455 lines
14 KiB
Python
455 lines
14 KiB
Python
import torch
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import torch.nn as nn
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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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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(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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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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## 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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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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### 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 ISNetBase(nn.Module):
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def __init__(self, in_ch=3, out_ch=1):
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super(ISNetBase, 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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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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hx6up = _upsample_like(hx6, hx5)
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# -------------------- decoder --------------------
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hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
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hx5dup = _upsample_like(hx5d, hx4)
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hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
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hx4dup = _upsample_like(hx4d, hx3)
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hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
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hx3dup = _upsample_like(hx3d, hx2)
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hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
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hx2dup = _upsample_like(hx2d, hx1)
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hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
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# side output
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d1 = self.side1(hx1d)
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d1 = _upsample_like(d1, x)
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return d1.sigmoid()
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