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

README_ch.md

@@ -81,7 +81,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 | 模型简介     | 模型名称     |推荐场景          | 检测模型 | 方向分类器 | 识别模型 |
 | ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| 中英文超轻量PP-OCRv2模型（13.0M） |  ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
+| 中英文超轻量PP-OCRv2模型（13.0M） |  ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
 | 中英文超轻量PP-OCR mobile模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar)      |
 | 中英文通用PP-OCR server模型（143.4M）   |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar)  |  
 

configs/det/det_mv3_pse.yml

+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_mv3_pse/
+  save_epoch_step: 600
+  # evaluation is run every 63 iterations
+  eval_batch_step: [ 0,63 ]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: FPN
+    out_channels: 96
+  Head:
+    name: PSEHead
+    hidden_dim: 96
+    out_channels: 7
+
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

configs/det/det_r50_vd_pse.yml

+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_r50_vd_pse/
+  save_epoch_step: 600
+  # evaluation is run every 125 iterations
+  eval_batch_step: [ 0,125 ]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: FPN
+    out_channels: 256
+  Head:
+    name: PSEHead
+    hidden_dim: 256
+    out_channels: 7
+
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.0001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 800
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: ./output/rec_chinese_lite_distillation_v2.1
+  save_model_dir: ./output/rec_mobile_pp-OCRv2
   save_epoch_step: 3
   eval_batch_step: [0, 2000]
   cal_metric_during_train: true
@@ -19,7 +19,7 @@ Global:
   infer_mode: false
   use_space_char: true
   distributed: true
-  save_res_path: ./output/rec/predicts_chinese_lite_distillation_v2.1.txt
+  save_res_path: ./output/rec/predicts_mobile_pp-OCRv2.txt
 
 
 Optimizer:
@@ -35,79 +35,32 @@ Optimizer:
     name: L2
     factor: 2.0e-05
 
+
 Architecture:
-  model_type: &model_type "rec"
-  name: DistillationModel
-  algorithm: Distillation
-  Models:
-    Teacher:
-      pretrained:
-      freeze_params: false
-      return_all_feats: true
-      model_type: *model_type
-      algorithm: CRNN
-      Transform:
-      Backbone:
-        name: MobileNetV1Enhance
-        scale: 0.5
-      Neck:
-        name: SequenceEncoder
-        encoder_type: rnn
-        hidden_size: 64
-      Head:
-        name: CTCHead
-        mid_channels: 96
-        fc_decay: 0.00002
-    Student:
-      pretrained:
-      freeze_params: false
-      return_all_feats: true
-      model_type: *model_type
-      algorithm: CRNN
-      Transform:
-      Backbone:
-        name: MobileNetV1Enhance
-        scale: 0.5
-      Neck:
-        name: SequenceEncoder
-        encoder_type: rnn
-        hidden_size: 64
-      Head:
-        name: CTCHead
-        mid_channels: 96
-        fc_decay: 0.00002
-  
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 64
+  Head:
+    name: CTCHead
+    mid_channels: 96
+    fc_decay: 0.00002
 
 Loss:
-  name: CombinedLoss
-  loss_config_list:
-  - DistillationCTCLoss:
-      weight: 1.0
-      model_name_list: ["Student", "Teacher"]
-      key: head_out
-  - DistillationDMLLoss:
-      weight: 1.0
-      act: "softmax"
-      model_name_pairs:
-      - ["Student", "Teacher"]
-      key: head_out
-  - DistillationDistanceLoss:
-      weight: 1.0
-      mode: "l2"
-      model_name_pairs:
-      - ["Student", "Teacher"]
-      key: backbone_out
+  name: CTCLoss
 
 PostProcess:
-  name: DistillationCTCLabelDecode
-  model_name: ["Student", "Teacher"]
-  key: head_out
+  name: CTCLabelDecode
 
 Metric:
-  name: DistillationMetric
-  base_metric_name: RecMetric
+  name: RecMetric
   main_indicator: acc
-  key: "Student"
 
