Merge branch 'dygraph' into dygraph

StyleText/engine/text_drawers.py

@@ -66,6 +66,7 @@ class StdTextDrawer(object):
                     corpus_list.append(corpus[0:i])
                     text_input_list.append(text_input)
                     corpus = corpus[i:]
+                    i = 0
                     break
                 draw.text((char_x, 2), char_i, fill=(0, 0, 0), font=font)
                 char_x += char_size
@@ -78,7 +79,6 @@ class StdTextDrawer(object):
 
                 corpus_list.append(corpus[0:i])
                 text_input_list.append(text_input)
-                corpus = corpus[i:]
                 break
 
         return corpus_list, text_input_list

configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [3000, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Student:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Student2:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Teacher:
+      pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
+      freeze_params: true
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: ResNet
+        layers: 18
+      Neck:
+        name: DBFPN
+        out_channels: 256
+      Head:
+        name: DBHead
+        k: 50
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDilaDBLoss:
+      weight: 1.0
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      - ["Student2", "Teacher"]
+      key: maps
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+  - DistillationDMLLoss:
+      model_name_pairs:
+      - ["Student", "Student2"]
+      maps_name: "thrink_maps"
+      weight: 1.0
+      # act: None
+      model_name_pairs: ["Student", "Student2"]
+      key: maps
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Student2"]
+      # key: maps
+      # name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student", "Student2", "Teacher"]
+  # key: maps
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+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
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - 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', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - 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: 2

configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [3000, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Student:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Teacher:
+      pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
+      freeze_params: true
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: ResNet
+        layers: 18
+      Neck:
+        name: DBFPN
+        out_channels: 256
+      Head:
+        name: DBHead
+        k: 50
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDilaDBLoss:
+      weight: 1.0
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: maps
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Teacher"]
+      # key: maps
+      name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student", "Student2"]
+  key: head_out
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+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
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - 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', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - 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: 2

configs/det/ch_ppocr_v2.1/ch_det_lite_train_dml_v2.1.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [3000, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Student:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Student2:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDMLLoss:
+      model_name_pairs:
+      - ["Student", "Student2"]
+      maps_name: "thrink_maps"
+      weight: 1.0
+      act: "softmax"
+      model_name_pairs: ["Student", "Student2"]
+      key: maps
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Student2"]
+      # key: maps
+      name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student", "Student2"]
+  key: head_out
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+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
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - 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', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - 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: 2

configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml

@@ -17,7 +17,7 @@ Global:
   character_type: ch
   max_text_length: 25
   infer_mode: false
-  use_space_char: false
+  use_space_char: true
   distributed: true
   save_res_path: ./output/rec/predicts_chinese_lite_distillation_v2.1.txt
 
@@ -27,28 +27,29 @@ Optimizer:
   beta1: 0.9
   beta2: 0.999
   lr:
-    name: Cosine
-    learning_rate: 0.0005
+    name: Piecewise
+    decay_epochs : [700, 800]
+    values : [0.001, 0.0001]
     warmup_epoch: 5
   regularizer:
     name: L2
-    factor: 1.0e-05
+    factor: 2.0e-05
+
 Architecture:
+  model_type: &model_type "rec"
   name: DistillationModel
   algorithm: Distillation
   Models:
-    Student:
+    Teacher:
       pretrained:
       freeze_params: false
       return_all_feats: true
-      model_type: rec
+      model_type: *model_type
       algorithm: CRNN
       Transform:
       Backbone:
-        name: MobileNetV3
+        name: MobileNetV1Enhance
         scale: 0.5
-        model_name: small
-        small_stride: [1, 2, 2, 2]
       Neck:
         name: SequenceEncoder
         encoder_type: rnn
@@ -56,19 +57,17 @@ Architecture:
       Head:
         name: CTCHead
         mid_channels: 96
-        fc_decay: 0.00001
-    Teacher:
+        fc_decay: 0.00002
+    Student:
       pretrained:
       freeze_params: false
       return_all_feats: true
-      model_type: rec
+      model_type: *model_type
       algorithm: CRNN
       Transform:
       Backbone:
-        name: MobileNetV3
+        name: MobileNetV1Enhance
         scale: 0.5
-        model_name: small
-        small_stride: [1, 2, 2, 2]
       Neck:
         name: SequenceEncoder
         encoder_type: rnn
@@ -76,7 +75,7 @@ Architecture:
       Head:
         name: CTCHead
         mid_channels: 96
-        fc_decay: 0.00001
+        fc_decay: 0.00002
   
 
 Loss:

deploy/hubserving/readme.md

@@ -29,7 +29,7 @@ deploy/hubserving/ocr_system/
 ### 1. 准备环境
 ```shell
 # 安装paddlehub  
-pip3 install paddlehub==1.8.3 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ### 2. 下载推理模型

deploy/hubserving/readme_en.md

@@ -30,7 +30,7 @@ The following steps take the 2-stage series service as an example. If only the d
 ### 1. Prepare the environment
 ```shell
 # Install paddlehub  
-pip3 install paddlehub==1.8.3 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ### 2. Download inference model

deploy/slim/quantization/export_model.py

@@ -37,6 +37,17 @@ from paddleslim.dygraph.quant import QAT
 from ppocr.data import build_dataloader
 
 
+def export_single_model(quanter, model, infer_shape, save_path, logger):
+    quanter.save_quantized_model(
+        model,
+        save_path,
+        input_spec=[
+            paddle.static.InputSpec(
+                shape=[None] + infer_shape, dtype='float32')
+        ])
+    logger.info('inference QAT model is saved to {}'.format(save_path))
+
+
 def main():
     ############################################################################################################
     # 1. quantization configs
@@ -76,14 +87,21 @@ def main():
     # for rec algorithm
     if hasattr(post_process_class, 'character'):
         char_num = len(getattr(post_process_class, 'character'))
-        config['Architecture']["Head"]['out_channels'] = char_num
+        if config['Architecture']["algorithm"] in ["Distillation",
+                                                   ]:  # distillation model
+            for key in config['Architecture']["Models"]:
+                config['Architecture']["Models"][key]["Head"][
+                    'out_channels'] = char_num
+        else:  # base rec model
+            config['Architecture']["Head"]['out_channels'] = char_num
+
     model = build_model(config['Architecture'])
 
     # get QAT model
     quanter = QAT(config=quant_config)
     quanter.quantize(model)
 
-    init_model(config, model, logger)
+    init_model(config, model)
     model.eval()
 
     # build metric
@@ -92,25 +110,30 @@ def main():
     # build dataloader
     valid_dataloader = build_dataloader(config, 'Eval', device, logger)
 
+    use_srn = config['Architecture']['algorithm'] == "SRN"
+    model_type = config['Architecture']['model_type']
     # start eval
-    metirc = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class)
+    metric = program.eval(model, valid_dataloader, post_process_class,
+                          eval_class, model_type, use_srn)
+
     logger.info('metric eval ***************')
-    for k, v in metirc.items():
+    for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))
 
-    save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
     infer_shape = [3, 32, 100] if config['Architecture'][
         'model_type'] != "det" else [3, 640, 640]
 
