Delete models/isnet.py

models/isnet.py

@@ -1,611 +0,0 @@
-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)