base_model.py 3.2 KB
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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
from ppocr.modeling.necks import build_neck
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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        the module for OCR.
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        args:
            config (dict): the super parameters for module.
        """
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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,
        # for rec, transfrom can be TPS,None
        # 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:
            self.use_transform = False
        else:
            self.use_transform = True
            config['Transform']['in_channels'] = in_channels
            self.transform = build_transform(config['Transform'])
            in_channels = self.transform.out_channels

        # build backbone, backbone is need for del, rec and cls
        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
        # for rec, neck can be cnn,rnn or reshape(None)
        # for det, neck can be FPN, BIFPN and so on.
        # for cls, neck should be none
        if 'Neck' not in config or config['Neck'] is None:
            self.use_neck = False
        else:
            self.use_neck = True
            config['Neck']['in_channels'] = in_channels
            self.neck = build_neck(config['Neck'])
            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
        self.head = build_head(config["Head"])

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        self.return_all_feats = config.get("return_all_feats", False)

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    def forward(self, x, data=None):
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        y = dict()
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        if self.use_transform:
            x = self.transform(x)
        x = self.backbone(x)
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        y["backbone_out"] = x
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        if self.use_neck:
            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:
            return y
        else:
            return x