def __init__(self, opt):
super(NET, self).__init__()
n_resblocks = opt.n_resblocks
n_feats = opt.channels
bias = opt.bias
norm_type = opt.norm_type
act_type = opt.act_type
block_type = opt.block_type
head = [common.ConvBlock(4, n_feats, 5, act_type=act_type, bias=True)]
if block_type.lower() == 'rrdb':
resblock = [common.RRDB(n_feats, n_feats, 3,
1, bias, norm_type, act_type, 0.2)
for _ in range(n_resblocks)]
elif block_type.lower() == 'res':
resblock = [common.ResBlock(n_feats, 3, norm_type, act_type, res_scale=1, bias=bias)
for _ in range(n_resblocks)]
else:
raise RuntimeError('block_type is not supported')
resblock += [common.ConvBlock(n_feats, n_feats, 3, bias=True)]
tail = [common.ConvBlock(n_feats, 3, 3, bias=True)]
self.model = nn.Sequential(*head, common.ShortcutBlock(nn.Sequential(*resblock)), *tail)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
def __init__(self, opt):
super(NET, self).__init__()
# parameter
denoise = opt.denoise
block_type = opt.block_type
n_feats = opt.channels
act_type = opt.act_type
bias = opt.bias
norm_type = opt.norm_type
n_resblocks = opt.n_resblocks
# architecture
if denoise:
dm_head = [
common.ConvBlock(5, n_feats, 5, act_type=act_type, bias=True)
]
else:
dm_head = [
common.ConvBlock(4, n_feats, 5, act_type=act_type, bias=True)
]
if block_type.lower() == 'rrdb':
dm_resblock = [
common.RRDB(n_feats, n_feats, 3, 1, bias, norm_type, act_type,
0.2) for _ in range(n_resblocks)
]
elif block_type.lower() == 'res':
dm_resblock = [
common.ResBlock(n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(n_resblocks)
]
else:
raise RuntimeError('block_type is not supported')
dm_resblock += [common.ConvBlock(n_feats, n_feats, 3, bias=True)]
m_dm_up = [
common.Upsampler(2, n_feats, norm_type, act_type, bias=bias),
common.ConvBlock(n_feats, 3, 3, bias=True)
]
self.model_dm = nn.Sequential(
*dm_head, common.ShortcutBlock(nn.Sequential(*dm_resblock)),
*m_dm_up)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
def __init__(self, opt):
super(NET, self).__init__()
sr_n_resblocks = opt.sr_n_resblocks
dm_n_resblocks = opt.dm_n_resblocks
sr_n_feats = opt.channels
dm_n_feats = opt.channels
scale = opt.scale
denoise = opt.denoise
block_type = opt.block_type
act_type = opt.act_type
bias = opt.bias
norm_type = opt.norm_type
# define sr module
if denoise:
m_sr_head = [
common.ConvBlock(5,
sr_n_feats,
5,
act_type=act_type,
bias=True)
]
else:
m_sr_head = [
common.ConvBlock(4,
sr_n_feats,
5,
act_type=act_type,
bias=True)
]
if block_type.lower() == 'rrdb':
m_sr_resblock = [
common.RRDB(sr_n_feats, sr_n_feats, 3, 1, bias, norm_type,
act_type, 0.2) for _ in range(sr_n_resblocks)
]
elif block_type.lower() == 'dudb':
m_sr_resblock = [
common.DUDB(sr_n_feats, 3, 1, bias, norm_type, act_type, 0.2)
for _ in range(sr_n_resblocks)
]
elif block_type.lower() == 'res':
m_sr_resblock = [
common.ResBlock(sr_n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(sr_n_resblocks)
]
else:
raise RuntimeError('block_type is not supported')
m_sr_resblock += [
common.ConvBlock(sr_n_feats, sr_n_feats, 3, bias=bias)
]
m_sr_up = [
common.Upsampler(scale, sr_n_feats, norm_type, act_type,
bias=bias),
common.ConvBlock(sr_n_feats, 4, 3, bias=True)
]
# branch for sr_raw output
m_sr_tail = [nn.PixelShuffle(2)]
# define demosaick module
m_dm_head = [
