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			79 lines
		
	
	
		
			2.8 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			79 lines
		
	
	
		
			2.8 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
# Copyright (c) 2020 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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__all__ = ['BaseModel']
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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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        # 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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        # 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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        # # 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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    def forward(self, x):
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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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        if self.use_neck:
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            x = self.neck(x)
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        x = self.head(x)
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        return x
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