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import torch
from torch import nn
from mapper import latent_mappers
from models.stylegan2.model import Generator
def get_keys(d, name):
if 'state_dict' in d:
d = d['state_dict']
d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
return d_filt
class StyleCLIPMapper(nn.Module):
def __init__(self, opts):
super(StyleCLIPMapper, self).__init__()
self.opts = opts
# Define architecture
self.mapper = self.set_mapper()
self.decoder = Generator(self.opts.stylegan_size, 512, 8)
self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
if self.opts.use_weight_delta_mapper:
self.delta_mapper = latent_mappers.WeightDeltasMapper(self.opts)
# Load weights if needed
self.load_weights()
def set_mapper(self):
if self.opts.work_in_stylespace:
mapper = latent_mappers.WithoutToRGBStyleSpaceMapper(self.opts)
elif self.opts.mapper_type == 'SingleMapper':
mapper = latent_mappers.SingleMapper(self.opts)
elif self.opts.mapper_type == 'LevelsMapper':
mapper = latent_mappers.LevelsMapper(self.opts)
else:
raise Exception('{} is not a valid mapper'.format(self.opts.mapper_type))
return mapper
def load_weights(self):
if self.opts.checkpoint_path is not None:
print('Loading from checkpoint: {}'.format(self.opts.checkpoint_path))
ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
self.mapper.load_state_dict(get_keys(ckpt, 'mapper'), strict=True)
self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True)
if self.opts.use_weight_delta_mapper:
self.delta_mapper.load_state_dict(get_keys(ckpt, 'delta_mapper'), strict=True)
else:
print('Loading decoder weights from pretrained!')
ckpt = torch.load(self.opts.stylegan_weights)
self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
def forward(self, x, weights_deltas=None, resize=True, latent_mask=None, input_code=False, randomize_noise=True,
inject_latent=None, return_latents=False, alpha=None):
if input_code:
codes = x
else:
codes = self.mapper(x)
if weights_deltas is not None and self.opts.use_weight_delta_mapper:
weights_deltas = self.weight_deltas_mapper(weights_deltas)
if latent_mask is not None:
for i in latent_mask:
if inject_latent is not None:
if alpha is not None:
codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
else:
codes[:, i] = inject_latent[:, i]
else:
codes[:, i] = 0
input_is_latent = not input_code
images, result_latent = self.decoder([codes], weights_deltas=weights_deltas,
input_is_latent=input_is_latent,
randomize_noise=randomize_noise,
return_latents=return_latents)
if resize:
images = self.face_pool(images)
if return_latents:
return images, result_latent
else:
return images
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