import torch
import torch.nn as nn
from torchvision import models
import torch.nn.functional as F
# https://github.com/xuebinqin/DIS/blob/main/IS-Net/models/isnet.py
bce_loss = nn.BCELoss(size_average=True)
def muti_loss_fusion(preds, target):
loss0 = 0.0
loss = 0.0
for i in range(0, len(preds)):
# print("i: ", i, preds[i].shape)
if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]:
# tmp_target = _upsample_like(target,preds[i])
tmp_target = F.interpolate(
target, size=preds[i].size()[2:], mode="bilinear", align_corners=True
)
loss = loss + bce_loss(preds[i], tmp_target)
else:
loss = loss + bce_loss(preds[i], target)
if i == 0:
loss0 = loss
return loss0, loss
fea_loss = nn.MSELoss(size_average=True)
kl_loss = nn.KLDivLoss(size_average=True)
l1_loss = nn.L1Loss(size_average=True)
smooth_l1_loss = nn.SmoothL1Loss(size_average=True)
def muti_loss_fusion_kl(preds, target, dfs, fs, mode="MSE"):
loss0 = 0.0
loss = 0.0
for i in range(0, len(preds)):
# print("i: ", i, preds[i].shape)
if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]:
# tmp_target = _upsample_like(target,preds[i])
tmp_target = F.interpolate(
target, size=preds[i].size()[2:], mode="bilinear", align_corners=True
)
loss = loss + bce_loss(preds[i], tmp_target)
else:
loss = loss + bce_loss(preds[i], target)
if i == 0:
loss0 = loss
for i in range(0, len(dfs)):
if mode == "MSE":
loss = loss + fea_loss(
dfs[i], fs[i]
) ### add the mse loss of features as additional constraints
# print("fea_loss: ", fea_loss(dfs[i],fs[i]).item())
elif mode == "KL":
loss = loss + kl_loss(F.log_softmax(dfs[i], dim=1), F.softmax(fs[i], dim=1))
# print("kl_loss: ", kl_loss(F.log_softmax(dfs[i],dim=1),F.softmax(fs[i],dim=1)).item())
elif mode == "MAE":
loss = loss + l1_loss(dfs[i], fs[i])
# print("ls_loss: ", l1_loss(dfs[i],fs[i]))
elif mode == "SmoothL1":
loss = loss + smooth_l1_loss(dfs[i], fs[i])
# print("SmoothL1: ", smooth_l1_loss(dfs[i],fs[i]).item())
return loss0, loss
class REBNCONV(nn.Module):
def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
super(REBNCONV, self).__init__()
self.conv_s1 = nn.Conv2d(
in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride
)
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.upsample(src, size=tar.shape[2:], mode="bilinear")
return src
### RSU-7 ###
class RSU7(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
super(RSU7, self).__init__()
self.in_ch = in_ch
self.mid_ch = mid_ch
self.out_ch = out_ch
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2
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):
b, c, h, w = x.shape
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)
hx = self.pool5(hx5)
hx6 = self.rebnconv6(hx)
hx7 = self.rebnconv7(hx6)
hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
hx6dup = _upsample_like(hx6d, hx5)
hx5d = self.rebnconv5d(torch.cat((hx6dup, 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-6 ###
class RSU6(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):
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):
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):
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
class myrebnconv(nn.Module):
def __init__(
self,
in_ch=3,
out_ch=1,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
groups=1,
):
super(myrebnconv, self).__init__()
self.conv = nn.Conv2d(
in_ch,
out_ch,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
)
self.bn = nn.BatchNorm2d(out_ch)
self.rl = nn.ReLU(inplace=True)
def forward(self, x):
return self.rl(self.bn(self.conv(x)))
class ISNetGTEncoder(nn.Module):
def __init__(self, in_ch=1, out_ch=1):
super(ISNetGTEncoder, self).__init__()
self.conv_in = myrebnconv(
in_ch, 16, 3, stride=2, padding=1
) # nn.Conv2d(in_ch,64,3,stride=2,padding=1)
self.stage1 = RSU7(16, 16, 64)
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage2 = RSU6(64, 16, 64)
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage3 = RSU5(64, 32, 128)
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage4 = RSU4(128, 32, 256)
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage5 = RSU4F(256, 64, 512)
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage6 = RSU4F(512, 64, 512)
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)
def compute_loss(self, preds, targets):
return muti_loss_fusion(preds, targets)
def forward(self, x):
hx = x
hxin = self.conv_in(hx)
# hx = self.pool_in(hxin)
# stage 1
hx1 = self.stage1(hxin)
hx = self.pool12(hx1)
# stage 2
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
# stage 3
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
# stage 4
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
# stage 5
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
# stage 6
hx6 = self.stage6(hx)
# side output
d1 = self.side1(hx1)
d1 = _upsample_like(d1, x)
d2 = self.side2(hx2)
d2 = _upsample_like(d2, x)
d3 = self.side3(hx3)
d3 = _upsample_like(d3, x)
d4 = self.side4(hx4)
d4 = _upsample_like(d4, x)
d5 = self.side5(hx5)
d5 = _upsample_like(d5, x)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, x)
# d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
return [
F.sigmoid(d1),
F.sigmoid(d2),
F.sigmoid(d3),
F.sigmoid(d4),
F.sigmoid(d5),
F.sigmoid(d6),
], [hx1, hx2, hx3, hx4, hx5, hx6]
class ISNetDIS(nn.Module):
def __init__(self, in_ch=3, out_ch=1):
super(ISNetDIS, self).__init__()
self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1)
self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.stage1 = RSU7(64, 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 compute_loss_kl(self, preds, targets, dfs, fs, mode="MSE"):
# return muti_loss_fusion(preds,targets)
return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode)
def compute_loss(self, preds, targets):
# return muti_loss_fusion(preds,targets)
return muti_loss_fusion(preds, targets)
def forward(self, x):
hx = x
hxin = self.conv_in(hx)
# hx = self.pool_in(hxin)
# stage 1
hx1 = self.stage1(hxin)
hx = self.pool12(hx1)
# stage 2
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
# stage 3
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
# stage 4
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
# stage 5
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
# stage 6
hx6 = self.stage6(hx)
hx6up = _upsample_like(hx6, hx5)
# -------------------- decoder --------------------
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
# side output
d1 = self.side1(hx1d)
d1 = _upsample_like(d1, x)
d2 = self.side2(hx2d)
d2 = _upsample_like(d2, x)
d3 = self.side3(hx3d)
d3 = _upsample_like(d3, x)
d4 = self.side4(hx4d)
d4 = _upsample_like(d4, x)
d5 = self.side5(hx5d)
d5 = _upsample_like(d5, x)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, x)
# d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
return [
F.sigmoid(d1),
F.sigmoid(d2),
F.sigmoid(d3),
F.sigmoid(d4),
F.sigmoid(d5),
F.sigmoid(d6),
], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]