-    quanter.save_quantized_model(
-        model,
-        save_path,
-        input_spec=[
-            paddle.static.InputSpec(
-                shape=[None] + infer_shape, dtype='float32')
-        ])
-    logger.info('inference QAT model is saved to {}'.format(save_path))
+    save_path = config["Global"]["save_inference_dir"]
+
+    arch_config = config["Architecture"]
+    if arch_config["algorithm"] in ["Distillation", ]:  # distillation model
+        for idx, name in enumerate(model.model_name_list):
+            sub_model_save_path = os.path.join(save_path, name, "inference")
+            export_single_model(quanter, model.model_list[idx], infer_shape,
+                                sub_model_save_path, logger)
+    else:
+        save_path = os.path.join(save_path, "inference")
+        export_single_model(quanter, model, infer_shape, save_path, logger)
 
 
 if __name__ == "__main__":

deploy/slim/quantization/quant.py

@@ -109,9 +109,18 @@ def main(config, device, logger, vdl_writer):
     # for rec algorithm
     if hasattr(post_process_class, 'character'):
         char_num = len(getattr(post_process_class, 'character'))
-        config['Architecture']["Head"]['out_channels'] = char_num
+        if config['Architecture']["algorithm"] in ["Distillation",
+                                                   ]:  # distillation model
+            for key in config['Architecture']["Models"]:
+                config['Architecture']["Models"][key]["Head"][
+                    'out_channels'] = char_num
+        else:  # base rec model
+            config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
 
+    quanter = QAT(config=quant_config, act_preprocess=PACT)
+    quanter.quantize(model)
+
     if config['Global']['distributed']:
         model = paddle.DataParallel(model)
 
@@ -132,8 +141,6 @@ def main(config, device, logger, vdl_writer):
 
     logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                 format(len(train_dataloader), len(valid_dataloader)))
-    quanter = QAT(config=quant_config, act_preprocess=PACT)
-    quanter.quantize(model)
 
     # start train
     program.train(config, train_dataloader, valid_dataloader, device, model,

doc/doc_ch/knowledge_distillation.md

+# 知识蒸馏
+
+
+## 1. 简介
+
+### 1.1 知识蒸馏介绍
+
+近年来，深度神经网络在计算机视觉、自然语言处理等领域被验证是一种极其有效的解决问题的方法。通过构建合适的神经网络，加以训练，最终网络模型的性能指标基本上都会超过传统算法。
+
+在数据量足够大的情况下，通过合理构建网络模型的方式增加其参数量，可以显著改善模型性能，但是这又带来了模型复杂度急剧提升的问题。大模型在实际场景中使用的成本较高。
+
+深度神经网络一般有较多的参数冗余，目前有几种主要的方法对模型进行压缩，减小其参数量。如裁剪、量化、知识蒸馏等，其中知识蒸馏是指使用教师模型(teacher model)去指导学生模型(student model)学习特定任务，保证小模型在参数量不变的情况下，得到比较大的性能提升。
+
+此外，在知识蒸馏任务中，也衍生出了互学习的模型训练方法，论文[Deep Mutual Learning](https://arxiv.org/abs/1706.00384)中指出，使用两个完全相同的模型在训练的过程中互相监督，可以达到比单个模型训练更好的效果。
+
+### 1.2 PaddleOCR知识蒸馏简介
+
+无论是大模型蒸馏小模型，还是小模型之间互相学习，更新参数，他们本质上是都是不同模型之间输出或者特征图(feature map)之间的相互监督，区别仅在于 (1) 模型是否需要固定参数。(2) 模型是否需要加载预训练模型。
+
+对于大模型蒸馏小模型的情况，大模型一般需要加载预训练模型并固定参数；对于小模型之间互相蒸馏的情况，小模型一般都不加载预训练模型，参数也都是可学习的状态。
+
+在知识蒸馏任务中，不只有2个模型之间进行蒸馏的情况，多个模型之间互相学习的情况也非常普遍。因此在知识蒸馏代码框架中，也有必要支持该种类别的蒸馏方法。
+
+PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要的特点：
+- 支持任意网络的互相学习，不要求子网络结构完全一致或者具有预训练模型；同时子网络数量也没有任何限制，只需要在配置文件中添加即可。
+- 支持loss函数通过配置文件任意配置，不仅可以使用某种loss，也可以使用多种loss的组合
+- 支持知识蒸馏训练、预测、评估与导出等所有模型相关的环境，方便使用与部署。
+
+
+通过知识蒸馏，在中英文通用文字识别任务中，不增加任何预测耗时的情况下，可以给模型带来3%以上的精度提升，结合学习率调整策略以及模型结构微调策略，最终提升提升超过5%。
+
+
+
+## 2. 配置文件解析
+
+在知识蒸馏训练的过程中，数据预处理、优化器、学习率、全局的一些属性没有任何变化。模型结构、损失函数、后处理、指标计算等模块的配置文件需要进行微调。
+
+下面以识别与检测的知识蒸馏配置文件为例，对知识蒸馏的训练与配置进行解析。
+
+### 2.1 识别配置文件解析
+
+配置文件在[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)。
+
+#### 2.1.1 模型结构
+
+知识蒸馏任务中，模型结构配置如下所示。
+
+```yaml
+Architecture:
+  model_type: &model_type "rec"    # 模型类别，rec、det等，每个子网络的的模型类别都与
+  name: DistillationModel          # 结构名称，蒸馏任务中，为DistillationModel，用于构建对应的结构
+  algorithm: Distillation          # 算法名称
+  Models:                          # 模型，包含子网络的配置信息
+    Teacher:                       # 子网络名称，至少需要包含`pretrained`与`freeze_params`信息，其他的参数为子网络的构造参数
+      pretrained:                  # 该子网络是否需要加载预训练模型
+      freeze_params: false         # 是否需要固定参数
+      return_all_feats: true       # 子网络的参数，表示是否需要返回所有的features，如果为False，则只返回最后的输出
+      model_type: *model_type      # 模型类别
+      algorithm: CRNN              # 子网络的算法名称，该子网络剩余参与均为构造参数，与普通的模型训练配置一致
+      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:                       # 另外一个子网络，这里给的是DML的蒸馏示例，两个子网络结构相同，均需要学习参数
+      pretrained:                  # 下面的组网参数同上
+      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
+```
+
+当然，这里如果希望添加更多的子网络进行训练，也可以按照`Student`与`Teacher`的添加方式，在配置文件中添加相应的字段。比如说如果希望有3个模型互相监督，共同训练，那么`Architecture`可以写为如下格式。
+
+```yaml
+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
+    Student2:                       # 知识蒸馏任务中引入的新的子网络，其他部分与上述配置相同
+      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
+```
+
+最终该模型训练时，包含3个子网络：`Teacher`, `Student`, `Student2`。
+
+蒸馏模型`DistillationModel`类的具体实现代码可以参考[distillation_model.py](../../ppocr/modeling/architectures/distillation_model.py)。
+
+最终模型`forward`输出为一个字典，key为所有的子网络名称，例如这里为`Student`与`Teacher`，value为对应子网络的输出，可以为`Tensor`（只返回该网络的最后一层）和`dict`（也返回了中间的特征信息）。
+
+在识别任务中，为了添加更多损失函数，保证蒸馏方法的可扩展性，将每个子网络的输出保存为`dict`，其中包含子模块输出。以该识别模型为例，每个子网络的输出结果均为`dict`，key包含`backbone_out`,`neck_out`, `head_out`，`value`为对应模块的tensor，最终对于上述配置文件，`DistillationModel`的输出格式如下。
+
+```json
+{
+  "Teacher": {
+    "backbone_out": tensor,
+    "neck_out": tensor,
+    "head_out": tensor,
+  },
+  "Student": {
+    "backbone_out": tensor,
+    "neck_out": tensor,
+    "head_out": tensor,
+  }
+}
+```
+
+#### 2.1.2 损失函数
+
+知识蒸馏任务中，损失函数配置如下所示。
+
+```yaml
+Loss:
+  name: CombinedLoss                           # 损失函数名称，基于改名称，构建用于损失函数的类
+  loss_config_list:                            # 损失函数配置文件列表，为CombinedLoss的必备函数
+  - DistillationCTCLoss:                       # 基于蒸馏的CTC损失函数，继承自标准的CTC loss
+      weight: 1.0                              # 损失函数的权重，loss_config_list中，每个损失函数的配置都必须包含该字段
+      model_name_list: ["Student", "Teacher"]  # 对于蒸馏模型的预测结果，提取这两个子网络的输出，与gt计算CTC loss
+      key: head_out                            # 取子网络输出dict中，该key对应的tensor
+  - DistillationDMLLoss:                       # 蒸馏的DML损失函数，继承自标准的DMLLoss
+      weight: 1.0                              # 权重
+      act: "softmax"                           # 激活函数，对输入使用激活函数处理，可以为softmax, sigmoid或者为None，默认为None
+      model_name_pairs:                        # 用于计算DML loss的子网络名称对，如果希望计算其他子网络的DML loss，可以在列表下面继续填充
+      - ["Student", "Teacher"]
+      key: head_out                            # 取子网络输出dict中，该key对应的tensor
+  - DistillationDistanceLoss:                  # 蒸馏的距离损失函数
+      weight: 1.0                              # 权重
+      mode: "l2"                               # 距离计算方法，目前支持l1, l2, smooth_l1
+      model_name_pairs:                        # 用于计算distance loss的子网络名称对
+      - ["Student", "Teacher"]
+      key: backbone_out                        # 取子网络输出dict中，该key对应的tensor
+```
+
+上述损失函数中，所有的蒸馏损失函数均继承自标准的损失函数类，主要功能为: 对蒸馏模型的输出进行解析，找到用于计算损失的中间节点(tensor)，再使用标准的损失函数类去计算。
+
+以上述配置为例，最终蒸馏训练的损失函数包含下面3个部分。
+
+- `Student`和`Teacher`的最终输出(`head_out`)与gt的CTC loss，权重为1。在这里因为2个子网络都需要更新参数，因此2者都需要计算与g的loss。
+- `Student`和`Teacher`的最终输出(`head_out`)之间的DML loss，权重为1。
+- `Student`和`Teacher`的骨干网络输出(`backbone_out`)之间的l2 loss，权重为1。
+
+关于`CombinedLoss`更加具体的实现可以参考: [combined_loss.py](../../ppocr/losses/combined_loss.py#L23)。关于`DistillationCTCLoss`等蒸馏损失函数更加具体的实现可以参考[distillation_loss.py](../../ppocr/losses/distillation_loss.py)。
+
+
+#### 2.1.3 后处理
+
+知识蒸馏任务中，后处理配置如下所示。
+
+```yaml
+PostProcess:
+  name: DistillationCTCLabelDecode       # 蒸馏任务的CTC解码后处理，继承自标准的CTCLabelDecode类
+  model_name: ["Student", "Teacher"]     # 对于蒸馏模型的预测结果，提取这两个子网络的输出，进行解码
+  key: head_out                          # 取子网络输出dict中，该key对应的tensor
+```
+
+以上述配置为例，最终会同时计算`Student`和`Teahcer` 2个子网络的CTC解码输出，返回一个`dict`，`key`为用于处理的子网络名称，`value`为用于处理的子网络列表。
+
+关于`DistillationCTCLabelDecode`更加具体的实现可以参考: [rec_postprocess.py](../../ppocr/postprocess/rec_postprocess.py#L128)
+
+
+#### 2.1.4 指标计算
+
+知识蒸馏任务中，指标计算配置如下所示。
+
+```yaml
+Metric:
+  name: DistillationMetric         # 蒸馏任务的CTC解码后处理，继承自标准的CTCLabelDecode类
+  base_metric_name: RecMetric      # 指标计算的基类，对于模型的输出，会基于该类，计算指标
+  main_indicator: acc              # 指标的名称
+  key: "Student"                   # 选取该子网络的 main_indicator 作为作为保存保存best model的判断标准
+```
+
+以上述配置为例，最终会使用`Student`子网络的acc指标作为保存best model的判断指标，同时，日志中也会打印出所有子网络的acc指标。
+
+关于`DistillationMetric`更加具体的实现可以参考: [distillation_metric.py](../../ppocr/metrics/distillation_metric.py#L24)。
+
+
+### 2.2 检测配置文件解析
+
+* coming soon!

