text: import torch
  import torch.nn as nn
  from torchvision import models
  import torch.nn.functional as F
  import numpy as np
  
  
  class sobel_net(nn.Module):
      def __init__(self):
          super().__init__()
          self.conv_opx = nn.Conv2d(1, 1, 3, bias=False)
          self.conv_opy = nn.Conv2d(1, 1, 3, bias=False)
          sobel_kernelx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype='float32').reshape((1, 1, 3, 3))
          sobel_kernely = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype='float32').reshape((1, 1, 3, 3))
          self.conv_opx.weight.data = torch.from_numpy(sobel_kernelx)
          self.conv_opy.weight.data = torch.from_numpy(sobel_kernely)
  
          for p in self.parameters():
              p.requires_grad = False
  
      def forward(self, im):  # input rgb
          x = (0.299 * im[:, 0, :, :] + 0.587 * im[:, 1, :, :] + 0.114 * im[:, 2, :, :]).unsqueeze(1)  # rgb2gray
          gradx = self.conv_opx(x)
          grady = self.conv_opy(x)
  
          x = (gradx ** 2 + grady ** 2) ** 0.5
          x = (x - x.min()) / (x.max() - x.min())
          x = F.pad(x, (1, 1, 1, 1))
  
          x = torch.cat([im, x], dim=1)
          return x
  
  
  class REBNCONV(nn.Module):
      def __init__(self, in_ch=3, out_ch=3, dirate=1):
          super(REBNCONV, self).__init__()
  
          self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate)
          self.bn_s1 = nn.BatchNorm2d(out_ch)
          self.relu_s1 = nn.ReLU(inplace=True)
  
      def forward(self, x):
          hx = x
          xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
  
          return xout
  
  
  ## upsample tensor 'src' to have the same spatial size with tensor 'tar'
  def _upsample_like(src, tar):
      src = F.interpolate(src, size=tar.shape[2:], mode='bilinear', align_corners=False)
  
      return src
  
  
  ### RSU-7 ###
  class RSU7(nn.Module):  # UNet07DRES(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU7, self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
          self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
  
          self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
  
          self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
  
      def forward(self, x):
          hx = x
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
          hx = self.pool3(hx3)
  
          hx4 = self.rebnconv4(hx)
          hx = self.pool4(hx4)
  
          hx5 = self.rebnconv5(hx)
          hx = self.pool5(hx5)
  
          hx6 = self.rebnconv6(hx)
  
          hx7 = self.rebnconv7(hx6)
  
          hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
          hx6dup = _upsample_like(hx6d, hx5)
  
          hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
          hx5dup = _upsample_like(hx5d, hx4)
  
          hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
          hx4dup = _upsample_like(hx4d, hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
          hx3dup = _upsample_like(hx3d, hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
          hx2dup = _upsample_like(hx2d, hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
  
          return hx1d + hxin
  
  
  ### RSU-6 ###
  class RSU6(nn.Module):  # UNet06DRES(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU6, self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
          self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
  
          self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
  
          self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
  
      def forward(self, x):
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
          hx = self.pool3(hx3)
  
          hx4 = self.rebnconv4(hx)
          hx = self.pool4(hx4)
  
          hx5 = self.rebnconv5(hx)
  
          hx6 = self.rebnconv6(hx5)
  
          hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
          hx5dup = _upsample_like(hx5d, hx4)
  
          hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
          hx4dup = _upsample_like(hx4d, hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
          hx3dup = _upsample_like(hx3d, hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
          hx2dup = _upsample_like(hx2d, hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
  
          return hx1d + hxin
  
  
  ### RSU-5 ###
  class RSU5(nn.Module):  # UNet05DRES(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU5, self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
          self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
  
          self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
  
          self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
  
      def forward(self, x):
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
          hx = self.pool3(hx3)
  
          hx4 = self.rebnconv4(hx)
  
          hx5 = self.rebnconv5(hx4)
  
          hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
          hx4dup = _upsample_like(hx4d, hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
          hx3dup = _upsample_like(hx3d, hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
          hx2dup = _upsample_like(hx2d, hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
  
          return hx1d + hxin
  
  
  ### RSU-4 ###
  class RSU4(nn.Module):  # UNet04DRES(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU4, self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
          self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
          self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
  
          self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
  
          self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
  
      def forward(self, x):
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
  
          hx4 = self.rebnconv4(hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
          hx3dup = _upsample_like(hx3d, hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
          hx2dup = _upsample_like(hx2d, hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
  
          return hx1d + hxin
  
  
  ### RSU-4F ###
  class RSU4F(nn.Module):  # UNet04FRES(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU4F, self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
          self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
          self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
  
          self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
  
          self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
          self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
          self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
  
      def forward(self, x):
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx2 = self.rebnconv2(hx1)
          hx3 = self.rebnconv3(hx2)
  
          hx4 = self.rebnconv4(hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
          hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
          hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
  
          return hx1d + hxin
  
  
  ##### U^2-Net ####
  class U2NET(nn.Module):
  
      def __init__(self, in_ch=3, out_ch=1):
          super(U2NET, self).__init__()
          self.edge = sobel_net()
  
          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):
          x = self.edge(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)
  
  
  def get_parameter_number(net):
      total_num = sum(p.numel() for p in net.parameters())
      trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad)
      return {'Total': total_num, 'Trainable': trainable_num}
  
  
  if __name__ == '__main__':
      net = U2NET(4, 1)
      print(get_parameter_number(net))  # 69090500 加attention后69442032
      with torch.no_grad():
          inputs = torch.zeros(1, 3, 256, 256)
          outs = net(inputs)
          print(outs[0].shape)  # torch.Size([2, 3, 256, 256]) torch.Size([2, 2, 256, 256])
  