support rec v4

configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml

@@ -1,38 +1,33 @@
 Global:
-  debug: false
-  use_gpu: true
+  device: gpu
   epoch_num: 200
   log_smooth_window: 20
   print_batch_step: 10
   output_dir: ./output/rec_ppocr_v4
-  save_epoch_step: 10
-  eval_batch_step: [0, 2000]
+  eval_epoch_step: [0, 1]
   cal_metric_during_train: true
   pretrained_model:
   checkpoints:
-  save_inference_dir:
-  use_visualdl: false
+  use_tensorboard: false
   infer_img: doc/imgs_words/ch/word_1.jpg
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  character_dict_path: &character_dict_path ppocr/utils/ppocr_keys_v1.txt
   max_text_length: &max_text_length 25
   infer_mode: false
-  use_space_char: true
-  distributed: true
-  save_res_path: ./output/rec/predicts_ppocrv3.txt
+  use_space_char: &use_space_char true
 
+Export:
+  export_dir:
+  export_shape: [ 1, 3, 48, 320 ]
+  dynamic_axes: [ 0, 2, 3 ]
 
 Optimizer:
   name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  lr:
-    name: Cosine
-    learning_rate: 0.001
-    warmup_epoch: 5
-  regularizer:
-    name: L2
-    factor: 3.0e-05
+  lr: 0.001
+  weight_decay: 3.0e-05
 
+LRScheduler:
+  name: CosineAnnealingLR
+  warmup_epoch: 5
 
 Architecture:
   model_type: rec
@@ -66,6 +61,8 @@ Loss:
 
 PostProcess:  
   name: CTCLabelDecode
+  character_dict_path: *character_dict_path
+  use_space_char: *use_space_char
 
 Metric:
   name: RecMetric
@@ -109,7 +106,7 @@ Train:
     shuffle: true
     batch_size_per_card: *bs
     drop_last: true
-    num_workers: 8
+    num_workers: 0
 Eval:
   dataset:
     name: SimpleDataSet