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