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net.py
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net.py
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#!/usr/bin/python3
#coding=utf-8
from lib.origin.resnet import resnet50
import timm
from GCF import *
def weight_init(module):
for n, m in module.named_children():
print('initialize: '+n)
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, BasicConv2d):
None
elif isinstance(m, nn.AdaptiveAvgPool2d):
None
elif isinstance(m, GGD):
None
else:
m.initialize()
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.sharedMLP = nn.Sequential(
nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False), nn.ReLU(),
nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False))
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avgout = self.sharedMLP(self.avg_pool(x))
maxout = self.sharedMLP(self.max_pool(x))
return self.sigmoid(avgout + maxout)
def initialize(self):
print()
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3,7), "kernel size must be 3 or 7"
padding = 3 if kernel_size == 7 else 1
self.conv = nn.Conv2d(2,1,kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avgout = torch.mean(x, dim=1, keepdim=True)
maxout, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avgout, maxout], dim=1)
x = self.conv(x)
return self.sigmoid(x)
def initialize(self):
print()
class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=(3*dilation-1)//2, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*4)
self.downsample = downsample
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)), inplace=True)
out = F.relu(self.bn2(self.conv2(out)), inplace=True)
out = self.bn3(self.conv3(out))
if self.downsample is not None:
residual = self.downsample(x)
return F.relu(out+residual, inplace=True)
class ResNet(nn.Module):
def __init__(self):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self.make_layer( 64, 3, stride=1, dilation=1)
self.layer2 = self.make_layer(128, 4, stride=2, dilation=1)
self.layer3 = self.make_layer(256, 6, stride=2, dilation=1)
self.layer4 = self.make_layer(512, 3, stride=2, dilation=1)
self.initialize()
def make_layer(self, planes, blocks, stride, dilation):
downsample = None
if stride != 1 or self.inplanes != planes*4:
downsample = nn.Sequential(nn.Conv2d(self.inplanes, planes*4, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes*4))
layers = [Bottleneck(self.inplanes, planes, stride, downsample, dilation=dilation)]
self.inplanes = planes*4
for _ in range(1, blocks):
layers.append(Bottleneck(self.inplanes, planes, dilation=dilation))
return nn.Sequential(*layers)
def forward(self, x):
out1 = F.relu(self.bn1(self.conv1(x)), inplace=True)
out1 = F.max_pool2d(out1, kernel_size=3, stride=2, padding=1)
out2 = self.layer1(out1)
out3 = self.layer2(out2)
out4 = self.layer3(out3)
out5 = self.layer4(out4)
return out1, out2, out3, out4, out5
def initialize(self):
self.load_state_dict(torch.load('resnet50-19c8e357.pth'), strict=False)
class ResNet_4(nn.Module):
def __init__(self):
super(ResNet_4, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(4, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self.make_layer( 64, 3, stride=1, dilation=1)
self.layer2 = self.make_layer(128, 4, stride=2, dilation=1)
self.layer3 = self.make_layer(256, 6, stride=2, dilation=1)
self.layer4 = self.make_layer(512, 3, stride=2, dilation=1)
self.initialize()
def make_layer(self, planes, blocks, stride, dilation):
downsample = None
if stride != 1 or self.inplanes != planes*4:
downsample = nn.Sequential(nn.Conv2d(self.inplanes, planes*4, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes*4))
