import torch.nn.functional as F
from mmcv.cnn import xavier_init
from torch import nn
from mmdet.models.builder import NECKS
class FPEM(nn.Module):
"""FPN-like feature fusion module in PANet."""
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):
# upsample
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))
# downsample
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:]) + y
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super().__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
[docs]@NECKS.register_module()
class FPEM_FFM(nn.Module):
"""This code is from https://github.com/WenmuZhou/PAN.pytorch."""
def __init__(self,
in_channels,
conv_out=128,
fpem_repeat=2,
align_corners=False):
super().__init__()
# reduce layers
self.reduce_conv_c2 = nn.Sequential(
nn.Conv2d(
in_channels=in_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=in_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=in_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=in_channels[3],
out_channels=conv_out,
kernel_size=1), nn.BatchNorm2d(conv_out), nn.ReLU())
self.align_corners = align_corners
self.fpems = nn.ModuleList()
for _ in range(fpem_repeat):
self.fpems.append(FPEM(conv_out))
[docs] def init_weights(self):
"""Initialize the weights of FPN module."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution='uniform')
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',
align_corners=self.align_corners)
c4 = F.interpolate(
c4_ffm,
c2_ffm.size()[-2:],
mode='bilinear',
align_corners=self.align_corners)
c3 = F.interpolate(
c3_ffm,
c2_ffm.size()[-2:],
mode='bilinear',
align_corners=self.align_corners)
outs = [c2_ffm, c3, c4, c5]
return tuple(outs)