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+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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+from torchvision import models
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+
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+
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+class REBNCONV(nn.Module):
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+ def __init__(self, in_ch=3, out_ch=3, dirate=1):
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+ super(REBNCONV, self).__init__()
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+
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+ self.conv_s1 = nn.Conv2d(
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+ in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate
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+ )
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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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+
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+ def forward(self, x):
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+
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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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+
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+ return xout
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+
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+
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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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+
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+ src = F.interpolate(src, size=tar.shape[2:], mode="bilinear", align_corners=False)
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+
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+ return src
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+
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+
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+### RSU-7 ###
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+class RSU7(nn.Module): # UNet07DRES(nn.Module):
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+ def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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+ super(RSU7, self).__init__()
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+
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+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
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+
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+ self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
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+
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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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+
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+ def forward(self, x):
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+
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+ hx = x
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+ hxin = self.rebnconvin(hx)
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+
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+ hx1 = self.rebnconv1(hxin)
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+ hx = self.pool1(hx1)
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+
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+ hx2 = self.rebnconv2(hx)
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+ hx = self.pool2(hx2)
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+
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+ hx3 = self.rebnconv3(hx)
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+ hx = self.pool3(hx3)
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+
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+ hx4 = self.rebnconv4(hx)
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+ hx = self.pool4(hx4)
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+
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+ hx5 = self.rebnconv5(hx)
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+ hx = self.pool5(hx5)
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+
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+ hx6 = self.rebnconv6(hx)
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+
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+ hx7 = self.rebnconv7(hx6)
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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+
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+ return hx1d + hxin
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+
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+
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+### RSU-6 ###
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+class RSU6(nn.Module): # UNet06DRES(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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+
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+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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+
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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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+
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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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+
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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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+
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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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+
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+ self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
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+
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+ self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
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+
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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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+
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+ def forward(self, x):
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+
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+ hx = x
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+
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+ hxin = self.rebnconvin(hx)
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+
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+ hx1 = self.rebnconv1(hxin)
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+ hx = self.pool1(hx1)
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+
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+ hx2 = self.rebnconv2(hx)
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+ hx = self.pool2(hx2)
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+
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+ hx3 = self.rebnconv3(hx)
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+ hx = self.pool3(hx3)
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+
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+ hx4 = self.rebnconv4(hx)
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+ hx = self.pool4(hx4)
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+
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+ hx5 = self.rebnconv5(hx)
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+
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+ hx6 = self.rebnconv6(hx5)
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+
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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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+
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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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+
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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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+
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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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+
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+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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+
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+ return hx1d + hxin
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+
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+
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+### RSU-5 ###
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+class RSU5(nn.Module): # UNet05DRES(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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+
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+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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+
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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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+
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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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+
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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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+
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+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
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+