layers = [Bottleneck(self.inplanes, planes, stride, downsample, dilation=dilation)]
self.inplanes = planes*4
for _ in range(1, blocks):
layers.append(Bottleneck(self.inplanes, planes, dilation=dilation))
return nn.Sequential(*layers)
def forward(self, x):
out1 = F.relu(self.bn1(self.conv1(x)), inplace=True)
out1 = F.max_pool2d(out1, kernel_size=3, stride=2, padding=1)
out2 = self.layer1(out1)
out3 = self.layer2(out2)
out4 = self.layer3(out3)
out5 = self.layer4(out4)
return out1, out2, out3, out4, out5
def initialize(self):
m = timm.create_model('resnet50', pretrained=True, in_chans=4)
self.load_state_dict(m.state_dict(), strict=False)
class CA(nn.Module):
def __init__(self, in_channel_left, in_channel_down):
super(CA, self).__init__()
self.conv0 = nn.Conv2d(in_channel_left, 256, kernel_size=1, stride=1, padding=0)
self.bn0 = nn.BatchNorm2d(256)
self.conv1 = nn.Conv2d(in_channel_down, 256, kernel_size=1, stride=1, padding=0)
self.conv2 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
def forward(self, left, down):
left = F.relu(self.bn0(self.conv0(left)), inplace=True) #256
down = down.mean(dim=(2,3), keepdim=True)
down = F.relu(self.conv1(down), inplace=True)
down = torch.sigmoid(self.conv2(down))
return left * down
def initialize(self):
weight_init(self)
class SRM(nn.Module):
def __init__(self, in_channel):
super(SRM, self).__init__()
self.conv1 = nn.Conv2d(in_channel, 256, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(256)
self.conv2 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
def forward(self, x):
out1 = F.relu(self.bn1(self.conv1(x)), inplace=True) #256
out2 = self.conv2(out1)
w, b = out2[:, :256, :, :], out2[:, 256:, :, :]
return F.relu(w * out1 + b, inplace=True)
def initialize(self):
weight_init(self)
""" Blender with Guidance and Aggregation Mechanisms """
class BGA(nn.Module):
def __init__(self, in_channel_left, in_channel_down, in_channel_right):
super(BGA, self).__init__()
#self.conv0 = nn.Conv2d(in_channel_left, 256, kernel_size=1, stride=1, padding=0)
self.conv0 = nn.Conv2d(in_channel_left, 256, kernel_size=3, stride=1, padding=1)
self.bn0 = nn.BatchNorm2d(256)
self.conv1 = nn.Conv2d(in_channel_down, 256, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(256)
self.conv2 = nn.Conv2d(in_channel_right, 256, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(256)
self.global_w=GGD(256)
self.sa = SpatialAttention()
self.conv_d1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv_d2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv_l = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(256)
def forward(self, left, down, right): ##left:encoder down:decoder right:global
left = F.relu(self.bn0(self.conv0(left)), inplace=True) #256 channels
down = F.relu(self.bn1(self.conv1(down)), inplace=True) #256 channels
right = F.relu(self.bn2(self.conv2(right)), inplace=True) #256
if left.size()[2:] != right.size()[2:]:
left = F.interpolate(right, size=right.size()[2:], mode='bilinear')
right = self.global_w(right, left)
down_1 = self.conv_d1(down)
if down_1.size()[2:] != right.size()[2:]:
down_1 = F.interpolate(down_1, size=right.size()[2:], mode='bilinear')
z = down_1 * F.relu(right)
w1 = self.conv_l(left)
if w1.size()[2:] != z.size()[2:]:
w1 = F.interpolate(w1, size=z.size()[2:], mode='bilinear')
w1 = self.sa(w1) * w1
z = w1 + z
return F.relu(self.bn3(self.conv3(z)), inplace=True)
def initialize(self):
weight_init(self)
class SA(nn.Module):
def __init__(self, in_channel_left, in_channel_down):
super(SA, self).__init__()
self.conv0 = nn.Conv2d(in_channel_left, 256, kernel_size=3, stride=1, padding=1)
self.bn0 = nn.BatchNorm2d(256)
self.conv2 = nn.Conv2d(in_channel_down, 512, kernel_size=3, stride=1, padding=1)
