def __init__(self, args, conv=common.default_conv):
super(BSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3 # 卷积核大小
self.scale_idx = 0
act = nn.ReLU(True) # 激活函数
self.DWT = common.DWT() # 二维离散小波
self.IWT = common.IWT() # 逆向的二维离散小波
n = 3
# downsample的第一层，维度变化4->16
m_head = [common.BBlock(conv, 4, 160, 3, act=act)]
d_l1 = []
for _ in range(n):
d_l1.append(common.BBlock(conv, 160, 160, 3, act=act))
# downsample的第二层，维度变化640->256（默认的feature map == 64）
d_l2 = [common.BBlock(conv, 640, n_feats * 4, 3, act=act)]
for _ in range(n):
d_l2.append(
common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
# downsample的第三层，并与upsample进行连接，也是upsample的第三层
# 维度变化1024->256，256->1024
pro_l3 = [common.BBlock(conv, n_feats * 16, n_feats * 4, 3, act=act)]
for _ in range(n * 2):
pro_l3.append(
common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3.append(
common.BBlock(conv, n_feats * 4, n_feats * 16, 3, act=act))
# upsample的第二层，维度变化256->640
i_l2 = []
for _ in range(n):
i_l2.append(
common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
i_l2.append(common.BBlock(conv, n_feats * 4, 640, 3, act=act))
# upsample的第一层，维度变化160->4
i_l1 = []
for _ in range(n):
i_l1.append((common.BBlock(conv, 160, 160, 3, act=act)))
m_tail = [conv(160, 4, 3)]
# downsample的第一层
self.head = nn.Sequential(*m_head)
self.d_l1 = nn.Sequential(*d_l1)
# downsample的第二层
self.d_l2 = nn.Sequential(*d_l2)
# 第三层连接层
self.pro_l3 = nn.Sequential(*pro_l3)
# upsample的第二层
self.i_l2 = nn.Sequential(*i_l2)
# upsample的第一层
self.i_l1 = nn.Sequential(*i_l1)
self.tail = nn.Sequential(*m_tail)
def __init__(self):
super(MWRN_lv2_head, self).__init__()
self.color_channel = 3
self.conv2_head_1 = common.BBlock(common.default_conv0,16*self.color_channel,256,3,bn=True)
self.conv2_head_2 = common.BBlock(common.default_conv0,512,256,3,bn=True)
self.res2_head = nn.Sequential(*[common.ResBlock(common.default_conv0,256,3) for i in range(4)])
self.conv2_head_3 = common.BBlock(common.default_conv0,1024,512,3,bn=True)
self.DWT = common.DWT()
def __init__(self, args, conv=common.default_conv):
super(BSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
self.scale_idx = 0
act = nn.ReLU(True)
self.DWT = common.DWT()
self.IWT = common.IWT()
n = 3
m_head = [common.BBlock(conv, 4, 160, 3, act=act)]
d_l1 = []
for _ in range(n):
d_l1.append(common.BBlock(conv, 160, 160, 3, act=act))
d_l2 = [common.BBlock(conv, 640, n_feats * 4, 3, act=act)]
for _ in range(n):
d_l2.append(
common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3 = [common.BBlock(conv, n_feats * 16, n_feats * 4, 3, act=act)]
for _ in range(n * 2):
pro_l3.append(
common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3.append(
common.BBlock(conv, n_feats * 4, n_feats * 16, 3, act=act))
i_l2 = []
for _ in range(n):
i_l2.append(
common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
i_l2.append(common.BBlock(conv, n_feats * 4, 640, 3, act=act))
i_l1 = []
for _ in range(n):
i_l1.append((common.BBlock(conv, 160, 160, 3, act=act)))
m_tail = [conv(160, 4, 3)]
self.head = nn.Sequential(*m_head)
self.d_l2 = nn.Sequential(*d_l2)
self.d_l1 = nn.Sequential(*d_l1)
self.pro_l3 = nn.Sequential(*pro_l3)
self.i_l2 = nn.Sequential(*i_l2)
