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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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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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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) |
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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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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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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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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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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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dilation=dilation, |
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groups=groups) |
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self.bn = nn.BatchNorm2d(out_ch) |
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self.rl = nn.ReLU(inplace=True) |
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def forward(self,x): |
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return self.rl(self.bn(self.conv(x))) |
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class BriaRMBG(nn.Module, PyTorchModelHubMixin): |
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def __init__(self,config:dict={"in_ch":3,"out_ch":1}): |
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super(BriaRMBG,self).__init__() |
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in_ch=config["in_ch"] |
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out_ch=config["out_ch"] |
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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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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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self.side2 = nn.Conv2d(64,out_ch,3,padding=1) |
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self.side3 = nn.Conv2d(128,out_ch,3,padding=1) |
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self.side4 = nn.Conv2d(256,out_ch,3,padding=1) |
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self.side5 = nn.Conv2d(512,out_ch,3,padding=1) |
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self.side6 = nn.Conv2d(512,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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hx1 = self.stage1(hxin) |
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hx = self.pool12(hx1) |
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hx2 = self.stage2(hx) |
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hx = self.pool23(hx2) |
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hx3 = self.stage3(hx) |
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hx = self.pool34(hx3) |
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hx4 = self.stage4(hx) |
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hx = self.pool45(hx4) |
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hx5 = self.stage5(hx) |
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hx = self.pool56(hx5) |
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hx6 = self.stage6(hx) |
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hx6up = _upsample_like(hx6,hx5) |
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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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d1 = self.side1(hx1d) |
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d1 = _upsample_like(d1,x) |
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d2 = self.side2(hx2d) |
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d2 = _upsample_like(d2,x) |
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d3 = self.side3(hx3d) |
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d3 = _upsample_like(d3,x) |
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d4 = self.side4(hx4d) |
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d4 = _upsample_like(d4,x) |
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d5 = self.side5(hx5d) |
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d5 = _upsample_like(d5,x) |
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d6 = self.side6(hx6) |
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d6 = _upsample_like(d6,x) |
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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] |