def forward(self, left, down):
left = F.relu(self.bn0(self.conv0(left)), inplace=True) #256 channels
down_1 = self.conv2(down) #wb
if down_1.size()[2:] != left.size()[2:]:
down_1 = F.interpolate(down_1, size=left.size()[2:], mode='bilinear')
w,b = down_1[:,:256,:,:], down_1[:,256:,:,:]
return F.relu(w*left+b, inplace=True)
def initialize(self):
weight_init(self)
class conv_2nV1(nn.Module):
def __init__(self, in_hc=64, in_lc=256, out_c=64, main=0):
super(conv_2nV1, self).__init__()
self.main = main
mid_c = min(in_hc, in_lc)
self.relu = nn.ReLU(True)
self.h2l_pool = nn.AvgPool2d((2, 2), stride=2)
self.l2h_up = nn.Upsample(scale_factor=2, mode="nearest")
# stage 0
self.h2h_0 = nn.Conv2d(in_hc, mid_c, 3, 1, 1)
self.l2l_0 = nn.Conv2d(in_lc, mid_c, 3, 1, 1)
self.bnh_0 = nn.BatchNorm2d(mid_c)
self.bnl_0 = nn.BatchNorm2d(mid_c)
# stage 1
self.h2h_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.h2l_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2h_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2l_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.bnl_1 = nn.BatchNorm2d(mid_c)
self.bnh_1 = nn.BatchNorm2d(mid_c)
if self.main == 0:
# stage 2
self.h2h_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2h_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.bnh_2 = nn.BatchNorm2d(mid_c)
# stage 3
self.h2h_3 = nn.Conv2d(mid_c, out_c, 3, 1, 1)
self.bnh_3 = nn.BatchNorm2d(out_c)
self.identity = nn.Conv2d(in_hc, out_c, 1)
elif self.main == 1:
# stage 2
self.h2l_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2l_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.bnl_2 = nn.BatchNorm2d(mid_c)
# stage 3
self.l2l_3 = nn.Conv2d(mid_c, out_c, 3, 1, 1)
self.bnl_3 = nn.BatchNorm2d(out_c)
self.identity = nn.Conv2d(in_lc, out_c, 1)
else:
raise NotImplementedError
def forward(self, in_h, in_l):
# stage 0
h = self.relu(self.bnh_0(self.h2h_0(in_h)))
l = self.relu(self.bnl_0(self.l2l_0(in_l)))
# stage 1
h2h = self.h2h_1(h)
h2l = self.h2l_1(self.h2l_pool(h))
l2l = self.l2l_1(l)
l2h = self.l2h_1(self.l2h_up(l))
h = self.relu(self.bnh_1(h2h + l2h))
l = self.relu(self.bnl_1(l2l + h2l))
if self.main == 0:
# stage 2
h2h = self.h2h_2(h)
l2h = self.l2h_2(self.l2h_up(l))
h_fuse = self.relu(self.bnh_2(h2h + l2h))
# stage 3
out = self.relu(self.bnh_3(self.h2h_3(h_fuse)) + self.identity(in_h))
# 这里使用的不是in_h,而是h
elif self.main == 1:
# stage 2
h2l = self.h2l_2(self.h2l_pool(h))
l2l = self.l2l_2(l)
l_fuse = self.relu(self.bnl_2(h2l + l2l))
# stage 3
out = self.relu(self.bnl_3(self.l2l_3(l_fuse)) + self.identity(in_l))
else:
raise NotImplementedError
return out
class conv_3nV1(nn.Module):
def __init__(self, in_hc=64, in_mc=256, in_lc=512, out_c=64):
super(conv_3nV1, self).__init__()
self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
self.downsample = nn.AvgPool2d((2, 2), stride=2)
mid_c = min(in_hc, in_mc, in_lc)
self.relu = nn.ReLU(True)
# stage 0
self.h2h_0 = nn.Conv2d(in_hc, mid_c, 3, 1, 1)
self.m2m_0 = nn.Conv2d(in_mc, mid_c, 3, 1, 1)
self.l2l_0 = nn.Conv2d(in_lc, mid_c, 3, 1, 1)
self.bnh_0 = nn.BatchNorm2d(mid_c)
self.bnm_0 = nn.BatchNorm2d(mid_c)
self.bnl_0 = nn.BatchNorm2d(mid_c)
# stage 1
self.h2h_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.h2m_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.m2h_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.m2m_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.m2l_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2m_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2l_1 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.bnh_1 = nn.BatchNorm2d(mid_c)
self.bnm_1 = nn.BatchNorm2d(mid_c)
self.bnl_1 = nn.BatchNorm2d(mid_c)
