Merge branch 'dygraph' into dygraph

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/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

@@ -132,7 +132,9 @@ 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_) {

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？
 

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_en/PP-OCRv3_introduction_en.md

@@ -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/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

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

@@ -22,6 +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, \
     SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg
 from .ssl_img_aug import SSLRotateResize

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
 
@@ -370,7 +431,7 @@ def flag():
     return 1 if random.random() > 0.5000001 else -1
 
 
-def cvtColor(img):
+def hsv_aug(img):
     """
     cvtColor
     """
@@ -438,50 +499,6 @@ 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
@@ -565,48 +582,3 @@ 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)
 

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/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_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_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_PACT/train_infer_python.txt

@@ -4,7 +4,7 @@ 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=50
+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

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_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

 ===========================kl_quant_params===========================
-model_name:PPOCRv3_ocr_rec_kl
+model_name:ch_PP-OCRv3_rec_KL
 python:python3.7
-Global.pretrained_model:null
+Global.pretrained_model:
 Global.save_inference_dir:null
 infer_model:./inference/ch_PP-OCRv3_rec_infer/
 infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_PP-OCRv3_rec/ch_PP-OCRv3_rec_distillation.yml -o
 infer_quant:True
 inference:tools/infer/predict_rec.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|6
---use_tensorrt:True
+--use_tensorrt:False
 --precision:int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference

test_tipc/configs/ch_PP-OCRv3_rec_PACT/train_infer_python.txt

@@ -4,7 +4,7 @@ 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=50
+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-OCRv3_rec_train/best_accuracy

test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -8,10 +8,10 @@ infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/c
 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_ppocr_mobile_v2.0_det_PACT/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:null
-Global.epoch_num:lite_train_lite_infer=20|whole_train_whole_infer=50
+Global.epoch_num:lite_train_lite_infer=2|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

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -6,12 +6,12 @@ Global.save_inference_dir:null
 infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
 infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.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:True
---cpu_threads:1|6
+--enable_mkldnn:False
+--cpu_threads:6
 --rec_batch_num:1
---use_tensorrt:False|True
+--use_tensorrt:False
 --precision:int8
 --rec_model_dir:
 --image_dir:./inference/rec_inference