:web_service.py --config=config.yml --opt op.det.concurrency="1" op.rec.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2_det
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+--det:True
+--rec:False
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ch_PP-OCRv2_det
+python:python3.7
+2onnx: paddle2onnx
+--det_model_dir:./inference/ch_PP-OCRv2_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_save_file:./inference/det_v2_onnx/model.onnx
+--rec_model_dir:
+--rec_save_file:
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--det_model_dir:
+--rec_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_det
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_client/
+--rec_dirname:null
+--rec_serving_server:null
+--rec_serving_client:null
+serving_dir:./deploy/pdserving
+web_service:web_service_det.py --config=config.yml --opt op.det.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv2_det
+model_name:ch_PP-OCRv2_det
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 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
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv2_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

+===========================train_params===========================
+model_name:ch_PP-OCRv2_det
+python:python3.7
+gpu_list:192.168.0.1,192.168.0.2;0,1
+Global.use_gpu:True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
+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
+norm_train:tools/train.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
+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.checkpoints:
+norm_export:tools/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
+quant_export:null
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]

test_tipc/configs/ch_PP-OCRv2_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv2_det
+model_name:ch_PP-OCRv2_det
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:amp
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 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
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2_det_KL
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_det_klquant_infer
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+--det:True
+--rec:False
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
-model_name:PPOCRv2_ocr_det_kl
+model_name:ch_PP-OCRv2_det_KL
 python:python3.7
 Global.pretrained_model:null
 Global.save_inference_dir:null
@@ -8,10 +8,10 @@ infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_PP-OCRv2/ch_
 infer_quant:True
 inference:tools/infer/predict_det.py
 --use_gpu:False|True
---enable_mkldnn:True
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
+--use_tensorrt:False
 --precision:int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_det_KL
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_klquant_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_kl_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_kl_client/
+--rec_dirname:./inference/ch_PP-OCRv2_rec_klquant_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_kl_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_kl_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_det_KL
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_klquant_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_kl_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_kl_client/
+--rec_dirname:null
+--rec_serving_server:null
+--rec_serving_client:null
+serving_dir:./deploy/pdserving
+web_service:web_service_det.py --config=config.yml --opt op.det.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_det_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2_det_PACT
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_det_pact_infer
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+--det:True
+--rec:False
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_det_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_det_PACT
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_pact_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_pact_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_pact_client/
+--rec_dirname:./inference/ch_PP-OCRv2_rec_pact_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_pact_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_pact_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_det_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_det_PACT
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_pact_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_pact_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_pact_client/
+--rec_dirname:null
+--rec_serving_server:null
+--rec_serving_client:null
+serving_dir:./deploy/pdserving
+web_service:web_service_det.py --config=config.yml --opt op.det.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv2_det_PACT
+model_name:ch_PP-OCRv2_det_PACT
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=1|whole_train_whole_infer=4
 Global.pretrained_model:null
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv2_det_PACT/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv2_det_PACT
+model_name:ch_PP-OCRv2_det_PACT
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:amp
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 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
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv2_rec/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2_rec
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_rec_infer/
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_img_h=32
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:6
+--use_tensorrt:False
+--precision:fp32
+--rec_model_dir:
+--image_dir:./inference/rec_inference/
+null:null
+--benchmark:True
+--det:False
+--rec:True
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ch_PP-OCRv2_rec
+python:python3.7
+2onnx: paddle2onnx
+--det_model_dir:
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_save_file:
+--rec_model_dir:./inference/ch_PP-OCRv2_rec_infer/
+--rec_save_file:./inference/rec_v2_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py --rec_image_shape="3,32,320"
+--use_gpu:True|False
+--det_model_dir:
+--rec_model_dir:
+--image_dir:./inference/rec_inference/
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_rec
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:null
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:null
+--det_serving_client:null
+--rec_dirname:./inference/ch_PP-OCRv2_rec_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py --det=False
+--image_dir:../../inference/rec_inference

test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt

 ===========================train_params===========================
-model_name:PPOCRv2_ocr_rec
+model_name:ch_PP-OCRv2_rec
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
 Global.pretrained_model:null
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_rec.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1|6
---use_tensorrt:False|True
---precision:fp32|int8
+--use_tensorrt:False
+--precision:fp32
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 null:null

test_tipc/configs/ch_PP-OCRv2_rec/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

+===========================train_params===========================
+model_name:ch_PP-OCRv2_rec
+python:python3.7
+gpu_list:192.168.0.1,192.168.0.2;0,1
+Global.use_gpu:True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o 
+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.checkpoints:
+norm_export:tools/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o 
+quant_export:
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv2_rec_infer
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_rec.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1|6
+--use_tensorrt:False
+--precision:fp32
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,32,320]}]

test_tipc/configs/ch_PP-OCRv2_rec/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt

