mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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5cac747656
* support min_area_rect crop * add check_install * fix requirement.txt * fix check_install * add lanms-neo for drrg * fix * fix doc * fix
111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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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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import math
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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def get_bias_attr(k):
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stdv = 1.0 / math.sqrt(k * 1.0)
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initializer = paddle.nn.initializer.Uniform(-stdv, stdv)
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bias_attr = ParamAttr(initializer=initializer)
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return bias_attr
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class Head(nn.Layer):
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def __init__(self, in_channels, kernel_list=[3, 2, 2], **kwargs):
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super(Head, self).__init__()
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self.conv1 = nn.Conv2D(
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in_channels=in_channels,
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out_channels=in_channels // 4,
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kernel_size=kernel_list[0],
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padding=int(kernel_list[0] // 2),
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weight_attr=ParamAttr(),
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bias_attr=False)
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self.conv_bn1 = nn.BatchNorm(
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num_channels=in_channels // 4,
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param_attr=ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=1.0)),
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bias_attr=ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=1e-4)),
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act='relu')
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self.conv2 = nn.Conv2DTranspose(
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in_channels=in_channels // 4,
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out_channels=in_channels // 4,
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kernel_size=kernel_list[1],
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stride=2,
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weight_attr=ParamAttr(
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initializer=paddle.nn.initializer.KaimingUniform()),
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bias_attr=get_bias_attr(in_channels // 4))
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self.conv_bn2 = nn.BatchNorm(
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num_channels=in_channels // 4,
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param_attr=ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=1.0)),
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bias_attr=ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=1e-4)),
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act="relu")
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self.conv3 = nn.Conv2DTranspose(
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in_channels=in_channels // 4,
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out_channels=1,
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kernel_size=kernel_list[2],
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stride=2,
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weight_attr=ParamAttr(
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initializer=paddle.nn.initializer.KaimingUniform()),
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bias_attr=get_bias_attr(in_channels // 4), )
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv_bn1(x)
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x = self.conv2(x)
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x = self.conv_bn2(x)
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x = self.conv3(x)
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x = F.sigmoid(x)
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return x
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class DBHead(nn.Layer):
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"""
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Differentiable Binarization (DB) for text detection:
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see https://arxiv.org/abs/1911.08947
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args:
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params(dict): super parameters for build DB network
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"""
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def __init__(self, in_channels, k=50, **kwargs):
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super(DBHead, self).__init__()
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self.k = k
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self.binarize = Head(in_channels, **kwargs)
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self.thresh = Head(in_channels, **kwargs)
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def step_function(self, x, y):
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return paddle.reciprocal(1 + paddle.exp(-self.k * (x - y)))
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def forward(self, x, targets=None):
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shrink_maps = self.binarize(x)
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if not self.training:
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return {'maps': shrink_maps}
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threshold_maps = self.thresh(x)
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binary_maps = self.step_function(shrink_maps, threshold_maps)
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y = paddle.concat([shrink_maps, threshold_maps, binary_maps], axis=1)
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return {'maps': y}
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