doc/doc_ch/recognition.md

@@ -375,7 +375,9 @@ PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi
 
 更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)
 
-多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。
+多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以通过下面两种方式下载。
+* [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA)。提取码：frgi。
+* [google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
 
 如您希望在现有模型效果的基础上调优，请参考下列说明修改配置文件：
 

doc/doc_en/recognition_en.md

@@ -375,7 +375,9 @@ Currently, the multi-language algorithms supported by PaddleOCR are:
 
 For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
 
-The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
+The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
+* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
+* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
 
 If you want to finetune on the basis of the existing model effect, please refer to the following instructions to modify the configuration file:
 

ppocr/data/imaug/copy_paste.py

@@ -73,11 +73,14 @@ class CopyPaste(object):
         box_img_pil = Image.fromarray(box_img).convert('RGBA')
         src_w, src_h = src_img.size
         box_w, box_h = box_img_pil.size
-        if box_w > src_w or box_h > src_h:
-            return src_img, None
+
         angle = np.random.randint(0, 360)
         box = np.array([[[0, 0], [box_w, 0], [box_w, box_h], [0, box_h]]])
         box = rotate_bbox(box_img, box, angle)[0]
+        box_img_pil = box_img_pil.rotate(angle, expand=1)
+        box_w, box_h = box_img_pil.width, box_img_pil.height
+        if src_w - box_w < 0 or src_h - box_h < 0:
+            return src_img, None
 
         paste_x, paste_y = self.select_coord(src_polys, box, src_w - box_w,
                                              src_h - box_h)
@@ -85,7 +88,6 @@ class CopyPaste(object):
             return src_img, None
         box[:, 0] += paste_x
         box[:, 1] += paste_y
-        box_img_pil = box_img_pil.rotate(angle, expand=1)
         r, g, b, A = box_img_pil.split()
         src_img.paste(box_img_pil, (paste_x, paste_y), mask=A)
 
@@ -105,7 +107,7 @@ class CopyPaste(object):
 
                 num_poly_in_rect = 0
                 for poly in src_polys:
-                    if not is_poly_outside_rect(poly, xmax1, ymin1,
+                    if not is_poly_outside_rect(poly, xmin1, ymin1,
                                                 xmax1 - xmin1, ymax1 - ymin1):
                         num_poly_in_rect += 1
                         break

ppocr/losses/basic_loss.py

@@ -54,6 +54,27 @@ class CELoss(nn.Layer):
         return loss
 
 
+class KLJSLoss(object):
+    def __init__(self, mode='kl'):
+        assert mode in ['kl', 'js', 'KL', 'JS'], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
+        self.mode = mode
+
+    def __call__(self, p1, p2, reduction="mean"):
+
+        loss = paddle.multiply(p2, paddle.log( (p2+1e-5)/(p1+1e-5) + 1e-5))
+
+        if self.mode.lower() == "js":
+            loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5))
+            loss *= 0.5
+        if reduction == "mean":
+            loss = paddle.mean(loss, axis=[1,2])
+        elif reduction=="none" or reduction is None:
+            return loss 
+        else:
+            loss = paddle.sum(loss, axis=[1,2])
+
+        return loss 
+
 class DMLLoss(nn.Layer):
     """
     DMLLoss
@@ -69,17 +90,21 @@ class DMLLoss(nn.Layer):
             self.act = nn.Sigmoid()
         else:
             self.act = None
+        
+        self.jskl_loss = KLJSLoss(mode="js")
 
     def forward(self, out1, out2):
         if self.act is not None:
             out1 = self.act(out1)
             out2 = self.act(out2)
-
-        log_out1 = paddle.log(out1)
-        log_out2 = paddle.log(out2)
-        loss = (F.kl_div(
-            log_out1, out2, reduction='batchmean') + F.kl_div(
-                log_out2, out1, reduction='batchmean')) / 2.0
+        if len(out1.shape) < 2:
+            log_out1 = paddle.log(out1)
+            log_out2 = paddle.log(out2)
+            loss = (F.kl_div(
+                log_out1, out2, reduction='batchmean') + F.kl_div(
+                    log_out2, out1, reduction='batchmean')) / 2.0
+        else:
+            loss = self.jskl_loss(out1, out2)
         return loss
 
