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	import torch
	import torch.nn as nn
	import torch.nn.functional as F


	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.upsample(src, size=tar.shape[2:], mode="bilinear")

	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))

	"""
	del hx1, hx2, hx3, hx4, hx5, hx6, hx7
	del hx6d, hx5d, hx3d, hx2d
	del hx2dup, hx3dup, hx4dup, hx5dup, hx6dup
	"""

	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))

	"""
	del hx1, hx2, hx3, hx4, hx5, hx6
	del hx5d, hx4d, hx3d, hx2d
	del hx2dup, hx3dup, hx4dup, hx5dup
	"""

	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))

	"""
	del hx1, hx2, hx3, hx4, hx5
	del hx4d, hx3d, hx2d
	del hx2dup, hx3dup, hx4dup
	"""

	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))

	"""
	del hx1, hx2, hx3, hx4
	del hx3d, hx2d
	del hx2dup, hx3dup
	"""

	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))

	"""
	del hx1, hx2, hx3, hx4
	del hx3d, hx2d
	"""

	return hx1d + hxin


	##### U^2-Net ####
	class U2NET(nn.Module):
	def __init__(self, in_ch=3, out_ch=1):
	super(U2NET, self).__init__()

	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, 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))

	"""
	del hx1, hx2, hx3, hx4, hx5, hx6
	del hx5d, hx4d, hx3d, hx2d, hx1d
	del hx6up, hx5dup, hx4dup, hx3dup, hx2dup
	"""

	return d0, d1, d2, d3, d4, d5, 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, 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 d0, d1, d2, d3, d4, d5, d6