add srn for dygraph

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -1,5 +1,5 @@
 Global:
-  use_gpu: true
+  use_gpu: True
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
@@ -59,7 +59,7 @@ Metric:
 
 Train:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
       - DecodeImage: # load image
@@ -78,7 +78,7 @@ Train:
 
 Eval:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/validation/
     transforms:
       - DecodeImage: # load image

configs/rec/rec_mv3_none_none_ctc.yml

@@ -58,7 +58,7 @@ Metric:
 
 Train:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
       - DecodeImage: # load image
@@ -77,7 +77,7 @@ Train:
 
 Eval:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/validation/
     transforms:
       - DecodeImage: # load image

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -63,7 +63,7 @@ Metric:
 
 Train:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
       - DecodeImage: # load image
@@ -82,7 +82,7 @@ Train:
 
 Eval:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/validation/
     transforms:
       - DecodeImage: # load image

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -58,7 +58,7 @@ Metric:
 
 Train:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
       - DecodeImage: # load image
@@ -77,7 +77,7 @@ Train:
 
 Eval:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/validation/
     transforms:
       - DecodeImage: # load image

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -56,7 +56,7 @@ Metric:
 
 Train:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
       - DecodeImage: # load image
@@ -75,7 +75,7 @@ Train:
 
 Eval:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/validation/
     transforms:
       - DecodeImage: # load image

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -62,7 +62,7 @@ Metric:
 
 Train:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/training/
     transforms:
       - DecodeImage: # load image
@@ -81,7 +81,7 @@ Train:
 
 Eval:
   dataset:
-    name: LMDBDateSet
+    name: LMDBDataSet
     data_dir: ./train_data/data_lmdb_release/validation/
     transforms:
       - DecodeImage: # load image

configs/rec/rec_r50_fpn_srn.yml

@@ -0,0 +1,106 @@
+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/rec/srn
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 5000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: 
+  character_type: en
+  max_text_length: 25
+  num_heads: 8
+  infer_mode: False
+  use_space_char: False
+
+
+Optimizer:
+  name: Adam
+  lr:
+    name: Cosine
+    learning_rate: 0.0001
+
+Architecture:
+  model_type: rec
+  algorithm: SRN
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: ResNetFPN
+  Head:
+    name: SRNHead
+    max_text_length: 25
+    num_heads: 8
+    num_encoder_TUs: 2
+    num_decoder_TUs: 4
+    hidden_dims: 512
+
+Loss:
+  name: SRNLoss
+
+PostProcess:
+  name: SRNLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/srn_train_data_duiqi
+    #label_file_list: ["./train_data/ic15_data/1.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SRNLabelEncode: # Class handling label
+      - SRNRecResizeImg:
+          image_shape: [1, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image',
+                      'label',
+                      'length',
+                      'encoder_word_pos',
+                      'gsrm_word_pos',
+                      'gsrm_slf_attn_bias1',
+                      'gsrm_slf_attn_bias2'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 64
+    drop_last: True
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/evaluation
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SRNLabelEncode: # Class handling label
+      - SRNRecResizeImg:
+          image_shape: [1, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image',
+                      'label',
+                      'length',
+                      'encoder_word_pos',
+                      'gsrm_word_pos',
+                      'gsrm_slf_attn_bias1',
+                      'gsrm_slf_attn_bias2'] 
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 32
+    num_workers: 4

ppocr/data/__init__.py

@@ -33,7 +33,7 @@
 
 from ppocr.data.imaug import transform, create_operators
 from ppocr.data.simple_dataset import SimpleDataSet
-from ppocr.data.lmdb_dataset import LMDBDateSet
+from ppocr.data.lmdb_dataset import LMDBDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -54,7 +54,7 @@ def term_mp(sig_num, frame):
 def build_dataloader(config, mode, device, logger):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDateSet']
+    support_dict = ['SimpleDataSet', 'LMDBDataSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))

ppocr/data/imaug/__init__.py

@@ -21,7 +21,7 @@
 from .make_shrink_map import MakeShrinkMap
 from .random_crop_data import EastRandomCropData, PSERandomCrop
 
-from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg
+from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg
 from .randaugment import RandAugment
 from .operators import *
 from .label_ops import *

ppocr/data/imaug/label_ops.py

@@ -98,6 +98,8 @@ def __init__(self,
             support_character_type, character_type)
 
         self.max_text_len = max_text_length
+        self.beg_str = "sos"
+        self.end_str = "eos"
         if character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
@@ -213,3 +215,49 @@ def get_beg_end_flag_idx(self, beg_or_end):
             assert False, "Unsupport type %s in get_beg_end_flag_idx" \
                           % beg_or_end
         return idx
+
+
+class SRNLabelEncode(BaseRecLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length=25,
+                 character_dict_path=None,
+                 character_type='en',
+                 use_space_char=False,
+                 **kwargs):
+        super(SRNLabelEncode,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+
+    def add_special_char(self, dict_character):
+        dict_character = dict_character + [self.beg_str, self.end_str]
+        return dict_character
+
+    def __call__(self, data):
+        text = data['label']
+        text = self.encode(text)
+        char_num = len(self.character_str)
+        if text is None:
+            return None
+        if len(text) > self.max_text_len:
+            return None
+        data['length'] = np.array(len(text))
+        text = text + [char_num] * (self.max_text_len - len(text))
+        data['label'] = np.array(text)
+        return data
+
+    def get_ignored_tokens(self):
+        beg_idx = self.get_beg_end_flag_idx("beg")
+        end_idx = self.get_beg_end_flag_idx("end")
+        return [beg_idx, end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if beg_or_end == "beg":
+            idx = np.array(self.dict[self.beg_str])
+        elif beg_or_end == "end":
+            idx = np.array(self.dict[self.end_str])
+        else:
+            assert False, "Unsupport type %s in get_beg_end_flag_idx" \
+                          % beg_or_end
+        return idx