 

ppocr/losses/combined_loss.py

@@ -17,7 +17,7 @@ import paddle.nn as nn
 
 from .distillation_loss import DistillationCTCLoss
 from .distillation_loss import DistillationDMLLoss
-from .distillation_loss import DistillationDistanceLoss
+from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss
 
 
 class CombinedLoss(nn.Layer):
@@ -44,15 +44,16 @@ class CombinedLoss(nn.Layer):
 
     def forward(self, input, batch, **kargs):
         loss_dict = {}
+        loss_all = 0.
         for idx, loss_func in enumerate(self.loss_func):
             loss = loss_func(input, batch, **kargs)
             if isinstance(loss, paddle.Tensor):
                 loss = {"loss_{}_{}".format(str(loss), idx): loss}
             weight = self.loss_weight[idx]
-            loss = {
-                "{}_{}".format(key, idx): loss[key] * weight
-                for key in loss
-            }
-            loss_dict.update(loss)
-        loss_dict["loss"] = paddle.add_n(list(loss_dict.values()))
+            for key in loss.keys():
+                if key == "loss":
+                    loss_all += loss[key] * weight
+                else:
+                    loss_dict["{}_{}".format(key, idx)] = loss[key]
+        loss_dict["loss"] = loss_all
         return loss_dict

ppocr/losses/distillation_loss.py

@@ -14,23 +14,76 @@
 
 import paddle
 import paddle.nn as nn
+import numpy as np
+import cv2
 
 from .rec_ctc_loss import CTCLoss
 from .basic_loss import DMLLoss
 from .basic_loss import DistanceLoss
+from .det_db_loss import DBLoss
+from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
+
+
+def _sum_loss(loss_dict):
+    if "loss" in loss_dict.keys():
+        return loss_dict
+    else:
+        loss_dict["loss"] = 0.
+        for k, value in loss_dict.items():
+            if k == "loss":
+                continue
+            else:
+                loss_dict["loss"] += value
+        return loss_dict
 
 
 class DistillationDMLLoss(DMLLoss):
     """
     """
 
-    def __init__(self, model_name_pairs=[], act=None, key=None,
-                 name="loss_dml"):
+    def __init__(self,
+                 model_name_pairs=[],
+                 act=None,
+                 key=None,
+                 maps_name=None,
+                 name="dml"):
         super().__init__(act=act)
         assert isinstance(model_name_pairs, list)
         self.key = key
-        self.model_name_pairs = model_name_pairs
+        self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
         self.name = name
+        self.maps_name = self._check_maps_name(maps_name)
+    
+    def _check_model_name_pairs(self, model_name_pairs):
+        if not isinstance(model_name_pairs, list):
+            return []
+        elif isinstance(model_name_pairs[0], list) and isinstance(model_name_pairs[0][0], str):
+            return model_name_pairs
+        else:
+            return [model_name_pairs]
+
+    def _check_maps_name(self, maps_name):
+        if maps_name is None:
+            return None
+        elif type(maps_name) == str:
+            return [maps_name]
+        elif type(maps_name) == list:
+            return [maps_name]
+        else:
+            return None
+
+    def _slice_out(self, outs):
+        new_outs = {}
+        for k in self.maps_name:
+            if k == "thrink_maps":
+                new_outs[k] = outs[:, 0, :, :]
+            elif k == "threshold_maps":
+                new_outs[k] = outs[:, 1, :, :]
+            elif k == "binary_maps":
+                new_outs[k] = outs[:, 2, :, :]
+            else:
+                continue
+        return new_outs
 
     def forward(self, predicts, batch):
         loss_dict = dict()
@@ -40,13 +93,30 @@ class DistillationDMLLoss(DMLLoss):
             if self.key is not None:
                 out1 = out1[self.key]
                 out2 = out2[self.key]
-            loss = super().forward(out1, out2)
-            if isinstance(loss, dict):
-                for key in loss:
-                    loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
-                                                   idx)] = loss[key]
+
+            if self.maps_name is None:
+                loss = super().forward(out1, out2)
+                if isinstance(loss, dict):
+                    for key in loss:
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
+                                                       idx)] = loss[key]
+                else:
+                    loss_dict["{}_{}".format(self.name, idx)] = loss
             else:
-                loss_dict["{}_{}".format(self.name, idx)] = loss
+                outs1 = self._slice_out(out1)
+                outs2 = self._slice_out(out2)
+                for _c, k in enumerate(outs1.keys()):
+                    loss = super().forward(outs1[k], outs2[k])
+                    if isinstance(loss, dict):
+                        for key in loss:
+                            loss_dict["{}_{}_{}_{}_{}".format(key, pair[
+                                0], pair[1], map_name, idx)] = loss[key]
+                    else:
+                        loss_dict["{}_{}_{}".format(self.name, self.maps_name[_c],
+                                                    idx)] = loss
+        
+        loss_dict = _sum_loss(loss_dict)
+
         return loss_dict
 
 
@@ -73,6 +143,98 @@ class DistillationCTCLoss(CTCLoss):
         return loss_dict
 
 
+class DistillationDBLoss(DBLoss):
+    def __init__(self,
+                 model_name_list=[],
+                 balance_loss=True,
+                 main_loss_type='DiceLoss',
+                 alpha=5,
+                 beta=10,
+                 ohem_ratio=3,
+                 eps=1e-6,
+                 name="db",
+                 **kwargs):
+        super().__init__()
+        self.model_name_list = model_name_list
+        self.name = name
+        self.key = None
+
+    def forward(self, predicts, batch):
+        loss_dict = {}
+        for idx, model_name in enumerate(self.model_name_list):
+            out = predicts[model_name]
+            if self.key is not None:
+                out = out[self.key]
+            loss = super().forward(out, batch)
+
+            if isinstance(loss, dict):
+                for key in loss.keys():
+                    if key == "loss":
+                        continue
+                    name = "{}_{}_{}".format(self.name, model_name, key)
+                    loss_dict[name] = loss[key]
+            else:
+                loss_dict["{}_{}".format(self.name, model_name)] = loss
+
+        loss_dict = _sum_loss(loss_dict)
+        return loss_dict
+
+
+class DistillationDilaDBLoss(DBLoss):
+    def __init__(self,
+                 model_name_pairs=[],
+                 key=None,
+                 balance_loss=True,
+                 main_loss_type='DiceLoss',
+                 alpha=5,
+                 beta=10,
+                 ohem_ratio=3,
+                 eps=1e-6,
+                 name="dila_dbloss"):
+        super().__init__()
+        self.model_name_pairs = model_name_pairs
+        self.name = name
+        self.key = key
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for idx, pair in enumerate(self.model_name_pairs):
+            stu_outs = predicts[pair[0]]
+            tch_outs = predicts[pair[1]]
+            if self.key is not None:
+                stu_preds = stu_outs[self.key]
+                tch_preds = tch_outs[self.key]
+
+            stu_shrink_maps = stu_preds[:, 0, :, :]
+            stu_binary_maps = stu_preds[:, 2, :, :]
+
+            # dilation to teacher prediction
+            dilation_w = np.array([[1, 1], [1, 1]])
+            th_shrink_maps = tch_preds[:, 0, :, :]
+            th_shrink_maps = th_shrink_maps.numpy() > 0.3  # thresh = 0.3 
+            dilate_maps = np.zeros_like(th_shrink_maps).astype(np.float32)
+            for i in range(th_shrink_maps.shape[0]):
+                dilate_maps[i] = cv2.dilate(
+                    th_shrink_maps[i, :, :].astype(np.uint8), dilation_w)
+            th_shrink_maps = paddle.to_tensor(dilate_maps)
+
+            label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = batch[
+                1:]
+
+            # calculate the shrink map loss
+            bce_loss = self.alpha * self.bce_loss(
+                stu_shrink_maps, th_shrink_maps, label_shrink_mask)
+            loss_binary_maps = self.dice_loss(stu_binary_maps, th_shrink_maps,
+                                              label_shrink_mask)
+
+            # k = f"{self.name}_{pair[0]}_{pair[1]}"
+            k = "{}_{}_{}".format(self.name, pair[0], pair[1])
+            loss_dict[k] = bce_loss + loss_binary_maps
+
+        loss_dict = _sum_loss(loss_dict)
+        return loss_dict
+
+
 class DistillationDistanceLoss(DistanceLoss):
     """
     """