# stage 2
self.h2m_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.l2m_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.m2m_2 = nn.Conv2d(mid_c, mid_c, 3, 1, 1)
self.bnm_2 = nn.BatchNorm2d(mid_c)
# stage 3
self.m2m_3 = nn.Conv2d(mid_c, out_c, 3, 1, 1)
self.bnm_3 = nn.BatchNorm2d(out_c)
self.identity = nn.Conv2d(in_mc, out_c, 1)
def forward(self, in_h, in_m, in_l):
# stage 0
h = self.relu(self.bnh_0(self.h2h_0(in_h)))
m = self.relu(self.bnm_0(self.m2m_0(in_m)))
l = self.relu(self.bnl_0(self.l2l_0(in_l)))
# stage 1
h2h = self.h2h_1(h)
m2h = self.m2h_1(self.upsample(m))
h2m = self.h2m_1(self.downsample(h))
m2m = self.m2m_1(m)
l2m = self.l2m_1(self.upsample(l))
m2l = self.m2l_1(self.downsample(m))
l2l = self.l2l_1(l)
h = self.relu(self.bnh_1(h2h + m2h))
m = self.relu(self.bnm_1(h2m + m2m + l2m))
l = self.relu(self.bnl_1(m2l + l2l))
# stage 2
h2m = self.h2m_2(self.downsample(h))
m2m = self.m2m_2(m)
l2m = self.l2m_2(self.upsample(l))
m = self.relu(self.bnm_2(h2m + m2m + l2m))
# stage 3
out = self.relu(self.bnm_3(self.m2m_3(m)) + self.identity(in_m))
return out
class BasicConv2d(nn.Module):
def __init__(
self,
in_planes,
out_planes,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=False,
):
super(BasicConv2d, self).__init__()
self.basicconv = nn.Sequential(
nn.Conv2d(
in_planes,
out_planes,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.basicconv(x)
#MINet AIM
class AIM(nn.Module):
def __init__(self, iC_list, oC_list):
super(AIM, self).__init__()
ic0, ic1, ic2, ic3, ic4 = iC_list
oc0, oc1, oc2, oc3, oc4 = oC_list
self.conv0 = conv_2nV1(in_hc=ic0, in_lc=ic1, out_c=oc0, main=0)
self.conv1 = conv_3nV1(in_hc=ic0, in_mc=ic1, in_lc=ic2, out_c=oc1)
self.conv2 = conv_3nV1(in_hc=ic1, in_mc=ic2, in_lc=ic3, out_c=oc2)
self.conv3 = conv_3nV1(in_hc=ic2, in_mc=ic3, in_lc=ic4, out_c=oc3)
self.conv4 = conv_2nV1(in_hc=ic3, in_lc=ic4, out_c=oc4, main=1)
def forward(self, *xs):
# in_data_2, in_data_4, in_data_8, in_data_16, in_data_32
out_xs = []
out_xs.append(self.conv0(xs[0], xs[1]))
out_xs.append(self.conv1(xs[0], xs[1], xs[2]))
out_xs.append(self.conv2(xs[1], xs[2], xs[3]))
out_xs.append(self.conv3(xs[2], xs[3], xs[4]))
out_xs.append(self.conv4(xs[3], xs[4]))
return out_xs
class SIM(nn.Module):
def cus_sample(self, feat, **kwargs):
"""
:param feat: 输入特征
:param kwargs: size或者scale_factor
"""
assert len(kwargs.keys()) == 1 and list(kwargs.keys())[0] in ["size", "scale_factor"]
return F.interpolate(feat, **kwargs, mode="bilinear", align_corners=False)
def __init__(self, h_C, l_C):
super(SIM, self).__init__()
self.h2l_pool = nn.AvgPool2d((2, 2), stride=2)
self.l2h_up = self.cus_sample
self.h2l_0 = nn.Conv2d(h_C, l_C, 3, 1, 1)
self.h2h_0 = nn.Conv2d(h_C, h_C, 3, 1, 1)
self.bnl_0 = nn.BatchNorm2d(l_C)
self.bnh_0 = nn.BatchNorm2d(h_C)
self.h2h_1 = nn.Conv2d(h_C, h_C, 3, 1, 1)
self.h2l_1 = nn.Conv2d(h_C, l_C, 3, 1, 1)
self.l2h_1 = nn.Conv2d(l_C, h_C, 3, 1, 1)
self.l2l_1 = nn.Conv2d(l_C, l_C, 3, 1, 1)
self.bnl_1 = nn.BatchNorm2d(l_C)
self.bnh_1 = nn.BatchNorm2d(h_C)
self.h2h_2 = nn.Conv2d(h_C, h_C, 3, 1, 1)
self.l2h_2 = nn.Conv2d(l_C, h_C, 3, 1, 1)
self.bnh_2 = nn.BatchNorm2d(h_C)
self.relu = nn.ReLU(True)
def forward(self, x):
h, w = x.shape[2:]
# first conv
x_h = self.relu(self.bnh_0(self.h2h_0(x)))
x_l = self.relu(self.bnl_0(self.h2l_0(self.h2l_pool(x))))
# mid conv
x_h2h = self.h2h_1(x_h)
x_h2l = self.h2l_1(self.h2l_pool(x_h))
x_l2l = self.l2l_1(x_l)
x_l2h = self.l2h_1(self.l2h_up(x_l, size=(h, w)))
x_h = self.relu(self.bnh_1(x_h2h + x_l2h))
x_l = self.relu(self.bnl_1(x_l2l + x_h2l))
# last conv
x_h2h = self.h2h_2(x_h)
x_l2h = self.l2h_2(self.l2h_up(x_l, size=(h, w)))
x_h = self.relu(self.bnh_2(x_h2h + x_l2h))