 Train:
   dataset:
@@ -132,7 +85,6 @@ Train:
     shuffle: true
     batch_size_per_card: 128
     drop_last: true
-    num_sections: 1
     num_workers: 8
 Eval:
   dataset:

configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 800
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_pp-OCRv2_distillation
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  character_type: ch
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_pp-OCRv2_distillation.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs : [700, 800]
+    values : [0.001, 0.0001]
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 2.0e-05
+
+Architecture:
+  model_type: &model_type "rec"
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Teacher:
+      pretrained:
+      freeze_params: false
+      return_all_feats: true
+      model_type: *model_type
+      algorithm: CRNN
+      Transform:
+      Backbone:
+        name: MobileNetV1Enhance
+        scale: 0.5
+      Neck:
+        name: SequenceEncoder
+        encoder_type: rnn
+        hidden_size: 64
+      Head:
+        name: CTCHead
+        mid_channels: 96
+        fc_decay: 0.00002
+    Student:
+      pretrained:
+      freeze_params: false
+      return_all_feats: true
+      model_type: *model_type
+      algorithm: CRNN
+      Transform:
+      Backbone:
+        name: MobileNetV1Enhance
+        scale: 0.5
+      Neck:
+        name: SequenceEncoder
+        encoder_type: rnn
+        hidden_size: 64
+      Head:
+        name: CTCHead
+        mid_channels: 96
+        fc_decay: 0.00002
+  
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationCTCLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Teacher"]
+      key: head_out
+  - DistillationDMLLoss:
+      weight: 1.0
+      act: "softmax"
+      use_log: true
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: head_out
+  - DistillationDistanceLoss:
+      weight: 1.0
+      mode: "l2"
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: backbone_out
+
+PostProcess:
+  name: DistillationCTCLabelDecode
+  model_name: ["Student", "Teacher"]
+  key: head_out
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: RecMetric
+  main_indicator: acc
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug:
+    - CTCLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_sections: 1
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 8

configs/rec/rec_mtb_nrtr.yml

@@ -46,7 +46,7 @@ Architecture:
     name: Transformer
     d_model: 512
     num_encoder_layers: 6
-    beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation.
+    beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation.
     
 
 Loss:
@@ -65,7 +65,7 @@ Train:
     name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
-      - NRTRDecodeImage: # load image
+      - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
       - NRTRLabelEncode: # Class handling label
@@ -85,7 +85,7 @@ Eval:
     name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/evaluation/
     transforms:
-      - NRTRDecodeImage: # load image
+      - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
       - NRTRLabelEncode: # Class handling label

configs/rec/rec_r31_sar.yml

@@ -79,7 +79,7 @@ Train:
 Eval:
   dataset:
     name: LMDBDataSet
-    data_dir: ./eval_data/evaluation/
+    data_dir: ./train_data/data_lmdb_release/evaluation/
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

deploy/cpp_infer/src/main.cpp

@@ -91,7 +91,7 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
                    FLAGS_use_tensorrt, FLAGS_precision);
     
     for (int i = 0; i < cv_all_img_names.size(); ++i) {
-      LOG(INFO) << "The predict img: " << cv_all_img_names[i];
+//       LOG(INFO) << "The predict img: " << cv_all_img_names[i];
 
       cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
       if (!srcimg.data) {
@@ -106,6 +106,16 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
       time_info[0] += det_times[0];
       time_info[1] += det_times[1];
       time_info[2] += det_times[2];
+    
+      if (FLAGS_benchmark) {
+          cout << cv_all_img_names[i] << '\t';
+          for (int n = 0; n < boxes.size(); n++) {
+            for (int m = 0; m < boxes[n].size(); m++) {
+              cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' ';
+            }
+          }
+          cout << endl;
+      }        
     }
     
     if (FLAGS_benchmark) {

deploy/hubserving/ocr_det/params.py

@@ -13,7 +13,7 @@ def read_params():
 
