content: """
  Source and Copyright Notice:
  This code is from briaai/RMBG-1.4
  Original repository: https://huggingface.co/briaai/RMBG-1.4
  Copyright belongs to briaai
  """
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from huggingface_hub import PyTorchModelHubMixin
  
  class REBNCONV(nn.Module):
      def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
          super(REBNCONV,self).__init__()
  
          self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
          self.bn_s1 = nn.BatchNorm2d(out_ch)
          self.relu_s1 = nn.ReLU(inplace=True)
  
      def forward(self,x):
  
          hx = x
          xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
  
          return xout
  
  ## upsample tensor 'src' to have the same spatial size with tensor 'tar'
  def _upsample_like(src,tar):
  
      src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
  
      return src
  
  
  ### RSU-7 ###
  class RSU7(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
          super(RSU7,self).__init__()
  
          self.in_ch = in_ch
          self.mid_ch = mid_ch
          self.out_ch = out_ch
  
          self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
  
          self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
          self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
  
          self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
  
          self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
  
      def forward(self,x):
          b, c, h, w = x.shape
  
          hx = x
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
          hx = self.pool3(hx3)
  
          hx4 = self.rebnconv4(hx)
          hx = self.pool4(hx4)
  
          hx5 = self.rebnconv5(hx)
          hx = self.pool5(hx5)
  
          hx6 = self.rebnconv6(hx)
  
          hx7 = self.rebnconv7(hx6)
  
          hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
          hx6dup = _upsample_like(hx6d,hx5)
  
          hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
          hx5dup = _upsample_like(hx5d,hx4)
  
          hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
          hx4dup = _upsample_like(hx4d,hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
          hx3dup = _upsample_like(hx3d,hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
          hx2dup = _upsample_like(hx2d,hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
  
          return hx1d + hxin
  
  
  ### RSU-6 ###
  class RSU6(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU6,self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
          self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
  
          self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
  
          self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
  
      def forward(self,x):
  
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
          hx = self.pool3(hx3)
  
          hx4 = self.rebnconv4(hx)
          hx = self.pool4(hx4)
  
          hx5 = self.rebnconv5(hx)
  
          hx6 = self.rebnconv6(hx5)
  
  
          hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
          hx5dup = _upsample_like(hx5d,hx4)
  
          hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
          hx4dup = _upsample_like(hx4d,hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
          hx3dup = _upsample_like(hx3d,hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
          hx2dup = _upsample_like(hx2d,hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
  
          return hx1d + hxin
  
  ### RSU-5 ###
  class RSU5(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU5,self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
          self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
  
          self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
  
          self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
  
      def forward(self,x):
  
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
          hx = self.pool3(hx3)
  
          hx4 = self.rebnconv4(hx)
  
          hx5 = self.rebnconv5(hx4)
  
          hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
          hx4dup = _upsample_like(hx4d,hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
          hx3dup = _upsample_like(hx3d,hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
          hx2dup = _upsample_like(hx2d,hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
  
          return hx1d + hxin
  
  ### RSU-4 ###
  class RSU4(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU4,self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
          self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
          self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
  
          self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
  
          self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
          self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
  
      def forward(self,x):
  
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx = self.pool1(hx1)
  
          hx2 = self.rebnconv2(hx)
          hx = self.pool2(hx2)
  
          hx3 = self.rebnconv3(hx)
  
          hx4 = self.rebnconv4(hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
          hx3dup = _upsample_like(hx3d,hx2)
  
          hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
          hx2dup = _upsample_like(hx2d,hx1)
  
          hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
  
          return hx1d + hxin
  
  ### RSU-4F ###
  class RSU4F(nn.Module):
  
      def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
          super(RSU4F,self).__init__()
  
          self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
  
          self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
          self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
          self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
  
          self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
  
          self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
          self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
          self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
  
      def forward(self,x):
  
          hx = x
  
          hxin = self.rebnconvin(hx)
  
          hx1 = self.rebnconv1(hxin)
          hx2 = self.rebnconv2(hx1)
          hx3 = self.rebnconv3(hx2)
  
          hx4 = self.rebnconv4(hx3)
  
          hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
          hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
          hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
  
          return hx1d + hxin
  
  
  class myrebnconv(nn.Module):
      def __init__(self, in_ch=3,
                         out_ch=1,
                         kernel_size=3,
                         stride=1,
                         padding=1,
                         dilation=1,
                         groups=1):
          super(myrebnconv,self).__init__()
  
          self.conv = nn.Conv2d(in_ch,
                                out_ch,
                                kernel_size=kernel_size,
                                stride=stride,
                                padding=padding,
                                dilation=dilation,
                                groups=groups)
          self.bn = nn.BatchNorm2d(out_ch)
          self.rl = nn.ReLU(inplace=True)
  
      def forward(self,x):
          return self.rl(self.bn(self.conv(x)))
  
  
  class BriaRMBG(nn.Module, PyTorchModelHubMixin):
  
      def __init__(self,config:dict={"in_ch":3,"out_ch":1}):
          super(BriaRMBG,self).__init__()
          in_ch=config["in_ch"]
          out_ch=config["out_ch"]
          self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
          self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.stage1 = RSU7(64,32,64)
          self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.stage2 = RSU6(64,32,128)
          self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.stage3 = RSU5(128,64,256)
          self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.stage4 = RSU4(256,128,512)
          self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.stage5 = RSU4F(512,256,512)
          self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
  
          self.stage6 = RSU4F(512,256,512)
  
          # decoder
          self.stage5d = RSU4F(1024,256,512)
          self.stage4d = RSU4(1024,128,256)
          self.stage3d = RSU5(512,64,128)
          self.stage2d = RSU6(256,32,64)
          self.stage1d = RSU7(128,16,64)
  
          self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
          self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
          self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
          self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
          self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
          self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
  
          # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
  
      def forward(self,x):
  
          hx = x
  
          hxin = self.conv_in(hx)
          #hx = self.pool_in(hxin)
  
          #stage 1
          hx1 = self.stage1(hxin)
          hx = self.pool12(hx1)
  
          #stage 2
          hx2 = self.stage2(hx)
          hx = self.pool23(hx2)
  
          #stage 3
          hx3 = self.stage3(hx)
          hx = self.pool34(hx3)
  
          #stage 4
          hx4 = self.stage4(hx)
          hx = self.pool45(hx4)
  
          #stage 5
          hx5 = self.stage5(hx)
          hx = self.pool56(hx5)
  
          #stage 6
          hx6 = self.stage6(hx)
          hx6up = _upsample_like(hx6,hx5)
  
          #-------------------- decoder --------------------
          hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
          hx5dup = _upsample_like(hx5d,hx4)
  
          hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
          hx4dup = _upsample_like(hx4d,hx3)
  
          hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
          hx3dup = _upsample_like(hx3d,hx2)
  
          hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
          hx2dup = _upsample_like(hx2d,hx1)
  
          hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
  
  
          #side output
          d1 = self.side1(hx1d)
          d1 = _upsample_like(d1,x)
  
          d2 = self.side2(hx2d)
          d2 = _upsample_like(d2,x)
  
          d3 = self.side3(hx3d)
          d3 = _upsample_like(d3,x)
  
          d4 = self.side4(hx4d)
          d4 = _upsample_like(d4,x)
  
          d5 = self.side5(hx5d)
          d5 = _upsample_like(d5,x)
  
          d6 = self.side6(hx6)
          d6 = _upsample_like(d6,x)
  
          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]
  
  