def __init__(self,in_channels, out_channels, num_features, bp_stages, upscale_factor=4, norm_type=None, act_type='prelu', mode='NAC', upsample_mode='upconv'):
super(D_DBPN, self).__init__()
if upscale_factor == 2:
stride = 2
padding = 2
projection_filter = 6
elif upscale_factor == 4:
stride = 4
padding = 2
projection_filter = 8
elif upscale_factor == 8:
stride = 8
padding = 2
projection_filter = 12
feature_extract_1 = B.ConvBlock(in_channels, 256, kernel_size=3, norm_type=norm_type, act_type=act_type)
feature_extract_2 = B.ConvBlock(256, num_features, kernel_size=1, norm_type=norm_type, act_type=act_type)
bp_units = B.DensebackprojBlock(num_features, num_features, projection_filter, bp_stages, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type)
conv_hr = B.ConvBlock(num_features*bp_stages, out_channels, kernel_size=3, norm_type=None, act_type=None)
self.network = B.sequential(feature_extract_1, feature_extract_2, bp_units, conv_hr)
def __init__(self,in_channels, out_channels, num_features, bp_stages, upscale_factor=4, norm_type=None, act_type='prelu', mode='NAC', upsample_mode='upconv'):
super(DBPN, self).__init__()
if upscale_factor == 2:
stride = 2
padding = 2
projection_filter = 6
elif upscale_factor == 4:
stride = 4
padding = 2
projection_filter = 8
elif upscale_factor == 8:
stride = 8
padding = 2
projection_filter = 12
feature_extract_1 = B.ConvBlock(in_channels, 128, kernel_size=3, norm_type=norm_type, act_type=act_type)
feature_extract_2 = B.ConvBlock(128, num_features, kernel_size=1, norm_type=norm_type, act_type=act_type)
bp_units = []
for _ in range(bp_stages-1):
bp_units.extend([B.UpprojBlock(num_features, num_features, projection_filter, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type),
B.DownprojBlock(num_features, num_features, projection_filter, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type)])
last_bp_unit = B.UpprojBlock(num_features, num_features, projection_filter, stride=stride, valid_padding=False,
padding=padding, norm_type=norm_type, act_type=act_type)
conv_hr = B.ConvBlock(num_features, out_channels, kernel_size=1, norm_type=None, act_type=None)
self.network = B.sequential(feature_extract_1, feature_extract_2, *bp_units, last_bp_unit, conv_hr)
def __init__(self, in_channels, out_channels, num_branch):
super(srcnn, self).__init__()
self.num_branch = num_branch
self.patch_extraction = B.ConvBlock(in_channels,
64,
kernel_size=9,
norm_type=None,
act_type='relu',
valid_padding=False,
padding=0)
self.mapping = B.ConvBlock(64,
32,
kernel_size=1,
norm_type=None,
act_type='relu',
valid_padding=False,
padding=0)
self.reconstruct = B.ConvBlock(32,
out_channels,
kernel_size=5,
norm_type=None,
act_type=None,
valid_padding=False,
padding=0)
def __init__(self, in_channels, out_channels, num_branch):
super(vdsr, self).__init__()
self.conv = nn.ModuleList()
self.conv_in = B.ConvBlock(in_channels, 64, kernel_size=3, norm_type=None, act_type='relu',valid_padding=True, bias=False)
for i in range(18):
self.conv.append(B.ConvBlock(64, 64, kernel_size=3, norm_type=None, act_type='relu',valid_padding=True, bias=False))
self.conv_out = B.ConvBlock(64, out_channels, kernel_size=3, norm_type=None, act_type=None, valid_padding=True, bias=False)
def __init__(self,
in_channels,
out_channels,
num_features,
num_blocks,
upscale_factor=4,
norm_type='bn',
act_type='relu',
mode='NAC',
upsample_mode='upconv'):
super(SRResNet, self).__init__()
feature_extract = B.ConvBlock(in_channels,
num_features,
kernel_size=9,
norm_type=None,
act_type='prelu')
res_blocks = [
B.ResBlock(num_features,
num_features,
num_features,
kernel_size=3,
norm_type=norm_type,
act_type=act_type,
mode=mode) for _ in range(num_blocks)
]
conv_lr = B.ConvBlock(num_features,
num_features,
kernel_size=3,
norm_type=norm_type,
act_type=act_type,
mode=mode)
if upsample_mode == 'upconv':
upsample_block = B.UpsampleConvBlock(upscale_factor=upscale_factor, in_channels=num_features, out_channels=num_features,\
kernel_size=3, stride=1)
elif upsample_mode == 'pixelshuffle':
upsample_block = B.PixelShuffleBlock()
else:
raise NotImplementedError('Upsample mode [%s] is not supported!' %
upsample_mode)
conv_hr = B.ConvBlock(num_features,
out_channels,
kernel_size=9,
norm_type=None,
act_type=None)
# TODO: dense connection
# TODO: Notice: We must unpack the residual blocks using ‘*’ before building a nn.Sequential
self.network = B.sequential(
feature_extract,
B.ShortcutBlock(B.sequential(*res_blocks,
conv_lr)), upsample_block, conv_hr)
def __init__(self, in_channels, out_channels, num_branch):
super(vdsr_k, self).__init__()
split_layer = 4
self.num_branch = num_branch
# self.conv = nn.Sequential()
conv_in = B.ConvBlock(in_channels, 64, kernel_size=3, norm_type=None, act_type='relu',valid_padding=True, bias=False)
conv_blocks = [B.ConvBlock(64, 64, kernel_size=3, norm_type=None, act_type='relu',valid_padding=True, bias=False) for _ in range(18 - split_layer)]
self.conv = B.sequential(conv_in, *conv_blocks)
self.conv_branch = nn.ModuleList()
for _ in range(self.num_branch):
sub_branch = [B.ConvBlock(64, 64, kernel_size=3, norm_type=None, act_type='relu',valid_padding=True, bias=False) for _ in range(split_layer)]
conv_out = B.ConvBlock(64, out_channels, kernel_size=3, norm_type=None, act_type=None, valid_padding=True, bias=False)
self.conv_branch.append(B.sequential(*sub_branch, conv_out))
def __init__(self,
in_channels,
out_channels,
num_features,
num_recurs,
upscale_factor,
norm_type=None,
act_type='prelu'):
super(DRUDN, self).__init__()
if upscale_factor == 2:
stride = 2
padding = 2
projection_filter = 6
if upscale_factor == 3:
stride = 3
padding = 2
projection_filter = 7
elif upscale_factor == 4:
stride = 4
padding = 2
projection_filter = 8
self.num_recurs = num_recurs
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = B.MeanShift(rgb_mean, rgb_std)
self.conv_in = B.ConvBlock(in_channels,
num_features,
kernel_size=3,
act_type=act_type,
norm_type=None)
self.up1_1 = B.DeconvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=act_type)
self.up1_2 = B.DeconvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=act_type)
self.up1_3 = B.DeconvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=act_type)
self.down1_1 = B.ConvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=act_type)
self.down1_2 = B.ConvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=act_type)
self.down1_3 = B.ConvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=None)
self.deconv1 = B.DeconvBlock(num_features,
num_features,
projection_filter,
stride=stride,
padding=padding,
norm_type=norm_type,
act_type=act_type)
self.conv_feat = B.ConvBlock(num_features,
num_features,
kernel_size=3,
act_type=act_type,
norm_type=None)
self.conv_out = B.ConvBlock(num_features,
out_channels,
kernel_size=3,
act_type=None,
norm_type=None)
self.add_mean = B.MeanShift(rgb_mean, rgb_std, 1)