init
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# 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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import copy
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import importlib
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from paddle.jit import to_static
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from paddle.static import InputSpec
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from .base_model import BaseModel
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from .distillation_model import DistillationModel
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__all__ = ["build_model", "apply_to_static"]
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def build_model(config):
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config = copy.deepcopy(config)
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if not "name" in config:
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arch = BaseModel(config)
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else:
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name = config.pop("name")
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mod = importlib.import_module(__name__)
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arch = getattr(mod, name)(config)
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return arch
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def apply_to_static(model, config, logger):
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if config["Global"].get("to_static", False) is not True:
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return model
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assert "d2s_train_image_shape" in config[
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"Global"], "d2s_train_image_shape must be assigned for static training mode..."
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supported_list = [
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"DB", "SVTR_LCNet", "TableMaster", "LayoutXLM", "SLANet", "SVTR"
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]
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if config["Architecture"]["algorithm"] in ["Distillation"]:
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algo = list(config["Architecture"]["Models"].values())[0]["algorithm"]
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else:
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algo = config["Architecture"]["algorithm"]
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assert algo in supported_list, f"algorithms that supports static training must in in {supported_list} but got {algo}"
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specs = [
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InputSpec(
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[None] + config["Global"]["d2s_train_image_shape"], dtype='float32')
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]
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if algo == "SVTR_LCNet":
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specs.append([
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InputSpec(
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[None, config["Global"]["max_text_length"]],
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dtype='int64'), InputSpec(
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[None, config["Global"]["max_text_length"]], dtype='int64'),
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InputSpec(
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[None], dtype='int64'), InputSpec(
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[None], dtype='float64')
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])
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elif algo == "TableMaster":
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specs.append(
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[
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InputSpec(
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[None, config["Global"]["max_text_length"]], dtype='int64'),
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InputSpec(
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[None, config["Global"]["max_text_length"], 4],
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dtype='float32'),
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InputSpec(
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[None, config["Global"]["max_text_length"], 1],
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dtype='float32'),
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InputSpec(
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[None, 6], dtype='float32'),
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])
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elif algo == "LayoutXLM":
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specs = [[
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InputSpec(
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shape=[None, 512], dtype="int64"), # input_ids
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InputSpec(
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shape=[None, 512, 4], dtype="int64"), # bbox
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InputSpec(
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shape=[None, 512], dtype="int64"), # attention_mask
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InputSpec(
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shape=[None, 512], dtype="int64"), # token_type_ids
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InputSpec(
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shape=[None, 3, 224, 224], dtype="float32"), # image
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InputSpec(
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shape=[None, 512], dtype="int64"), # label
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]]
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elif algo == "SLANet":
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specs.append([
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InputSpec(
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[None, config["Global"]["max_text_length"] + 2], dtype='int64'),
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InputSpec(
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[None, config["Global"]["max_text_length"] + 2, 4],
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dtype='float32'),
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InputSpec(
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[None, config["Global"]["max_text_length"] + 2, 1],
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dtype='float32'),
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InputSpec(
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[None, 6], dtype='float64'),
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])
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elif algo == "SVTR":
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specs.append([
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InputSpec(
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[None, config["Global"]["max_text_length"]], dtype='int64'),
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InputSpec(
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[None], dtype='int64')
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])
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model = to_static(model, input_spec=specs)
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logger.info("Successfully to apply @to_static with specs: {}".format(specs))
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return model
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@@ -0,0 +1,118 @@
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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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@@ -0,0 +1,60 @@
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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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from .base_model import BaseModel
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from ppocr.utils.save_load import load_pretrained_params
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__all__ = ['DistillationModel']
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class DistillationModel(nn.Layer):
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def __init__(self, config):
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"""
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the module for OCR distillation.
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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().__init__()
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self.model_list = []
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self.model_name_list = []
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for key in config["Models"]:
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model_config = config["Models"][key]
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freeze_params = False
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pretrained = None
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if "freeze_params" in model_config:
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freeze_params = model_config.pop("freeze_params")
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if "pretrained" in model_config:
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pretrained = model_config.pop("pretrained")
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model = BaseModel(model_config)
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if pretrained is not None:
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load_pretrained_params(model, pretrained)
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if freeze_params:
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for param in model.parameters():
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param.trainable = False
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self.model_list.append(self.add_sublayer(key, model))
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self.model_name_list.append(key)
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def forward(self, x, data=None):
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result_dict = dict()
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for idx, model_name in enumerate(self.model_name_list):
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result_dict[model_name] = self.model_list[idx](x, data)
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return result_dict
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