mirror of
https://github.com/xuebinqin/DIS.git
synced 2024-11-30 02:24:32 +01:00
611 lines
17 KiB
Python
611 lines
17 KiB
Python
import torch
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import torch.nn as nn
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from torchvision import models
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import torch.nn.functional as F
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bce_loss = nn.BCELoss(size_average=True)
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def muti_loss_fusion(preds, target):
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loss0 = 0.0
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loss = 0.0
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for i in range(0,len(preds)):
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# print("i: ", i, preds[i].shape)
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if(preds[i].shape[2]!=target.shape[2] or preds[i].shape[3]!=target.shape[3]):
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# tmp_target = _upsample_like(target,preds[i])
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tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True)
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loss = loss + bce_loss(preds[i],tmp_target)
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else:
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loss = loss + bce_loss(preds[i],target)
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if(i==0):
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loss0 = loss
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return loss0, loss
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fea_loss = nn.MSELoss(size_average=True)
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kl_loss = nn.KLDivLoss(size_average=True)
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l1_loss = nn.L1Loss(size_average=True)
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smooth_l1_loss = nn.SmoothL1Loss(size_average=True)
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def muti_loss_fusion_kl(preds, target, dfs, fs, mode='MSE'):
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loss0 = 0.0
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loss = 0.0
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for i in range(0,len(preds)):
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# print("i: ", i, preds[i].shape)
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if(preds[i].shape[2]!=target.shape[2] or preds[i].shape[3]!=target.shape[3]):
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# tmp_target = _upsample_like(target,preds[i])
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tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True)
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loss = loss + bce_loss(preds[i],tmp_target)
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else:
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loss = loss + bce_loss(preds[i],target)
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if(i==0):
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loss0 = loss
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for i in range(0,len(dfs)):
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if(mode=='MSE'):
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loss = loss + fea_loss(dfs[i],fs[i]) ### add the mse loss of features as additional constraints
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# print("fea_loss: ", fea_loss(dfs[i],fs[i]).item())
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elif(mode=='KL'):
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loss = loss + kl_loss(F.log_softmax(dfs[i],dim=1),F.softmax(fs[i],dim=1))
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# print("kl_loss: ", kl_loss(F.log_softmax(dfs[i],dim=1),F.softmax(fs[i],dim=1)).item())
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elif(mode=='MAE'):
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loss = loss + l1_loss(dfs[i],fs[i])
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# print("ls_loss: ", l1_loss(dfs[i],fs[i]))
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elif(mode=='SmoothL1'):
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loss = loss + smooth_l1_loss(dfs[i],fs[i])
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# print("SmoothL1: ", smooth_l1_loss(dfs[i],fs[i]).item())
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return loss0, loss
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class REBNCONV(nn.Module):
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def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
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super(REBNCONV,self).__init__()
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self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
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self.bn_s1 = nn.BatchNorm2d(out_ch)
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self.relu_s1 = nn.ReLU(inplace=True)
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def forward(self,x):
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hx = x
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xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
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return xout
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## upsample tensor 'src' to have the same spatial size with tensor 'tar'
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def _upsample_like(src,tar):
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src = F.upsample(src,size=tar.shape[2:],mode='bilinear')
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return src
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### RSU-7 ###
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class RSU7(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
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super(RSU7,self).__init__()
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self.in_ch = in_ch
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self.mid_ch = mid_ch
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self.out_ch = out_ch
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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b, c, h, w = x.shape
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx = self.pool4(hx4)
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hx5 = self.rebnconv5(hx)
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hx = self.pool5(hx5)
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hx6 = self.rebnconv6(hx)
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hx7 = self.rebnconv7(hx6)
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hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1))
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hx6dup = _upsample_like(hx6d,hx5)
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hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1))
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hx5dup = _upsample_like(hx5d,hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
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hx4dup = _upsample_like(hx4d,hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
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hx3dup = _upsample_like(hx3d,hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
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hx2dup = _upsample_like(hx2d,hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
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return hx1d + hxin
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### RSU-6 ###
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class RSU6(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU6,self).__init__()
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx = self.pool4(hx4)
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hx5 = self.rebnconv5(hx)
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hx6 = self.rebnconv6(hx5)
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hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1))
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hx5dup = _upsample_like(hx5d,hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
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hx4dup = _upsample_like(hx4d,hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
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hx3dup = _upsample_like(hx3d,hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
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hx2dup = _upsample_like(hx2d,hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
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return hx1d + hxin
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### RSU-5 ###
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class RSU5(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU5,self).__init__()
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx5 = self.rebnconv5(hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
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hx4dup = _upsample_like(hx4d,hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
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hx3dup = _upsample_like(hx3d,hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
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hx2dup = _upsample_like(hx2d,hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
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return hx1d + hxin
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### RSU-4 ###
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class RSU4(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU4,self).__init__()
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx4 = self.rebnconv4(hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
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hx3dup = _upsample_like(hx3d,hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
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hx2dup = _upsample_like(hx2d,hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
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return hx1d + hxin
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### RSU-4F ###
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class RSU4F(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU4F,self).__init__()
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx2 = self.rebnconv2(hx1)
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hx3 = self.rebnconv3(hx2)
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hx4 = self.rebnconv4(hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
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hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
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hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
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return hx1d + hxin
