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import torch
from torch import nn
from torch.nn import Module
from models.StyleCLIP.models.stylegan2.model import EqualLinear, PixelNorm
class Mapper(Module):
def __init__(self, opts):
super(Mapper, self).__init__()
self.opts = opts
layers = [PixelNorm()]
for i in range(4):
layers.append(
EqualLinear(
512, 512, lr_mul=0.01, activation='fused_lrelu'
)
)
self.mapping = nn.Sequential(*layers)
def forward(self, x):
x = self.mapping(x)
return x
class SingleMapper(Module):
def __init__(self, opts):
super(SingleMapper, self).__init__()
self.opts = opts
self.mapping = Mapper(opts)
def forward(self, x):
out = self.mapping(x)
return out
class LevelsMapper(Module):
def __init__(self, opts):
super(LevelsMapper, self).__init__()
self.opts = opts
if not opts.no_coarse_mapper:
self.course_mapping = Mapper(opts)
if not opts.no_medium_mapper:
self.medium_mapping = Mapper(opts)
if not opts.no_fine_mapper:
self.fine_mapping = Mapper(opts)
def forward(self, x):
x_coarse = x[:, :4, :]
x_medium = x[:, 4:8, :]
x_fine = x[:, 8:, :]
if not self.opts.no_coarse_mapper:
x_coarse = self.course_mapping(x_coarse)
else:
x_coarse = torch.zeros_like(x_coarse)
if not self.opts.no_medium_mapper:
x_medium = self.medium_mapping(x_medium)
else:
x_medium = torch.zeros_like(x_medium)
if not self.opts.no_fine_mapper:
x_fine = self.fine_mapping(x_fine)
else:
x_fine = torch.zeros_like(x_fine)
out = torch.cat([x_coarse, x_medium, x_fine], dim=1)
return out
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