| import matplotlib |
|
|
| matplotlib.use('Agg') |
| import torch |
| from torch import nn |
| from e4e.models.encoders import psp_encoders |
| from e4e.models.stylegan2.model import Generator |
| from e4e.configs.paths_config import model_paths |
|
|
|
|
| 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 pSp(nn.Module): |
|
|
| def __init__(self, opts, device): |
| super(pSp, self).__init__() |
| self.opts = opts |
| self.device = device |
| |
| self.encoder = self.set_encoder() |
| self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2) |
| self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) |
| |
| self.load_weights() |
|
|
| def set_encoder(self): |
| if self.opts.encoder_type == 'GradualStyleEncoder': |
| encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts) |
| elif self.opts.encoder_type == 'Encoder4Editing': |
| encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts) |
| else: |
| raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type)) |
| return encoder |
|
|
| def load_weights(self): |
| if self.opts.checkpoint_path is not None: |
| print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path)) |
| ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') |
| self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True) |
| self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True) |
| self.__load_latent_avg(ckpt) |
| else: |
| print('Loading encoders weights from irse50!') |
| encoder_ckpt = torch.load(model_paths['ir_se50']) |
| self.encoder.load_state_dict(encoder_ckpt, strict=False) |
| print('Loading decoder weights from pretrained!') |
| ckpt = torch.load(self.opts.stylegan_weights) |
| self.decoder.load_state_dict(ckpt['g_ema'], strict=False) |
| self.__load_latent_avg(ckpt, repeat=self.encoder.style_count) |
|
|
| 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.encoder(x) |
| |
| if self.opts.start_from_latent_avg: |
| if codes.ndim == 2: |
| codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :] |
| else: |
| codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) |
|
|
| 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 |
|
|
| def __load_latent_avg(self, ckpt, repeat=None): |
| if 'latent_avg' in ckpt: |
| self.latent_avg = ckpt['latent_avg'].to(self.device) |
| if repeat is not None: |
| self.latent_avg = self.latent_avg.repeat(repeat, 1) |
| else: |
| self.latent_avg = None |
|
|
