def __init__(self, **kwargs):
super(Net, self).__init__()
scale = kwargs.get("scale")
multi_scale = kwargs.get("multi_scale")
reduce_upsample = kwargs.get("reduce_upsample", False)
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.relu = nn.ReLU()
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)
self.c2 = ops.BasicBlock(64 * 3, 64, 1)
self.c3 = ops.BasicBlock(64 * 4, 64, 1)
self.upsample = ops.UpsampleBlock(64,
scale=scale,
multi_scale=multi_scale,
reduce=reduce_upsample)
self.exit = nn.Sequential(nn.Conv2d(64, 3, 3, 1, 1))
def __init__(self, **kwargs):
super(Net, self).__init__()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.carnchannel = 32
#self.conv1_e = nn.Conv2d(1, self.carnchannel//2, 3, 1, 2, bias=False).to(self.device)
#self.conv2 = nn.Conv2d(self.carnchannel//2, self.carnchannel//2, 3, 1, 1, bias=False).to(self.device)
#torch.nn.init.xavier_normal_(self.conv1_e.weight)
#torch.nn.init.xavier_normal_(self.conv2.weight)
self.entry = nn.Conv2d(3, self.carnchannel, 5, 1, 2,
bias=True).to(self.device)
torch.nn.init.xavier_normal_(self.entry.weight)
self.b1 = Block(self.carnchannel, self.carnchannel).to(self.device)
self.b2 = Block(self.carnchannel, self.carnchannel).to(self.device)
self.c1 = ops.BasicBlock(self.carnchannel * 2, self.carnchannel, 1, 1,
0).to(self.device)
self.c2 = ops.BasicBlock(self.carnchannel * 3, self.carnchannel, 1, 1,
0).to(self.device)
self.upsample = ops.UpsampleBlock(self.carnchannel,
scale=4).to(self.device)
self.exit = nn.Conv2d(self.carnchannel, 3, 3, 1, 1,
bias=True).to(self.device)
torch.nn.init.xavier_normal_(self.exit.weight)
def __init__(
self,
scale=4, multi_scale=True,
num_channels=64,
mobile=False, groups=1
):
super().__init__()
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, num_channels, 3, 1, 1)
self.b1 = Block(num_channels, mobile, groups)
self.b2 = Block(num_channels, mobile, groups)
self.b3 = Block(num_channels, mobile, groups)
self.c1 = nn.Conv2d(num_channels*2, num_channels, 1, 1, 0)
self.c2 = nn.Conv2d(num_channels*3, num_channels, 1, 1, 0)
self.c3 = nn.Conv2d(num_channels*4, num_channels, 1, 1, 0)
self.upsample = ops.UpsampleBlock(
num_channels,
scale=scale, multi_scale=multi_scale,
groups=groups
)
self.exit = nn.Conv2d(num_channels, 3, 3, 1, 1)
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, args):
super(CARN, self).__init__()
#scale = kwargs.get("scale")
#multi_scale = kwargs.get("multi_scale")
#group = kwargs.get("group", 1)
multi_scale = len(args.scale) > 1
self.scale_idx = 0
scale = args.scale[self.scale_idx]
group = 1
self.scale = args.scale
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.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 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)
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=scale,
multi_scale=multi_scale,
group=group)
self.exit = nn.Conv2d(64, 3, 3, 1, 1)
def __init__(self, args):
super(Net, self).__init__()
self.args = args
#scale = args.scale[0]
self.scale = args.scale[0]
#print(self.scale)
multi_scale = args.multi_scale
group = args.group
#group = kwargs.get("group", 1)
self.num_types = args.num_types
self.neuf = args.neuf
self.neufed = False
self.part = args.part
#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)
if self.args.use_real:
self.input_channels = self.args.n_colors + 3
else:
self.input_channels = self.args.n_colors
self.entry = nn.Conv2d(self.input_channels, self.args.n_feats, 3, 1, 1)
if self.args.split:
self.b1 = BlockBucket(self.args, self.args.n_feats,
self.args.n_feats)
if self.args.bottom_only:
self.b2 = Block(self.args.n_feats, self.args.n_feats)
self.b3 = Block(self.args.n_feats, self.args.n_feats)
else:
self.b2 = BlockBucket(self.args, self.args.n_feats,
self.args.n_feats)
self.b3 = BlockBucket(self.args, self.args.n_feats,
self.args.n_feats)
else:
self.b1 = Block(self.args.n_feats, self.args.n_feats)
self.b2 = Block(self.args.n_feats, self.args.n_feats)
self.b3 = Block(self.args.n_feats, self.args.n_feats)
self.c1 = ops.BasicBlock(self.args.n_feats * 2, self.args.n_feats, 1,
1, 0)
self.c2 = ops.BasicBlock(self.args.n_feats * 3, self.args.n_feats, 1,
1, 0)
self.c3 = ops.BasicBlock(self.args.n_feats * 4, self.args.n_feats, 1,
1, 0)
if self.scale != 1:
self.upsample = ops.UpsampleBlock(self.args.n_feats,
scale=self.scale,
multi_scale=multi_scale,
group=group)
else:
self.upsample = None
#print(self.upsample)
self.exit = nn.Conv2d(self.args.n_feats, args.n_colors, 3, 1, 1)
def __init__(self, scale_factor, num_channels=1, d=56, s=12, m=4):
super(FSRCNN_ps1, self).__init__()
self.first_part = nn.Sequential(
nn.Conv2d(num_channels, d, kernel_size=5, padding=5 // 2),
nn.PReLU(d))
self.mid_part = [nn.Conv2d(d, s, kernel_size=1), nn.PReLU(s)]
for _ in range(m):
self.mid_part.extend(
[nn.Conv2d(s, s, kernel_size=3, padding=3 // 2),
nn.PReLU(s)])
self.mid_part.extend([nn.Conv2d(s, d, kernel_size=1), nn.PReLU(d)])
self.mid_part = nn.Sequential(*self.mid_part)
self.upsample_part = ops.UpsampleBlock(d, scale=scale_factor, group=1)
self.refine_part = nn.Conv2d(d, 1, kernel_size=3, padding=1)
self._initialize_weights()
def __init__(self):
super(CARN, self).__init__()
self.b1 = Block(64, 64)
self.b2 = Block(64, 64)
self.b3 = Block(64, 64)
self.b4 = Block(64, 64)
self.b5 = Block(64, 64)
self.b6 = Block(64, 64)
self.b7 = Block(64, 64)
self.b8 = Block(64, 64)
self.c1 = ops.BasicBlock(64 * 2, 64, 1)
self.c2 = ops.BasicBlock(64 * 3, 64, 1)
self.c3 = ops.BasicBlock(64 * 4, 64, 1)
self.c4 = ops.BasicBlock(64 * 5, 64, 1)
self.c5 = ops.BasicBlock(64 * 6, 64, 1)
self.c6 = ops.BasicBlock(64 * 7, 64, 1)
self.c7 = ops.BasicBlock(64 * 8, 64, 1)
self.c8 = ops.BasicBlock(64 * 9, 64, 1)
self.up = ops.UpsampleBlock(64, scale=2)
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, **kwargs):
super(Net, self).__init__()
scale = kwargs.get("scale") #value of scale is scale.
