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			89 lines
		
	
	
		
			3.2 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			89 lines
		
	
	
		
			3.2 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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| #
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| # Licensed under the Apache License, Version 2.0 (the "License");
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| # you may not use this file except in compliance with the License.
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| # You may obtain a copy of the License at
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| #
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| #     http://www.apache.org/licenses/LICENSE-2.0
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| #
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| # Unless required by applicable law or agreed to in writing, software
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| # distributed under the License is distributed on an "AS IS" BASIS,
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| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| # See the License for the specific language governing permissions and
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| # limitations under the License.
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| from __future__ import absolute_import
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| from __future__ import division
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| from __future__ import print_function
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| from paddle import nn
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| from ppocr.modeling.transforms import build_transform
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| from ppocr.modeling.backbones import build_backbone
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| from ppocr.modeling.necks import build_neck
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| from ppocr.modeling.heads import build_head
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| 
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| __all__ = ['BaseModel']
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| 
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| 
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| class BaseModel(nn.Layer):
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|     def __init__(self, config):
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|         """
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|         the module for OCR.
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|         args:
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|             config (dict): the super parameters for module.
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|         """
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|         super(BaseModel, self).__init__()
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|         in_channels = config.get('in_channels', 3)
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|         model_type = config['model_type']
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|         # build transfrom,
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|         # for rec, transfrom can be TPS,None
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|         # for det and cls, transfrom shoule to be None,
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|         # if you make model differently, you can use transfrom in det and cls
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|         if 'Transform' not in config or config['Transform'] is None:
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|             self.use_transform = False
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|         else:
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|             self.use_transform = True
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|             config['Transform']['in_channels'] = in_channels
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|             self.transform = build_transform(config['Transform'])
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|             in_channels = self.transform.out_channels
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| 
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|         # build backbone, backbone is need for del, rec and cls
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|         config["Backbone"]['in_channels'] = in_channels
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|         self.backbone = build_backbone(config["Backbone"], model_type)
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|         in_channels = self.backbone.out_channels
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| 
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|         # build neck
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|         # for rec, neck can be cnn,rnn or reshape(None)
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|         # for det, neck can be FPN, BIFPN and so on.
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|         # for cls, neck should be none
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|         if 'Neck' not in config or config['Neck'] is None:
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|             self.use_neck = False
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|         else:
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|             self.use_neck = True
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|             config['Neck']['in_channels'] = in_channels
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|             self.neck = build_neck(config['Neck'])
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|             in_channels = self.neck.out_channels
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| 
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|         # # build head, head is need for det, rec and cls
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|         config["Head"]['in_channels'] = in_channels
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|         self.head = build_head(config["Head"])
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| 
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|         self.return_all_feats = config.get("return_all_feats", False)
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| 
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|     def forward(self, x, data=None):
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|         y = dict()
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|         if self.use_transform:
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|             x = self.transform(x)
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|         x = self.backbone(x)
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|         y["backbone_out"] = x
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|         if self.use_neck:
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|             x = self.neck(x)
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|         y["neck_out"] = x
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|         x = self.head(x, targets=data)
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|         if isinstance(x, dict):
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|             y.update(x)
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|         else:
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|             y["head_out"] = x
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|         if self.return_all_feats:
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|             return y
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|         else:
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|             return x
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