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111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
# copyright (c) 2021 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 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 TableFPN(nn.Layer):
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def __init__(self, in_channels, out_channels, **kwargs):
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super(TableFPN, self).__init__()
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self.out_channels = 512
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weight_attr = paddle.nn.initializer.KaimingUniform()
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self.in2_conv = nn.Conv2D(
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in_channels=in_channels[0],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.in3_conv = nn.Conv2D(
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in_channels=in_channels[1],
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out_channels=self.out_channels,
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kernel_size=1,
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stride = 1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.in4_conv = nn.Conv2D(
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in_channels=in_channels[2],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.in5_conv = nn.Conv2D(
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in_channels=in_channels[3],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p5_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p4_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p3_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p2_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.fuse_conv = nn.Conv2D(
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in_channels=self.out_channels * 4,
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out_channels=512,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False)
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def forward(self, x):
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c2, c3, c4, c5 = x
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in5 = self.in5_conv(c5)
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in4 = self.in4_conv(c4)
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in3 = self.in3_conv(c3)
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in2 = self.in2_conv(c2)
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out4 = in4 + F.upsample(
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in5, size=in4.shape[2:4], mode="nearest", align_mode=1) # 1/16
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out3 = in3 + F.upsample(
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out4, size=in3.shape[2:4], mode="nearest", align_mode=1) # 1/8
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out2 = in2 + F.upsample(
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out3, size=in2.shape[2:4], mode="nearest", align_mode=1) # 1/4
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p4 = F.upsample(out4, size=in5.shape[2:4], mode="nearest", align_mode=1)
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p3 = F.upsample(out3, size=in5.shape[2:4], mode="nearest", align_mode=1)
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p2 = F.upsample(out2, size=in5.shape[2:4], mode="nearest", align_mode=1)
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fuse = paddle.concat([in5, p4, p3, p2], axis=1)
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fuse_conv = self.fuse_conv(fuse) * 0.005
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return [c5 + fuse_conv]
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