def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, upscale=4):
super(MSRResNet, self).__init__()
self.upscale = upscale
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
basic_block = functools.partial(mutil.ResidualBlock_noBN, nf=nf)
self.recon_trunk = mutil.make_layer(basic_block, nb)
# upsampling
if self.upscale == 2:
self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
elif self.upscale == 3:
self.upconv1 = nn.Conv2d(nf, nf * 9, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(3)
elif self.upscale == 4:
self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
mutil.initialize_weights(
[self.conv_first, self.upconv1, self.HRconv, self.conv_last], 0.1)
if self.upscale == 4:
mutil.initialize_weights(self.upconv2, 0.1)
def __init__(self, nf=64, gc=32, bias=True, use_snorm=False):
super(ResidualDenseBlock_5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
if use_snorm:
self.conv1 = nn.utils.spectral_norm(
nn.Conv2d(nf, gc, 3, 1, 1, bias=bias))
self.conv2 = nn.utils.spectral_norm(
nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias))
self.conv3 = nn.utils.spectral_norm(
nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias))
self.conv4 = nn.utils.spectral_norm(
nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias))
self.conv5 = nn.utils.spectral_norm(
nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias))
else:
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
mutil.initialize_weights(
[self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def __init__(
self,
channel_in: int,
channel_out: int,
init: str = "xavier",
gc: int = 32,
bias=True,
):
# https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
super(DenseBlock, self).__init__()
self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(channel_in + 4 * gc,
channel_out,
3,
1,
1,
bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
if init == "xavier":
mutil.initialize_weights_xavier(
[self.conv1, self.conv2, self.conv3, self.conv4], 0.1)
else:
mutil.initialize_weights(
[self.conv1, self.conv2, self.conv3, self.conv4], 0.1)
mutil.initialize_weights(self.conv5, 0)
def __init__(self,
channel_in,
channel_out,
init='xavier',
gc=32,
bias=True):
super(DenseBlock, self).__init__()
self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(channel_in + 4 * gc,
channel_out,
3,
1,
1,
bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
if init == 'xavier':
mutil.initialize_weights_xavier(
[self.conv1, self.conv2, self.conv3, self.conv4], 0.1)
else:
mutil.initialize_weights(
[self.conv1, self.conv2, self.conv3, self.conv4], 0.1)
mutil.initialize_weights(self.conv5, 0)
def __init__(self, filters=64, bias=True):
super(ResidualDenseBlock_2C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(filters, filters, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(filters, filters, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
mutil.initialize_weights([self.conv1, self.conv2], 0.1)
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
mutil.initialize_weights(
[self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def __init__(self, nf=64, gc=32, bias=True):
super(TimeResidualDenseBlock5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = ConcatConv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = ConcatConv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = ConcatConv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = ConcatConv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = ConcatConv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def __init__(self, in_nc, out_nc, nf, nb, gc=32, differential=None, time_dependent=False, adjoint=False, sb=5):
super(RRDBNet, self).__init__()
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if differential == "checkpointed":
self.conv_trunk = SRTrunk(nf, nb, make_odeblock(5, 'RK4'))
mutil.initialize_weights(self.conv_trunk.odefunc.convs)
elif differential == "standard":
self.conv_trunk = ODEBlock(ODEfunc(nf, nb=nb, normalization=False, time_dependent=time_dependent), adjoint=adjoint)
mutil.initialize_weights(self.conv_trunk.odefunc.convs)
elif differential == "sequential":
self.conv_trunk = nn.Sequential(*[ODEBlock(ODEfunc(nf, nb=sb, normalization=False, time_dependent=time_dependent), adjoint=adjoint) for _ in range(nb)])
for block in self.conv_trunk:
mutil.initialize_weights(block.odefunc.convs)
elif differential == "augmented":
augment_dim = nf//4
method = 'dopri5'
if method == 'euler':
warnings.warn("euler mode")
self.conv_trunk = AugBlock(AugFunc(nf=nf, nb=nb, augment_dim=augment_dim, time_dependent=time_dependent), adjoint=adjoint, is_conv=True, method=method)
self.trunk_conv = nn.Conv2d(nf+augment_dim, nf, 3, 1, 1, bias=True)
mutil.initialize_weights(self.conv_trunk.odefunc.convs)
elif differential is None or differential == "nodiff":
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.conv_trunk = mutil.make_layer(RRDB_block_f, nb)
else:
raise NotImplementedError("unrecognized differential system passed")
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16):
super(MSRResNet, self).__init__()
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
basic_block = functools.partial(mutil.ResidualBlock_noBN, nf=nf)
self.recon_trunk = mutil.make_layer(basic_block, nb)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
mutil.initialize_weights(
[self.conv_first, self.HRconv, self.conv_last], 0.1)
def __init__(self, nf=64, gc=32, bias=True, use_snorm=False):
super(Multi_extfea, self).__init__()
# gc: growth channel, i.e. intermediate channels
if use_snorm:
self.conv0 = nn.utils.spectral_norm(
nn.Conv2d(nf, gc, 1, 1, 1, bias=bias))
self.conv1 = nn.utils.spectral_norm(
nn.Conv2d(nf, gc, 3, 1, 1, bias=bias))
self.conv2 = nn.utils.spectral_norm(
nn.Conv2d(nf, gc, 5, 1, 2, bias=bias))
self.conv3 = nn.utils.spectral_norm(
nn.Conv2d(nf, gc, 7, 1, 3, bias=bias))
else:
self.conv0 = nn.Conv2d(nf, gc, 1, 1, 1, bias=bias)
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf, gc, 5, 1, 2, bias=bias)
self.conv3 = nn.Conv2d(nf, gc, 7, 1, 3, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv_out = nn.Conv2d(gc * 4, gc, 3, 1, 1, bias=bias)
# initialization
mutil.initialize_weights(
[self.conv0, self.conv1, self.conv2, self.conv3, self.conv_out],
0.1)
def __init__(self,
in_nc=3,
out_nc=3,
nf=64,
nb=16,
upscale=4,
differential=False):
super(MSRResNet, self).__init__()
self.upscale = upscale
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
if differential == "checkpointed":
self.conv_trunk = SRTrunk(nf, nb, make_odeblock(2, 'RK4'))
mutil.initialize_weights(self.conv_trunk.odefunc.convs)
elif differential == "standard":
self.conv_trunk = ODEBlock(ODEfunc(nf, nb=nb, normalization=False))
mutil.initialize_weights(self.conv_trunk.odefunc.convs)
elif differential is None:
basic_block = functools.partial(mutil.ResidualBlock_noBN, nf=nf)
self.conv_trunk = mutil.make_layer(basic_block, nb)
else:
raise NotImplementedError(
"unrecognized differential system passed")
# upsampling
if self.upscale == 2:
self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
elif self.upscale == 3:
self.upconv1 = nn.Conv2d(nf, nf * 9, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(3)
elif self.upscale == 4:
self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=False)
# initialization
mutil.initialize_weights(
[self.conv_first, self.upconv1, self.HRconv, self.conv_last], 0.1)
if self.upscale == 4:
mutil.initialize_weights(self.upconv2, 0.1)
