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import torch
from torch import nn
from adapter import clipadapter
from models.stylegan2.model_remapper 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 CLIPAdapterWithDecoder(nn.Module):
def __init__(self, opts):
super(CLIPAdapterWithDecoder, self).__init__()
self.opts = opts
# Define architecture
self.adapter = clipadapter.CLIPAdapter(self.opts)
self.decoder = Generator(self.opts.stylegan_size, 512, 8)
self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
# Load weights if needed
self.load_weights()
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.adapter.load_state_dict(get_keys(ckpt, 'mapper'), strict=False)
self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), 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, 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.adapter(x)
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],
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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