import torch import torch.nn as nn from torchvision import models import torch.nn.functional as F 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] # return F.sigmoid(d1)