ppocr/metrics/det_metric.py

@@ -55,6 +55,7 @@ class DetMetric(object):
             result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
             self.results.append(result)
 
+
     def get_metric(self):
         """
         return metrics {

ppocr/metrics/distillation_metric.py

@@ -24,8 +24,8 @@ from .cls_metric import ClsMetric
 class DistillationMetric(object):
     def __init__(self,
                  key=None,
-                 base_metric_name="RecMetric",
-                 main_indicator='acc',
+                 base_metric_name=None,
+                 main_indicator=None,
                  **kwargs):
         self.main_indicator = main_indicator
         self.key = key
@@ -42,16 +42,13 @@ class DistillationMetric(object):
                 main_indicator=self.main_indicator, **self.kwargs)
             self.metrics[key].reset()
 
-    def __call__(self, preds, *args, **kwargs):
+    def __call__(self, preds, batch, **kwargs):
         assert isinstance(preds, dict)
         if self.metrics is None:
             self._init_metrcis(preds)
         output = dict()
         for key in preds:
-            metric = self.metrics[key].__call__(preds[key], *args, **kwargs)
-            for sub_key in metric:
-                output["{}_{}".format(key, sub_key)] = metric[sub_key]
-        return output
+            self.metrics[key].__call__(preds[key], batch, **kwargs)
 
     def get_metric(self):
         """

ppocr/modeling/architectures/base_model.py

@@ -79,7 +79,10 @@ class BaseModel(nn.Layer):
             x = self.neck(x)
         y["neck_out"] = x
         x = self.head(x, targets=data)
-        y["head_out"] = x
+        if isinstance(x, dict):
+            y.update(x)
+        else:
+            y["head_out"] = x
         if self.return_all_feats:
             return y
         else:

ppocr/modeling/architectures/distillation_model.py

@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone
 from ppocr.modeling.necks import build_neck
 from ppocr.modeling.heads import build_head
 from .base_model import BaseModel
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_pretrained_params
 
 __all__ = ['DistillationModel']
 
@@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
                 pretrained = model_config.pop("pretrained")
             model = BaseModel(model_config)
             if pretrained is not None:
-                init_model(model, path=pretrained)
+                model = load_pretrained_params(model, pretrained)
             if freeze_params:
                 for param in model.parameters():
                     param.trainable = False

ppocr/modeling/backbones/__init__.py

@@ -12,33 +12,36 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__all__ = ['build_backbone']
+__all__ = ["build_backbone"]
 
 
 def build_backbone(config, model_type):
-    if model_type == 'det':
+    if model_type == "det":
         from .det_mobilenet_v3 import MobileNetV3
         from .det_resnet_vd import ResNet
         from .det_resnet_vd_sast import ResNet_SAST
-        support_dict = ['MobileNetV3', 'ResNet', 'ResNet_SAST']
-    elif model_type == 'rec' or model_type == 'cls':
+        support_dict = ["MobileNetV3", "ResNet", "ResNet_SAST"]
+    elif model_type == "rec" or model_type == "cls":
         from .rec_mobilenet_v3 import MobileNetV3
         from .rec_resnet_vd import ResNet
         from .rec_resnet_fpn import ResNetFPN
-        support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
-    elif model_type == 'e2e':
+        from .rec_mv1_enhance import MobileNetV1Enhance
+        support_dict = [
+            "MobileNetV1Enhance", "MobileNetV3", "ResNet", "ResNetFPN"
+        ]
+    elif model_type == "e2e":
         from .e2e_resnet_vd_pg import ResNet
-        support_dict = ['ResNet']
+        support_dict = ["ResNet"]
     elif model_type == "table":
         from .table_resnet_vd import ResNet
         from .table_mobilenet_v3 import MobileNetV3
-        support_dict = ['ResNet', 'MobileNetV3']
+        support_dict = ["ResNet", "MobileNetV3"]
     else:
         raise NotImplementedError
 
-    module_name = config.pop('name')
+    module_name = config.pop("name")
     assert module_name in support_dict, Exception(
-        'when model typs is {}, backbone only support {}'.format(model_type,
+        "when model typs is {}, backbone only support {}".format(model_type,
                                                                  support_dict))
     module_class = eval(module_name)(**config)
     return module_class