 ===========================train_params===========================
-model_name:PPOCRv2_ocr_rec
+model_name:ch_PP-OCRv2_rec
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:amp
-Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
 Global.pretrained_model:null
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_rec.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1|6
---use_tensorrt:False|True
---precision:fp32|int8
+--use_tensorrt:False
+--precision:fp32
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 null:null

test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2_rec_KL
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_rec_klquant_infer
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_img_h=32
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:6
+--use_tensorrt:False
+--precision:fp32
+--rec_model_dir:
+--image_dir:./inference/rec_inference/
+null:null
+--benchmark:True
+--det:False
+--rec:True
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
-model_name:PPOCRv2_ocr_rec_kl
+model_name:ch_PP-OCRv2_rec_KL
 python:python3.7
 Global.pretrained_model:null
 Global.save_inference_dir:null
 infer_model:./inference/ch_PP-OCRv2_rec_infer/
 infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
 infer_quant:True
-inference:tools/infer/predict_rec.py
+inference:tools/infer/predict_rec.py --rec_image_shape="3,32,320"
 --use_gpu:False|True
---enable_mkldnn:False|True
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1|6
---use_tensorrt:True
+--use_tensorrt:False
 --precision:int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference

test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_rec_KL
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_klquant_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_kl_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_kl_client/
+--rec_dirname:./inference/ch_PP-OCRv2_rec_klquant_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_kl_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_kl_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_rec_KL
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:null
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:null
+--det_serving_client:null
+--rec_dirname:./inference/ch_PP-OCRv2_rec_klquant_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_kl_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_kl_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py --det=False
+--image_dir:../../inference/rec_inference

test_tipc/configs/ch_PP-OCRv2_rec_PACT/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv2_rec_PACT
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv2_rec_pact_infer
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_img_h=32
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:6
+--use_tensorrt:False
+--precision:fp32
+--rec_model_dir:
+--image_dir:./inference/rec_inference/
+null:null
+--benchmark:True
+--det:False
+--rec:True
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_rec_PACT/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_rec_PACT
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv2_det_pact_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v2_pact_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v2_pact_client/
+--rec_dirname:./inference/ch_PP-OCRv2_rec_pact_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_pact_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_pact_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv2_rec_PACT/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv2_rec_PACT
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:null
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:null
+--det_serving_client:null
+--rec_dirname:./inference/ch_PP-OCRv2_rec_pact_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v2_pact_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v2_pact_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py --det=False
+--image_dir:../../inference/rec_inference

test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv2_rec_PACT
+model_name:ch_PP-OCRv2_rec_PACT
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=6|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
 Global.pretrained_model:pretrain_models/ch_PP-OCRv2_rec_train/best_accuracy
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:True
 inference:tools/infer/predict_rec.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1|6
---use_tensorrt:False|True
---precision:fp32|int8
+--use_tensorrt:False
+--precision:fp32
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 null:null

test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv2_rec_PACT
+model_name:ch_PP-OCRv2_rec_PACT
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:amp
-Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
-Global.pretrained_model:null
+Global.pretrained_model:pretrain_models/ch_PP-OCRv2_rec_train/best_accuracy
 train_model_name:latest
 train_infer_img_dir:./inference/rec_inference
 null:null
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:True
 inference:tools/infer/predict_rec.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1|6
---use_tensorrt:False|True
---precision:fp32|int8
+--use_tensorrt:False
+--precision:fp32
 --rec_model_dir:
 --image_dir:./inference/rec_inference
 null:null

test_tipc/configs/ch_PP-OCRv3/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

 ===========================cpp_infer_params===========================
-model_name:ocr_system_v3
+model_name:ch_PP-OCRv3
 use_opencv:True
 infer_model:./inference/ch_PP-OCRv3_det_infer/
 infer_quant:False
 inference:./deploy/cpp_infer/build/ppocr --rec_img_h=48 --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 --rec_model_dir:./inference/ch_PP-OCRv3_rec_infer/

test_tipc/configs/ch_PP-OCRv3/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -6,10 +6,10 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_system.py --rec_image_shape="3,48,320"
 --use_gpu:False|True
---enable_mkldnn:False|True
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
+--use_tensorrt:False
 --precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/

test_tipc/configs/ch_PP-OCRv3/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ch_PP-OCRv3
+python:python3.7
+2onnx: paddle2onnx
+--det_model_dir:./inference/ch_PP-OCRv3_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_save_file:./inference/det_v3_onnx/model.onnx
+--rec_model_dir:./inference/ch_PP-OCRv3_rec_infer/
+--rec_save_file:./inference/rec_v3_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_system.py --rec_image_shape="3,48,320"
+--use_gpu:True|False
+--det_model_dir:
+--rec_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/00008790.jpg
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv3/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv3
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv3_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v3_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v3_client/
+--rec_dirname:./inference/ch_PP-OCRv3_rec_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v3_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v3_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv3/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv3
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv3_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v3_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v3_client/
+--rec_dirname:./inference/ch_PP-OCRv3_rec_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v3_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v3_client/
+serving_dir:./deploy/pdserving
+web_service:web_service.py --config=config.yml --opt op.det.concurrency="1" op.rec.concurrency="1"
+op.det.local_service_conf.devices:gpu|null
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv3_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