return x_h + x
class WSLNet_UP(nn.Module):
def __init__(self, cfg):
super(WSLNet_UP, self).__init__()
self.cfg = cfg
self.bkbone = ResNet_4()
self.ca45 = CA(2048, 2048)
self.ca35 = CA(2048, 2048)
self.ca25 = CA(2048, 2048)
self.ca55 = CA(256, 2048)
self.sa55 = SA(2048, 2048)
self.bga45 = BGA(1024, 256, 4096)
self.bga34 = BGA( 512, 256, 4096)
self.bga23 = BGA( 256, 256, 4096)
self.w_global = GGD(2048)
self.srm5 = SRM(256)
self.srm4 = SRM(256)
self.srm3 = SRM(256)
self.srm2 = SRM(256)
self.ca1 = ChannelAttention(128)
self.ca2 = ChannelAttention(512)
self.ca3 = ChannelAttention(1024)
self.ca4 = ChannelAttention(2048)
self.sa = SpatialAttention()
self.downconv1 = nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0)
self.downconv2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
self.downconv3 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0)
self.downconv4 = nn.Conv2d(2048, 1024, kernel_size=1, stride=1, padding=0)
self.linear5 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.linear4 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.linear3 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.linear2 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.initialize()
def forward(self, x, y, down_out1, down_out2, down_out3, down_out4, down_out5):
x = torch.cat((x, y), dim=1)
out1, out2, out3, out4, out5_ = self.bkbone(x)
out5_global = torch.cat((out5_, down_out5), dim=1)
out1_sa = self.sa(down_out1)
out2_sa = self.sa(down_out2)
out3_sa = self.sa(down_out3)
out4_sa = self.sa(down_out4)
out1 = torch.cat((down_out1, out1), dim=1)
out2 = torch.cat((down_out2, out2), dim=1)
out3 = torch.cat((down_out3, out3), dim=1)
out4 = torch.cat((down_out4, out4), dim=1)
out1 = self.ca1(out1) * out1
out1 = out1 * out1_sa + out1
out1 = self.downconv1(out1)
out2 = self.ca2(out2) * out2
out2 = out2 * out2_sa + out2
out2 = self.downconv2(out2)
out3 = self.ca3(out3) * out3
out3 = out3 * out3_sa + out3
out3 = self.downconv3(out3)
out4 = self.ca4(out4) * out4
out4 = out4 * out4_sa + out4
out4 = self.downconv4(out4)
out5_a = self.sa55(out5_, out5_)
out5 = self.ca55(out5_a, out5_)
# out
out5 = self.srm5(out5)
out4 = self.srm4(self.bga45(out4, out5, out5_global))
out3 = self.srm3(self.bga34(out3, out4, out5_global))
out2 = self.srm2(self.bga23(out2, out3, out5_global))
# we use bilinear interpolation instead of transpose convolution
out5 = F.interpolate(self.linear5(out5), size=x.size()[2:], mode='bilinear')
out4 = F.interpolate(self.linear4(out4), size=x.size()[2:], mode='bilinear')
out3 = F.interpolate(self.linear3(out3), size=x.size()[2:], mode='bilinear')
out2 = F.interpolate(self.linear2(out2), size=x.size()[2:], mode='bilinear')
return out2, out3, out4, out5
def initialize(self):
if self.cfg.snapshot:
try:
self.load_state_dict(torch.load(self.cfg.snapshot))
except:
print("Warning: please check the snapshot file:", self.cfg.snapshot)
pass
else:
weight_init(self)
class WSLNet_DOWN(nn.Module):
def __init__(self, cfg):
super(WSLNet_DOWN, self).__init__()
self.cfg = cfg
self.bkbone = ResNet()
self.ca45 = CA(2048, 2048)
self.ca35 = CA(2048, 2048)
self.ca25 = CA(2048, 2048)
self.ca55 = CA(256, 2048)
self.sa55 = SA(2048, 2048)
self.bga45 = BGA(1024, 256, 256)
self.bga34 = BGA( 512, 256, 256)
self.bga23 = BGA( 256, 256, 256)
self.srm5 = SRM(256)
self.srm4 = SRM(256)
self.srm3 = SRM(256)
self.srm2 = SRM(256)
self.linear5 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.linear4 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.linear3 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.linear2 = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
self.initialize()
def forward(self, x):
