""" This file defines the core research contribution """ import matplotlib matplotlib.use('Agg') import math import torch from torch import nn from models.encoders import psp_encoders from models.stylegan2.model import Generator from configs.paths_config import model_paths import torch.nn.functional as F 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, ckpt=None): 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(ckpt) def set_encoder(self): if self.opts.encoder_type == 'GradualStyleEncoder': encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts) elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoW': encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts) elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoWPlus': encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoWPlus(50, 'ir_se', self.opts) else: raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type)) return encoder def load_weights(self, ckpt=None): if self.opts.checkpoint_path is not None: print('Loading pSp from checkpoint: {}'.format(self.opts.checkpoint_path)) if ckpt is None: ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=False) self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=False) 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) # for video toonification, we load G0' model if self.opts.toonify_weights is not None: ##### modified ckpt = torch.load(self.opts.toonify_weights) self.decoder.load_state_dict(ckpt['g_ema'], strict=False) self.opts.toonify_weights = None # x1: image for first-layer feature f. # x2: image for style latent code w+. If not specified, x2=x1. # inject_latent: for sketch/mask-to-face translation, another latent code to fuse with w+ # latent_mask: fuse w+ and inject_latent with the mask (1~7 use w+ and 8~18 use inject_latent) # use_feature: use f. Otherwise, use the orginal StyleGAN first-layer constant 4*4 feature # first_layer_feature_ind: always=0, means the 1st layer of G accept f # use_skip: use skip connection. # zero_noise: use zero noises. # editing_w: the editing vector v for video face editing def forward(self, x1, x2=None, resize=True, latent_mask=None, randomize_noise=True, inject_latent=None, return_latents=False, alpha=None, use_feature=True, first_layer_feature_ind=0, use_skip=False, zero_noise=False, editing_w=None): ##### modified feats = None # f and the skipped encoder features codes, feats = self.encoder(x1, return_feat=True, return_full=use_skip) ##### modified if x2 is not None: ##### modified codes = self.encoder(x2) ##### modified # normalize with respect to the center of an average face if self.opts.start_from_latent_avg: if self.opts.learn_in_w: codes = codes + self.latent_avg.repeat(codes.shape[0], 1) else: codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) # E_W^{1:7}(T(x1)) concatenate E_W^{8:18}(w~) 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 first_layer_feats, skip_layer_feats, fusion = None, None, None ##### modified if use_feature: ##### modified first_layer_feats = feats[0:2] # use f if use_skip: ##### modified skip_layer_feats = feats[2:] # use skipped encoder feature fusion = self.encoder.fusion # use fusion layer to fuse encoder feature and decoder feature. images, result_latent = self.decoder([codes], input_is_latent=True, randomize_noise=randomize_noise, return_latents=return_latents, first_layer_feature=first_layer_feats, first_layer_feature_ind=first_layer_feature_ind, skip_layer_feature=skip_layer_feats, fusion_block=fusion, zero_noise=zero_noise, editing_w=editing_w) ##### modified if resize: if self.opts.output_size == 1024: ##### modified images = F.adaptive_avg_pool2d(images, (images.shape[2]//4, images.shape[3]//4)) ##### modified else: 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