ppocr/modeling/backbones/rec_mv1_enhance.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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.nn.initializer import KaimingNormal
+import math
+import numpy as np
+import paddle
+from paddle import ParamAttr, reshape, transpose, concat, split
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.nn.initializer import KaimingNormal
+import math
+from paddle.nn.functional import hardswish, hardsigmoid
+from paddle.regularizer import L2Decay
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 filter_size,
+                 num_filters,
+                 stride,
+                 padding,
+                 channels=None,
+                 num_groups=1,
+                 act='hard_swish'):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            weight_attr=ParamAttr(initializer=KaimingNormal()),
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(regularizer=L2Decay(0.0)),
+            bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class DepthwiseSeparable(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters1,
+                 num_filters2,
+                 num_groups,
+                 stride,
+                 scale,
+                 dw_size=3,
+                 padding=1,
+                 use_se=False):
+        super(DepthwiseSeparable, self).__init__()
+        self.use_se = use_se
+        self._depthwise_conv = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=int(num_filters1 * scale),
+            filter_size=dw_size,
+            stride=stride,
+            padding=padding,
+            num_groups=int(num_groups * scale))
+        if use_se:
+            self._se = SEModule(int(num_filters1 * scale))
+        self._pointwise_conv = ConvBNLayer(
+            num_channels=int(num_filters1 * scale),
+            filter_size=1,
+            num_filters=int(num_filters2 * scale),
+            stride=1,
+            padding=0)
+
+    def forward(self, inputs):
+        y = self._depthwise_conv(inputs)
+        if self.use_se:
+            y = self._se(y)
+        y = self._pointwise_conv(y)
+        return y
+
+
+class MobileNetV1Enhance(nn.Layer):
+    def __init__(self, in_channels=3, scale=0.5, **kwargs):
+        super().__init__()
+        self.scale = scale
+        self.block_list = []
+
+        self.conv1 = ConvBNLayer(
+            num_channels=3,
+            filter_size=3,
+            channels=3,
+            num_filters=int(32 * scale),
+            stride=2,
+            padding=1)
+
+        conv2_1 = DepthwiseSeparable(
+            num_channels=int(32 * scale),
+            num_filters1=32,
+            num_filters2=64,
+            num_groups=32,
+            stride=1,
+            scale=scale)
+        self.block_list.append(conv2_1)
+
+        conv2_2 = DepthwiseSeparable(
+            num_channels=int(64 * scale),
+            num_filters1=64,
+            num_filters2=128,
+            num_groups=64,
+            stride=1,
+            scale=scale)
+        self.block_list.append(conv2_2)
+
+        conv3_1 = DepthwiseSeparable(
+            num_channels=int(128 * scale),
+            num_filters1=128,
+            num_filters2=128,
+            num_groups=128,
+            stride=1,
+            scale=scale)
+        self.block_list.append(conv3_1)
+
+        conv3_2 = DepthwiseSeparable(
+            num_channels=int(128 * scale),
+            num_filters1=128,
+            num_filters2=256,
+            num_groups=128,
+            stride=(2, 1),
+            scale=scale)
+        self.block_list.append(conv3_2)
+
+        conv4_1 = DepthwiseSeparable(
+            num_channels=int(256 * scale),
+            num_filters1=256,
+            num_filters2=256,
+            num_groups=256,
+            stride=1,
+            scale=scale)
+        self.block_list.append(conv4_1)
+
+        conv4_2 = DepthwiseSeparable(
+            num_channels=int(256 * scale),
+            num_filters1=256,
+            num_filters2=512,
+            num_groups=256,
+            stride=(2, 1),
+            scale=scale)
+        self.block_list.append(conv4_2)
+
+        for _ in range(5):
+            conv5 = DepthwiseSeparable(
+                num_channels=int(512 * scale),
+                num_filters1=512,
+                num_filters2=512,
+                num_groups=512,
+                stride=1,
+                dw_size=5,
+                padding=2,
+                scale=scale,
+                use_se=False)
+            self.block_list.append(conv5)
+
+        conv5_6 = DepthwiseSeparable(
+            num_channels=int(512 * scale),
+            num_filters1=512,
+            num_filters2=1024,
+            num_groups=512,
+            stride=(2, 1),
+            dw_size=5,
+            padding=2,
+            scale=scale,
+            use_se=True)
+        self.block_list.append(conv5_6)
+
+        conv6 = DepthwiseSeparable(
+            num_channels=int(1024 * scale),
+            num_filters1=1024,
+            num_filters2=1024,
+            num_groups=1024,
+            stride=1,
+            dw_size=5,
+            padding=2,
+            use_se=True,
+            scale=scale)
+        self.block_list.append(conv6)
+
+        self.block_list = nn.Sequential(*self.block_list)
+
+        self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
+        self.out_channels = int(1024 * scale)
+
+    def forward(self, inputs):
+        y = self.conv1(inputs)
+        y = self.block_list(y)
+        y = self.pool(y)
+        return y
+
+
+class SEModule(nn.Layer):
+    def __init__(self, channel, reduction=4):
+        super(SEModule, self).__init__()
+        self.avg_pool = AdaptiveAvgPool2D(1)
+        self.conv1 = Conv2D(
+            in_channels=channel,
+            out_channels=channel // reduction,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            weight_attr=ParamAttr(),
+            bias_attr=ParamAttr())
+        self.conv2 = Conv2D(
+            in_channels=channel // reduction,
+            out_channels=channel,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            weight_attr=ParamAttr(),
+            bias_attr=ParamAttr())
+
+    def forward(self, inputs):
+        outputs = self.avg_pool(inputs)
+        outputs = self.conv1(outputs)
+        outputs = F.relu(outputs)
+        outputs = self.conv2(outputs)
+        outputs = hardsigmoid(outputs)
+        return paddle.multiply(x=inputs, y=outputs)

ppocr/postprocess/__init__.py

@@ -21,7 +21,7 @@ import copy
 
 __all__ = ['build_post_process']
 
-from .db_postprocess import DBPostProcess
+from .db_postprocess import DBPostProcess, DistillationDBPostProcess
 from .east_postprocess import EASTPostProcess
 from .sast_postprocess import SASTPostProcess
 from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
@@ -34,7 +34,8 @@ def build_post_process(config, global_config=None):
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
         'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
-        'DistillationCTCLabelDecode', 'TableLabelDecode'
+        'DistillationCTCLabelDecode', 'TableLabelDecode',
+        'DistillationDBPostProcess'
     ]
 
     config = copy.deepcopy(config)

ppocr/postprocess/db_postprocess.py

@@ -187,3 +187,29 @@ class DBPostProcess(object):
 
             boxes_batch.append({'points': boxes})
         return boxes_batch
+
+
+class DistillationDBPostProcess(object):
+    def __init__(self, model_name=["student"],
+                 key=None,
+                 thresh=0.3,
+                 box_thresh=0.6,
+                 max_candidates=1000,
+                 unclip_ratio=1.5,
+                 use_dilation=False,
+                 score_mode="fast",
+                 **kwargs):
+        self.model_name = model_name
+        self.key = key
+        self.post_process = DBPostProcess(thresh=thresh,
+                 box_thresh=box_thresh,
+                 max_candidates=max_candidates,
+                 unclip_ratio=unclip_ratio,
+                 use_dilation=use_dilation,
+                 score_mode=score_mode)
+
+    def __call__(self, predicts, shape_list):
+        results = {}
+        for k in self.model_name:
+            results[k] = self.post_process(predicts[k], shape_list=shape_list)
+        return results

ppocr/utils/save_load.py

@@ -91,14 +91,14 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
 
 def load_dygraph_params(config, model, logger, optimizer):
     ckp = config['Global']['checkpoints']
-    if ckp and os.path.exists(ckp):
+    if ckp and os.path.exists(ckp + ".pdparams"):
         pre_best_model_dict = init_model(config, model, optimizer)
         return pre_best_model_dict
     else:
         pm = config['Global']['pretrained_model']
         if pm is None:
             return {}
-        if not os.path.exists(pm) or not os.path.exists(pm + ".pdparams"):
+        if not os.path.exists(pm) and not os.path.exists(pm + ".pdparams"):
             logger.info(f"The pretrained_model {pm} does not exists!")
             return {}
         pm = pm if pm.endswith('.pdparams') else pm + '.pdparams'
@@ -116,6 +116,27 @@ def load_dygraph_params(config, model, logger, optimizer):
         logger.info(f"loaded pretrained_model successful from {pm}")
         return {}
 
+def load_pretrained_params(model, path):
+    if path is None:
+        return False
+    if not os.path.exists(path) and not os.path.exists(path + ".pdparams"):
+        print(f"The pretrained_model {path} does not exists!")
+        return False
+
+    path = path if path.endswith('.pdparams') else path + '.pdparams'
+    params = paddle.load(path)
+    state_dict = model.state_dict()
+    new_state_dict = {}
+    for k1, k2 in zip(state_dict.keys(), params.keys()):
+        if list(state_dict[k1].shape) == list(params[k2].shape):
+            new_state_dict[k1] = params[k2]
+        else:
+            print(
+                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
+            )
+    model.set_state_dict(new_state_dict)
+    print(f"load pretrain successful from {path}")
+    return model
 
 def save_model(model,
                optimizer,

test/ocr_det_params.txt

 model_name:ocr_det
 python:python3.7
-gpu_list:-1|0|0,1
-Global.auto_cast:False|True
+gpu_list:0|0,1
+Global.auto_cast:null
 Global.epoch_num:10
 Global.save_model_dir:./output/
-Global.save_inference_dir:./output/
 Train.loader.batch_size_per_card:
-Global.use_gpu
-Global.pretrained_model
+Global.use_gpu:
+Global.pretrained_model:null
 
