AIStoryBoard/python/PaddleOCR-main/ppocr/modeling/heads/e2e_pg_head.py

# 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