 ===========================cpp_infer_params===========================
-model_name:ocr_det_v3
+model_name:ch_PP-OCRv3_det
 use_opencv:True
 infer_model:./inference/ch_PP-OCRv3_det_infer/
 infer_quant:False
 inference:./deploy/cpp_infer/build/ppocr
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv3_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

 ===========================paddle2onnx_params===========================
-model_name:ocr_det_v3
+model_name:ch_PP-OCRv3_det
 python:python3.7
 2onnx: paddle2onnx
---model_dir:./inference/ch_PP-OCRv3_det_infer/
+--det_model_dir:./inference/ch_PP-OCRv3_det_infer/
 --model_filename:inference.pdmodel
 --params_filename:inference.pdiparams
---save_file:./inference/det_v3_onnx/model.onnx
+--det_save_file:./inference/det_v3_onnx/model.onnx
+--rec_model_dir:
+--rec_save_file:
 --opset_version:10
 --enable_onnx_checker:True
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
 --det_model_dir:
+--rec_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv3_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

 ===========================serving_params===========================
-model_name:ocr_det_v3
+model_name:ch_PP-OCRv3_det
 python:python3.7
 trans_model:-m paddle_serving_client.convert
---dirname:./inference/ch_PP-OCRv3_det_infer/
+--det_dirname:./inference/ch_PP-OCRv3_det_infer/
 --model_filename:inference.pdmodel
 --params_filename:inference.pdiparams
---serving_server:./deploy/pdserving/ppocr_det_v3_serving/
---serving_client:./deploy/pdserving/ppocr_det_v3_client/
+--det_serving_server:./deploy/pdserving/ppocr_det_v3_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v3_client/
+--rec_dirname:null
+--rec_serving_server:null
+--rec_serving_client:null
 serving_dir:./deploy/pdserving
 web_service:web_service_det.py --config=config.yml --opt op.det.concurrency="1"
 op.det.local_service_conf.devices:gpu|null
-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
+op.det.local_service_conf.use_mkldnn:False
+op.det.local_service_conf.thread_num:6
+op.det.local_service_conf.use_trt:False
+op.det.local_service_conf.precision:fp32
+op.det.local_service_conf.model_config:
+op.rec.local_service_conf.model_config:
+pipline:pipeline_http_client.py
+--image_dir:../../doc/imgs/1.jpg

test_tipc/configs/ch_PP-OCRv3_det/train_infer_python.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv3_det
+model_name:ch_PP-OCRv3_det
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 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
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv3_det/train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt

+===========================train_params===========================
+model_name:ch_PP-OCRv3_det
+python:python3.7
+gpu_list:192.168.0.1,192.168.0.2;0,1
+Global.use_gpu:True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
+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
+norm_train:tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o 
+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.checkpoints:
+norm_export:tools/export_model.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o 
+quant_export:null
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv3_det_infer/
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]

test_tipc/configs/ch_PP-OCRv3_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt

 ===========================train_params===========================
-model_name:ch_PPOCRv3_det
+model_name:ch_PP-OCRv3_det
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:amp
-Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 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
@@ -39,11 +39,11 @@ infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16|int8
+--use_tensorrt:False
+--precision:fp32
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null

test_tipc/configs/ch_PP-OCRv3_det_KL/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ch_PP-OCRv3_det_KL
+use_opencv:True
+infer_model:./inference/ch_PP-OCRv3_det_klquant_infer
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+--det:True
+--rec:False
+--cls:False
+--use_angle_cls:False
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv3_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
-model_name:PPOCRv3_ocr_det_kl
+model_name:ch_PP-OCRv3_det_KL
 python:python3.7
 Global.pretrained_model:null
 Global.save_inference_dir:null
@@ -8,10 +8,10 @@ infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_PP-OCRv3/ch_
 infer_quant:True
 inference:tools/infer/predict_det.py
 --use_gpu:False|True
---enable_mkldnn:True
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
+--use_tensorrt:False
 --precision:int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/

test_tipc/configs/ch_PP-OCRv3_det_KL/model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ch_PP-OCRv3_det_KL
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--det_dirname:./inference/ch_PP-OCRv3_det_klquant_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--det_serving_server:./deploy/pdserving/ppocr_det_v3_kl_serving/
+--det_serving_client:./deploy/pdserving/ppocr_det_v3_kl_client/
+--rec_dirname:./inference/ch_PP-OCRv3_rec_klquant_infer/
+--rec_serving_server:./deploy/pdserving/ppocr_rec_v3_kl_serving/
+--rec_serving_client:./deploy/pdserving/ppocr_rec_v3_kl_client/
+serving_dir:./deploy/pdserving
+web_service:-m paddle_serving_server.serve
+--op:GeneralDetectionOp GeneralInferOp
+--port:8181
+--gpu_id:"0"|null
+cpp_client:ocr_cpp_client.py
+--image_dir:../../doc/imgs/1.jpg