139 lines
4.9 KiB
Python
139 lines
4.9 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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"""
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This code is refer from:
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https://github.com/whai362/PSENet/blob/python3/models/neck/fpn.py
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"""
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import paddle.nn as nn
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import paddle
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import math
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import paddle.nn.functional as F
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class Conv_BN_ReLU(nn.Layer):
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def __init__(self,
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in_planes,
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out_planes,
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kernel_size=1,
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stride=1,
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padding=0):
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super(Conv_BN_ReLU, self).__init__()
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self.conv = nn.Conv2D(
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in_planes,
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out_planes,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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bias_attr=False)
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self.bn = nn.BatchNorm2D(out_planes, momentum=0.1)
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self.relu = nn.ReLU()
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
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m.weight = paddle.create_parameter(
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shape=m.weight.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Normal(
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0, math.sqrt(2. / n)))
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elif isinstance(m, nn.BatchNorm2D):
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m.weight = paddle.create_parameter(
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shape=m.weight.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(1.0))
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m.bias = paddle.create_parameter(
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shape=m.bias.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(0.0))
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def forward(self, x):
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return self.relu(self.bn(self.conv(x)))
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class FPN(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super(FPN, self).__init__()
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# Top layer
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self.toplayer_ = Conv_BN_ReLU(
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in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)
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# Lateral layers
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self.latlayer1_ = Conv_BN_ReLU(
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in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)
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self.latlayer2_ = Conv_BN_ReLU(
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in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)
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self.latlayer3_ = Conv_BN_ReLU(
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in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)
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# Smooth layers
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self.smooth1_ = Conv_BN_ReLU(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.smooth2_ = Conv_BN_ReLU(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.smooth3_ = Conv_BN_ReLU(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.out_channels = out_channels * 4
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
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m.weight = paddle.create_parameter(
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shape=m.weight.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Normal(
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0, math.sqrt(2. / n)))
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elif isinstance(m, nn.BatchNorm2D):
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m.weight = paddle.create_parameter(
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shape=m.weight.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(1.0))
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m.bias = paddle.create_parameter(
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shape=m.bias.shape,
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(0.0))
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def _upsample(self, x, scale=1):
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return F.upsample(x, scale_factor=scale, mode='bilinear')
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def _upsample_add(self, x, y, scale=1):
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return F.upsample(x, scale_factor=scale, mode='bilinear') + y
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def forward(self, x):
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f2, f3, f4, f5 = x
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p5 = self.toplayer_(f5)
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f4 = self.latlayer1_(f4)
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p4 = self._upsample_add(p5, f4, 2)
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p4 = self.smooth1_(p4)
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f3 = self.latlayer2_(f3)
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p3 = self._upsample_add(p4, f3, 2)
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p3 = self.smooth2_(p3)
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f2 = self.latlayer3_(f2)
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p2 = self._upsample_add(p3, f2, 2)
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p2 = self.smooth3_(p2)
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p3 = self._upsample(p3, 2)
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p4 = self._upsample(p4, 4)
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p5 = self._upsample(p5, 8)
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fuse = paddle.concat([p2, p3, p4, p5], axis=1)
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return fuse
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