-trainer:norm|pact|fpgm
+trainer:norm|pact
 norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
 fpgm_train:null
@@ -17,6 +16,8 @@ distill_train:null
 
 eval:tools/eval.py -c configs/det/det_mv3_db.yml -o 
 
+Global.save_inference_dir:./output/
+Global.pretrained_model:
 norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o 
 quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o 
 fpgm_export:deploy/slim/prune/export_prune_model.py
@@ -29,7 +30,6 @@ inference:tools/infer/predict_det.py
 --rec_batch_num:1
 --use_tensorrt:True|False
 --precision:fp32|fp16|int8
---det_model_dir
---image_dir
---save_log_path
-
+--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--save_log_path:./test/output/

test/prepare.sh

@@ -26,8 +26,10 @@ IFS=$'\n'
 # The training params
 model_name=$(func_parser_value "${lines[0]}")
 train_model_list=$(func_parser_value "${lines[0]}")
+
 trainer_list=$(func_parser_value "${lines[10]}")
 
+
 # MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
 MODE=$2
 # prepare pretrained weights and dataset 
@@ -62,8 +64,8 @@ else
     rm -rf ./train_data/icdar2015
     wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
     if [ ${model_name} = "ocr_det" ]; then
-        eval_model_name="ch_ppocr_mobile_v2.0_det_train"
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
+        eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
         cd ./inference && tar xf ${eval_model_name}.tar && cd ../
     else 
         eval_model_name="ch_ppocr_mobile_v2.0_rec_train"

test/test.sh

@@ -41,59 +41,51 @@ gpu_list=$(func_parser_value "${lines[2]}")
 autocast_list=$(func_parser_value "${lines[3]}")
 autocast_key=$(func_parser_key "${lines[3]}")
 epoch_key=$(func_parser_key "${lines[4]}")
+epoch_num=$(func_parser_value "${lines[4]}")
 save_model_key=$(func_parser_key "${lines[5]}")
-save_infer_key=$(func_parser_key "${lines[6]}")
-train_batch_key=$(func_parser_key "${lines[7]}")
-train_use_gpu_key=$(func_parser_key "${lines[8]}")
-pretrain_model_key=$(func_parser_key "${lines[9]}")
-
-trainer_list=$(func_parser_value "${lines[10]}")
-norm_trainer=$(func_parser_value "${lines[11]}")
-pact_trainer=$(func_parser_value "${lines[12]}")
-fpgm_trainer=$(func_parser_value "${lines[13]}")
-distill_trainer=$(func_parser_value "${lines[14]}")
-
-eval_py=$(func_parser_value "${lines[15]}")
-norm_export=$(func_parser_value "${lines[16]}")
-pact_export=$(func_parser_value "${lines[17]}")
-fpgm_export=$(func_parser_value "${lines[18]}")
-distill_export=$(func_parser_value "${lines[19]}")
-
-inference_py=$(func_parser_value "${lines[20]}")
-use_gpu_key=$(func_parser_key "${lines[21]}")
-use_gpu_list=$(func_parser_value "${lines[21]}")
-use_mkldnn_key=$(func_parser_key "${lines[22]}")
-use_mkldnn_list=$(func_parser_value "${lines[22]}")
-cpu_threads_key=$(func_parser_key "${lines[23]}")
-cpu_threads_list=$(func_parser_value "${lines[23]}")
-batch_size_key=$(func_parser_key "${lines[24]}")
-batch_size_list=$(func_parser_value "${lines[24]}")
-use_trt_key=$(func_parser_key "${lines[25]}")
-use_trt_list=$(func_parser_value "${lines[25]}")
-precision_key=$(func_parser_key "${lines[26]}")
-precision_list=$(func_parser_value "${lines[26]}")
-model_dir_key=$(func_parser_key "${lines[27]}")
-image_dir_key=$(func_parser_key "${lines[28]}")
-save_log_key=$(func_parser_key "${lines[29]}")
+train_batch_key=$(func_parser_key "${lines[6]}")
+train_use_gpu_key=$(func_parser_key "${lines[7]}")
+pretrain_model_key=$(func_parser_key "${lines[8]}")
+pretrain_model_value=$(func_parser_value "${lines[8]}")
+
+trainer_list=$(func_parser_value "${lines[9]}")
+norm_trainer=$(func_parser_value "${lines[10]}")
+pact_trainer=$(func_parser_value "${lines[11]}")
+fpgm_trainer=$(func_parser_value "${lines[12]}")
+distill_trainer=$(func_parser_value "${lines[13]}")
+
+eval_py=$(func_parser_value "${lines[14]}")
+
+save_infer_key=$(func_parser_key "${lines[15]}")
+export_weight=$(func_parser_key "${lines[16]}")
+norm_export=$(func_parser_value "${lines[17]}")
+pact_export=$(func_parser_value "${lines[18]}")
+fpgm_export=$(func_parser_value "${lines[19]}")
+distill_export=$(func_parser_value "${lines[20]}")
+
+inference_py=$(func_parser_value "${lines[21]}")
+use_gpu_key=$(func_parser_key "${lines[22]}")
+use_gpu_list=$(func_parser_value "${lines[22]}")
+use_mkldnn_key=$(func_parser_key "${lines[23]}")
+use_mkldnn_list=$(func_parser_value "${lines[23]}")
+cpu_threads_key=$(func_parser_key "${lines[24]}")
+cpu_threads_list=$(func_parser_value "${lines[24]}")
+batch_size_key=$(func_parser_key "${lines[25]}")
+batch_size_list=$(func_parser_value "${lines[25]}")
+use_trt_key=$(func_parser_key "${lines[26]}")
+use_trt_list=$(func_parser_value "${lines[26]}")
+precision_key=$(func_parser_key "${lines[27]}")
+precision_list=$(func_parser_value "${lines[27]}")
+infer_model_key=$(func_parser_key "${lines[28]}")
+infer_model=$(func_parser_value "${lines[28]}")
+image_dir_key=$(func_parser_key "${lines[29]}")
+infer_img_dir=$(func_parser_value "${lines[29]}")
+save_log_key=$(func_parser_key "${lines[30]}")
 
 LOG_PATH="./test/output"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results.log"
 
-if [ ${MODE} = "lite_train_infer" ]; then
-    export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-    export epoch_num=10
-elif [ ${MODE} = "whole_infer" ]; then
-    export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-    export epoch_num=10
-elif [ ${MODE} = "whole_train_infer" ]; then
-    export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-    export epoch_num=300
-else
-    export infer_img_dir="./inference/ch_det_data_50/all-sum-510"
-    export infer_model_dir="./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy"
-fi
-
 
 function func_inference(){
     IFS='|'
@@ -109,8 +101,8 @@ function func_inference(){
             for use_mkldnn in ${use_mkldnn_list[*]}; do
                 for threads in ${cpu_threads_list[*]}; do
                     for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}"
+                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True"
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -123,8 +115,8 @@ function func_inference(){
                         continue
                     fi
                     for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}"
+                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}  --benchmark=True"
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -144,6 +136,7 @@ for gpu in ${gpu_list[*]}; do
         env=""
     elif [ ${#gpu} -le 1 ];then
         env="export CUDA_VISIBLE_DEVICES=${gpu}"
+        eval ${env}
     elif [ ${#gpu} -le 15 ];then
         IFS=","
         array=(${gpu})
@@ -155,6 +148,7 @@ for gpu in ${gpu_list[*]}; do
         ips=${array[0]}
         gpu=${array[1]}
         IFS="|"
+        env=" "
     fi
     for autocast in ${autocast_list[*]}; do 
         for trainer in ${trainer_list[*]}; do 
@@ -179,13 +173,32 @@ for gpu in ${gpu_list[*]}; do
                 continue
             fi
 
