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# 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 |