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import torch
from torch import nn
from torch.nn import Module
from models.stylegan2.model import EqualLinear, PixelNorm
from models.hyperstyle.hypernetworks.refinement_blocks import PARAMETERS as HYPERSTYLE_PARAMETERS
STYLESPACE_DIMENSIONS = [512 for _ in range(15)] + [256, 256, 256] + [128, 128, 128] + [64, 64, 64] + [32, 32]
class Mapper(Module):
def __init__(self, opts, latent_dim=512):
super(Mapper, self).__init__()
self.opts = opts
layers = [PixelNorm()]
for i in range(4):
layers.append(
EqualLinear(
latent_dim, latent_dim, 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
class FullStyleSpaceMapper(Module):
def __init__(self, opts):
super(FullStyleSpaceMapper, self).__init__()
self.opts = opts
for c, c_dim in enumerate(STYLESPACE_DIMENSIONS):
setattr(self, f"mapper_{c}", Mapper(opts, latent_dim=c_dim))
def forward(self, x):
out = []
for c, x_c in enumerate(x):
curr_mapper = getattr(self, f"mapper_{c}")
x_c_res = curr_mapper(x_c.view(x_c.shape[0], -1)).view(x_c.shape)
out.append(x_c_res)
return out
class WithoutToRGBStyleSpaceMapper(Module):
def __init__(self, opts):
super(WithoutToRGBStyleSpaceMapper, self).__init__()
self.opts = opts
indices_without_torgb = list(range(1, len(STYLESPACE_DIMENSIONS), 3))
self.STYLESPACE_INDICES_WITHOUT_TORGB = [i for i in range(len(STYLESPACE_DIMENSIONS)) if i not in indices_without_torgb]
for c in self.STYLESPACE_INDICES_WITHOUT_TORGB:
setattr(self, f"mapper_{c}", Mapper(opts, latent_dim=STYLESPACE_DIMENSIONS[c]))
def forward(self, x):
out = []
for c in range(len(STYLESPACE_DIMENSIONS)):
x_c = x[c]
if c in self.STYLESPACE_INDICES_WITHOUT_TORGB:
curr_mapper = getattr(self, f"mapper_{c}")
x_c_res = curr_mapper(x_c.view(x_c.shape[0], -1)).view(x_c.shape)
else:
x_c_res = torch.zeros_like(x_c)
out.append(x_c_res)
return out
class WeightDeltasMapper(Module):
def __init__(self, opts):
super(WeightDeltasMapper, self).__init__()
self.opts = opts
self.weight_deltas_indicies = [int(l) for l in opts.layers_to_tune.split(',')]
for c in self.weight_deltas_indicies:
_, _, latent_dim = HYPERSTYLE_PARAMETERS[c]
setattr(self, f"mapper_{c}", Mapper(opts, latent_dim=latent_dim))
def forward(self, x):
out = []
for c in range(len(STYLESPACE_DIMENSIONS)):
x_c = x[c]
if c in self.weight_deltas_indicies:
curr_mapper = getattr(self, f"mapper_{c}")
x_c_res = curr_mapper(x_c.view(x_c.shape[0], -1)).view(x_c.shape)
else:
x_c_res = None
out.append(x_c_res)
return out |