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u2_net.py
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# code has been adapted from
# https://github.com/xuebinqin/U-2-Net/blob/master/model/u2net.py
# reference
# https://arxiv.org/pdf/2005.09007.pdf
import torch
import torch.nn as nn
import torch.nn.functional as F
class REBNCONV(nn.Module):
def __init__(self,in_ch=3,out_ch=3,dirate=1):
super(REBNCONV,self).__init__()
self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)
self.bn_s1 = nn.BatchNorm2d(out_ch)
self.relu_s1 = nn.ReLU(inplace=True)
def forward(self,x):
hx = x
xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
return xout
## upsample tensor 'src' to have the same spatial size with tensor 'tar'
def _upsample_like(src,tar):
src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
return src
### RSU-7 ###
class RSU7(nn.Module):#UNet07DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU7,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
print('--RSU7')
print(f'--input: {x.size()}')
hx = x
hxin = self.rebnconvin(hx)
print(f'--conv: {hxin.size()}')
hx1 = self.rebnconv1(hxin)
print(f'--conv: {hx1.size()}')
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
print(f'--conv: {hx2.size()}')
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
print(f'--conv: {hx3.size()}')
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
print(f'--conv: {hx4.size()}')
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
print(f'--conv: {hx5.size()}')
hx = self.pool5(hx5)
hx6 = self.rebnconv6(hx)
print(f'--conv: {hx6.size()}')
hx7 = self.rebnconv7(hx6)
print(f'--conv: {hx7.size()}')
hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1))
print(f'--conv d: {hx6d.size()}')
hx6dup = _upsample_like(hx6d,hx5)
print(f'--upsample: {hx6dup.size()}')
hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1))
print(f'--conv d: {hx5d.size()}')
hx5dup = _upsample_like(hx5d,hx4)
print(f'--upsample: {hx5dup.size()}')
hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
print(f'--conv d: {hx4d.size()}')
hx4dup = _upsample_like(hx4d,hx3)
print(f'--upsample: {hx4dup.size()}')
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
print(f'--conv d: {hx3d.size()}')
hx3dup = _upsample_like(hx3d,hx2)
print(f'--upsample: {hx3dup.size()}')
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
print(f'--conv d: {hx2d.size()}')
hx2dup = _upsample_like(hx2d,hx1)
print(f'--upsample: {hx2dup.size()}')
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
print(f'--conv d: {hx1d.size()}')
print(f'--return: {(hx1d+hxin).size()}')
return hx1d + hxin
### RSU-6 ###
class RSU6(nn.Module):#UNet06DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU6,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx6 = self.rebnconv6(hx5)
hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1))
hx5dup = _upsample_like(hx5d,hx4)
hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
hx4dup = _upsample_like(hx4d,hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-5 ###
class RSU5(nn.Module):#UNet05DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU5,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx5 = self.rebnconv5(hx4)
hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
hx4dup = _upsample_like(hx4d,hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-4 ###
class RSU4(nn.Module):#UNet04DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
hx3dup = _upsample_like(hx3d,hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
hx2dup = _upsample_like(hx2d,hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
return hx1d + hxin
### RSU-4F ###
class RSU4F(nn.Module):#UNet04FRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4F,self).__init__()
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
def forward(self,x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx2 = self.rebnconv2(hx1)
hx3 = self.rebnconv3(hx2)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
return hx1d + hxin
#----U^2-Net----
class U2Net(nn.Module):
'''
main model class defining the U-Net structure
'''
def __init__(self, args):
super().__init__()
in_ch = args.in_ch
out_ch = args.out_ch
# encoder
# RSU nested model
self.stage1 = RSU7(in_ch, 32, 64)
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage2 = RSU6(64,32,128)
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage3 = RSU5(128,64,256)
self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage4 = RSU4(256,128,512)
self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage5 = RSU4F(512,256,512)
self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
self.stage6 = RSU4F(512,256,512)
# decoder
self.stage5d = RSU4F(1024,256,512)
self.stage4d = RSU4(1024,128,256)
self.stage3d = RSU5(512,64,128)
self.stage2d = RSU6(256,32,64)
self.stage1d = RSU7(128,16,64)
self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
def forward(self,x):
hx = x
print('start U2 Net')
print(f'input: {x.size()}')
# stage 1
hx1 = self.stage1(hx)
print(f'after stage1: {hx1.size()}')
hx = self.pool12(hx1)
print(f'after pooling: {hx.size()}')
#stage 2
hx2 = self.stage2(hx)
print(f'after stage2: {hx2.size()}')
hx = self.pool23(hx2)
print(f'after pooling: {hx.size()}')
#stage 3
hx3 = self.stage3(hx)
print(f'after stage3: {hx3.size()}')
hx = self.pool34(hx3)
print(f'after pooling: {hx.size()}')
#stage 4
hx4 = self.stage4(hx)
print(f'after stage4: {hx4.size()}')
hx = self.pool45(hx4)
print(f'after pooling: {hx.size()}')
#stage 5
hx5 = self.stage5(hx)
print(f'after stage5: {hx5.size()}')
hx = self.pool56(hx5)
print(f'after pooling: {hx.size()}')
#stage 6
hx6 = self.stage6(hx)
print(f'after stage6: {hx6.size()}')
hx6up = _upsample_like(hx6,hx5)
print(f'after pooling: {hx6up.size()}')
#-------------------- decoder --------------------
print('decoder')
hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
print(f'after stage5d: {hx5d.size()}')
hx5dup = _upsample_like(hx5d,hx4)
print(f'after upsample: {hx5dup.size()}')
hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
print(f'after stage4d: {hx4d.size()}')
hx4dup = _upsample_like(hx4d,hx3)
print(f'after upsample: {hx4dup.size()}')
hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
print(f'after stage3d: {hx3d.size()}')
hx3dup = _upsample_like(hx3d,hx2)
print(f'after upsample: {hx3dup.size()}')
hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
print(f'after stage2d: {hx2d.size()}')
hx2dup = _upsample_like(hx2d,hx1)
print(f'after upsample: {hx2dup.size()}')
hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
print(f'after stage1d: {hx1d.size()}')
#side output
d1 = self.side1(hx1d)
print(f'after side1: {d1.size()}')
d2 = self.side2(hx2d)
print(f'after side2: {d2.size()}')
d2 = _upsample_like(d2,d1)
print(f'after upsample: {d2.size()}')
d3 = self.side3(hx3d)
print(f'after side3: {d3.size()}')
d3 = _upsample_like(d3,d1)
print(f'after upsample: {d3.size()}')
d4 = self.side4(hx4d)
print(f'after side4: {d4.size()}')
d4 = _upsample_like(d4,d1)
print(f'after upsample: {d4.size()}')
d5 = self.side5(hx5d)
print(f'after side5: {d5.size()}')
d5 = _upsample_like(d5,d1)
print(f'after upsample: {d5.size()}')
d6 = self.side6(hx6)
print(f'after side6: {d6.size()}')
d6 = _upsample_like(d6,d1)
print(f'after upsample: {d6.size()}')
d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
print(f'd0: {d0.size()}')
return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)