def __init__(self, downs, n_res, input_dim, dim, norm, activ, pad_type):
super(ContentEncoder, self).__init__()
self.model = []
self.model += [
Conv2dBlock(input_dim,
dim,
7,
1,
3,
norm=norm,
activation=activ,
pad_type=pad_type)
]
for i in range(downs):
self.model += [
Conv2dBlock(dim,
2 * dim,
4,
2,
1,
norm=norm,
activation=activ,
pad_type=pad_type)
]
dim *= 2
self.model += [
ResBlocks(n_res,
dim,
norm=norm,
activation=activ,
pad_type=pad_type)
]
self.model = nn.Sequential(*self.model)
self.output_dim = dim
def __init__(self, ups, n_res, dim, out_dim, res_norm, activ, pad_type):
super(Decoder, self).__init__()
self.model = []
self.model += [
ResBlocks(n_res, dim, res_norm, activ, pad_type=pad_type)
]
for i in range(ups):
self.model += [
nn.Upsample(scale_factor=2),
Conv2dBlock(dim,
dim // 2,
5,
1,
2,
norm='in',
activation=activ,
pad_type=pad_type)
]
dim //= 2
self.model += [
Conv2dBlock(dim,
out_dim,
7,
1,
3,
norm='none',
activation='tanh',
pad_type=pad_type)
]
self.model = nn.Sequential(*self.model)
def __init__(self, batch_size, downs, ind_im, dim, latent_dim, norm, activ, pad_type):
super(ClassModelEncoder, self).__init__()
s_s_layers = []
dim_size = dim
for i in range(downs[0]):
if i == 0:
s_s_layers.append(Conv2dBlock(ind_im, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type))
else:
dim = dim*2
s_s_layers.append(Conv2dBlock(dim // 2, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type))
self.enc_s_s = nn.Sequential(*s_s_layers)
dim = dim_size
s_c_layers = []
for i in range(downs[1]):
if i == 0:
s_c_layers.append(Conv2dBlock(ind_im, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type))
else:
dim = dim*2
s_c_layers.append(Conv2dBlock(dim // 2, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type))
dim = dim * 2
s_c_layers.append(Conv2dBlock(dim // 2, dim, 3, 1, 1, norm=norm, activation=activ, pad_type=pad_type))
s_c_layers.append(ResBlocks(2, dim, norm=norm, activation=activ, pad_type=pad_type))
self.enc_s_c = nn.Sequential(*s_c_layers)
self.linear_s = nn.Linear(dim*2, latent_dim)
self.linear_c = nn.Linear(dim, latent_dim)
self.csb = torch.randn(batch_size, dim).cuda()
def __init__(self, ups, n_res, dim, out_dim, res_norm, activ, pad_type):
super(Decoder, self).__init__()
self.model = []
self.model += [ResBlocks(n_res, dim, res_norm, activ, pad_type=pad_type)]
for i in range(ups):
self.model.append(AdainUpResBlock(dim, activation=activ, pad_type=pad_type))
dim = dim // 2
self.model.append(Conv2dBlock(dim, out_dim, 3, 1, 1, norm='none', activation='tanh', pad_type=pad_type))
self.model = nn.Sequential(*self.model)
def __init__(self, downs, n_res, input_dim, dim, norm, activ, pad_type):
super(ContentEncoder, self).__init__()
#InceptionBlock = Conv2dBlock
self.model = []
self.model += [
InceptionBlock(input_dim,
dim,
KERNEL_SIZE_7,
1,
3,
norm=norm,
activation=activ,
pad_type=pad_type)
]
"""
for i in range(downs):
self.model += [InceptionBlock(dim, 2 * dim, KERNEL_SIZE_4, 1,#2,
1,
norm=norm,
activation=activ,
pad_type=pad_type)]
if (i == downs-1):
self.model += [
nn.MaxPool2d(KERNEL_SIZE_4, 2, padding=1)
]
else:
self.model += [
nn.MaxPool2d(KERNEL_SIZE_4, 2, padding=0)
]
dim *= 2
"""
for i in range(downs):
self.model += [
Conv2dBlock(dim,
2 * dim,
KERNEL_SIZE_4,
2,
1,
norm=norm,
activation=activ,
pad_type=pad_type)
]
dim *= 2
self.model += [
ResBlocks(n_res,
dim,
norm=norm,
activation=activ,
pad_type=pad_type,
inception=True)
]
self.model = nn.Sequential(*self.model)
self.output_dim = dim
def __init__(self, downs, n_res, input_dim, dim, norm, activ, pad_type):
super(ContentEncoder, self).__init__()
s_c_layers = []
for i in range(downs):
if i == 0:
s_c_layers.append(Conv2dBlock(input_dim, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type))
else:
dim = dim * 2
s_c_layers.append(Conv2dBlock(dim // 2, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type))
dim = dim * 2
s_c_layers.append(Conv2dBlock(dim // 2, dim, 3, 1, 1, norm=norm, activation=activ, pad_type=pad_type))
s_c_layers.append(ResBlocks(n_res, dim, norm=norm, activation=activ, pad_type=pad_type))
self.model = nn.Sequential(*s_c_layers)
self.output_dim = dim
def __init__(self, args):
super(Refiner, self).__init__()
c_up = args.c_up // 2 # 32
down = args.down # 2
self.model = [
Conv2dBlock(6, c_up, 7, 1, 3, norm='in',
pad_type='reflect'), # RGB + target
]
for i in range(down):
self.model.append(
Conv2dBlock(c_up,
2 * c_up,
4,
2,
1,
norm='in',
pad_type='reflect'))
c_up *= 2
self.model.append(
ResBlocks(5,
c_up,
norm='in',
activation='relu',
pad_type='reflect'))
for i in range(down):
self.model.append(
UpConv2dBlock(c_up,
norm='in',
activation='relu',
pad_type='reflect'))
c_up //= 2
self.model.append(
Conv2dBlock(c_up,
3,
7,
1,
padding=3,
norm='none',
activation='none',
pad_type='reflect'))
self.model = nn.Sequential(*self.model)