self.i_l1 = nn.Sequential(*i_l1)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(KERNEL_EST, self).__init__()
n_resblocks = args.n_resblocks
n_feats = 48#args.n_feats
kernel_size = 3
self.scale_idx = 0
act = nn.ReLU(True)
self.DWT = common.DWT()
self.IWT = common.IWT()
m_head = [conv(args.n_colors*4+20, n_feats*2, kernel_size)]
m_body = []
for _ in range(10):
m_body.append(common.BBlock(conv, n_feats*2, n_feats*2, kernel_size, act=act))
m_tail = [conv(n_feats*2, args.n_colors*4, kernel_size)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self):
super(MWRN_lv1_tail, self).__init__()
self.color_channel = 3
self.conv1_tail_1 = common.BBlock(common.default_conv0,
256,
640,
3,
bn=True)
self.IWT = common.IWT()
self.res1_tail = nn.Sequential(
*[common.ResBlock(common.default_conv0, 160, 3) for i in range(4)])
self.conv1_tail_2 = common.BBlock(common.default_conv0,
160,
4 * self.color_channel,
3,
bn=True)
def __init__(self):
super(MWRN_lv2_tail, self).__init__()
self.color_channel = 3
self.conv2_tail_1 = common.BBlock(common.default_conv0,
512,
1024,
3,
bn=True)
self.IWT = common.IWT()
self.res2_tail = nn.Sequential(
*[common.ResBlock(common.default_conv0, 256, 3) for i in range(4)])
self.conv2_tail_img = common.BBlock(common.default_conv0,
256,
16 * self.color_channel,
3,
bn=True)
def __init__(self):
super(MWRN_lv3, self).__init__()
self.color_channel = 3
self.conv3_1 = common.BBlock(common.default_conv0,
64 * self.color_channel,
512,
3,
bn=True)
self.conv3_2 = common.BBlock(common.default_conv0,
1024,
512,
3,
bn=True)
self.res3 = nn.Sequential(
*[common.ResBlock(common.default_conv0, 512, 3) for i in range(8)])
self.conv3_img = common.BBlock(common.default_conv0,
512,
64 * self.color_channel,
3,
bn=True)
def __init__(self, args, conv=common.default_conv):
super().__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
self.scale_idx = 0
nColor = args.n_colors
act = nn.ReLU(True)
self.DWT = common.DWT()
self.IWT = common.IWT()
n = 1
m_head = [common.BBlock(conv, nColor, n_feats, kernel_size, act=act)]
d_l0 = []
d_l0.append(common.DBlock_com1(conv, n_feats, n_feats, kernel_size, act=act, bn=False))
d_l1 = [common.BBlock(conv, n_feats * 4, n_feats * 2, kernel_size, act=act, bn=False)]
d_l1.append(common.DBlock_com1(conv, n_feats * 2, n_feats * 2, kernel_size, act=act, bn=False))
d_l2 = []
d_l2.append(common.BBlock(conv, n_feats * 8, n_feats * 4, kernel_size, act=act, bn=False))
d_l2.append(common.DBlock_com1(conv, n_feats * 4, n_feats * 4, kernel_size, act=act, bn=False))
pro_l3 = []
pro_l3.append(common.BBlock(conv, n_feats * 16, n_feats * 8, kernel_size, act=act, bn=False))
pro_l3.append(common.DBlock_com(conv, n_feats * 8, n_feats * 8, kernel_size, act=act, bn=False))
pro_l3.append(common.DBlock_inv(conv, n_feats * 8, n_feats * 8, kernel_size, act=act, bn=False))
pro_l3.append(common.BBlock(conv, n_feats * 8, n_feats * 16, kernel_size, act=act, bn=False))
i_l2 = [common.DBlock_inv1(conv, n_feats * 4, n_feats * 4, kernel_size, act=act, bn=False)]
i_l2.append(common.BBlock(conv, n_feats * 4, n_feats * 8, kernel_size, act=act, bn=False))
i_l1 = [common.DBlock_inv1(conv, n_feats * 2, n_feats * 2, kernel_size, act=act, bn=False)]
i_l1.append(common.BBlock(conv, n_feats * 2, n_feats * 4, kernel_size, act=act, bn=False))