     #params for text detector
     cfg.det_algorithm = "DB"
-    cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/"
+    cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/"
     cfg.det_limit_side_len = 960
     cfg.det_limit_type = 'max'
 

deploy/hubserving/ocr_rec/params.py

@@ -13,7 +13,7 @@ def read_params():
 
     #params for text recognizer
     cfg.rec_algorithm = "CRNN"
-    cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/"
+    cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/"
 
     cfg.rec_image_shape = "3, 32, 320"
     cfg.rec_char_type = 'ch'

deploy/hubserving/ocr_system/params.py

@@ -13,7 +13,7 @@ def read_params():
 
     #params for text detector
     cfg.det_algorithm = "DB"
-    cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/"
+    cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/"
     cfg.det_limit_side_len = 960
     cfg.det_limit_type = 'max'
 
@@ -31,7 +31,7 @@ def read_params():
 
     #params for text recognizer
     cfg.rec_algorithm = "CRNN"
-    cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/"
+    cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/"
 
     cfg.rec_image_shape = "3, 32, 320"
     cfg.rec_char_type = 'ch'

deploy/hubserving/readme.md

@@ -34,10 +34,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim
 ```
 
 ### 2. 下载推理模型
-安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v2.0版的超轻量模型，默认模型路径为：
+安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是PP-OCRv2模型，默认模型路径为：
 ```
-检测模型：./inference/ch_ppocr_mobile_v2.0_det_infer/
-识别模型：./inference/ch_ppocr_mobile_v2.0_rec_infer/
+检测模型：./inference/ch_PP-OCRv2_det_infer/
+识别模型：./inference/ch_PP-OCRv2_rec_infer/
 方向分类器：./inference/ch_ppocr_mobile_v2.0_cls_infer/
 ```  
 

deploy/hubserving/readme_en.md

@@ -35,10 +35,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim
 ```
 
 ### 2. Download inference model
-Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v2.0 is used, and the default model path is:  
+Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv2 models are used, and the default model path is:  
 ```
-detection model: ./inference/ch_ppocr_mobile_v2.0_det_infer/
-recognition model: ./inference/ch_ppocr_mobile_v2.0_rec_infer/
+detection model: ./inference/ch_PP-OCRv2_det_infer/
+recognition model: ./inference/ch_PP-OCRv2_rec_infer/
 text direction classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/
 ```  
 

