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# copyright (c) 2019 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
def get_bias_attr(k):
stdv = 1.0 / math.sqrt(k * 1.0)
initializer = paddle.nn.initializer.Uniform(-stdv, stdv)
bias_attr = ParamAttr(initializer=initializer)
return bias_attr
class Head(nn.Layer):
def __init__(self, in_channels, kernel_list=[3, 2, 2], **kwargs):
super(Head, self).__init__()
self.conv1 = nn.Conv2D(
in_channels=in_channels,
out_channels=in_channels // 4,
kernel_size=kernel_list[0],
padding=int(kernel_list[0] // 2),
weight_attr=ParamAttr(),
bias_attr=False)
self.conv_bn1 = nn.BatchNorm(
num_channels=in_channels // 4,
param_attr=ParamAttr(
initializer=paddle.nn.initializer.Constant(value=1.0)),
bias_attr=ParamAttr(
initializer=paddle.nn.initializer.Constant(value=1e-4)),
act='relu')
self.conv2 = nn.Conv2DTranspose(
in_channels=in_channels // 4,
out_channels=in_channels // 4,
kernel_size=kernel_list[1],
stride=2,
weight_attr=ParamAttr(
initializer=paddle.nn.initializer.KaimingUniform()),
bias_attr=get_bias_attr(in_channels // 4))
self.conv_bn2 = nn.BatchNorm(
num_channels=in_channels // 4,
param_attr=ParamAttr(
initializer=paddle.nn.initializer.Constant(value=1.0)),
bias_attr=ParamAttr(
initializer=paddle.nn.initializer.Constant(value=1e-4)),
act="relu")
self.conv3 = nn.Conv2DTranspose(
in_channels=in_channels // 4,
out_channels=1,
kernel_size=kernel_list[2],
stride=2,
weight_attr=ParamAttr(
initializer=paddle.nn.initializer.KaimingUniform()),
bias_attr=get_bias_attr(in_channels // 4), )
def forward(self, x):
x = self.conv1(x)
x = self.conv_bn1(x)
x = self.conv2(x)
x = self.conv_bn2(x)
x = self.conv3(x)
x = F.sigmoid(x)
return x
class DBHead(nn.Layer):
"""
Differentiable Binarization (DB) for text detection:
see https://arxiv.org/abs/1911.08947
args:
params(dict): super parameters for build DB network
"""
def __init__(self, in_channels, k=50, **kwargs):
super(DBHead, self).__init__()
self.k = k
self.binarize = Head(in_channels, **kwargs)
self.thresh = Head(in_channels, **kwargs)
def step_function(self, x, y):
return paddle.reciprocal(1 + paddle.exp(-self.k * (x - y)))
def forward(self, x, targets=None):
shrink_maps = self.binarize(x)
if not self.training:
return {'maps': shrink_maps}
threshold_maps = self.thresh(x)
binary_maps = self.step_function(shrink_maps, threshold_maps)
y = paddle.concat([shrink_maps, threshold_maps, binary_maps], axis=1)
return {'maps': y}
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