def __init__(self, input_nc, ndf=64, n_layers=5):
super(Discriminator, self).__init__()
model = [nn.ReflectionPad2d(1),
nn.utils.spectral_norm(nn.Conv2d(3, ndf, 4, 2, 0, bias=True)),
nn.LeakyReLU(0.2, True)]
for i in range(1, n_layers - 2):
mult = 2 ** (i - 1)
model += [nn.ReflectionPad2d(1),
nn.utils.spectral_norm(nn.Conv2d(ndf * mult, ndf * mult * 2, 4, 2, 0, bias=True)),
nn.LeakyReLU(0.2, True)]
mult = 2 ** (n_layers - 2 - 1)
model += [nn.ReflectionPad2d(1),
nn.utils.spectral_norm(nn.Conv2d(ndf * mult, ndf * mult * 2, 4, 1, 0, bias=True)),
nn.LeakyReLU(0.2, True)]
# Class Activation Map
mult = 2 ** (n_layers - 2)
self.gap_fc = nn.utils.spectral_norm(nn.Linear(ndf * mult, 1, bias=False))
self.gmp_fc = nn.utils.spectral_norm(nn.Linear(ndf * mult, 1, bias=False))
self.conv1x1 = nn.Conv2d(ndf * mult * 2, ndf * mult, 1, 1, bias=True)
self.leaky_relu = nn.LeakyReLU(0.2, True)
self.pad = nn.ReflectionPad2d(1)
self.conv = nn.utils.spectral_norm(nn.Conv2d(ndf * mult, 1, 4, 1, 0, bias=False))
self.model = nn.Sequential(*model)
def __init__(self, img_size=256, num_domains=2, max_conv_dim=512):
super().__init__()
dim_in = 2**14 // img_size
blocks = []
blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)]
repeat_num = int(np.log2(img_size)) - 2
for _ in range(repeat_num):
dim_out = min(dim_in * 2, max_conv_dim)
blocks += [ResBlk(dim_in, dim_out, downsample=True)]
dim_in = dim_out
blocks += [nn.LeakyReLU(0.2)]
blocks += [nn.Conv2d(dim_out, dim_out, 4, 1, 0)]
blocks += [nn.LeakyReLU(0.2)]
blocks += [nn.Conv2d(dim_out, num_domains, 1, 1, 0)]
self.main = nn.Sequential(*blocks)
def __init__(self,
dim_in,
dim_out,
actv=nn.LeakyReLU(0.2),
normalize=False,
downsample=False):
super().__init__()
self.actv = actv
self.normalize = normalize
self.downsample = downsample
self.learned_sc = dim_in != dim_out
self._build_weights(dim_in, dim_out)
def __init__(self,
dim_in,
dim_out,
style_dim=64,
w_hpf=0,
actv=nn.LeakyReLU(0.2),
upsample=False):
super().__init__()
self.w_hpf = w_hpf
self.actv = actv
self.upsample = upsample
self.learned_sc = dim_in != dim_out
self._build_weights(dim_in, dim_out, style_dim)
def __init__(self, img_size=256, style_dim=64, max_conv_dim=512, w_hpf=1):
super().__init__()
dim_in = 2**14 // img_size
self.img_size = img_size
self.from_rgb = nn.Conv2d(3, dim_in, 3, 1, 1)
self.encode = nn.ModuleList()
self.decode = nn.ModuleList()
self.to_rgb = nn.Sequential(nn.InstanceNorm2d(dim_in, affine=True),
nn.LeakyReLU(0.2),
nn.Conv2d(dim_in, 3, 1, 1, 0))
# down/up-sampling blocks
repeat_num = int(np.log2(img_size)) - 4
if w_hpf > 0:
repeat_num += 1
for _ in range(repeat_num):
dim_out = min(dim_in * 2, max_conv_dim)
self.encode.append(
ResBlk(dim_in, dim_out, normalize=True, downsample=True))
self.decode.insert(0,
AdainResBlk(dim_out,
dim_in,
style_dim,
w_hpf=w_hpf,
upsample=True)) # stack-like
dim_in = dim_out
# bottleneck blocks
for _ in range(2):
self.encode.append(ResBlk(dim_out, dim_out, normalize=True))
self.decode.insert(
0, AdainResBlk(dim_out, dim_out, style_dim, w_hpf=w_hpf))
if w_hpf > 0:
device = porch.device(
'cuda' if porch.cuda.is_available() else 'cpu')
self.hpf = HighPass(w_hpf, device)