deploy/slim/quantization/quant_kl.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
+sys.path.append(
+    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
+
+import yaml
+import paddle
+import paddle.distributed as dist
+
+paddle.seed(2)
+
+from ppocr.data import build_dataloader
+from ppocr.modeling.architectures import build_model
+from ppocr.losses import build_loss
+from ppocr.optimizer import build_optimizer
+from ppocr.postprocess import build_post_process
+from ppocr.metrics import build_metric
+from ppocr.utils.save_load import init_model
+import tools.program as program
+import paddleslim
+from paddleslim.dygraph.quant import QAT
+import numpy as np
+
+dist.get_world_size()
+
+
+class PACT(paddle.nn.Layer):
+    def __init__(self):
+        super(PACT, self).__init__()
+        alpha_attr = paddle.ParamAttr(
+            name=self.full_name() + ".pact",
+            initializer=paddle.nn.initializer.Constant(value=20),
+            learning_rate=1.0,
+            regularizer=paddle.regularizer.L2Decay(2e-5))
+
+        self.alpha = self.create_parameter(
+            shape=[1], attr=alpha_attr, dtype='float32')
+
+    def forward(self, x):
+        out_left = paddle.nn.functional.relu(x - self.alpha)
+        out_right = paddle.nn.functional.relu(-self.alpha - x)
+        x = x - out_left + out_right
+        return x
+
+
+quant_config = {
+    # weight preprocess type, default is None and no preprocessing is performed. 
+    'weight_preprocess_type': None,
+    # activation preprocess type, default is None and no preprocessing is performed.
+    'activation_preprocess_type': None,
+    # weight quantize type, default is 'channel_wise_abs_max'
+    'weight_quantize_type': 'channel_wise_abs_max',
+    # activation quantize type, default is 'moving_average_abs_max'
+    'activation_quantize_type': 'moving_average_abs_max',
+    # weight quantize bit num, default is 8
+    'weight_bits': 8,
+    # activation quantize bit num, default is 8
+    'activation_bits': 8,
+    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+    'dtype': 'int8',
+    # window size for 'range_abs_max' quantization. default is 10000
+    'window_size': 10000,
+    # The decay coefficient of moving average, default is 0.9
+    'moving_rate': 0.9,
+    # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
+    'quantizable_layer_type': ['Conv2D', 'Linear'],
+}
+
+
+def sample_generator(loader):
+    def __reader__():
+        for indx, data in enumerate(loader):
+            images = np.array(data[0])
+            yield images
+
+    return __reader__
+
+
+def main(config, device, logger, vdl_writer):
+    # init dist environment
+    if config['Global']['distributed']:
+        dist.init_parallel_env()
+
+    global_config = config['Global']
+
+    # build dataloader
+    config['Train']['loader']['num_workers'] = 0
+    train_dataloader = build_dataloader(config, 'Train', device, logger)
+    if config['Eval']:
+        config['Eval']['loader']['num_workers'] = 0
+        valid_dataloader = build_dataloader(config, 'Eval', device, logger)
+    else:
+        valid_dataloader = None
+
+    paddle.enable_static()
+    place = paddle.CPUPlace()
+    exe = paddle.static.Executor(place)
+
+    if 'inference_model' in global_config.keys():  # , 'inference_model'):
+        inference_model_dir = global_config['inference_model']
+    else:
+        inference_model_dir = os.path.dirname(global_config['pretrained_model'])
+        if  not (os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel")) and \
+            os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams")) ):
+            raise ValueError(
+                "Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantazition"
+            )
+
+    paddleslim.quant.quant_post_static(
+        executor=exe,
+        model_dir=inference_model_dir,
+        model_filename='inference.pdmodel',
+        params_filename='inference.pdiparams',
+        quantize_model_path=global_config['save_inference_dir'],
+        sample_generator=sample_generator(train_dataloader),
+        save_model_filename='inference.pdmodel',
+        save_params_filename='inference.pdiparams',
+        batch_size=1,
+        batch_nums=None)
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
+    main(config, device, logger, vdl_writer)

doc/doc_ch/algorithm_overview.md

@@ -9,11 +9,13 @@
 ### 1.文本检测算法
 
 PaddleOCR开源的文本检测算法列表：
-- [x]  DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐）
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
+- [x]  DB([paper]( https://arxiv.org/abs/1911.08947))（ppocr推荐）
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
 
 在ICDAR2015文本检测公开数据集上，算法效果如下：
+
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- |
 |EAST|ResNet50_vd|85.80%|86.71%|86.25%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
@@ -21,6 +23,8 @@ PaddleOCR开源的文本检测算法列表：
 |DB|ResNet50_vd|86.41%|78.72%|82.38%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 
@@ -39,15 +43,15 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训
 ### 2.文本识别算法
 
 PaddleOCR基于动态图开源的文本识别算法列表：
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))[5]
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐）
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
+- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
 - [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
 
-参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
+参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
 
 |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
 |---|---|---|---|---|

doc/doc_ch/benchmark.md

@@ -12,40 +12,27 @@
 ## 评估指标  
 
 说明：
-- v1.0是未添加优化策略的DB+CRNN模型，v1.1是添加多种优化策略和方向分类器的PP-OCR模型。slim_v1.1是使用裁剪或量化的模型。
+
 - 检测输入图像的的长边尺寸是960。
-- 评估耗时阶段为图像输入到结果输出的完整阶段，包括了图像的预处理和后处理。  
+- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。  
 - `Intel至强6148`为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。
 - `骁龙855`为移动端处理平台型号。  
 
