def __init__(self, args, conv=basenet.default_conv):
super(EDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale
act = nn.ReLU(True)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
basenet.ResBlock(conv, n_feats, kernel_size, act=act)
for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
basenet.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=basenet.default_conv):
super(Upscalar, self).__init__()
n_feats = args.n_feats
kernel_size = 3
scale = args.scale
act = nn.ReLU(True)
m_upscalar = [
basenet.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.upscalar = nn.Sequential(*m_upscalar)
def __init__(self, args, conv=basenet.default_conv):
super(RCAN, self).__init__()
n_resgroups = args.n_resgroups
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
reduction = args.reduction
#scale = args.scale[0]
scale = args.scale
act = nn.ReLU(True)
# RGB mean for DIV2K
#self.sub_mean = basenet.MeanShift(args.rgb_range)
# define head module
modules_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
modules_body = [
ResidualGroup(
conv, n_feats, kernel_size, reduction, act=act, res_scale=1, n_resblocks=n_resblocks) \
for _ in range(n_resgroups)]
modules_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
modules_tail = [
basenet.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
#self.add_mean = basenet.MeanShift(args.rgb_range, sign=1)
self.head = nn.Sequential(*modules_head)
self.body = nn.Sequential(*modules_body)
self.tail = nn.Sequential(*modules_tail)
def __init__(self, args, conv=basenet.default_conv):
super(MDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
act = nn.ReLU(True)
self.scale_idx = 0
self.url = url['r{}f{}'.format(n_resblocks, n_feats)]
self.sub_mean = basenet.MeanShift(args.rgb_range)
self.add_mean = basenet.MeanShift(args.rgb_range, sign=1)
m_head = [conv(args.n_colors, n_feats, kernel_size)]
self.pre_process = nn.ModuleList([
nn.Sequential(
basenet.ResBlock(conv, n_feats, 5, act=act),
basenet.ResBlock(conv, n_feats, 5, act=act)
) for _ in args.scale
])
m_body = [
basenet.ResBlock(
conv, n_feats, kernel_size, act=act
) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
self.upsample = nn.ModuleList([
basenet.Upsampler(conv, s, n_feats, act=False) for s in args.scale
])
m_tail = [conv(n_feats, args.n_colors, kernel_size)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args):
super(DRLN, self).__init__()
#n_resgroups = args.n_resgroups
#n_resblocks = args.n_resblocks
#n_feats = args.n_feats
#kernel_size = 3
#reduction = args.reduction
#scale = args.scale[0]
#act = nn.ReLU(True)
#self.scale = args.scale[0]
self.scale = args.scale
#chs=64
chs=args.n_feats
#self.sub_mean = MeanShift((0.4488, 0.4371, 0.4040), sub=True)
#self.add_mean = 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.c1 = basenet.BasicBlock(chs*2, chs, 3, 1, 1)
self.c2 = basenet.BasicBlock(chs*3, chs, 3, 1, 1)
self.c3 = basenet.BasicBlock(chs*4, chs, 3, 1, 1)
self.c4 = basenet.BasicBlock(chs*2, chs, 3, 1, 1)
self.c5 = basenet.BasicBlock(chs*3, chs, 3, 1, 1)
self.c6 = basenet.BasicBlock(chs*4, chs, 3, 1, 1)
self.c7 = basenet.BasicBlock(chs*2, chs, 3, 1, 1)
self.c8 = basenet.BasicBlock(chs*3, chs, 3, 1, 1)
self.c9 = basenet.BasicBlock(chs*4, chs, 3, 1, 1)
self.c10 = basenet.BasicBlock(chs*2, chs, 3, 1, 1)
self.c11 = basenet.BasicBlock(chs*3, chs, 3, 1, 1)
self.c12 = basenet.BasicBlock(chs*4, chs, 3, 1, 1)
self.c13 = basenet.BasicBlock(chs*2, chs, 3, 1, 1)
self.c14 = basenet.BasicBlock(chs*3, chs, 3, 1, 1)
self.c15 = basenet.BasicBlock(chs*4, chs, 3, 1, 1)
self.c16 = basenet.BasicBlock(chs*5, chs, 3, 1, 1)
self.c17 = basenet.BasicBlock(chs*2, chs, 3, 1, 1)
self.c18 = basenet.BasicBlock(chs*3, chs, 3, 1, 1)
self.c19 = basenet.BasicBlock(chs*4, chs, 3, 1, 1)
self.c20 = basenet.BasicBlock(chs*5, chs, 3, 1, 1)
#self.upsample = basenet.UpsampleBlock(chs, self.scale , multi_scale=False)
self.upsample = basenet.Upsampler(basenet.default_conv, self.scale, chs, act=False)
#self.convert = ops.ConvertBlock(chs, chs, 20)
self.tail = nn.Conv2d(chs, 3, 3, 1, 1)