rm load_dyg_pretrain

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

@@ -8,9 +8,9 @@ Global:
   save_epoch_step: 3
   eval_batch_step: [0, 2000]
   cal_metric_during_train: true
-  pretrained_model: null
-  checkpoints: null
-  save_inference_dir: null
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
   use_visualdl: false
   infer_img: doc/imgs_words/ch/word_1.jpg
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
@@ -38,7 +38,7 @@ Architecture:
   algorithm: Distillation
   Models:
     Student:
-      pretrained: null
+      pretrained:
       freeze_params: false
       return_all_feats: true
       model_type: rec
@@ -57,7 +57,7 @@ Architecture:
         name: CTCHead
         fc_decay: 0.00001
     Teacher:
-      pretrained: null
+      pretrained:
       freeze_params: false
       return_all_feats: true
       model_type: rec
@@ -118,8 +118,8 @@ Train:
     - DecodeImage:
         img_mode: BGR
         channel_first: false
-    - RecAug: null
-    - CTCLabelEncode: null
+    - RecAug:
+    - CTCLabelEncode:
     - RecResizeImg:
         image_shape: [3, 32, 320]
     - KeepKeys:
@@ -143,7 +143,7 @@ Eval:
     - DecodeImage:
         img_mode: BGR
         channel_first: false
-    - CTCLabelEncode: null
+    - CTCLabelEncode:
     - RecResizeImg:
         image_shape: [3, 32, 320]
     - KeepKeys:

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 load_dygraph_pretrain
+from ppocr.utils.save_load import init_model
 
 __all__ = ['DistillationModel']
 
@@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
                 pretrained = model_config.pop("pretrained")
             model = BaseModel(model_config)
             if pretrained is not None:
-                load_dygraph_pretrain(model, path=pretrained)
+                init_model(model, path=pretrained)
             if freeze_params:
                 for param in model.parameters():
                     param.trainable = False

ppocr/utils/save_load.py

@@ -23,6 +23,8 @@ import six
 
 import paddle
 
+from ppocr.utils.logging import get_logger
+
 __all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']
 
 
@@ -42,19 +44,11 @@ def _mkdir_if_not_exist(path, logger):
                 raise OSError('Failed to mkdir {}'.format(path))
 
 
-def load_dygraph_pretrain(model, logger=None, path=None):
-    if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
-        raise ValueError("Model pretrain path {} does not "
-                         "exists.".format(path))
-    param_state_dict = paddle.load(path + '.pdparams')
-    model.set_state_dict(param_state_dict)
-    return
-
-
-def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
+def init_model(config, model, optimizer=None, lr_scheduler=None):
     """
     load model from checkpoint or pretrained_model
     """
+    logger = get_logger()
     global_config = config['Global']
     checkpoints = global_config.get('checkpoints')
     pretrained_model = global_config.get('pretrained_model')
@@ -77,13 +71,17 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
             best_model_dict = states_dict.get('best_model_dict', {})
             if 'epoch' in states_dict:
                 best_model_dict['start_epoch'] = states_dict['epoch'] + 1
-
         logger.info("resume from {}".format(checkpoints))
     elif pretrained_model:
         if not isinstance(pretrained_model, list):
             pretrained_model = [pretrained_model]
         for pretrained in pretrained_model:
-            load_dygraph_pretrain(model, logger, path=pretrained)
+            if not (os.path.isdir(pretrained) or
+                    os.path.exists(pretrained + '.pdparams')):
+                raise ValueError("Model pretrain path {} does not "
+                                 "exists.".format(pretrained))
+            param_state_dict = paddle.load(pretrained + '.pdparams')
+            model.set_state_dict(param_state_dict)
             logger.info("load pretrained model from {}".format(
                 pretrained_model))
     else:

tools/eval.py

@@ -49,7 +49,7 @@ def main():
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
 
-    best_model_dict = init_model(config, model, logger)
+    best_model_dict = init_model(config, model)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/export_model.py

@@ -95,7 +95,7 @@ def main():
         else:  # base rec model
             config["Architecture"]["Head"]["out_channels"] = char_num
     model = build_model(config["Architecture"])
-    init_model(config, model, logger)
+    init_model(config, model)
     model.eval()
 
     save_path = config["Global"]["save_inference_dir"]

tools/infer_cls.py

@@ -47,7 +47,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    init_model(config, model)
 
     # create data ops
     transforms = []

tools/infer_det.py

@@ -61,7 +61,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    init_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'])

tools/infer_e2e.py

@@ -68,7 +68,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    init_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'],

tools/infer_rec.py

@@ -58,7 +58,7 @@ def main():
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    init_model(config, model)
 
     # create data ops
     transforms = []

tools/train.py

@@ -97,7 +97,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = init_model(config, model, logger, optimizer)
+    pre_best_model_dict = init_model(config, model, optimizer)
 
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None: