254 lines
7.6 KiB
Python
254 lines
7.6 KiB
Python
# 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, **kwargs):
|
|
super(PGHead, self).__init__()
|
|
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=37,
|
|
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
|