-            save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-            if [ ${#gpu} -le 2 ];then  # epoch_num #TODO
-                cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
-            elif [ ${#gpu} -le 15 ];then
-                cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num}  ${save_model_key}=${save_log}"
+            # not set autocast when autocast is null
+            if [ ${autocast} = "null" ]; then
+                set_autocast=" "
+            else
+                set_autocast="${autocast_key}=${autocast}"
+            fi
+            # not set epoch when whole_train_infer
+            if [ ${MODE} != "whole_train_infer" ]; then
+                set_epoch="${epoch_key}=${epoch_num}"
+            else
+                set_epoch=" "
+            fi
+            # set pretrain
+            if [ ${pretrain_model_value} != "null" ]; then
+                set_pretrain="${pretrain_model_key}=${pretrain_model_value}"
             else
-                cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
+                set_pretrain=" "
+            fi
+
+            save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+            if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
+                cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu}  ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
+            elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
+                cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${save_model_key}=${save_log}  ${set_epoch} ${set_pretrain} ${set_autocast}"
+            else     # train with multi-machine
+                cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast}"
             fi
             # run train
             eval $cmd
@@ -198,24 +211,27 @@ for gpu in ${gpu_list[*]}; do
 
             # run export model
             save_infer_path="${save_log}"
-            export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
+            export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
             eval $export_cmd
             status_check $? "${export_cmd}" "${status_log}"
 
             #run inference
+            eval $env
             save_infer_path="${save_log}"
             func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
+            eval "unset CUDA_VISIBLE_DEVICES"
         done
     done
 done
 
 else
-    save_infer_path="${LOG_PATH}/${MODE}"
-    run_export=${norm_export}
-    export_cmd="${python} ${run_export} ${save_model_key}=${save_infer_path} ${pretrain_model_key}=${infer_model_dir} ${save_infer_key}=${save_infer_path}"
-    eval $export_cmd
-    status_check $? "${export_cmd}" "${status_log}"
-
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
+        env=" "
+    else
+        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+    fi
+    echo $env
     #run inference
-    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
+    func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}"
 fi

test1/table/README_ch.md

@@ -19,7 +19,29 @@
 
 
 ### 2.1 训练
-TBD
+#### 数据准备  
+训练数据使用公开数据集[PubTabNet](https://arxiv.org/abs/1911.10683)，可以从[官网](https://github.com/ibm-aur-nlp/PubTabNet)下载。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
+
+#### 启动训练  
+*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
+```shell
+# 单机单卡训练
+python3 tools/train.py -c configs/table/table_mv3.yml
+# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
+```
+
+上述指令中，通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释，请参考[链接](./config.md)。
+
+#### 断点训练
+
+如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
+```shell
+python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model
+```
+
+**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
+
 
 ### 2.2 评估
 先cd到PaddleOCR/ppstructure目录下

tools/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_pretrained_params
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -55,7 +55,10 @@ def main():
 
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
-    model_type = config['Architecture']['model_type']
+    if "model_type" in config['Architecture'].keys():
+        model_type = config['Architecture']['model_type']
+    else:
+        model_type = None
 
     best_model_dict = init_model(config, model)
     if len(best_model_dict):
@@ -68,7 +71,7 @@ def main():
 
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class, model_type, use_srn)
+                        eval_class, model_type, use_srn)
     logger.info('metric eval ***************')
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))

tools/infer/predict_det.py

@@ -106,7 +106,7 @@ class TextDetector(object):
                 model_precision=args.precision,
                 batch_size=1,
                 data_shape="dynamic",
-                save_path="./output/auto_log.lpg",
+                save_path=args.save_log_path,
                 inference_config=self.config,
                 pids=pid,
                 process_name=None,
@@ -174,7 +174,7 @@ class TextDetector(object):
         data = {'image': img}
 
         st = time.time()
-        
+
         if self.args.benchmark:
             self.autolog.times.start()
 
@@ -212,7 +212,7 @@ class TextDetector(object):
         else:
             raise NotImplementedError
 
-        self.predictor.try_shrink_memory()
+        #self.predictor.try_shrink_memory()
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
         if self.det_algorithm == "SAST" and self.det_sast_polygon:
@@ -262,7 +262,6 @@ if __name__ == "__main__":
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
         logger.info("The visualized image saved in {}".format(img_path))
-    
+
     if args.benchmark:
         text_detector.autolog.report()
-

tools/infer/predict_system.py

@@ -174,8 +174,6 @@ def main(args):
     logger.info("The predict total time is {}".format(time.time() - _st))
     logger.info("\nThe predict total time is {}".format(total_time))
 
-    img_num = text_sys.text_detector.det_times.img_num
-
 
 if __name__ == "__main__":
     args = utility.parse_args()

tools/infer/utility.py

@@ -37,7 +37,7 @@ def init_args():
     parser.add_argument("--use_gpu", type=str2bool, default=True)
     parser.add_argument("--ir_optim", type=str2bool, default=True)
     parser.add_argument("--use_tensorrt", type=str2bool, default=False)
-    parser.add_argument("--min_subgraph_size", type=int, default=3)
+    parser.add_argument("--min_subgraph_size", type=int, default=10)
     parser.add_argument("--precision", type=str, default="fp32")
     parser.add_argument("--gpu_mem", type=int, default=500)
 
@@ -164,7 +164,7 @@ def create_predictor(args, mode, logger):
         config.enable_use_gpu(args.gpu_mem, 0)
         if args.use_tensorrt:
             config.enable_tensorrt_engine(
-                precision_mode=inference.PrecisionType.Float32,
+                precision_mode=precision,
                 max_batch_size=args.max_batch_size,
                 min_subgraph_size=args.min_subgraph_size)
             # skip the minmum trt subgraph
@@ -176,6 +176,7 @@ def create_predictor(args, mode, logger):
                 "conv2d_59.tmp_0": [1, 96, 20, 20],
                 "nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
                 "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
+                "conv2d_124.tmp_0": [1, 96, 20, 20],
                 "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
                 "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
                 "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
@@ -188,6 +189,7 @@ def create_predictor(args, mode, logger):
                 "conv2d_91.tmp_0": [1, 96, 200, 200],
                 "conv2d_59.tmp_0": [1, 96, 400, 400],
                 "nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
+                "conv2d_124.tmp_0": [1, 256, 400, 400],
                 "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
                 "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
                 "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
@@ -202,6 +204,7 @@ def create_predictor(args, mode, logger):
                 "conv2d_59.tmp_0": [1, 96, 160, 160],
                 "nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
                 "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
+                "conv2d_124.tmp_0": [1, 256, 160, 160],
                 "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
                 "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
                 "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
@@ -237,7 +240,7 @@ def create_predictor(args, mode, logger):
 
     # enable memory optim
     config.enable_memory_optim()
-    config.disable_glog_info()
+    #config.disable_glog_info()
 
     config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
     if mode == 'table':

tools/program.py

@@ -186,7 +186,10 @@ def train(config,
     model.train()
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
-    model_type = config['Architecture']['model_type']
+    try: 
+        model_type = config['Architecture']['model_type']
+    except: 
+        model_type = None
 
     if 'start_epoch' in best_model_dict:
         start_epoch = best_model_dict['start_epoch']

tools/train.py

@@ -98,7 +98,6 @@ def main(config, device, logger, vdl_writer):
     eval_class = build_metric(config['Metric'])
     # load pretrain model
     pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
-
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(