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# -*- coding: utf-8 -*-
import torch
from torch import nn
import torch.nn.functional as F
class FPN(nn.Module):
def __init__(self, backbone_out_channels, **kwargs):
"""
:param backbone_out_channels: 基础网络输出的维度
:param kwargs:
"""
super().__init__()
# result_num = kwargs.get('result_num', 6)
inplace = True
conv_out = 256
# reduce layers
self.reduce_conv_c2 = nn.Sequential(
nn.Conv2d(backbone_out_channels[0], conv_out, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
self.reduce_conv_c3 = nn.Sequential(
nn.Conv2d(backbone_out_channels[1], conv_out, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
self.reduce_conv_c4 = nn.Sequential(
nn.Conv2d(backbone_out_channels[2], conv_out, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
self.reduce_conv_c5 = nn.Sequential(
nn.Conv2d(backbone_out_channels[3], conv_out, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
# Smooth layers
self.smooth_p4 = nn.Sequential(
nn.Conv2d(conv_out, conv_out, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
self.smooth_p3 = nn.Sequential(
nn.Conv2d(conv_out, conv_out, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
self.smooth_p2 = nn.Sequential(
nn.Conv2d(conv_out, conv_out, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
self.conv = nn.Sequential(
nn.Conv2d(conv_out * 4, conv_out, kernel_size=3, padding=1, stride=1),
nn.BatchNorm2d(conv_out),
nn.ReLU(inplace=inplace)
)
# self.out_conv = nn.Conv2d(conv_out, result_num, kernel_size=1, stride=1)
self.pred_conv = nn.Sequential(
nn.Conv2d(conv_out, 2, kernel_size=1, stride=1, padding=0),
nn.Sigmoid()
)
def forward(self, x):
c2, c3, c4, c5 = x
# Top-down
p5 = self.reduce_conv_c5(c5)
p4 = self._upsample_add(p5, self.reduce_conv_c4(c4))
p4 = self.smooth_p4(p4)
p3 = self._upsample_add(p4, self.reduce_conv_c3(c3))
p3 = self.smooth_p3(p3)
p2 = self._upsample_add(p3, self.reduce_conv_c2(c2))
p2 = self.smooth_p2(p2)
x = self._upsample_cat(p2, p3, p4, p5)
x = self.conv(x)
# x = self.out_conv(x)
x = self.pred_conv(x)
return x
def _upsample_add(self, x, y):
return F.interpolate(x, size=y.size()[2:], mode='bilinear', align_corners=True) + y
def _upsample_cat(self, p2, p3, p4, p5):
h, w = p2.size()[2:]
p3 = F.interpolate(p3, size=(h, w), mode='bilinear', align_corners=True)
p4 = F.interpolate(p4, size=(h, w), mode='bilinear', align_corners=True)
p5 = F.interpolate(p5, size=(h, w), mode='bilinear', align_corners=True)
return torch.cat([p2, p3, p4, p5], dim=1)
class FPEM_FFM(nn.Module):
def __init__(self, backbone_out_channels, **kwargs):
"""
PANnet
:param backbone_out_channels: 基础网络输出的维度
"""
super().__init__()
fpem_repeat = kwargs.get('fpem_repeat', 2)
conv_out = 128
# reduce layers
self.reduce_conv_c2 = nn.Sequential(
nn.Conv2d(in_channels=backbone_out_channels[0], out_channels=conv_out, kernel_size=1),
nn.BatchNorm2d(conv_out),
nn.ReLU()
)
self.reduce_conv_c3 = nn.Sequential(
nn.Conv2d(in_channels=backbone_out_channels[1], out_channels=conv_out, kernel_size=1),
nn.BatchNorm2d(conv_out),
nn.ReLU()
)
self.reduce_conv_c4 = nn.Sequential(
nn.Conv2d(in_channels=backbone_out_channels[2], out_channels=conv_out, kernel_size=1),
nn.BatchNorm2d(conv_out),
nn.ReLU()
)
self.reduce_conv_c5 = nn.Sequential(
nn.Conv2d(in_channels=backbone_out_channels[3], out_channels=conv_out, kernel_size=1),
nn.BatchNorm2d(conv_out),
nn.ReLU()
)
self.fpems = nn.ModuleList()
for i in range(fpem_repeat):
self.fpems.append(FPEM(conv_out))
self.out_conv = nn.Conv2d(in_channels=conv_out * 4, out_channels=6, kernel_size=1)
def forward(self, x):
c2, c3, c4, c5 = x
# reduce channel
c2 = self.reduce_conv_c2(c2)
c3 = self.reduce_conv_c3(c3)
c4 = self.reduce_conv_c4(c4)
c5 = self.reduce_conv_c5(c5)
# FPEM
for i, fpem in enumerate(self.fpems):
c2, c3, c4, c5 = fpem(c2, c3, c4, c5)
if i == 0:
c2_ffm = c2
c3_ffm = c3
c4_ffm = c4
c5_ffm = c5
else:
c2_ffm += c2
c3_ffm += c3
c4_ffm += c4
c5_ffm += c5
# FFM
c5 = F.interpolate(c5_ffm, c2_ffm.size()[-2:], mode='bilinear')
c4 = F.interpolate(c4_ffm, c2_ffm.size()[-2:], mode='bilinear')
c3 = F.interpolate(c3_ffm, c2_ffm.size()[-2:], mode='bilinear')
Fy = torch.cat([c2_ffm, c3, c4, c5], dim=1)
y = self.out_conv(Fy)
return y
class FPEM(nn.Module):
def __init__(self, in_channels=128):
super().__init__()
self.up_add1 = SeparableConv2d(in_channels, in_channels, 1)
self.up_add2 = SeparableConv2d(in_channels, in_channels, 1)
self.up_add3 = SeparableConv2d(in_channels, in_channels, 1)
self.down_add1 = SeparableConv2d(in_channels, in_channels, 2)
self.down_add2 = SeparableConv2d(in_channels, in_channels, 2)
self.down_add3 = SeparableConv2d(in_channels, in_channels, 2)
def forward(self, c2, c3, c4, c5):
# up阶段
c4 = self.up_add1(self._upsample_add(c5, c4))
c3 = self.up_add2(self._upsample_add(c4, c3))
c2 = self.up_add3(self._upsample_add(c3, c2))
# down 阶段
c3 = self.down_add1(self._upsample_add(c3, c2))
c4 = self.down_add2(self._upsample_add(c4, c3))
c5 = self.down_add3(self._upsample_add(c5, c4))
return c2, c3, c4, c5
def _upsample_add(self, x, y):
return F.interpolate(x, size=y.size()[2:], mode='bilinear') + y
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(SeparableConv2d, self).__init__()
self.depthwise_conv = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, padding=1,
stride=stride, groups=in_channels)
self.pointwise_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self, x):
x = self.depthwise_conv(x)
x = self.pointwise_conv(x)
x = self.bn(x)
x = self.relu(x)
return x
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