-不同预测模型大小和整体识别精度对比
+预测模型大小和整体识别精度对比
 
 | 模型名称                     | 整体模型<br>大小\(M\) | 检测模型<br>大小\(M\) | 方向分类器<br>模型大小\(M\) | 识别模型<br>大小\(M\) | 整体识别<br>F\-score |
 |:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      |
-| ch\_ppocr\_server\_v1\.1 | 155\.1      | 47\.2       | 0\.9           | 107         | 0\.5414      |
-| ch\_ppocr\_mobile\_v1\.0 | 8\.6        | 4\.1        | \-             | 4\.5        | 0\.393       |
-| ch\_ppocr\_server\_v1\.0 | 203\.8      | 98\.5       | \-             | 105\.3      | 0\.4436      |
-
-不同预测模型在T4 GPU上预测速度对比，单位ms
-
-| 模型名称                     | 整体  | 检测 | 方向分类器 | 识别  |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24    | 78  |
-| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25    | 140 |
-| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \-    | 76  |
-| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \-    | 147 |
+| PP-OCRv2 | 11\.6        | 3\.0        | 0\.9           | 8\.6        | 0\.5224      |
+| PP-OCR mobile |   8\.1  | 2\.6        | 0\.9           | 4\.6        | 0\.503       |
+| PP-OCR server | 155\.1  | 47\.2       | 0\.9           | 107         | 0\.570       |
 
-不同预测模型在CPU上预测速度对比，单位ms
 
-| 模型名称                     | 整体   | 检测  | 方向分类器 | 识别  |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 421  | 164 | 51    | 206 |
-| ch\_ppocr\_mobile\_v1\.0 | 398  | 219 | \-    | 179 |
+预测模型在CPU和GPU上的速度对比，单位ms
 
-裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比
+| 模型名称                     | CPU   | T4 GPU  |
+|:-:|:-:|:-:|
+| PP-OCRv2 | 330  | 111 |
+| PP-OCR mobile | 356  | 11 6|
+| PP-OCR server | 1056  | 200 |
 
-| 模型名称                           | 整体模型<br>大小\(M\) | 检测模型<br>大小\(M\) | 方向分类器<br>模型大小\(M\) | 识别模型<br>大小\(M\) | 整体识别<br>F\-score | SD 855<br>\(ms\) |
-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1       | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      | 306          |
-| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5        | 1\.4        | 0\.5           | 1\.6        | 0\.521       | 268          |
+更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md)

doc/doc_ch/knowledge_distillation.md

@@ -39,7 +39,7 @@ PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要
 
 ### 2.1 识别配置文件解析
 
-配置文件在[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)。
+配置文件在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)。
 
 #### 2.1.1 模型结构
 
@@ -246,6 +246,39 @@ Metric:
 关于`DistillationMetric`更加具体的实现可以参考: [distillation_metric.py](../../ppocr/metrics/distillation_metric.py#L24)。
 
 
+#### 2.1.5 蒸馏模型微调
+
+对蒸馏得到的识别蒸馏进行微调有2种方式。
+
+（1）基于知识蒸馏的微调：这种情况比较简单，下载预训练模型，在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)中配置好预训练模型路径以及自己的数据路径，即可进行模型微调训练。
+
+（2）微调时不使用知识蒸馏：这种情况，需要首先将预训练模型中的学生模型参数提取出来，具体步骤如下。
+
+* 首先下载预训练模型并解压。
+```shell
+# 下面预训练模型并解压
+wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar
+tar -xf ch_PP-OCRv2_rec_train.tar
+```
+
+* 然后使用python，对其中的学生模型参数进行提取
+
+```python
+import paddle
+# 加载预训练模型
+all_params = paddle.load("ch_PP-OCRv2_rec_train/best_accuracy.pdparams")
+# 查看权重参数的keys
+print(all_params.keys())
+# 学生模型的权重提取
+s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Student." in key}
+# 查看学生模型权重参数的keys
+print(s_params.keys())
+# 保存
+paddle.save(s_params, "ch_PP-OCRv2_rec_train/student.pdparams")
+```
+
+转化完成之后，使用[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)，修改预训练模型的路径（为导出的`student.pdparams`模型路径）以及自己的数据路径，即可进行模型微调。
+
 ### 2.2 检测配置文件解析
 
