mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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122 lines
3.6 KiB
Python
122 lines
3.6 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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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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groups=1,
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if_act=True,
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act=None,
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name=None):
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super(ConvBNLayer, self).__init__()
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self.if_act = if_act
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self.act = act
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self.conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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weight_attr=ParamAttr(name=name + '_weights'),
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bias_attr=False)
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self.bn = nn.BatchNorm(
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num_channels=out_channels,
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act=act,
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param_attr=ParamAttr(name="bn_" + name + "_scale"),
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bias_attr=ParamAttr(name="bn_" + name + "_offset"),
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moving_mean_name="bn_" + name + "_mean",
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moving_variance_name="bn_" + name + "_variance")
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return x
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class EASTHead(nn.Layer):
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"""
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"""
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def __init__(self, in_channels, model_name, **kwargs):
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super(EASTHead, self).__init__()
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self.model_name = model_name
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if self.model_name == "large":
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num_outputs = [128, 64, 1, 8]
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else:
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num_outputs = [64, 32, 1, 8]
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self.det_conv1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=num_outputs[0],
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kernel_size=3,
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stride=1,
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padding=1,
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if_act=True,
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act='relu',
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name="det_head1")
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self.det_conv2 = ConvBNLayer(
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in_channels=num_outputs[0],
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out_channels=num_outputs[1],
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kernel_size=3,
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stride=1,
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padding=1,
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if_act=True,
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act='relu',
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name="det_head2")
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self.score_conv = ConvBNLayer(
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in_channels=num_outputs[1],
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out_channels=num_outputs[2],
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kernel_size=1,
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stride=1,
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padding=0,
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if_act=False,
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act=None,
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name="f_score")
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self.geo_conv = ConvBNLayer(
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in_channels=num_outputs[1],
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out_channels=num_outputs[3],
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kernel_size=1,
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stride=1,
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padding=0,
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if_act=False,
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act=None,
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name="f_geo")
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def forward(self, x, targets=None):
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f_det = self.det_conv1(x)
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f_det = self.det_conv2(f_det)
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f_score = self.score_conv(f_det)
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f_score = F.sigmoid(f_score)
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f_geo = self.geo_conv(f_det)
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f_geo = (F.sigmoid(f_geo) - 0.5) * 2 * 800
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pred = {'f_score': f_score, 'f_geo': f_geo}
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return pred
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