def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', \
mode='CNA', res_scale=1, upsample_mode='upconv'):
super(RRDB_Net, self).__init__()
n_upscale = int(math.log(upscale, 2))
if upscale == 3:
n_upscale = 1
fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
rb_blocks = [B.RRDB(nf, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=norm_type, act_type=act_type, mode='CNA') for _ in range(nb)]
LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
if upsample_mode == 'upconv':
upsample_block = B.upconv_blcok
elif upsample_mode == 'pixelshuffle':
upsample_block = B.pixelshuffle_block
else:
raise NotImplementedError('upsample mode [%s] is not found' % upsample_mode)
if upscale == 3:
upsampler = upsample_block(nf, nf, 3, act_type=act_type)
else:
upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)
self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\
*upsampler, HR_conv0, HR_conv1)
def __init__(self, ch_in=64, ch_out=64, res_type='res'):
super(TopDownBlock, self).__init__()
if res_type == 'rrdb':
self.res_block = B.RRDB(ch_in, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=None, act_type='leakyrelu', mode='CNA')
else:
self.res_block = ResidualBlock(ch_in=ch_in, ch_out=ch_out)
# self.res_block = ResidualInceptionBlock(ch_in=ch_in, ch_out=ch_out)
self.pool = nn.MaxPool2d(kernel_size=2)
def __init__(self, ch_in=64, ch_out=64, res_type='res'):
super(BottomUpBlock, self).__init__()
if res_type == 'rrdb':
self.res_block = B.RRDB(ch_in, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=None, act_type='leakyrelu', mode='CNA')
else:
self.res_block = ResidualBlock(ch_in=ch_in, ch_out=ch_out)
# self.res_block = ResidualInceptionBlock(ch_in=ch_in, ch_out=ch_out)
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
def __init__(self, in_nc=1, out_nc=1, nf=16, nb=16, gc=16, norm_type=None, act_type='leakyrelu', mode='CNA'):
super(DenoseNet, self).__init__()
fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
rb_blocks = [B.RRDB(nf, kernel_size=3, gc=gc, stride=1, bias=True, pad_type='zero',
norm_type=norm_type, act_type=act_type, mode='CNA', dilation=(1, 2, 4), groups=1) for _ in
range(nb)]
LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)
self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv))
, HR_conv0, HR_conv1, nn.Sigmoid())
def __init__(self,
in_nc,
out_nc,
nf,
nb,
gc=32,
upscale=4,
norm_type=None,
act_type='leakyrelu',
res_scale=1):
super(RRDB_Net, self).__init__()
n_upscale = int(math.log(upscale, 2))
fea_conv = B.conv_block(in_nc,
nf,
kernel_size=3,
norm_type=None,
act_type=None)
rb_blocks = [
B.RRDB(nf,
kernel_size=3,
gc=32,
stride=1,
bias=True,
norm_type=norm_type,
act_type=act_type) for _ in range(nb)
]
LR_conv = B.conv_block(nf,
nf,
kernel_size=3,
norm_type=norm_type,
act_type=None)
upsample_block = B.upconv_blcok
upsampler = [
upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)
]
HR_conv0 = B.conv_block(nf,
nf,
kernel_size=3,
norm_type=None,
act_type=act_type)
HR_conv1 = B.conv_block(nf,
out_nc,
kernel_size=3,
norm_type=None,
act_type=None)
self.model = B.sequential(
fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),
*upsampler, HR_conv0, HR_conv1)
