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briarmbg.py
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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 huggingface_hub import PyTorchModelHubMixin
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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.interpolate(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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| 339 |
-
dilation=dilation,
|
| 340 |
-
groups=groups)
|
| 341 |
-
self.bn = nn.BatchNorm2d(out_ch)
|
| 342 |
-
self.rl = nn.ReLU(inplace=True)
|
| 343 |
-
|
| 344 |
-
def forward(self,x):
|
| 345 |
-
return self.rl(self.bn(self.conv(x)))
|
| 346 |
-
|
| 347 |
-
|
| 348 |
-
class BriaRMBG(nn.Module, PyTorchModelHubMixin):
|
| 349 |
-
|
| 350 |
-
def __init__(self,config:dict={"in_ch":3,"out_ch":1}):
|
| 351 |
-
super(BriaRMBG,self).__init__()
|
| 352 |
-
in_ch=config["in_ch"]
|
| 353 |
-
out_ch=config["out_ch"]
|
| 354 |
-
self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
|
| 355 |
-
self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
| 356 |
-
|
| 357 |
-
self.stage1 = RSU7(64,32,64)
|
| 358 |
-
self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
| 359 |
-
|
| 360 |
-
self.stage2 = RSU6(64,32,128)
|
| 361 |
-
self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
| 362 |
-
|
| 363 |
-
self.stage3 = RSU5(128,64,256)
|
| 364 |
-
self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
| 365 |
-
|
| 366 |
-
self.stage4 = RSU4(256,128,512)
|
| 367 |
-
self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
| 368 |
-
|
| 369 |
-
self.stage5 = RSU4F(512,256,512)
|
| 370 |
-
self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
| 371 |
-
|
| 372 |
-
self.stage6 = RSU4F(512,256,512)
|
| 373 |
-
|
| 374 |
-
# decoder
|
| 375 |
-
self.stage5d = RSU4F(1024,256,512)
|
| 376 |
-
self.stage4d = RSU4(1024,128,256)
|
| 377 |
-
self.stage3d = RSU5(512,64,128)
|
| 378 |
-
self.stage2d = RSU6(256,32,64)
|
| 379 |
-
self.stage1d = RSU7(128,16,64)
|
| 380 |
-
|
| 381 |
-
self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
|
| 382 |
-
self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
|
| 383 |
-
self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
|
| 384 |
-
self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
|
| 385 |
-
self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
|
| 386 |
-
self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
|
| 387 |
-
|
| 388 |
-
# self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
|
| 389 |
-
|
| 390 |
-
def forward(self,x):
|
| 391 |
-
|
| 392 |
-
hx = x
|
| 393 |
-
|
| 394 |
-
hxin = self.conv_in(hx)
|
| 395 |
-
#hx = self.pool_in(hxin)
|
| 396 |
-
|
| 397 |
-
#stage 1
|
| 398 |
-
hx1 = self.stage1(hxin)
|
| 399 |
-
hx = self.pool12(hx1)
|
| 400 |
-
|
| 401 |
-
#stage 2
|
| 402 |
-
hx2 = self.stage2(hx)
|
| 403 |
-
hx = self.pool23(hx2)
|
| 404 |
-
|
| 405 |
-
#stage 3
|
| 406 |
-
hx3 = self.stage3(hx)
|
| 407 |
-
hx = self.pool34(hx3)
|
| 408 |
-
|
| 409 |
-
#stage 4
|
| 410 |
-
hx4 = self.stage4(hx)
|
| 411 |
-
hx = self.pool45(hx4)
|
| 412 |
-
|
| 413 |
-
#stage 5
|
| 414 |
-
hx5 = self.stage5(hx)
|
| 415 |
-
hx = self.pool56(hx5)
|
| 416 |
-
|
| 417 |
-
#stage 6
|
| 418 |
-
hx6 = self.stage6(hx)
|
| 419 |
-
hx6up = _upsample_like(hx6,hx5)
|
| 420 |
-
|
| 421 |
-
#-------------------- decoder --------------------
|
| 422 |
-
hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
|
| 423 |
-
hx5dup = _upsample_like(hx5d,hx4)
|
| 424 |
-
|
| 425 |
-
hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
|
| 426 |
-
hx4dup = _upsample_like(hx4d,hx3)
|
| 427 |
-
|
| 428 |
-
hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
|
| 429 |
-
hx3dup = _upsample_like(hx3d,hx2)
|
| 430 |
-
|
| 431 |
-
hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
|
| 432 |
-
hx2dup = _upsample_like(hx2d,hx1)
|
| 433 |
-
|
| 434 |
-
hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
|
| 435 |
-
|
| 436 |
-
|
| 437 |
-
#side output
|
| 438 |
-
d1 = self.side1(hx1d)
|
| 439 |
-
d1 = _upsample_like(d1,x)
|
| 440 |
-
|
| 441 |
-
d2 = self.side2(hx2d)
|
| 442 |
-
d2 = _upsample_like(d2,x)
|
| 443 |
-
|
| 444 |
-
d3 = self.side3(hx3d)
|
| 445 |
-
d3 = _upsample_like(d3,x)
|
| 446 |
-
|
| 447 |
-
d4 = self.side4(hx4d)
|
| 448 |
-
d4 = _upsample_like(d4,x)
|
| 449 |
-
|
| 450 |
-
d5 = self.side5(hx5d)
|
| 451 |
-
d5 = _upsample_like(d5,x)
|
| 452 |
-
|
| 453 |
-
d6 = self.side6(hx6)
|
| 454 |
-
d6 = _upsample_like(d6,x)
|
| 455 |
-
|
| 456 |
-
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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