def __init__(self,
nf=64,
nframes=5,
groups=8,
front_RBs=5,
back_RBs=10,
center=None,
predeblur=False,
HR_in=False):
super(EDVR, self).__init__()
self.nf = nf
self.center = nframes // 2 if center is None else center
self.is_predeblur = True if predeblur else False
self.HR_in = True if HR_in else False
ResidualBlock_noBN_f = functools.partial(mutil.ResidualBlock_noBN,
nf=nf)
#### extract features (for each frame)
if self.is_predeblur:
self.pre_deblur = Predeblur_ResNet_Pyramid(nf=nf, HR_in=self.HR_in)
self.conv_1x1 = nn.Conv2d(nf, nf, 1, 1, bias=True)
else:
if self.HR_in:
self.conv_first_1 = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
self.conv_first_2 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
self.conv_first_3 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
else:
self.conv_first = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
self.feature_extraction = mutil.make_layer(ResidualBlock_noBN_f,
front_RBs)
self.fea_L2_conv1 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
self.fea_L2_conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.fea_L3_conv1 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
self.fea_L3_conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.pcd_align = PCD_Align(nf=nf, groups=groups)
self.tsa_fusion = TSA_Fusion(nf=nf,
nframes=nframes,
center=self.center)
#### reconstruction
self.recon_trunk = mutil.make_layer(ResidualBlock_noBN_f, back_RBs)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nf, 64 * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
self.HRconv = nn.Conv2d(64, 64, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
#### activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def __init__(self, in_nc, out_nc, nf, nb, gc=32):
super(NRP, self).__init__()
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.RRDB_trunk = mutil.make_layer(RRDB_block_f, nb)
self.trunk_conv = nn.Conv2d(nf, 3, 3, 1, 1, bias=True)
def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23):
super(NRP_resG, 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.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)