def __init__(self, args, conv=common.default_conv, BBlock=common.BBlock):
super(BSR, self).__init__()
self.out_channels = 4
self.scale_idx = 0
self.is_fcSim = args.is_fcSim
self.toRGB = common.ApplyBayer2RGB(normalize=False)
self.n_colors = args.n_colors
# Sampling layer
if args.is_fcSim:
self.fc_sim = common.FlatCamSampSim(args.batch_size)
self.add_noise = common.AddNoise(nSig=args.sigma)
self.toBayer = common.ApplyRaw2Bayer()
# Initial Reconstruction
self.init_recon = common.KronConv(in_channels = 4, out_channels=self.out_channels,\
mid_channels=args.mid_channels, act = args.is_act)
self.conv = nn.Conv2d(4,
3,
kernel_size=1,
stride=1,
padding=0,
bias=False)
def __init__(self, args, conv=common.default_conv, BBlock = common.BBlock):
super(BSR, self).__init__()
self.out_channels = 4
self.scale_idx = 0
self.is_fcSim = args.is_fcSim
self.toRGB = common.ApplyBayer2RGB(normalize = False)
n_colors = args.n_colors
n_feats = args.n_feats
# Sampling layer
if args.is_fcSim:
self.fc_sim = common.FlatCamSampSim(args.batch_size)
self.add_noise = common.AddNoise(nSig = args.sigma)
self.toBayer = common.ApplyRaw2Bayer()
# Initial Reconstruction
self.init_recon = common.KronConv(in_channels = 4, out_channels=self.out_channels,\
mid_channels=args.mid_channels, act = args.is_act)
self.conv = nn.Conv2d(4, 3, kernel_size = 1, stride= 1, padding= 0, bias= False)
# Enhance reconstruction
act = nn.ReLU(True)
self.DWT = common.DWT()
self.IWT = common.IWT()
n = 3
m_head = [BBlock(conv, 4 * n_colors, 160, 3, act=act)]
d_l1 = []
for _ in range(n):
d_l1.append(BBlock(conv, 160, 160, 3, act=act))
d_l2 = [BBlock(conv, 640, n_feats * 4, 3, act=act)]
for _ in range(n):
d_l2.append(BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3 = [BBlock(conv, n_feats * 16, n_feats * 4, 3, act=act)]
for _ in range(n*2):
pro_l3.append(BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3.append(BBlock(conv, n_feats * 4, n_feats * 16, 3, act=act))
i_l2 = []
for _ in range(n):
i_l2.append(BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
i_l2.append(BBlock(conv, n_feats * 4,640, 3, act=act))
i_l1 = []
for _ in range(n):
i_l1.append((BBlock(conv,160, 160, 3, act=act)))
m_tail = [conv(160, 4 * n_colors, 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, BBlock=common.BBlock):
super(BSR, self).__init__()
n_colors = args.n_colors
self.scale_idx = 0
#self.in_channels = args.in_channels
self.is_fcSim = args.is_fcSim
self.toRGB = common.ApplyBayer2RGB(normalize=False)
# Sampling layer
if args.is_fcSim:
self.fc_sim = common.FlatCamSampSim(args.batch_size)
self.add_noise = common.AddNoise(nSig=args.sigma)
self.toBayer = common.ApplyRaw2Bayer()
# Initial Reconstruction
self.init_recon1 = common.KronConv(in_channels=1,
out_channels=1,
mid_channels=1,
act=args.is_act)
self.init_recon2 = common.KronConv(in_channels=1,
out_channels=1,
mid_channels=1,
act=args.is_act)
self.init_recon3 = common.KronConv(in_channels=1,
out_channels=1,
mid_channels=1,
act=args.is_act)
self.init_recon4 = common.KronConv(in_channels=1,
out_channels=1,
mid_channels=1,
act=args.is_act)
def __init__(self, args, conv=common.default_conv, BBlock=common.BBlock):
super(BSR, self).__init__()
#n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
n_colors = args.n_colors
self.scale_idx = 0
act = nn.ReLU(True)
self.is_fcSim = args.is_fcSim
# Sampling layer
if args.is_fcSim:
self.fc_sim = common.FlatCamSampSim(args.batch_size)
self.add_noise = common.AddNoise(nSig=args.sigma)
self.init_recon1 = common.FlatCamSimInitConv1()
self.init_recon2 = common.FlatCamSimInitConv2()
self.init_recon3 = common.FlatCamSimInitConv3()
self.init_recon4 = common.FlatCamSimInitFix()
self.shuffle = ChannelShuffle(groups=3)
self.conv = nn.Conv2d(in_channels=4 * 3,
out_channels=3,
kernel_size=1,
padding=0,
stride=1,
groups=3)
def __init__(self, args, conv=common.default_conv, BBlock=common.BBlock):
super(BSR, self).__init__()
#n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
n_colors = args.n_colors
self.scale_idx = 0
act = nn.ReLU(True)
self.is_fcSim = args.is_fcSim
# Sampling layer
if args.is_fcSim:
self.fc_sim = common.FlatCamSampSim(args.batch_size)
self.add_noise = common.AddNoise(nSig=args.sigma)
self.init_recon = common.FlatCamSimInitConv1()
self.DWT = common.DWT()
self.IWT = common.IWT()
n = args.n_resblocks
m_head = [BBlock(conv, 4 * n_colors, 160, 3, act=act)]
d_l1 = []
for _ in range(n):
d_l1.append(BBlock(conv, 160, 160, 3, act=act))
d_l2 = [BBlock(conv, 640, n_feats * 4, 3, act=act)]
for _ in range(n):
d_l2.append(BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3 = [BBlock(conv, n_feats * 16, n_feats * 4, 3, act=act)]
for _ in range(n * 2):
pro_l3.append(BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
pro_l3.append(BBlock(conv, n_feats * 4, n_feats * 16, 3, act=act))
i_l2 = []
for _ in range(n):
i_l2.append(BBlock(conv, n_feats * 4, n_feats * 4, 3, act=act))
i_l2.append(BBlock(conv, n_feats * 4, 640, 3, act=act))
i_l1 = []
for _ in range(n):
i_l1.append((BBlock(conv, 160, 160, 3, act=act)))
m_tail = [conv(160, 4 * n_colors, 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, BBlock=common.BBlock):
super(BSR, self).__init__()
#n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
n_colors = args.n_colors
self.scale_idx = 0
act = nn.ReLU(True)
self.is_fcSim = args.is_fcSim
# Sampling layer
if args.is_fcSim:
self.fc_sim = common.FlatCamSampSim(args.batch_size)
self.add_noise = common.AddNoise(nSig=args.sigma)
self.init_recon = common.FlatCamSimInitConv4()
