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118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
8 months ago
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# 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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__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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if "Backbone" not in config or config["Backbone"] is None:
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self.use_backbone = False
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else:
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self.use_backbone = True
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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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if "Head" not in config or config["Head"] is None:
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self.use_head = False
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else:
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self.use_head = True
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config["Head"]["in_channels"] = in_channels
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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:
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x = self.transform(x)
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if self.use_backbone:
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x = self.backbone(x)
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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["backbone_out"] = x
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final_name = "backbone_out"
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if self.use_neck:
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x = self.neck(x)
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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["neck_out"] = x
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final_name = "neck_out"
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if self.use_head:
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x = self.head(x, targets=data)
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# for multi head, save ctc neck out for udml
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if isinstance(x, dict) and "ctc_neck" in x.keys():
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y["neck_out"] = x["ctc_neck"]
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y["head_out"] = x
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elif 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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final_name = "head_out"
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if self.return_all_feats:
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if self.training:
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return y
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elif isinstance(x, dict):
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return x
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else:
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return {final_name: x}
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else:
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return x
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