i_l0 = [common.DBlock_inv1(conv, n_feats, n_feats, kernel_size, act=act, bn=False)]
m_tail = [conv(n_feats, nColor, kernel_size)]
self.head = nn.Sequential(*m_head)
self.d_l2 = nn.Sequential(*d_l2)
self.d_l1 = nn.Sequential(*d_l1)
self.d_l0 = nn.Sequential(*d_l0)
self.pro_l3 = nn.Sequential(*pro_l3)
self.i_l2 = nn.Sequential(*i_l2)
self.i_l1 = nn.Sequential(*i_l1)
self.i_l0 = nn.Sequential(*i_l0)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(NL_EST, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
n_feats = 48
kernel_size = 3
self.scale_idx = 0
act = nn.ReLU(True)
# rgb_mean = (0.4488, 0.4371, 0.4040)
# rgb_std = (1.0, 1.0, 1.0)
self.DWT = common.DWT()
self.IWT = common.IWT()
m_head = [conv(args.n_colors * 4 + 4, n_feats * 2, kernel_size)]
m_body = []
for _ in range(10):
m_body.append(
common.BBlock(conv,
n_feats * 2,
n_feats * 2,
kernel_size,
act=act))
# m_body.append(conv(n_feats, n_feats, kernel_size))
#
# self.upsample = nn.ModuleList([
# common.Upsampler(
# conv, s, n_feats, act=False
# ) for s in args.scale
# ])
m_tail = [conv(n_feats * 2, args.n_colors * 4, kernel_size)]
# self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(CBSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
self.scale_idx = 0
act = nn.ReLU(True)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
self.DWT = common.DWT()
self.IWT = common.IWT()
n = n_resblocks
m_head = [conv(args.n_colors * 4 + 20, n_feats * 2, kernel_size + 2)]
# d_l1 = [common.BBlock(conv, n_feats*4, n_feats*2, 3, act=act)]
d_l1 = []
for _ in range(n):
#d_l1.append(common.BBlock(conv, n_feats*2, n_feats*2, 3, act=act))
d_l1.append(common.ResBlock(conv, n_feats * 2, 3, act=act))
d_l1.append(act)
# dwt_l2 = [common.DWT]
d_l2 = [common.BBlock(conv, n_feats * 8, n_feats * 4, 3, act=act)]
for _ in range(n):
d_l2.append(common.ResBlock(conv, n_feats * 4, 3, act=act))
d_l2.append(act)
#d_l2.append(common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
# pro_l3 = [common.DWT]
pro_l3 = [common.BBlock(conv, n_feats * 16, n_feats * 8, 3, act=act)]
for _ in range(n * 2):
pro_l3.append(common.ResBlock(conv, n_feats * 8, 3, act=act))
pro_l3.append(act)
# pro_l3.append(common.BBlock(conv, n_feats * 8, n_feats * 16, 3, act=act))
pro_l3.append(conv(n_feats * 8, n_feats * 16, 3))
# pro_l3.append(common.IWT)
i_l2 = []
for _ in range(n):
#i_l2.append(common.BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
i_l2.append(common.ResBlock(conv, n_feats * 4, 3, act=act))
i_l2.append(act)
# i_l2.append(common.BBlock(conv, n_feats * 4, n_feats * 8, 3, act=act))
i_l2.append(conv(n_feats * 4, n_feats * 8, 3))
# IWT = common.IWT
# DWT = common.DWT
i_l1 = []
for _ in range(n):
#i_l1.append(common.BBlock(conv, n_feats * 2, n_feats * 2, 3, act=act))
i_l1.append(common.ResBlock(conv, n_feats * 2, 3, act=act))
i_l1.append(act)
m_tail = [conv(n_feats * 2, args.n_colors * 4, kernel_size + 4)]
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
# self.d_l0 = nn.Sequential(*d_l0)
self.d_l1 = nn.Sequential(*d_l1)
self.d_l2 = nn.Sequential(*d_l2)
self.pro_l3 = nn.Sequential(*pro_l3)
self.i_l2 = nn.Sequential(*i_l2)
self.i_l1 = nn.Sequential(*i_l1)
# self.i_l0 = nn.Sequential(*i_l0)
self.tail = nn.Sequential(*m_tail)
self.act = nn.Sequential(act)
