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- # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
- #
- # 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
- import math
- import paddle
- from paddle import nn
- import paddle.nn.functional as F
- from paddle import ParamAttr
- class ConvBNLayer(nn.Layer):
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- groups=1,
- if_act=True,
- act=None,
- name=None):
- super(ConvBNLayer, self).__init__()
- self.if_act = if_act
- self.act = act
- self.conv = nn.Conv2D(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- groups=groups,
- weight_attr=ParamAttr(name=name + '_weights'),
- bias_attr=False)
- self.bn = nn.BatchNorm(
- num_channels=out_channels,
- act=act,
- param_attr=ParamAttr(name="bn_" + name + "_scale"),
- bias_attr=ParamAttr(name="bn_" + name + "_offset"),
- moving_mean_name="bn_" + name + "_mean",
- moving_variance_name="bn_" + name + "_variance",
- use_global_stats=False)
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- return x
- class PGHead(nn.Layer):
- """
- """
- def __init__(self,
- in_channels,
- character_dict_path='ppocr/utils/ic15_dict.txt',
- **kwargs):
- super(PGHead, self).__init__()
- # get character_length
- with open(character_dict_path, "rb") as fin:
- lines = fin.readlines()
- character_length = len(lines) + 1
- self.conv_f_score1 = ConvBNLayer(
- in_channels=in_channels,
- out_channels=64,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_score{}".format(1))
- self.conv_f_score2 = ConvBNLayer(
- in_channels=64,
- out_channels=64,
- kernel_size=3,
- stride=1,
- padding=1,
- act='relu',
- name="conv_f_score{}".format(2))
- self.conv_f_score3 = ConvBNLayer(
- in_channels=64,
- out_channels=128,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_score{}".format(3))
- self.conv1 = nn.Conv2D(
- in_channels=128,
- out_channels=1,
- kernel_size=3,
- stride=1,
- padding=1,
- groups=1,
- weight_attr=ParamAttr(name="conv_f_score{}".format(4)),
- bias_attr=False)
- self.conv_f_boder1 = ConvBNLayer(
- in_channels=in_channels,
- out_channels=64,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_boder{}".format(1))
- self.conv_f_boder2 = ConvBNLayer(
- in_channels=64,
- out_channels=64,
- kernel_size=3,
- stride=1,
- padding=1,
- act='relu',
- name="conv_f_boder{}".format(2))
- self.conv_f_boder3 = ConvBNLayer(
- in_channels=64,
- out_channels=128,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_boder{}".format(3))
- self.conv2 = nn.Conv2D(
- in_channels=128,
- out_channels=4,
- kernel_size=3,
- stride=1,
- padding=1,
- groups=1,
- weight_attr=ParamAttr(name="conv_f_boder{}".format(4)),
- bias_attr=False)
- self.conv_f_char1 = ConvBNLayer(
- in_channels=in_channels,
- out_channels=128,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_char{}".format(1))
- self.conv_f_char2 = ConvBNLayer(
- in_channels=128,
- out_channels=128,
- kernel_size=3,
- stride=1,
- padding=1,
- act='relu',
- name="conv_f_char{}".format(2))
- self.conv_f_char3 = ConvBNLayer(
- in_channels=128,
- out_channels=256,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_char{}".format(3))
- self.conv_f_char4 = ConvBNLayer(
- in_channels=256,
- out_channels=256,
- kernel_size=3,
- stride=1,
- padding=1,
- act='relu',
- name="conv_f_char{}".format(4))
- self.conv_f_char5 = ConvBNLayer(
- in_channels=256,
- out_channels=256,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_char{}".format(5))
- self.conv3 = nn.Conv2D(
- in_channels=256,
- out_channels=character_length,
- kernel_size=3,
- stride=1,
- padding=1,
- groups=1,
- weight_attr=ParamAttr(name="conv_f_char{}".format(6)),
- bias_attr=False)
- self.conv_f_direc1 = ConvBNLayer(
- in_channels=in_channels,
- out_channels=64,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_direc{}".format(1))
- self.conv_f_direc2 = ConvBNLayer(
- in_channels=64,
- out_channels=64,
- kernel_size=3,
- stride=1,
- padding=1,
- act='relu',
- name="conv_f_direc{}".format(2))
- self.conv_f_direc3 = ConvBNLayer(
- in_channels=64,
- out_channels=128,
- kernel_size=1,
- stride=1,
- padding=0,
- act='relu',
- name="conv_f_direc{}".format(3))
- self.conv4 = nn.Conv2D(
- in_channels=128,
- out_channels=2,
- kernel_size=3,
- stride=1,
- padding=1,
- groups=1,
- weight_attr=ParamAttr(name="conv_f_direc{}".format(4)),
- bias_attr=False)
- def forward(self, x, targets=None):
- f_score = self.conv_f_score1(x)
- f_score = self.conv_f_score2(f_score)
- f_score = self.conv_f_score3(f_score)
- f_score = self.conv1(f_score)
- f_score = F.sigmoid(f_score)
- # f_border
- f_border = self.conv_f_boder1(x)
- f_border = self.conv_f_boder2(f_border)
- f_border = self.conv_f_boder3(f_border)
- f_border = self.conv2(f_border)
- f_char = self.conv_f_char1(x)
- f_char = self.conv_f_char2(f_char)
- f_char = self.conv_f_char3(f_char)
- f_char = self.conv_f_char4(f_char)
- f_char = self.conv_f_char5(f_char)
- f_char = self.conv3(f_char)
- f_direction = self.conv_f_direc1(x)
- f_direction = self.conv_f_direc2(f_direction)
- f_direction = self.conv_f_direc3(f_direction)
- f_direction = self.conv4(f_direction)
- predicts = {}
- predicts['f_score'] = f_score
- predicts['f_border'] = f_border
- predicts['f_char'] = f_char
- predicts['f_direction'] = f_direction
- return predicts
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