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class myrebnconv(nn.Module):
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def __init__(self, in_ch=3,
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out_ch=1,
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kernel_size=3,
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stride=1,
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padding=1,
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dilation=1,
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groups=1):
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super(myrebnconv,self).__init__()
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self.conv = nn.Conv2d(in_ch,
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out_ch,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups)
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self.bn = nn.BatchNorm2d(out_ch)
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self.rl = nn.ReLU(inplace=True)
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def forward(self,x):
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return self.rl(self.bn(self.conv(x)))
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class ISNetGTEncoder(nn.Module):
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def __init__(self,in_ch=1,out_ch=1):
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super(ISNetGTEncoder,self).__init__()
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self.conv_in = myrebnconv(in_ch,16,3,stride=2,padding=1) # nn.Conv2d(in_ch,64,3,stride=2,padding=1)
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self.stage1 = RSU7(16,16,64)
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self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage2 = RSU6(64,16,64)
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self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage3 = RSU5(64,32,128)
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self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage4 = RSU4(128,32,256)
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self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage5 = RSU4F(256,64,512)
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self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage6 = RSU4F(512,64,512)
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self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
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self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
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self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
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self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
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self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
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self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
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def compute_loss(self, preds, targets):
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return muti_loss_fusion(preds,targets)
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def forward(self,x):
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hx = x
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hxin = self.conv_in(hx)
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# hx = self.pool_in(hxin)
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#stage 1
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hx1 = self.stage1(hxin)
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hx = self.pool12(hx1)
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#stage 2
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hx2 = self.stage2(hx)
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hx = self.pool23(hx2)
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#stage 3
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hx3 = self.stage3(hx)
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hx = self.pool34(hx3)
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#stage 4
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hx4 = self.stage4(hx)
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hx = self.pool45(hx4)
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#stage 5
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hx5 = self.stage5(hx)
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hx = self.pool56(hx5)
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#stage 6
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hx6 = self.stage6(hx)
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#side output
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d1 = self.side1(hx1)
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d1 = _upsample_like(d1,x)
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d2 = self.side2(hx2)
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d2 = _upsample_like(d2,x)
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d3 = self.side3(hx3)
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d3 = _upsample_like(d3,x)
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d4 = self.side4(hx4)
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d4 = _upsample_like(d4,x)
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d5 = self.side5(hx5)
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d5 = _upsample_like(d5,x)
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d6 = self.side6(hx6)
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d6 = _upsample_like(d6,x)
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# d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
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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]
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class ISNetDIS(nn.Module):
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def __init__(self,in_ch=3,out_ch=1):
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super(ISNetDIS,self).__init__()
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self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
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self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage1 = RSU7(64,32,64)
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self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage2 = RSU6(64,32,128)
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self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage3 = RSU5(128,64,256)
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self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage4 = RSU4(256,128,512)
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self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage5 = RSU4F(512,256,512)
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self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.stage6 = RSU4F(512,256,512)
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# decoder
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self.stage5d = RSU4F(1024,256,512)
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self.stage4d = RSU4(1024,128,256)
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self.stage3d = RSU5(512,64,128)
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self.stage2d = RSU6(256,32,64)
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self.stage1d = RSU7(128,16,64)
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self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
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self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
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self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
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self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
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self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
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self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
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# self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
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def compute_loss_kl(self, preds, targets, dfs, fs, mode='MSE'):
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# return muti_loss_fusion(preds,targets)
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return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode)
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def compute_loss(self, preds, targets):
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|
|
# return muti_loss_fusion(preds,targets)
|
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return muti_loss_fusion(preds, targets)
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|
|
def forward(self,x):
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|
hx = x
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hxin = self.conv_in(hx)
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hx = self.pool_in(hxin)
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|
|
#stage 1
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hx1 = self.stage1(hxin)
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hx = self.pool12(hx1)
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#stage 2
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hx2 = self.stage2(hx)
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hx = self.pool23(hx2)
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|
#stage 3
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hx3 = self.stage3(hx)
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|
hx = self.pool34(hx3)
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|
#stage 4
|
|
hx4 = self.stage4(hx)
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hx = self.pool45(hx4)
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|
#stage 5
|
|
hx5 = self.stage5(hx)
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hx = self.pool56(hx5)
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|
#stage 6
|
|
hx6 = self.stage6(hx)
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|
hx6up = _upsample_like(hx6,hx5)
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|
|
#-------------------- 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)
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|
|
|
hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
|
|
hx3dup = _upsample_like(hx3d,hx2)
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|
|
|
hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
|
|
hx2dup = _upsample_like(hx2d,hx1)
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|
|
|
hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
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|
|
|
|
|
#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]
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