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LayerDiffuse-gradio-unofficial / briarmbg.py

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	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]