def __init__(self, **kwargs):
super(Net, self).__init__()
#scale = kwargs.get("scale")
scale = 2
multi_scale = kwargs.get("multi_scale")
group = kwargs.get("group", 1)
self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
#@#@ Protect this convolution operation with ABFT
self.entry = nn.Conv2d(3, 64, 3, 1, 1)
self.b1 = Block(64, 64, group=group)
self.b2 = Block(64, 64, group=group)
self.b3 = Block(64, 64, group=group)
self.c1 = ops.BasicBlock(64*2, 64, 1, 1, 0)
self.c2 = ops.BasicBlock(64*3, 64, 1, 1, 0)
self.c3 = ops.BasicBlock(64*4, 64, 1, 1, 0)
self.upsample = ops.UpsampleBlock(64, scale=scale,
multi_scale=multi_scale,
group=group)
#@#@ Protect this convolution operation with ABFT
self.exit = nn.Conv2d(64, 3, 3, 1, 1)
def __init__(self, opt):
super().__init__()
self.sub_mean = ops.MeanShift(255)
self.add_mean = ops.MeanShift(255, sign=1)
head = [
ops.DownBlock(opt.scale),
nn.Conv2d(3 * opt.scale**2, opt.num_channels, 3, 1, 1)
]
body = list()
for _ in range(opt.num_groups):
body += [
Group(opt.num_channels, opt.num_blocks, opt.reduction,
opt.res_scale)
]
body += [nn.Conv2d(opt.num_channels, opt.num_channels, 3, 1, 1)]
tail = [
ops.Upsampler(opt.num_channels, opt.scale),
nn.Conv2d(opt.num_channels, 3, 3, 1, 1)
]
self.head = nn.Sequential(*head)
self.body = nn.Sequential(*body)
self.tail = nn.Sequential(*tail)
self.opt = opt
def __init__(self, opt):
super().__init__()
self.sub_mean = ops.MeanShift(255)
self.add_mean = ops.MeanShift(255, sign=1)
head = [
ops.DownBlock(opt.scale),
nn.Conv2d(3 * opt.scale**2, opt.num_channels, 3, 1, 1)
]
# define body module
for ng in range(opt.num_groups):
setattr(
self, "c{}".format(ng + 1),
nn.Conv2d(opt.num_channels * (ng + 2), opt.num_channels, 1, 1,
0))
setattr(self, "b{}".format(ng + 1),
Group(opt.num_channels, opt.num_blocks))
tail = [
ops.Upsampler(opt.num_channels, opt.scale),
nn.Conv2d(opt.num_channels, 3, 3, 1, 1)
]
self.head = nn.Sequential(*head)
self.tail = nn.Sequential(*tail)
self.opt = opt
def __init__(self, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = 32
n_feats = 64
kernel_size = 3
scale = 2
act = 'relu'
#self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
# define head module
m_head = [conv(3, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(n_feats,
kernel_size,
act_type=act,
bias=True,
res_scale=1) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(scale, n_feats, norm_type=False, act_type=False),
conv(n_feats, 3, kernel_size)
]
self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self):
super(carn, self).__init__()
group = 2
self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
self.entry = nn.Conv2d(3, 64, 3, 1, 1)
self.b1 = Block(64, 64)
self.b2 = Block(64, 64)
self.b3 = Block(64, 64)
self.c1 = ops.BasicBlock(64 * 2, 64, 1, 1, 0)
self.c2 = ops.BasicBlock(64 * 3, 64, 1, 1, 0)
self.c3 = ops.BasicBlock(64 * 4, 64, 1, 1, 0)
self.upsample = ops.UpsampleBlock(64,
scale=2,
multi_scale=False,
group=group)
self.exit = nn.Conv2d(64, 3, 3, 1, 1)
def __init__(self, **kwargs):
super(Net, self).__init__()
self.scale = kwargs.get("scale")
self.multi_scale = kwargs.get("multi_scale")
self.group = kwargs.get("group", 1)
self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
self.entry = nn.Conv2d(3, 64, 3, 1, 1)
modules_body = [
SSPBlock(channels=64, split=4, groups=1, bias=True, add=True) \
for _ in range(16) ]
modules_body.append(nn.Conv2d(64, 64, 3, 1, 1))
self.body = nn.Sequential(*modules_body)
self.upsample = ops.UpsampleBlock(64,
scale=self.scale,
multi_scale=self.multi_scale,
group=self.group)
self.exit = nn.Conv2d(64, 3, 3, 1, 1)
def __init__(self, args):
super(DRLN, self).__init__()
self.stop_block = 1
self.scale = 3#args.scale[0]
chs=64
self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
self.head = nn.Conv2d(3, chs, 3, 1, 1)
self.b1 = Block(chs, chs)
self.b2 = Block(chs, chs)
self.b3 = Block(chs, chs)
self.b4 = Block(chs, chs)
self.b5 = Block(chs, chs)
self.b6 = Block(chs, chs)
self.b7 = Block(chs, chs)
self.b8 = Block(chs, chs)
self.b9 = Block(chs, chs)
self.b10 = Block(chs, chs)
self.b11 = Block(chs, chs)
self.b12 = Block(chs, chs)
self.b13 = Block(chs, chs)
self.b14 = Block(chs, chs)
self.b15 = Block(chs, chs)
self.b16 = Block(chs, chs)
self.b17 = Block(chs, chs)
self.b18 = Block(chs, chs)
self.b19 = Block(chs, chs)
self.b20 = Block(chs, chs)
self.b21 = Block(chs, chs)
self.b22 = Block(chs, chs)
self.b23 = Block(chs, chs)
self.b24 = Block(chs, chs)
self.c1 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c2 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c3 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c4 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c5 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c6 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c7 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c8 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c9 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c10 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c11 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c12 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c13 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c14 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c15 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c16 = ops.BasicBlock(chs*5, chs, 3, 1, 1)
self.c17 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c18 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c19 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c20 = ops.BasicBlock(chs*5, chs, 3, 1, 1)
self.c21 = ops.BasicBlock(chs*2, chs, 3, 1, 1)
self.c22 = ops.BasicBlock(chs*3, chs, 3, 1, 1)
self.c23 = ops.BasicBlock(chs*4, chs, 3, 1, 1)
self.c24 = ops.BasicBlock(chs*5, chs, 3, 1, 1)
self.upsample = ops.UpsampleBlock(chs, self.scale , multi_scale=False)
self.tail = nn.Conv2d(chs, 3, 3, 1, 1)