common.ConvBlock(4, dm_n_feats, 5, act_type=act_type, bias=True)
]
if block_type.lower() == 'rrdb':
m_dm_resblock = [
common.RRDB(dm_n_feats, dm_n_feats, 3, 1, bias, norm_type,
act_type, 0.2) for _ in range(dm_n_resblocks)
]
elif block_type.lower() == 'dudb':
m_dm_resblock = [
common.DUDB(dm_n_feats, 3, 1, bias, norm_type, act_type, 0.2)
for _ in range(dm_n_resblocks)
]
elif block_type.lower() == 'res':
m_dm_resblock = [
common.ResBlock(dm_n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(dm_n_resblocks)
]
else:
raise RuntimeError('block_type is not supported')
m_dm_resblock += [
common.ConvBlock(dm_n_feats, dm_n_feats, 3, bias=bias)
]
m_dm_up = [
common.Upsampler(2, dm_n_feats, norm_type, act_type, bias=bias),
common.ConvBlock(dm_n_feats, 3, 3, bias=True)
]
self.model_sr = nn.Sequential(
*m_sr_head, common.ShortcutBlock(nn.Sequential(*m_sr_resblock)),
*m_sr_up)
self.sr_output = nn.Sequential(*m_sr_tail)
self.model_dm = nn.Sequential(
*m_dm_head, common.ShortcutBlock(nn.Sequential(*m_dm_resblock)),
*m_dm_up)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
def __init__(self,
sr_n_resblocks=6,
dm_n_resblock=6,
sr_n_feats=64,
dm_n_feats=64,
scale=2,
denoise=True,
bias=True,
norm_type=False,
act_type='relu',
block_type='rrdb'):
super(NET, self).__init__()
# act = nn.LeakyReLU(negative_slope=0.1, inplace=False)
# act = nn.PReLU(n_feats)
# define sr module
if denoise:
m_sr_head = [
common.ConvBlock(5,
sr_n_feats,
5,
act_type=act_type,
bias=True)
]
else:
m_sr_head = [
common.ConvBlock(4,
sr_n_feats,
5,
act_type=act_type,
bias=True)
]
if block_type.lower() == 'rrdb':
m_sr_resblock = [
common.RRDB(sr_n_feats, sr_n_feats, 3, 1, bias, norm_type,
act_type, 0.2) for _ in range(sr_n_resblocks)
]
elif block_type.lower() == 'dudb':
m_sr_resblock = [
common.DUDB(sr_n_feats, 3, 1, bias, norm_type, act_type, 0.2)
for _ in range(sr_n_resblocks)
]
elif block_type.lower() == 'res':
m_sr_resblock = [
common.ResBlock(sr_n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(sr_n_resblocks)
]
else:
raise RuntimeError('block_type is not supported')
m_sr_resblock += [
common.ConvBlock(sr_n_feats, sr_n_feats, 3, bias=bias)
]
m_sr_up = [
common.Upsampler(scale, sr_n_feats, norm_type, act_type,
bias=bias),
common.ConvBlock(sr_n_feats, 4, 3, bias=True)
]
# branch for sr_raw output
m_sr_tail = [nn.PixelShuffle(2)]
# define demosaick module
m_dm_head = [
common.ConvBlock(4, dm_n_feats, 5, act_type=act_type, bias=True)
]
if block_type.lower() == 'rrdb':
m_dm_resblock = [
common.RRDB(dm_n_feats, dm_n_feats, 3, 1, bias, norm_type,
act_type, 0.2) for _ in range(dm_n_resblock)
]
elif block_type.lower() == 'dudb':
m_dm_resblock = [
common.DUDB(dm_n_feats, 3, 1, bias, norm_type, act_type, 0.2)
for _ in range(dm_n_resblock)
]
elif block_type.lower() == 'res':
m_dm_resblock = [
common.ResBlock(dm_n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(dm_n_resblock)
]
else:
raise RuntimeError('block_type is not supported')
m_dm_resblock += [
common.ConvBlock(dm_n_feats, dm_n_feats, 3, bias=bias)
]
m_dm_up = [
common.Upsampler(2, dm_n_feats, norm_type, act_type, bias=bias),
common.ConvBlock(dm_n_feats, 3, 3, bias=True)
]
self.model_sr = nn.Sequential(
*m_sr_head, common.ShortcutBlock(nn.Sequential(*m_sr_resblock)),
*m_sr_up)