multi_scale = kwargs.get("multi_scale") # value of multi_scale is multi_scale in args.
group = kwargs.get("group", 1) #if valule of group isn't given, group is 1.
kernel_size = 3 #tcw 201904091123
kernel_size1 = 1 #tcw 201904091123
padding1 = 0 #tcw 201904091124
padding = 1 #tcw201904091123
features = 64 #tcw201904091124
groups = 1 #tcw201904091124
channels = 3
features1 = 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)
'''
in_channels, out_channels, kernel_size, stride, padding,dialation, groups,
'''
self.conv1_1 = nn.Sequential(nn.Conv2d(in_channels=channels,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv1_2 = nn.Sequential(nn.Conv2d(in_channels=channels,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv1_3 = nn.Sequential(nn.Conv2d(in_channels=channels,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv2_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv2_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv2_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv3_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv3_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv3_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv4_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv4_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv4_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv5_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv5_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv5_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv6_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv6_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv6_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv7_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv7_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv7_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv8_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv8_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv8_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv9_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv9_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv9_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv10_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv10_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv10_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv11_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv11_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv11_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv12_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv12_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv12_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv13_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv13_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv13_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv14_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv14_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv14_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv15_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv15_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv15_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv16_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv16_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv16_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv17_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(1,3),padding=(0,1),groups=1,bias=False))
self.conv17_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,1),padding=(1,0),groups=1,bias=False))
self.conv17_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv18_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False),nn.ReLU(inplace=True))
self.conv18_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False),nn.ReLU(inplace=True))
self.conv19_1 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv19_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False))
self.conv20 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False),nn.ReLU(inplace=True))
self.conv21 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=(3,3),padding=(1,1),groups=1,bias=False),nn.ReLU(inplace=True))
self.conv22 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=3,kernel_size=kernel_size,padding=padding,groups=groups,bias=False))
self.ReLU=nn.ReLU(inplace=True)
#self.BN = nn.BatchNorm2d(features)
self.upsample = ops.UpsampleBlock(64, scale=scale, multi_scale=multi_scale,group=1)
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]
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) # feature extraction
self.b1 = Block(chs, chs) # DRLM
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 = 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.upsample = ops.UpsampleBlock(chs, self.scale, multi_scale=False)
#self.convert = ops.ConvertBlock(chs, chs, 20)
self.tail = nn.Conv2d(chs, 3, 3, 1, 1)
def __init__(self, **kwargs):
super(Net, self).__init__()
scale = kwargs.get("scale") #value of scale is scale.
multi_scale = kwargs.get(
"multi_scale") # value of multi_scale is multi_scale in args.
group = kwargs.get("group",
1) #if valule of group isn't given, group is 1.
kernel_size = 3 #tcw 201904091123
kernel_size1 = 1 #tcw 201904091123
padding1 = 0 #tcw 201904091124
padding = 1 #tcw201904091123
features = 64 #tcw201904091124
groups = 1 #tcw201904091124
channels = 3
features1 = 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)
'''
in_channels, out_channels, kernel_size, stride, padding,dialation, groups,
'''
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=channels,
out_channels=features,
kernel_size=kernel_size,
padding=padding,
groups=1,
bias=False))
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv4 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv5 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv6 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv7 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv8 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv9 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv10 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv11 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv12 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv13 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv14 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv15 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv16 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv17 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size1,
padding=0,
groups=groups,
bias=False))
self.conv17_1 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv17_2 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv17_3 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv17_4 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=features,
kernel_size=kernel_size,
padding=1,
groups=1,
bias=False), nn.ReLU(inplace=True))
self.conv18 = nn.Sequential(
nn.Conv2d(in_channels=features,
out_channels=3,
kernel_size=kernel_size,
padding=padding,
groups=groups,
bias=False))
'''
self.conv18 = nn.Conv2d(in_channels=features,out_channels=features1,kernel_size=kernel_size1,padding=padding1,groups=groups,bias=False)
self.ReLU = nn.ReLU(inplace=True)
'''
self.ReLU = nn.ReLU(inplace=True)
self.upsample = ops.UpsampleBlock(64,
scale=scale,
multi_scale=multi_scale,
group=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)