 * coming soon!

doc/doc_en/algorithm_overview_en.md

@@ -11,9 +11,10 @@ This tutorial lists the text detection algorithms and text recognition algorithm
 ### 1. Text Detection Algorithm
 
 PaddleOCR open source text detection algorithms list:
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[2]
-- [x]  DB([paper](https://arxiv.org/abs/1911.08947))[1]
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
+- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  PSE([paper](https://arxiv.org/abs/1903.12473v2))
 
 On the ICDAR2015 dataset, the text detection result is as follows:
 
@@ -24,6 +25,8 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 On Total-Text dataset, the text detection result is as follows:
 
@@ -41,11 +44,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re
 ### 2. Text Recognition Algorithm
 
 PaddleOCR open-source text recognition algorithms list:
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))[5]
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
+- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
 - [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
 

doc/doc_en/benchmark_en.md

@@ -13,7 +13,6 @@ We collected 300 images for different real application scenarios to evaluate the
 ## MEASUREMENT
 
 Explanation:
-- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization.
 
 - The long size of the input for the text detector is 960.
 
@@ -27,30 +26,16 @@ Compares the model size and F-score:
 
 | Model Name                    | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score |
 |:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      |
-| ch\_ppocr\_server\_v1\.1 | 155\.1      | 47\.2       | 0\.9           | 107         | 0\.5414      |
-| ch\_ppocr\_mobile\_v1\.0 | 8\.6        | 4\.1        | \-             | 4\.5        | 0\.393       |
-| ch\_ppocr\_server\_v1\.0 | 203\.8      | 98\.5       | \-             | 105\.3      | 0\.4436      |
+| PP-OCRv2                 | 11\.6        | 3\.0        | 0\.9           | 8\.6        | 0\.5224      |
+| PP-OCR mobile            |   8\.1       | 2\.6        | 0\.9           | 4\.6        | 0\.503       |
+| PP-OCR server            | 155\.1       | 47\.2       | 0\.9           | 107         | 0\.570       |
 
-Compares the time-consuming on T4 GPU (ms):
+Compares the time-consuming on CPU and T4 GPU (ms):
 
-| Model Name                     | Overall  | Text Detector  | Direction Classifier  | Text Recognizer |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24    | 78  |
-| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25    | 140 |
-| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \-    | 76  |
-| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \-    | 147 |
+| Model Name    | CPU  | T4 GPU |
+|:-:|:-:|:-:|
+| PP-OCRv2      | 330  | 111 |
+| PP-OCR mobile | 356  | 116|
+| PP-OCR server | 1056 | 200 |
 
-Compares the time-consuming on CPU (ms):
-
-| Model Name                     | Overall  | Text Detector  | Direction Classifier  | Text Recognizer |
-|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1 | 421  | 164 | 51    | 206 |
-| ch\_ppocr\_mobile\_v1\.0 | 398  | 219 | \-    | 179 |
-
-Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models:
-
-| Model Name                          | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score | SD 855<br>\(ms\) |
-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
-| ch\_ppocr\_mobile\_v1\.1       | 8\.1        | 2\.6        | 0\.9           | 4\.6        | 0\.5193      | 306          |
-| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5        | 1\.4        | 0\.5           | 1\.6        | 0\.521       | 268          |
+More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md)

ppocr/data/imaug/ColorJitter.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.
+from paddle.vision.transforms import ColorJitter as pp_ColorJitter
+
+__all__  = ['ColorJitter']
+
+class ColorJitter(object):
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0,**kwargs):
+        self.aug = pp_ColorJitter(brightness, contrast, saturation, hue)
+
+    def __call__(self, data):
+        image = data['image']
+        image = self.aug(image)
+        data['image'] = image
+        return data