self.sr_output = nn.Sequential(*m_sr_tail)
self.model_dm = nn.Sequential(
*m_dm_head, common.ShortcutBlock(nn.Sequential(*m_dm_resblock)),
*m_dm_up)
for m in self.modules():
# pdb.set_trace()
if isinstance(m, nn.Conv2d):
# Xavier
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
def __init__(self, opt):
super(NET, self).__init__()
sr_n_resblocks = opt.sr_n_resblocks
dm_n_resblocks = opt.dm_n_resblocks
sr_n_feats = opt.channels
dm_n_feats = opt.channels
scale = opt.scale
denoise = opt.denoise
block_type = opt.block_type
act_type = opt.act_type
bias = opt.bias
norm_type = opt.norm_type
# define sr module
if denoise:
m_sr_head = [
common.ConvBlock(6,
sr_n_feats,
5,
act_type=act_type,
bias=True)
]
else:
m_sr_head = [
common.ConvBlock(4,
sr_n_feats,
5,
act_type=act_type,
bias=True)
]
if block_type.lower() == 'rrdb':
m_sr_resblock = [
common.RRDB(sr_n_feats, sr_n_feats, 3, 1, bias, norm_type,
act_type, 0.2) for _ in range(sr_n_resblocks)
]
elif block_type.lower() == 'dudb':
m_sr_resblock = [
common.DUDB(sr_n_feats, 3, 1, bias, norm_type, act_type, 0.2)
for _ in range(sr_n_resblocks)
]
elif block_type.lower() == 'res':
m_sr_resblock = [
common.ResBlock(sr_n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(sr_n_resblocks)
]
else:
raise RuntimeError('block_type is not supported')
m_sr_resblock += [
common.ConvBlock(sr_n_feats, sr_n_feats, 3, bias=bias)
]
m_sr_up = [
common.Upsampler(scale, sr_n_feats, norm_type, act_type,
bias=bias),
common.ConvBlock(sr_n_feats, 4, 3, bias=True)
]
# branch for sr_raw output
m_sr_tail = [nn.PixelShuffle(2)]
# define demosaick module
m_dm_head = [
common.ConvBlock(4, dm_n_feats, 5, act_type=act_type, bias=True)
]
if block_type.lower() == 'rrdb':
m_dm_resblock = m_res(
opt) #[common.RRDB(dm_n_feats, dm_n_feats, 3,
#1, bias, norm_type, act_type, 0.2)
#for _ in range(dm_n_resblocks)]
elif block_type.lower() == 'dudb':
m_dm_resblock = [
common.DUDB(dm_n_feats, 3, 1, bias, norm_type, act_type, 0.2)
for _ in range(dm_n_resblocks)
]
elif block_type.lower() == 'res':
m_dm_resblock = [
common.ResBlock(dm_n_feats,
3,
norm_type,
act_type,
res_scale=1,
bias=bias) for _ in range(dm_n_resblocks)
]
else:
raise RuntimeError('block_type is not supported')
#m_dm_resblock += [common.ConvBlock(dm_n_feats, dm_n_feats, 3, bias=bias)]
m_dm_up = [
common.Upsampler(2, dm_n_feats, norm_type, act_type, bias=bias)
]
#common.ConvBlock(dm_n_feats, 3, 3, bias=True)]
self.model_sr = nn.Sequential(
*m_sr_head, common.ShortcutBlock(nn.Sequential(*m_sr_resblock)),
*m_sr_up)
self.sr_output = nn.Sequential(*m_sr_tail)
self.model_dm1 = nn.Sequential(*m_dm_head)
self.model_dm2 = m_dm_resblock
self.model_dm3 = nn.Sequential(*m_dm_up)
greenresblock = green_res(opt)
self.green = greenresblock
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
#self.sft = SFTLayer()
self.combine = nn.Sequential(
common.ConvBlock(dm_n_feats + 1, dm_n_feats, 1, bias=True),
nn.LeakyReLU(0.2, inplace=True))
self.greenup = nn.Sequential(common.ConvBlock(1, 4, 1, bias=True),
nn.LeakyReLU(0.2, inplace=True),
common.ConvBlock(4, 8, 1, bias=True),
nn.LeakyReLU(0.2, inplace=True))
self.pac = PacConvTranspose2d(64,
64,
kernel_size=5,
stride=2,
padding=2,
output_padding=1)
self.final = common.ConvBlock(dm_n_feats, 3, 3, bias=True)
self.norm = nn.InstanceNorm2d(1)