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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. | |
| """ | |
| This code is refer from: | |
| https://github.com/whai362/PSENet/blob/python3/models/neck/fpn.py | |
| """ | |
| import paddle.nn as nn | |
| import paddle | |
| import math | |
| import paddle.nn.functional as F | |
| class Conv_BN_ReLU(nn.Layer): | |
| def __init__(self, | |
| in_planes, | |
| out_planes, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0): | |
| super(Conv_BN_ReLU, self).__init__() | |
| self.conv = nn.Conv2D( | |
| in_planes, | |
| out_planes, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| bias_attr=False) | |
| self.bn = nn.BatchNorm2D(out_planes, momentum=0.1) | |
| self.relu = nn.ReLU() | |
| for m in self.sublayers(): | |
| if isinstance(m, nn.Conv2D): | |
| n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels | |
| m.weight = paddle.create_parameter( | |
| shape=m.weight.shape, | |
| dtype='float32', | |
| default_initializer=paddle.nn.initializer.Normal( | |
| 0, math.sqrt(2. / n))) | |
| elif isinstance(m, nn.BatchNorm2D): | |
| m.weight = paddle.create_parameter( | |
| shape=m.weight.shape, | |
| dtype='float32', | |
| default_initializer=paddle.nn.initializer.Constant(1.0)) | |
| m.bias = paddle.create_parameter( | |
| shape=m.bias.shape, | |
| dtype='float32', | |
| default_initializer=paddle.nn.initializer.Constant(0.0)) | |
| def forward(self, x): | |
| return self.relu(self.bn(self.conv(x))) | |
| class FPN(nn.Layer): | |
| def __init__(self, in_channels, out_channels): | |
| super(FPN, self).__init__() | |
| # Top layer | |
| self.toplayer_ = Conv_BN_ReLU( | |
| in_channels[3], out_channels, kernel_size=1, stride=1, padding=0) | |
| # Lateral layers | |
| self.latlayer1_ = Conv_BN_ReLU( | |
| in_channels[2], out_channels, kernel_size=1, stride=1, padding=0) | |
| self.latlayer2_ = Conv_BN_ReLU( | |
| in_channels[1], out_channels, kernel_size=1, stride=1, padding=0) | |
| self.latlayer3_ = Conv_BN_ReLU( | |
| in_channels[0], out_channels, kernel_size=1, stride=1, padding=0) | |
| # Smooth layers | |
| self.smooth1_ = Conv_BN_ReLU( | |
| out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.smooth2_ = Conv_BN_ReLU( | |
| out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.smooth3_ = Conv_BN_ReLU( | |
| out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.out_channels = out_channels * 4 | |
| for m in self.sublayers(): | |
| if isinstance(m, nn.Conv2D): | |
| n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels | |
| m.weight = paddle.create_parameter( | |
| shape=m.weight.shape, | |
| dtype='float32', | |
| default_initializer=paddle.nn.initializer.Normal( | |
| 0, math.sqrt(2. / n))) | |
| elif isinstance(m, nn.BatchNorm2D): | |
| m.weight = paddle.create_parameter( | |
| shape=m.weight.shape, | |
| dtype='float32', | |
| default_initializer=paddle.nn.initializer.Constant(1.0)) | |
| m.bias = paddle.create_parameter( | |
| shape=m.bias.shape, | |
| dtype='float32', | |
| default_initializer=paddle.nn.initializer.Constant(0.0)) | |
| def _upsample(self, x, scale=1): | |
| return F.upsample(x, scale_factor=scale, mode='bilinear') | |
| def _upsample_add(self, x, y, scale=1): | |
| return F.upsample(x, scale_factor=scale, mode='bilinear') + y | |
| def forward(self, x): | |
| f2, f3, f4, f5 = x | |
| p5 = self.toplayer_(f5) | |
| f4 = self.latlayer1_(f4) | |
| p4 = self._upsample_add(p5, f4, 2) | |
| p4 = self.smooth1_(p4) | |
| f3 = self.latlayer2_(f3) | |
| p3 = self._upsample_add(p4, f3, 2) | |
| p3 = self.smooth2_(p3) | |
| f2 = self.latlayer3_(f2) | |
| p2 = self._upsample_add(p3, f2, 2) | |
| p2 = self.smooth3_(p2) | |
| p3 = self._upsample(p3, 2) | |
| p4 = self._upsample(p4, 4) | |
| p5 = self._upsample(p5, 8) | |
| fuse = paddle.concat([p2, p3, p4, p5], axis=1) | |
| return fuse | |