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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 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") | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| return x | |
| class DeConvBNLayer(nn.Layer): | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| stride, | |
| padding, | |
| groups=1, | |
| if_act=True, | |
| act=None, | |
| name=None): | |
| super(DeConvBNLayer, self).__init__() | |
| self.if_act = if_act | |
| self.act = act | |
| self.deconv = nn.Conv2DTranspose( | |
| 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") | |
| def forward(self, x): | |
| x = self.deconv(x) | |
| x = self.bn(x) | |
| return x | |
| class EASTFPN(nn.Layer): | |
| def __init__(self, in_channels, model_name, **kwargs): | |
| super(EASTFPN, self).__init__() | |
| self.model_name = model_name | |
| if self.model_name == "large": | |
| self.out_channels = 128 | |
| else: | |
| self.out_channels = 64 | |
| self.in_channels = in_channels[::-1] | |
| self.h1_conv = ConvBNLayer( | |
| in_channels=self.out_channels+self.in_channels[1], | |
| out_channels=self.out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_h_1") | |
| self.h2_conv = ConvBNLayer( | |
| in_channels=self.out_channels+self.in_channels[2], | |
| out_channels=self.out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_h_2") | |
| self.h3_conv = ConvBNLayer( | |
| in_channels=self.out_channels+self.in_channels[3], | |
| out_channels=self.out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_h_3") | |
| self.g0_deconv = DeConvBNLayer( | |
| in_channels=self.in_channels[0], | |
| out_channels=self.out_channels, | |
| kernel_size=4, | |
| stride=2, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_g_0") | |
| self.g1_deconv = DeConvBNLayer( | |
| in_channels=self.out_channels, | |
| out_channels=self.out_channels, | |
| kernel_size=4, | |
| stride=2, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_g_1") | |
| self.g2_deconv = DeConvBNLayer( | |
| in_channels=self.out_channels, | |
| out_channels=self.out_channels, | |
| kernel_size=4, | |
| stride=2, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_g_2") | |
| self.g3_conv = ConvBNLayer( | |
| in_channels=self.out_channels, | |
| out_channels=self.out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| if_act=True, | |
| act='relu', | |
| name="unet_g_3") | |
| def forward(self, x): | |
| f = x[::-1] | |
| h = f[0] | |
| g = self.g0_deconv(h) | |
| h = paddle.concat([g, f[1]], axis=1) | |
| h = self.h1_conv(h) | |
| g = self.g1_deconv(h) | |
| h = paddle.concat([g, f[2]], axis=1) | |
| h = self.h2_conv(h) | |
| g = self.g2_deconv(h) | |
| h = paddle.concat([g, f[3]], axis=1) | |
| h = self.h3_conv(h) | |
| g = self.g3_conv(h) | |
| return g |