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+ self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
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+
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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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+
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+ def forward(self, x):
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+
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+ hx = x
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+
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+ hxin = self.rebnconvin(hx)
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+
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+ hx1 = self.rebnconv1(hxin)
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+ hx = self.pool1(hx1)
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+
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+ hx2 = self.rebnconv2(hx)
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+ hx = self.pool2(hx2)
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+
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+ hx3 = self.rebnconv3(hx)
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+ hx = self.pool3(hx3)
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+
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+ hx4 = self.rebnconv4(hx)
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+
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+ hx5 = self.rebnconv5(hx4)
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+
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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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+
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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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+
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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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+
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+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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+
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+ return hx1d + hxin
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+
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+
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+### RSU-4 ###
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+class RSU4(nn.Module): # UNet04DRES(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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+
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+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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+
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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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+
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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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+
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+ self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
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+
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+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
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+
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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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+
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+ def forward(self, x):
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+
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+ hx = x
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+
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+ hxin = self.rebnconvin(hx)
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+
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+ hx1 = self.rebnconv1(hxin)
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+ hx = self.pool1(hx1)
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+
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+ hx2 = self.rebnconv2(hx)
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+ hx = self.pool2(hx2)
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+
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+ hx3 = self.rebnconv3(hx)
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+
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+ hx4 = self.rebnconv4(hx3)
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+
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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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+
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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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+
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+ hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
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+
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+ return hx1d + hxin
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+
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+
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+### RSU-4F ###
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+class RSU4F(nn.Module): # UNet04FRES(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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+
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+ self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
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+
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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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+
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+ self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
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+
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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):
|
|
|
|
+
|
|
|
|
+ hx = x
|
|
|
|
+
|
|
|
|
+ hxin = self.rebnconvin(hx)
|
|
|
|
+
|
|
|
|
+ hx1 = self.rebnconv1(hxin)
|
|
|
|
+ hx2 = self.rebnconv2(hx1)
|
|
|
|
+ hx3 = self.rebnconv3(hx2)
|
|
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|
+
|
|
|
|
+ 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
|
|
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|
+
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|
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|
+
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|
|
|
+##### U^2-Net ####
|
|
|
|
+class U2NET(nn.Module):
|
|
|
|
+ def __init__(self, in_ch=3, out_ch=1):
|
|
|
|
+ super(U2NET, self).__init__()
|
|
|
|
+
|
|
|
|
+ self.stage1 = RSU7(in_ch, 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, 1)
|
|
|
|
+
|
|
|
|
+ def forward(self, x):
|
|
|
|
+
|
|
|
|
+ hx = x
|
|
|
|
+
|
|
|
|
+ # stage 1
|
|
|
|
+ hx1 = self.stage1(hx)
|
|
|
|
+ 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)
|
|
|
|
+
|
|
|
|
+ d2 = self.side2(hx2d)
|
|
|
|
+ d2 = _upsample_like(d2, d1)
|
|
|
|
+
|
|
|
|
+ d3 = self.side3(hx3d)
|
|
|
|
+ d3 = _upsample_like(d3, d1)
|
|
|
|
+
|
|
|
|
+ d4 = self.side4(hx4d)
|
|
|
|
+ d4 = _upsample_like(d4, d1)
|
|
|
|
+
|
|
|
|
+ d5 = self.side5(hx5d)
|
|
|
|
+ d5 = _upsample_like(d5, d1)
|
|
|
|
+
|
|
|
|
+ d6 = self.side6(hx6)
|
|
|
|
+ d6 = _upsample_like(d6, d1)
|
|
|
|
+
|
|
|
|
+ d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
|
|
|
|
+
|
|
|
|
+ return (
|
|
|
|
+ torch.sigmoid(d0),
|
|
|
|
+ torch.sigmoid(d1),
|
|
|
|
+ torch.sigmoid(d2),
|
|
|
|
+ torch.sigmoid(d3),
|
|
|
|
+ torch.sigmoid(d4),
|
|
|
|
+ torch.sigmoid(d5),
|
|
|
|
+ torch.sigmoid(d6),
|
|
|
|
+ )
|
|
|
|
+
|
|
|
|
+
|
|
|
|
+### U^2-Net small ###
|
|
|
|
+class U2NETP(nn.Module):
|
|
|
|
+ def __init__(self, in_ch=3, out_ch=1):
|
|
|
|
+ super(U2NETP, self).__init__()
|
|
|
|
+
|
|
|
|
+ self.stage1 = RSU7(in_ch, 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, 16, 64)
|
|
|
|
+ self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
|
|
|
+
|
|
|
|
+ self.stage4 = RSU4(64, 16, 64)
|
|
|
|
+ self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
|
|
|
+
|
|
|
|
+ self.stage5 = RSU4F(64, 16, 64)
|
|
|
|
+ self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
|
|
|
+
|
|
|
|
+ self.stage6 = RSU4F(64, 16, 64)
|
|
|
|
+
|
|
|
|
+ # decoder
|
|
|
|
+ self.stage5d = RSU4F(128, 16, 64)
|
|
|
|
+ self.stage4d = RSU4(128, 16, 64)
|
|
|
|
+ self.stage3d = RSU5(128, 16, 64)
|
|
|
|
+ self.stage2d = RSU6(128, 16, 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(64, out_ch, 3, padding=1)
|
|
|
|
+ self.side4 = nn.Conv2d(64, out_ch, 3, padding=1)
|
|
|
|
+ self.side5 = nn.Conv2d(64, out_ch, 3, padding=1)
|
|
|
|
+ self.side6 = nn.Conv2d(64, out_ch, 3, padding=1)
|
|
|
|
+
|
|
|
|
+ self.outconv = nn.Conv2d(6, out_ch, 1)
|
|
|
|
+
|
|
|
|
+ def forward(self, x):
|
|
|
|
+
|
|
|
|
+ hx = x
|
|
|
|
+
|
|
|
|
+ # stage 1
|
|
|
|
+ hx1 = self.stage1(hx)
|
|
|
|
+ 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)
|
|
|
|
+
|
|
|
|
+ d2 = self.side2(hx2d)
|
|
|
|
+ d2 = _upsample_like(d2, d1)
|
|
|
|
+
|
|
|
|
+ d3 = self.side3(hx3d)
|
|
|
|
+ d3 = _upsample_like(d3, d1)
|
|
|
|
+
|
|
|
|
+ d4 = self.side4(hx4d)
|
|
|
|
+ d4 = _upsample_like(d4, d1)
|
|
|
|
+
|
|
|
|
+ d5 = self.side5(hx5d)
|
|
|
|
+ d5 = _upsample_like(d5, d1)
|
|
|
|
+
|
|
|
|
+ d6 = self.side6(hx6)
|
|
|
|
+ d6 = _upsample_like(d6, d1)
|
|
|
|
+
|
|
|
|
+ d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
|
|
|
|
+
|
|
|
|
+ return (
|
|
|
|
+ torch.sigmoid(d0),
|
|
|
|
+ torch.sigmoid(d1),
|
|
|
|
+ torch.sigmoid(d2),
|
|
|
|
+ torch.sigmoid(d3),
|
|
|
|
+ torch.sigmoid(d4),
|
|
|
|
+ torch.sigmoid(d5),
|
|
|
|
+ torch.sigmoid(d6),
|
|
|
|
+ )
|