base_model.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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
from ppocr.modeling.transforms import build_transform
from ppocr.modeling.backbones import build_backbone
from ppocr.modeling.necks import build_neck
from ppocr.modeling.heads import build_head

__all__ = ['BaseModel']


class BaseModel(nn.Layer):
    def __init__(self, config):
        """
        the module for OCR.
        args:
            config (dict): the super parameters for module.
        """
        super(BaseModel, self).__init__()
        in_channels = config.get('in_channels', 3)
        model_type = config['model_type']
        # build transfrom,
        # for rec, transfrom can be TPS,None
        # for det and cls, transfrom shoule to be None,
        # if you make model differently, you can use transfrom in det and cls
        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
        if 'Backbone' not in config or config['Backbone'] is None:
            self.use_backbone = False
        else:
            self.use_backbone = True
            config["Backbone"]['in_channels'] = in_channels
            self.backbone = build_backbone(config["Backbone"], model_type)
            in_channels = self.backbone.out_channels

        # 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

        # # build head, head is need for det, rec and cls
        if 'Head' not in config or config['Head'] is None:
            self.use_head = False
        else:
            self.use_head = True
            config["Head"]['in_channels'] = in_channels
            self.head = build_head(config["Head"])

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

    def forward(self, x, data=None):

        y = dict()
        if self.use_transform:
            x = self.transform(x)
        if self.use_backbone:
            x = self.backbone(x)
        if isinstance(x, dict):
            y.update(x)
        else:
            y["backbone_out"] = x
        final_name = "backbone_out"
        if self.use_neck:
            x = self.neck(x)
            if isinstance(x, dict):
                y.update(x)
            else:
                y["neck_out"] = x
            final_name = "neck_out"
        if self.use_head:
            x = self.head(x, targets=data)
            # for multi head, save ctc neck out for udml
            if isinstance(x, dict) and 'ctc_neck' in x.keys():
                y["neck_out"] = x["ctc_neck"]
                y["head_out"] = x
            elif isinstance(x, dict):
                y.update(x)
            else:
                y["head_out"] = x
            final_name = "head_out"
        if self.return_all_feats:
            if self.training:
                return y
            elif isinstance(x, dict):
                return x
            else:
                return {final_name: x}
        else:
            return x