out1, out2, out3, out4, out5_ = self.bkbone(x)
down_out1, down_out2, down_out3, down_out4, down_out5 = out1, out2, out3, out4, out5_
out4_a = self.ca45(out5_, out5_)
out3_a = self.ca35(out5_, out5_)
out2_a = self.ca25(out5_, out5_)
out5_a = self.sa55(out5_, out5_)
out5 = self.ca55(out5_a, out5_)
# out
out5 = self.srm5(out5)
out4 = self.srm4(self.bga45(out4, out5, out4_a))
out3 = self.srm3(self.bga34(out3, out4, out3_a))
out2 = self.srm2(self.bga23(out2, out3, out2_a))
# we use bilinear interpolation instead of transpose convolution
out5 = F.interpolate(self.linear5(out5), size=x.size()[2:], mode='bilinear')
out4 = F.interpolate(self.linear4(out4), size=x.size()[2:], mode='bilinear')
out3 = F.interpolate(self.linear3(out3), size=x.size()[2:], mode='bilinear')
out2 = F.interpolate(self.linear2(out2), size=x.size()[2:], mode='bilinear')
return out2, out3, out4, out5, down_out1, down_out2, down_out3, down_out4, down_out5
def initialize(self):
if self.cfg.snapshot:
try:
self.load_state_dict(torch.load(self.cfg.snapshot))
except:
print("Warning: please check the snapshot file:", self.cfg.snapshot)
pass
else:
weight_init(self)
class MINet_ResNet50(nn.Module):
def upsample_add(self, *xs):
y = xs[-1]
for x in xs[:-1]:
y = y + F.interpolate(x, size=y.size()[2:], mode="bilinear", align_corners=False)
return y
def cus_sample(self, feat, **kwargs):
"""
:param feat: 输入特征
:param kwargs: size或者scale_factor
"""
assert len(kwargs.keys()) == 1 and list(kwargs.keys())[0] in ["size", "scale_factor"]
return F.interpolate(feat, **kwargs, mode="bilinear", align_corners=False)
def Backbone_ResNet50_in3(self):
net = resnet50(pretrained=True)
div_2 = nn.Sequential(*list(net.children())[:3])
div_4 = nn.Sequential(*list(net.children())[3:5])
div_8 = net.layer2
div_16 = net.layer3
div_32 = net.layer4
return div_2, div_4, div_8, div_16, div_32
def __init__(self, cfg):
super(MINet_ResNet50, self).__init__()
self.cfg =cfg
self.div_2, self.div_4, self.div_8, self.div_16, self.div_32 = self.Backbone_ResNet50_in3()
self.upsample_add = self.upsample_add
self.upsample = self.cus_sample
self.trans = AIM(iC_list=(64, 256, 512, 1024, 2048), oC_list=(64, 64, 64, 64, 64))
self.sim32 = SIM(64, 32)
self.sim16 = SIM(64, 32)
self.sim8 = SIM(64, 32)
self.sim4 = SIM(64, 32)
self.sim2 = SIM(64, 32)
self.upconv32 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv16 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv8 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv4 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv2 = BasicConv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.upconv1 = BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.classifier = nn.Conv2d(32, 1, 1)
def forward(self, in_data):
in_data_2 = self.div_2(in_data)
in_data_4 = self.div_4(in_data_2)
in_data_8 = self.div_8(in_data_4)
in_data_16 = self.div_16(in_data_8)
in_data_32 = self.div_32(in_data_16)
in_data_2, in_data_4, in_data_8, in_data_16, in_data_32 = self.trans(
in_data_2, in_data_4, in_data_8, in_data_16, in_data_32
)
out_data_32 = self.upconv32(self.sim32(in_data_32)) # 1024
out_data_16 = self.upsample_add(out_data_32, in_data_16) # 1024
out_data_16 = self.upconv16(self.sim16(out_data_16))
out_data_8 = self.upsample_add(out_data_16, in_data_8)
out_data_8 = self.upconv8(self.sim8(out_data_8)) # 512
out_data_4 = self.upsample_add(out_data_8, in_data_4)
out_data_4 = self.upconv4(self.sim4(out_data_4)) # 256
out_data_2 = self.upsample_add(out_data_4, in_data_2)
out_data_2 = self.upconv2(self.sim2(out_data_2)) # 64
out_data_1 = self.upconv1(self.upsample(out_data_2, scale_factor=2)) # 32
out_data = self.classifier(out_data_1)
return out_data