删除backbone里的pretrained_model字段

configs/det/det_r50_drrg_ctw.yml

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 1260 iterations
   eval_batch_step: [37800, 1260]
   cal_metric_during_train: False
-  pretrained_model:
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained.pdparams 
   checkpoints: 
   save_inference_dir: 
   use_visualdl: False
@@ -23,8 +23,6 @@ Architecture:
   Backbone:
     name: ResNet_vd
     layers: 50
-    pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained.pdparams 
-  
   Neck:
     name: FPN_UNet
     in_channels: [256, 512, 1024, 2048]

ppocr/modeling/backbones/det_resnet_vd.py

@@ -16,8 +16,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
@@ -27,8 +25,6 @@ from paddle.vision.ops import DeformConv2D
 from paddle.regularizer import L2Decay
 from paddle.nn.initializer import Normal, Constant, XavierUniform
 
-from ppocr.utils.logging import get_logger
-
 __all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"]
 
 
@@ -250,7 +246,6 @@ class ResNet_vd(nn.Layer):
                  layers=50,
                  dcn_stage=None,
                  out_indices=None,
-                 pretrained_model=None,
                  **kwargs):
         super(ResNet_vd, self).__init__()
 
@@ -344,30 +339,6 @@ class ResNet_vd(nn.Layer):
                     self.out_channels.append(num_filters[block])
                 self.stages.append(nn.Sequential(*block_list))
 
-        if pretrained_model is not None:
-            self.load_pretrained_params(pretrained_model)
-
-    def load_pretrained_params(self, path):
-        logger = get_logger()
-        if path.endswith('.pdparams'):
-            path = path.replace('.pdparams', '')
-        assert os.path.exists(path + ".pdparams"), \
-            "The {}.pdparams does not exists!".format(path)
-
-        params = paddle.load(path + '.pdparams')
-        state_dict = self.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:
-                logger.info(
-                    f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
-                )
-        self.set_state_dict(new_state_dict)
-        logger.info(f"loaded backbone pretrained_model successful from {path}")
-
     def forward(self, inputs):
         y = self.conv1_1(inputs)
         y = self.conv1_2(y)