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| """ | |
| This file defines the core research contribution | |
| """ | |
| import math | |
| import matplotlib | |
| matplotlib.use('Agg') | |
| import torch | |
| from torch import nn | |
| from models.hyperstyle.encoders import w_encoder | |
| from models.stylegan2.model import Generator | |
| from models.hyperstyle.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): | |
| super(pSp, self).__init__() | |
| self.set_opts(opts) | |
| # compute number of style inputs based on the output resolution | |
| self.opts.n_styles = int(math.log(self.opts.output_size, 2)) * 2 - 2 | |
| # Define architecture | |
| self.encoder = self.set_encoder() | |
| self.decoder = Generator(self.opts.output_size, 512, 8) | |
| self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) | |
| # Load weights if needed | |
| self.load_weights() | |
| def set_encoder(self): | |
| if self.opts.encoder_type == 'WEncoder': | |
| encoder = w_encoder.WEncoder(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 WEncoder 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']) | |
| # if input to encoder is not an RGB image, do not load the input layer weights | |
| if self.opts.label_nc != 0: | |
| encoder_ckpt = {k: v for k, v in encoder_ckpt.items() if "input_layer" not in k} | |
| 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) | |
| if self.opts.learn_in_w: | |
| self.__load_latent_avg(ckpt, repeat=1) | |
| else: | |
| self.__load_latent_avg(ckpt, repeat=self.opts.n_styles) | |
| 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 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 set_opts(self, opts): | |
| self.opts = opts | |
| def __load_latent_avg(self, ckpt, repeat=None): | |
| if 'latent_avg' in ckpt: | |
| self.latent_avg = ckpt['latent_avg'].to(self.opts.device) | |
| if repeat is not None: | |
| self.latent_avg = self.latent_avg.repeat(repeat, 1) | |
| else: | |
| self.latent_avg = None |